Biopython Tutorial and CookbookJeff Chang, Brad Chapman, Iddo Friedberg, Thomas Hamelryck, Michiel de Hoon, Peter CockLast Update–16 March 2007 |
The Biopython Project is an international association of developers of freely available Python (http://www.python.org) tools for computational molecular biology. The web site http://www.biopython.org provides an online resource for modules, scripts, and web links for developers of Python-based software for life science research.
Basically, we just like to program in python and want to make it as easy as possible to use python for bioinformatics by creating high-quality, reusable modules and scripts.
The main Biopython releases have lots of functionality, including:
We hope this gives you plenty of reasons to download and start using Biopython!
All of the installation information for Biopython was separated from this document to make it easier to keep updated. The instructions cover installation of python, Biopython dependencies and Biopython itself. It is available in pdf (http://biopython.org/DIST/docs/install/Installation.pdf) and html formats (http://biopython.org/DIST/docs/install/Installation.html).
Bio.SeqIO work? It imports fine but there is no parse function etc.
You need Biopython 1.43 or later. Older versions did contain some related code under the Bio.SeqIO name which has since been deprecated - and this is why the import “works”.Bio.Blast work with the latest plain text NCBI blast output?
The NCBI keep tweaking the plain text output from the BLAST tools, and keeping our parser up to date is an ongoing struggle. We recommend you to use the XML output instead, which is designed to be read by a computer program.__init__.py files. If you are not used to looking for code in this file this can be confusing. The reason we do this is to make the imports easier for users. For instance, instead of having to do a “repetitive” import like from Bio.GenBank import GenBank, you can just import like from Bio import GenBank.This section is designed to get you started quickly with Biopython, and to give a general overview of what is available and how to use it. All of the examples in this section assume that you have some general working knowledge of python, and that you have successfully installed Biopython on your system. If you think you need to brush up on your python, the main python web site provides quite a bit of free documentation to get started with (http://www.python.org/doc/).
Since much biological work on the computer involves connecting with databases on the internet, some of the examples will also require a working internet connection in order to run.
Now that that is all out of the way, let’s get into what we can do with Biopython.
As mentioned in the introduction, Biopython is a set of libraries to provide the ability to deal with ”things” of interest to biologists working on the computer. In general this means that you will need to have at least some programming experience (in python, of course!) or at least an interest in learning to program. Biopython’s job is to make your job easier as a programmer by supplying reusable libraries so that you can focus on answering your specific question of interest, instead of focusing on the internals of parsing a particular file format (of course, if you want to help by writing a parser that doesn’t exist and contributing it to Biopython, please go ahead!). So Biopython’s job is to make you happy!
One thing to note about Biopython is that it often provides multiple ways of “doing the same thing.” To me, this can be frustrating since I often way to just know the one right way to do something. However, this is also a real benefit because it gives you lots of flexibility and control over the libraries. The tutorial helps to show you the common or easy ways to do things so that you can just make things work. To learn more about the alternative possibilities, look into the Cookbook section (which tells you some cools tricks and tips) and the Advanced section (which provides you with as much detail as you’d ever want to know!).
Disputedly (of course!), the central object in bioinformatics is the sequence. Thus, we’ll start with a quick introduction to the Biopython mechanisms for dealing with sequences, the Seq object, which we’ll discuss in more detail in Chapter 3.
Most of the time when we think about sequences we have in my mind a string of letters like 'AGTACACTGGT'. You can create such Seq object with this sequence as follows - the “>>>” represents the python prompt followed by what you would type in:
>>> from Bio.Seq import Seq
>>> my_seq = Seq("AGTACACTGGT")
>>> my_seq.alphabet
Alphabet()
>>> print my_seq.tostring()
AGTACACTGGT
What we have here is a sequence object with a generic alphabet - reflecting the fact we have specified if this is a DNA or protein sequence (okay, a protein with a lot of Alanines, Glycines, Cysteines and Threonines!). We’ll talk more about alphabets in Chapter 3.
In addition to having an alphabet, the Seq object differs from the python string in the methods it supports. You can’t do this with a plain string:
>>> my_seq
Seq('AGTACACTGGT', Alphabet())
>>> my_seq.complement()
Seq('TCATGTGACCA', Alphabet())
>>> my_seq.reverse_complement()
Seq('ACCAGTGTACT', Alphabet())
The next most important class is the SeqRecord or Sequence Record. This holds a sequence with additional annotation including an identifier, name and description. The Bio.SeqIO module for reading and writing sequence file formats works with SeqRecord objects, which will be introduced below and cover in more detail by Chapter 4.
This covers the basic features and uses of the Biopython sequence class. There is a more detailed description of the design ideas behind the sequence class in the Advanced section of this tutorial. This class is still under development and comments on the design and use are, of course, very welcome. Now that you’ve got some idea of what it is like to interact with the Biopython libraries, it’s time to delve into the fun, fun world of dealing with biological file formats!
Before we jump right into parsers and everything else to do with Biopython, let’s set up an example to motivate everything we do and make life more interesting. After all, if there wasn’t any biology in this tutorial, why would you want you read it?
Since I love plants, I think we’re just going to have to have a plant based example (sorry to all the fans of other organisms out there!). Having just completed a recent trip to our local greenhouse, we’ve suddenly developed an incredible obsession with Lady Slipper Orchids (if you wonder why, have a look at some Lady Slipper Orchids photos on Flickr, or try a Google Image Search).
Of course, orchids are not only beautiful to look at, they are also extremely interesting for people studying evolution and systematics. So let’s suppose we’re thinking about writing a funding proposal to do a molecular study of Lady Slipper evolution, and would like to see what kind of research has already been done and how we can add to that.
After a little bit of reading up we discover that the Lady Slipper Orchids are in the Orchidaceae family and the Cypripedioideae sub-family and are made up of 5 genera: Cypripedium, Paphiopedilum, Phragmipedium, Selenipedium and Mexipedium.
That gives us enough to get started delving for more information. So, let’s look at how the Biopython tools can help us. We’ll start with sequence parsing in Section 2.4, but the orchids will be back later on as well - for example we’ll extra data from Swiss-Prot from certain orchid proteins in Section 6.1, search PubMed for papers about orchids in Section 6.2, extract sequence data from GenBank in Section 6.3.1, and work with ClustalW multiple sequence alignments of orchid proteins in Section 6.4.1.
A large part of much bioinformatics work involves dealing with the many types of file formats designed to hold biological data. These files are loaded with interesting biological data, and a special challenge is parsing these files into a format so that you can manipulate them with some kind of programming language. However the task of parsing these files can be frustrated by the fact that the formats can change quite regularly, and that formats may contain small subtleties which can break even the most well designed parsers.
We are going to briefly introduce the Bio.SeqIO module, available in Biopython 1.43 and later. If you are using an older version of Biopython we encourage you to update (or find an old edition of this tutorial!). You can find out more in Chapter 4.
We’ll start with an online search for our friends, the lady slipper orchids. Let’s just take a look through the nucleotide databases at NCBI, using an Entrez online search (http://www.ncbi.nlm.nih.gov:80/entrez/query.fcgi?db=Nucleotide) for everything mentioning the text Cypripedioideae (this is the subfamily of lady slipper orchids). When this tutorial was originally written, this search gave us only 94 hits, which we saved as a FASTA formatted text file (ls_orchid.fasta; also available online here) and as a GenBank formatted text file (ls_orchid.gbk; also available online here).
If you run the search today, you’ll get hundreds of results! When following the tutorial, if you want to see the same list of genes, just download the two files above or copy them from docs/examples/ in the Biopython source code. In Section 2.5 we will look at how to do a search like this from within python.
If you open the lady slipper orchids FASTA file in your favourite text editor, you’ll see that the file starts like this:
>gi|2765658|emb|Z78533.1|CIZ78533 C.irapeanum 5.8S rRNA gene and ITS1 and ITS2 DNA CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGATGAGACCGTGGAATAAACGATCGAGTG AATCCGGAGGACCGGTGTACTCAGCTCACCGGGGGCATTGCTCCCGTGGTGACCCTGATTTGTTGTTGGG ...
It contains 94 records, each has a line starting with “>” (greater-than symbol) followed by the sequence on one or more lines. Now try this in python:
from Bio import SeqIO
handle = open("ls_orchid.fasta")
for seq_record in SeqIO.parse(handle, "fasta") :
print seq_record.id
print seq_record.seq
print len(seq_record.seq)
handle.close()
You should get something like this on your screen:
gi|2765658|emb|Z78533.1|CIZ78533
Seq('CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGATGAGACCGTGGAATAAA ...', SingleLetterAlphabet())
740
...
gi|2765564|emb|Z78439.1|PBZ78439
Seq('CATTGTTGAGATCACATAATAATTGATCGAGTTAATCTGGAGGATCTGTTTACTTTGGTC ...', SingleLetterAlphabet())
592
Notice that the FASTA format does not specify the alphabet, so Bio.SeqIO has defaulted to the rather generic SingleLetterAlphabet() rather than something DNA specific.
Now let’s load the GenBank file instead - notice that the code to do this is almost identical to the snippet used above for a FASTA file - the only difference is we changed the filename and the format string:
from Bio import SeqIO
handle = open("ls_orchid.gbk")
for seq_record in SeqIO.parse(handle, "genbank") :
print seq_record.id
print seq_record.seq
print len(seq_record.seq)
handle.close()
This should give:
Z78533.1
Seq('CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGATGAGACCGTGGAATAAA ...', IUPACAmbiguousDNA())
740
...
Z78439.1
Seq('CATTGTTGAGATCACATAATAATTGATCGAGTTAATCTGGAGGATCTGTTTACTTTGGTC ...', IUPACAmbiguousDNA())
592
This time Bio.SeqIO has been able to choose a sensible alphabet, IUPAC Ambiguous DNA. You’ll also notice that a shorter string has been used as the seq_record.id in this case.
Biopython has a lot of parsers, and each has its own little special niches based on the sequence format it is parsing and all of that. While the most popular file formats have parsers integrated into Bio.SeqIO, for some of the rarer and unloved file formats there is either no parser at all, or an old parser which has not been linked in yet.
Chapter 4 covers Bio.SeqIO in more detail. Please also check the wiki page (http://biopython.org/wiki/SeqIO) for the latest information, or ask on the mailing list. The wiki page should includes an up to date list of supported file types, and more examples including writing sequences to a file, and converting between file formats.
The next place to look for information about specific parsers and how to do cool things with them is in the Cookbook, Section 6 of this Tutorial. If you don’t find the information you are looking for, please consider helping out your poor overworked documentors and submitting a cookbook entry about it! (once you figure out how to do it, that is!)
One of the very common things that you need to do in bioinformatics is extract information from biological databases. It can be quite tedious to access these databases manually, especially if you have a lot of repetitive work to do. Biopython attempts to save you time and energy by making some on-line databases available from python scripts. Currently, Biopython has code to extract information from the following databases:
The code is these modules basically makes it easy to write python code that interact with the CGI scripts on these pages, so that you can get results in an easy to deal with format. In some cases, the results can be tightly integrated with the Biopython parsers to make it even easier to extract information.
Here we’ll show a simple example of performing a remote Entrez query. More information on the other services is available in the Cookbook, which begins on page ??.
In section 2.3 of the parsing examples, we talked about using Entrez website to search the NCBI nucleotide databases for info on Cypripedioideae, our friends the lady slipper orchids. Now, we’ll look at how to automate that process using a python script. For Entrez searching, this is more useful for displaying results then as a tool for getting sequences. The NCBI web site is mostly set up to allow remote queries so that you could write our own local CGI scripts that return information from NCBI pages. For this reason, the results are returned as HTML and it is pretty tough to get a flat file in a quick manner.
In this example, we’ll just show how to connect, get the results, and display them in a web browser. First, we’ll start by defining our search and how to display the results:
search_command = 'Search' search_database = 'Nucleotide' return_format = 'FASTA' search_term = 'Cypripedioideae' my_browser = 'lynx'
The first four terms define the search we are going to do. To use the Entrez module, you’ll need to know a bit about how the remote CGI scripts at NCBI work, and you can find out more about this at http://www.ncbi.nlm.nih.gov/entrez/query/static/linking.html. The final term just describes the browser to display the results in.
Now that we’ve got this all set up, we can query Entrez and get a handle with the results. This is done with the following code:
from Bio.WWW import NCBI
result_handle = NCBI.query(search_command, search_database, term = search_term,
doptcmdl = return_format)
The query function does all of the work of preparing the CGI script command line and rounding up the HTML results.
Now that we’ve got the results, we are ready to save them to a file and display them in our browser, which we can do with code like:
import os
result_file_name = os.path.join(os.getcwd(), "results.html")
result_file = open(result_file_name, "w")
result_file.write(result_handle.read())
result_file.close()
if my_browser == "lynx":
os.system("lynx -force_html " + result_file_name)
elif my_browser == "netscape":
os.system("netscape file:" + result_file_name)
Snazzy! We can fetch things and display them automatically – you could use this to quickly set up searches that you want to repeat on a daily basis and check by hand, or to set up a small CGI script to do queries and locally save the results before displaying them (as a kind of lab notebook of our search results). Hopefully whatever your task, the database connectivity code will make things lots easier for you!
Now that you’ve made it this far, you hopefully have a good understanding of the basics of Biopython and are ready to start using it for doing useful work. The best thing to do now is to start snooping around in the source code and looking at the automatically generated documentation.
Once you get a picture of what you want to do, and what libraries in Biopython will do it, you should take a peak at the Cookbook, which may have example code to do something similar to what you want to do.
If you know what you want to do, but can’t figure out how to do it, please feel free to post questions to the main biopython list (biopython@biopython.org). This will not only help us answer your question, it will also allow us to improve the documentation so it can help the next person do what you want to do.
Enjoy the code!
Biological sequences are arguably the central object in Bioinformatics, and in this chapter we’ll introduce the Biopython mechanism for dealing with sequences, the Seq object (see also the SeqRecord object which combines the sequence information with annotation). Sequences are essentially strings of letters like AGTACACTGGT, which seems very natural since this is the most common way that sequences are seen in biological file formats.
There are two important differences between the Seq object and standard python strings. First of all the Seq object has a slightly different set of methods to a plain python string (for example, a reverse_complement() method used for nucleotide sequences).
Secondly, the Seq object has an important attribute, alphabet, which is an object describing what the individual characters making up the sequence string “mean”, and how they should be interpreted. For example, is AGTACACTGGT a DNA sequence, or just a protein sequence that happens to be rich in Alanines, Glycines, Cysteines
and Threonines?
The alphabet object is perhaps the important thing that makes the Seq object more than just a string. The currently available alphabets for Biopython are defined in the Bio.Alphabet module. We’ll use the IUPAC alphabets (http://www.chem.qmw.ac.uk/iupac/) here to deal with some of our favorite objects: DNA, RNA and Proteins.
Bio.Alphabet.IUPAC provides basic definitions for proteins, DNA and RNA, but additionally provides the ability to extend and customize the basic definitions. For instance, for proteins, there is a basic IUPACProtein class, but there is an additional ExtendedIUPACProtein class providing for the additional elements “Asx” (asparagine or aspartic acid), “Sec” (selenocysteine), and “Glx” (glutamine or glutamic acid). For DNA you’ve got choices of IUPACUnambiguousDNA, which provides for just the basic letters, IUPACAmbiguousDNA (which provides for ambiguity letters for every possible situation) and ExtendedIUPACDNA, which allows letters for modified bases. Similarly, RNA can be represented by IUPACAmbiguousRNA or IUPACUnambiguousRNA.
The advantages of having an alphabet class are two fold. First, this gives an idea of the type of information the Seq object contains. Secondly, this provides a means of constraining the information, as a means of type checking.
Now that we know what we are dealing with, let’s look at how to utilize this class to do interesting work. You can create an ambiguous sequence with the default generic alphabet like this:
>>> from Bio.Seq import Seq
>>> my_seq = Seq("AGTACACTGGT")
>>> my_seq
Seq('AGTACACTGGT', Alphabet())
>>> my_seq.alphabet
Alphabet()
However, where possible you should specify the alphabet explicitly when creating your sequence objects - in this case an unambiguous DNA alphabet object:
>>> from Bio.Seq import Seq
>>> from Bio.Alphabet import IUPAC
>>> my_seq = Seq('AGTACACTGGT', IUPAC.unambiguous_dna)
>>> my_seq
Seq('AGTACACTGGT', IUPACUnambiguousDNA())
>>> my_seq.alphabet
IUPACUnambiguousDNA()
In many ways, we can deal with Seq objects as if they were normal python strings, for example getting the length, or iterating over the elements:
from Bio.Seq import Seq
from Bio.Alphabet import IUPAC
my_seq = Seq('GATCGATGGGCCTATATAGGATCGAAAATCGC', IUPAC.unambiguous_dna)
for index, letter in enumerate(my_seq) :
print index, letter
print len(letter)
You can access elements of the sequence in the same way as for strings (but remember, python counts from zero!):
>>> print my_seq[0] #first element >>> print my_seq[2] #third element >>> print my_seq[-1] #list element
The Seq object has a .count() method, just like a string:
>>> len(my_seq)
32
>>> my_seq.count("G")
10
>>> float(my_seq.count("G") + my_seq.count("C")) / len(my_seq)
0.46875
While you could use the above snippet of code to calculate a GC%, note that Biopython does have some GC functions already built in, see the Bio.SeqUtils module.
A more complicated example, let’s get a slice of the sequence:
>>> my_seq[4:12]
Seq('GATGGGCC', IUPACUnambiguousDNA())
Two things are interesting to note. First, this follows the normal conventions for python strings. So the first element of the sequence is 0 (which is normal for computer science, but not so normal for biology). When you do a slice the first item is included (i. e. 4 in this case) and the last is excluded (12 in this case), which is the way things work in python, but of course not necessarily the way everyone in the world would expect. The main goal is to stay consistent with what python does.
The second thing to notice is that the slice is performed on the sequence data string, but the new object produced is another Seq object which retains the alphabet information from the original Seq object.
Also like a python string, you can do slices with a start, stop and stride (the step size, which defaults to one). For example, we can get the first, second and third codon positions of this DNA sequence:
>>> my_seq[0::3]
Seq('GCTGTAGTAAG', IUPACUnambiguousDNA())
>>> my_seq[1::3]
Seq('AGGCATGCATC', IUPACUnambiguousDNA())
>>> my_seq[2::3]
Seq('TAGCTAAGAC', IUPACUnambiguousDNA())
Another stride trick you might have seen with a python string is the use of a -1 stride to reverse the string. You can do this with a Seq object too:
>>> my_seq[::-1]
Seq('CGCTAAAAGCTAGGATATATCCGGGTAGCTAG', IUPACUnambiguousDNA())
If you are really do just need a plain string, for example to print out, write to a file, or insert into a database, then this is very easy to get:
>>> my_seq.tostring() 'GATCGATGGGCCTATATAGGATCGAAAATCGC'
For nucleotide sequences, you can easily obtain the complement or reverse complement of a Seq object:
>>> my_seq
Seq('GATCGATGGGCCTATATAGGATCGAAAATCGC', IUPACUnambiguousDNA())
>>> my_seq.complement()
Seq('CTAGCTACCCGGATATATCCTAGCTTTTAGCG', IUPACUnambiguousDNA())
>>> my_seq.reverse_complement()
Seq('GCGATTTTCGATCCTATATAGGCCCATCGATC', IUPACUnambiguousDNA())
In all of these operations, the alphabet property is maintained. This is very useful in case you accidentally end up trying to do something weird like take the (reverse)complement of a protein seuqence:
>>> protein_seq = Seq("EVRNAK", IUPAC.protein)
>>> dna_seq = Seq("ACGT", IUPAC.unambiguous_dna)
>>> protein_seq.complement()
Traceback (most recent call last):
File "<stdin>", line 1, in ?
File "/usr/local/lib/python2.4/site-packages/Bio/Seq.py", line 108, in complement
raise ValueError, "Proteins do not have complements!"
ValueError: Proteins do not have complements!
Naturally, you can in principle add any two Seq objects together - just like you can with python strings to concatenate them. However, you can’t add sequences with incompatible alphabets, such as a protein sequence and a DNA sequence:
>>> protein_seq + dna_seq
Traceback (most recent call last):
File "<stdin>", line 1, in ?
File "/usr/local/lib/python2.4/site-packages/Bio/Seq.py", line 42, in __add__
raise TypeError, ("incompatable alphabets", str(self.alphabet),
TypeError: ('incompatable alphabets', 'IUPACProtein()', 'IUPACUnambiguousDNA()')
If you really wanted to do this, you’d have to first give both sequences generic alphabets:
>>> from Bio.Alphabet import generic_alphabet
>>> protein_seq.alphabet = generic_alphabet
>>> dna_seq.alphabet = generic_alphabet
>>> protein_seq + dna_seq
Seq('EVRNAKACGT', Alphabet())
Here is an example of adding a generic nucleotide sequence to an unambiguous IUPAC DNA sequence, resulting in an ambiguous nucleotide sequence:
>>> from Bio.Seq import Seq
>>> from Bio.Alphabet import generic_nucleotide
>>> from Bio.Alphabet import IUPAC
>>> nuc_seq = Seq('GATCGATGC', generic_nucleotide)
>>> dna_seq = Seq('ACGT', IUPAC.unambiguous_dna)
>>> nuc_seq
Seq('GATCGATGC', NucleotideAlphabet())
>>> dna_seq
Seq('ACGT', IUPACUnambiguousDNA())
>>> nuc_seq + dna_seq
Seq('GATCGATGCACGT', NucleotideAlphabet())
Just like the normal python string, the Seq object is “read only”, or in python terminology, not mutable. Apart from the wanting the Seq object to act like a string, this is also a useful default since in many biological applications you want to ensure you are not changing your sequence data:
>>> my_seq[5] = "G" Traceback (most recent call last): File "<stdin>", line 1, in ? AttributeError: 'Seq' instance has no attribute '__setitem__'
However, you can convert it into a mutable sequence (a MutableSeq object) and do pretty much anything you want with it:
>>> mutable_seq = my_seq.tomutable()
>>> print mutable_seq
MutableSeq('GATCGATGGGCCTATATAGGATCGAAAATCGC', IUPACUnambiguousDNA())
>>> mutable_seq[5] = "T"
>>> print mutable_seq
MutableSeq('GATCGTTGGGCCTATATAGGATCGAAAATCGC', IUPACUnambiguousDNA())
>>> mutable_seq.remove("T")
>>> print mutable_seq
MutableSeq('GACGTTGGGCCTATATAGGATCGAAAATCGC', IUPACUnambiguousDNA())
>>> mutable_seq.reverse()
>>> print mutable_seq
MutableSeq('CGCTAAAAGCTAGGATATATCCGGGTTGCAG', IUPACUnambiguousDNA())
Now that the nature of the sequence object makes some sense, the next
thing to look at is what kind of things we can do with a sequence. The
Bio directory contains two useful modules to transcribe and
translate a sequence object. These tools work based on the alphabet of
the sequence.
For instance, let’s supposed we want to transcribe a DNA sequence:
>>> from Bio.Seq import Seq
>>> from Bio.Alphabet import IUPAC
>>> my_seq = Seq("GATCGATGGGCCTATATAGGATCGAAAATCGC", IUPAC.unambiguous_dna)
This contains an unambiguous alphabet, so to transcribe we would do the following:
>>> from Bio import Transcribe
>>> transcriber = Transcribe.unambiguous_transcriber
>>> my_rna_seq = transcriber.transcribe(my_seq)
>>> print my_rna_seq
Seq('GAUCGAUGGGCCUAUAUAGGAUCGAAAAUCGC', IUPACUnambiguousRNA())
The alphabet of the new RNA Seq object is created for free, so again, dealing with a Seq object is no more difficult then dealing with a simple string.
You can also reverse transcribe RNA sequences:
>>> transcriber.back_transcribe(my_rna_seq)
Seq('GATCGATGGGCCTATATAGGATCGAAAATCGC', IUPACUnambiguousDNA())
To translate our DNA object we have quite a few choices. First, we can use any number of translation tables depending on what we know about our DNA sequence. The translation tables available in biopython were taken from information at ftp://ftp.ncbi.nlm.nih.gov/entrez/misc/data/gc.prt. So, you have tons of choices to pick from. For this, let’s just focus on two choices: the Standard translation table, and the Translation table for Vertebrate Mitochondrial DNA. These tables are labeled with id numbers 1 and 2, respectively. Now that we know what tables we are looking to get, we’re all set to perform a basic translation. First, we need to get our translators that use these tables. Since we are still dealing with our unambiguous DNA object, we want to fetch translators that take this into account:
>>> from Bio import Translate >>> standard_translator = Translate.unambiguous_dna_by_id[1] >>> mito_translator = Translate.unambiguous_dna_by_id[2]
Once we’ve got the proper translators, it’s time to go ahead and translate a sequence:
>>> my_seq = Seq("GCCATTGTAATGGGCCGCTGAAAGGGTGCCCGA", IUPAC.unambiguous_dna)
>>> standard_translator.translate(my_seq)
Seq('AIVMGR*KGAR', IUPACProtein())
>>> mito_translator.translate(my_seq)
Seq('AIVMGRWKGAR', IUPACProtein())
Notice that the default translation will just go ahead and proceed blindly through a stop codon. If you are aware that you are translating some kind of open reading frame and want to just see everything up until the stop codon, this can be easily done with the translate_to_stop function:
>>> standard_translator.translate_to_stop(my_seq)
Seq('AIVMGR', IUPACProtein())
Similar to the transcriber, it is also possible to reverse translate a protein into a DNA sequence:
>>> my_protein = Seq("AVMGRWKGGRAAG", IUPAC.protein)
>>> standard_translator.back_translate(my_protein)
Seq('GCTGTTATGGGTCGTTGGAAGGGTGGTCGTGCTGCTGGT', IUPACUnambiguousDNA())
In this chapter we’ll discuss the Bio.SeqIO module introduced earlier in more detail. This is a new interface added to Biopython 1.43, which aims to provide a simple interface for working with assorted sequence file formats in a uniform way. The “catch” is that you have to work with SeqRecord ojects - which contain a Seq object plus annotation like an identifier and description.
The workhorse function Bio.SeqIO.parse() is used to read in sequence data as SeqRecord objects. This function expects two arguments. The first argument is a handle to read the data from. A handle is typically a file opened for reading, but could be the output from a command line program, see Section 9.1 for more details. The second argument is a lower case string specifying sequence format – we don’t try and guess the file format for you!
What you get back is an iterator which gives SeqRecord objects, which we’ll talk about in more detail later, and is typically used in a for loop.
In general Bio.SeqIO.parse() is used to read in sequence files as SeqRecord objects, and is typically used with a for loop like this:
from Bio import SeqIO
handle = open("ls_orchid.fasta")
for seq_record in SeqIO.parse(handle, "fasta") :
print seq_record.id
print seq_record.seq
print len(seq_record.seq)
handle.close()
The above example is repeated from the introduction in Section 2.4, and will load the orchid DNA sequences in the FASTA format file ls_orchid.fasta. If instead you wanted to load a GenBank format file like ls_orchid.gbk then all you need to do is change the filename and the format string:
from Bio import SeqIO
handle = open("ls_orchid.gbk")
for seq_record in SeqIO.parse(handle, one"genbank") :
print seq_record.id
print seq_record.seq
print len(seq_record.seq)
handle.close()
Similarly, if you wanted to read in a file in another file format, then assuming Bio.SeqIO.parse() supports it you would just need to change the format string as appropriate, for example “swiss” for SwissProt files or “embl” for EMBL text files. There is a full listing on the wiki page (http://biopython.org/wiki/SeqIO).
In the previous section, we looked at parsing sequence data from a file handle. We hinted that handles are not always from files, and in this and the next section we’ll use handles to internet connections to download sequences.
Section 6.3.1 covers fetching sequences from GenBank in more depth, including how to do searches to get lists of GI numbers, but for now let’s just connect to the NCBI and get a few orchid proteins from GenBank using their GI numbers:
from Bio import GenBank
from Bio import SeqIO
handle = GenBank.download_many(['6273291', '6273290', '6273289'])
for seq_record in SeqIO.parse(handle, "genbank") :
print seq_record.id, seq_record.description[:50] + "..."
print "Sequence length %i," % len(seq_record.seq),
print "%i features," % len(seq_record.features),
print "from: %s" % seq_record.annotations['source']
handle.close()
That should give the following output:
AF191665.1 Opuntia marenae rpl16 gene; chloroplast gene for c... Sequence length 902, 3 features, from: chloroplast Opuntia marenae AF191664.1 Opuntia clavata rpl16 gene; chloroplast gene for c... Sequence length 899, 3 features, from: chloroplast Grusonia clavata AF191663.1 Opuntia bradtiana rpl16 gene; chloroplast gene for... Sequence length 899, 3 features, from: chloroplast Opuntia bradtianaa
Now let’s use a handle to download a SwissProt file from ExPASy, something covered in more depth in Section 6.1:
from Bio.WWW import ExPASy
from Bio import SeqIO
handle = ExPASy.get_sprot_raw("O23729")
for seq_record in SeqIO.parse(handle, "swiss") :
print seq_record.id
print seq_record.name
print seq_record.description
print seq_record.seq
print len(seq_record.seq)
print seq_record.annotations['keywords']
handle.close()
Assuming your network connection is OK, you should get back:
O23729
CHS3_BROFI
Chalcone synthase 3 (EC 2.3.1.74) (Naringenin-chalcone synthase 3).
Seq('MAPAMEEIRQAQRAEGPAAVLAIGTSTPPNALYQADYPDYYFRITKSEHLTELKEKFKRM ...', ProteinAlphabet())
394
['Acyltransferase', 'Flavonoid biosynthesis', 'Transferase']
The function Bio.WWW.ExPASy.get_sprot_raw only ever returns the data for a single record, so using a for loop here is a little redundant. How about this version:
from Bio.WWW import ExPASy
from Bio import SeqIO
seq_record = SeqIO.parse(ExPASy.get_sprot_raw("O23729"), "swiss").next()
print seq_record.id
print seq_record.seq
print len(seq_record.seq)
What’s going on here with the .next() method? Well its an alternative to using an iterator in a for loop, and we discuss this in the next section.
In the above examples, we have usually used a for loop to iterate over all the records one by one. You can use the for loop for all sorts of Python objects (including lists, tuples and strings) which support the iteration interface.
The object returned by Bio.SeqIO is actually an iterator which returns SeqRecord objects. You get to see each record in turn, but once and only once. The plus point is that an iterator can save you memory when dealing with large files.
Instead of using a for loop, can also use the .next() method of an iterator to step through the entries, like this:
from Bio import SeqIO
handle = open("ls_orchid.fasta")
record_iterator = SeqIO.parse(handle, "fasta")
first_record = record_iterator.next()
print first_record.id
print first_record.description
second_record = record_iterator.next()
print second_record.id
print second_record.description
handle.close()
The above style is useful if your sequence files have only one record, like the ExPASy SwissProt example above in Section 4.1.3, or a GenBank file for a single chromosome.
Note that if you try and use .next() and there are no more results, you’ll either get back the special Python object None or a StopIteration exception.
In the previous section we talked about the fact that Bio.SeqIO gives you a SeqRecord iterator, and that you get the records one by one. Very often you need to be able to access the records in any order. The Python list data type is perfect for this, and we can turn the SeqRecord iterator into a list of SeqRecord objects using the built-in Python function list() like so:
from Bio import SeqIO
handle = open("ls_orchid.gbk")
records = list(SeqIO.parse(handle, "genbank"))
handle.close()
print "Found %i records" % len(records)
print "The last record"
last_record = records[-1] #using Python's list tricks
print last_record.id
print last_record.seq
print len(last_record.seq)
print "The first record"
first_record = records[0] #remember, Python counts from zero
print first_record.id
print first_record.seq
print len(first_record.seq)
Giving:
Found 94 records
The last record
Z78439.1
Seq('CATTGTTGAGATCACATAATAATTGATCGAGTTAATCTGGAGGATCTGTTTACTTTGGTC ...', IUPACAmbiguousDNA())
592
The first record
Z78533.1
Seq('CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGATGAGACCGTGGAATAAA ...', IUPACAmbiguousDNA())
740
You can of course still use a for loop with a list of SeqRecord objects. Using a list is much more flexible than an iterator (for example, you can determine the number of records from the length of the list), but does need more memory because it will hold all the records in memory at once.
Suppose you wanted to extract a list of the species from the ls_orchid.gbk GenBank file. Let’s have a close look at the first record in the file and see where the species gets stored:
from Bio import SeqIO
record_iterator = SeqIO.parse(open("ls_orchid.gbk"), "genbank")
first_record = record_iterator.next()
print first_record
That should give something like this:
ID: Z78533.1
Name: Z78533
Desription: C.irapeanum 5.8S rRNA gene and ITS1 and ITS2 DNA.
/source=Cypripedium irapeanum
/taxonomy=['Eukaryota', 'Viridiplantae', 'Streptophyta', ..., 'Cypripedium']
/keywords=['5.8S ribosomal RNA', '5.8S rRNA gene', 'internal transcribed spacer', 'ITS1', 'ITS2']
/references=[...]
/data_file_division=PLN
/date=30-NOV-2006
/organism=Cypripedium irapeanum
/gi=2765658
Seq('CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGATGAGACCGTGGAATAAA ...', IUPACAmbiguousDNA())
The information we want, Cypripedium irapeanum, is held in the annotations dictionary under ‘source’ and ‘organism’, which we can access like this:
print first_record.annotations["source"]
or:
print first_record.annotations["organism"]
Now let’s go through all the records, building up a list of the species each orchid sequence is from:
from Bio import SeqIO
handle = open("ls_orchid.gbk")
all_species = []
for seq_record in SeqIO.parse(handle, "genbank") :
all_species.append(seq_record.annotations["organism"])
handle.close()
print all_species
Another way of writing this code is to use a list comprehension (introduced in Python 2.0) like this:
from Bio import SeqIO
all_species = [seq_record.annotations["organism"] for seq_record in \
SeqIO.parse(open("ls_orchid.gbk"), "genbank")]
print all_species
In either case, the result is:
['Cypripedium irapeanum', 'Cypripedium californicum', ..., 'Paphiopedilum barbatum']
Great. That was pretty easy because GenBank files are annotated in a standardised way. Now, let’s suppose you wanted to extract a list of the species from your FASTA file, rather than the GenBank file. The bad news is you will have to write some code to extract the data you want from the record’s description line - if the information is in the file in the first place!
For this example, notice that if you break up the description line at the spaces, then the species is there as field number one (field zero is the record identifier). That means we can do this:
from Bio import SeqIO
handle = open("ls_orchid.fasta")
all_species = []
for seq_record in SeqIO.parse(handle, "fasta") :
all_species.append(seq_record.description.split()[1])
handle.close()
print all_species
This gives:
['C.irapeanum', 'C.californicum', 'C.fasciculatum', 'C.margaritaceum', ..., 'P.barbatum']
The concise alternative using list comprehensions (Python 2.0 or later) would be:
from Bio import SeqIO
all_species == [seq_record.description.split()[1] for seq_record in \
SeqIO.parse(open("ls_orchid.fasta"), "fasta")]
print all_species
In general, extracting information from the FASTA description line is not very nice. If you can get your sequences in a well annotated file format like GenBank or EMBL, then this sort of annotation information is much easier to deal with.
The next thing that we’ll do with our ubiquitous orchid files is to show how to index them and access them like a database using the Python dictionary datatype (like a hash in Perl). This is very useful for large files where you only need to access certain elements of the file, and makes for a nice quick ’n dirty database.
You can use the function SeqIO.to_dict() to make a SeqRecord dictionary (in memory). By default this will use each record’s identifier (i.e. the .id attribute) as the key. Let’s try this using our GenBank file:
from Bio import SeqIO
handle = open("ls_orchid.gbk")
orchid_dict = SeqIO.to_dict(SeqIO.parse(handle, "genbank"))
handle.close()
Since this variable orchid_dict is an ordinary Python dictionary, we can look at all of the keys we have available:
>>> print orchid_dict.keys() ['Z78484.1', 'Z78464.1', 'Z78455.1', 'Z78442.1', 'Z78532.1', 'Z78453.1', ..., 'Z78471.1']
We can access a single SeqRecord object via the keys and manipulate the object as normal:
>>> seq_record = orchid_dict["Z78475.1"]
>>> print seq_record.description
P.supardii 5.8S rRNA gene and ITS1 and ITS2 DNA
>>> print seq_record.seq
Seq('CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGATCACATAATAAT ...', IUPACAmbiguousDNA())
So, it is very easy to create an in memory “database” of our GenBank records. Next we’ll try this for the FASTA file instead.
Using the same code as above, but for the FASTA file instead:
from Bio import SeqIO
handle = open("ls_orchid.fasta")
orchid_dict = SeqIO.to_dict(SeqIO.parse(handle, "fasta"))
handle.close()
print orchid_dict.keys()
This time the keys are:
['gi|2765596|emb|Z78471.1|PDZ78471', 'gi|2765646|emb|Z78521.1|CCZ78521', ... ..., 'gi|2765613|emb|Z78488.1|PTZ78488', 'gi|2765583|emb|Z78458.1|PHZ78458']
You should recognise these strings from when we parsed the FASTA file earlier in Section 2.4.1. Suppose you would rather have something else as the keys - like the accesion numbers. This brings us nicely to SeqIO.to_dict()’s optional argument key_function, which lets you define what to use as the dictionary key for your records.
First you must write your own function to return the key you want (as a string) when given a SeqRecord object. In general, the details of function will depend on the sort of input records you are dealing with. But for our orchids, we can just split up the record’s identifier using the “pipe” character (the vertical line) and return the fourth entry (field three):
def get_accession(record) :
""""Given a SeqRecord, return the accession number as a string
e.g. "gi|2765613|emb|Z78488.1|PTZ78488" -> "Z78488.1"
"""
parts = record.id.split("|")
assert len(parts) == 5 and parts[0] == "gi" and parts[2] == "emb"
return parts[3]
Then we can give this function to the SeqIO.to_dict() function to use in building the dictionary:
from Bio import SeqIO
handle = open("ls_orchid.fasta")
orchid_dict = SeqIO.to_dict(SeqIO.parse(handle, "fasta"), key_function=get_accession)
handle.close()
print orchid_dict.keys()
Finally, as desired, the new dictionary keys:
>>> print orchid_dict.keys() ['Z78484.1', 'Z78464.1', 'Z78455.1', 'Z78442.1', 'Z78532.1', 'Z78453.1', ..., 'Z78471.1']
Not too complicated, I hope!
To give another example of working with dictionaries of SeqRecord objects, we’ll use the SEGUID checksum function (added in Biopython 1.44). This is a relatively recent checksum, and collisions should be very rare (i.e. two different sequences with the same checksum), an improvement on the CRC64 checksum.
Once again, working with the orchids GenBank file:
from Bio import SeqIO
from Bio.SeqUtils.CheckSum import seguid
for record in SeqIO.parse(open("ls_orchid.gbk"), "genbank") :
print record.id, seguid(record.seq)
This should give:
Z78533.1 JUEoWn6DPhgZ9nAyowsgtoD9TTo Z78532.1 MN/s0q9zDoCVEEc+k/IFwCNF2pY ... Z78439.1 H+JfaShya/4yyAj7IbMqgNkxdxQ
Now, recall the Bio.SeqIO.to_dict() function’s key_function argument expects a function which turns a SeqRecord into a string. We can’t use the seguid() function directly because it expects to be given a Seq object (or a string). However, we can use python’s lambda feature to create a “one off” function to give to Bio.SeqIO.to_dict() instead:
from Bio import SeqIO
from Bio.SeqUtils.CheckSum import seguid
seguid_dict = SeqIO.to_dict(SeqIO.parse(open("ls_orchid.gbk"), "genbank"),
lambda rec : seguid(rec.seq))
record = seguid_dict["MN/s0q9zDoCVEEc+k/IFwCNF2pY"]
print record.id
print record.description
That should have retrieved the record Z78532.1, the second entry in the file.
We’ve talked about using Bio.SeqIO.parse() for sequence input (reading files), and now we’ll look at Bio.SeqIO.write() which is for sequence output (writing files). This is a function taking three arguments: some SeqRecord objects, a handle to write to, and a sequence format.
Here is an example, where we start by creating a few SeqRecord objects the hard way (by hand, rather than by loading them from a file):
from Bio.Seq import Seq
from Bio.SeqRecord import SeqRecord
from Bio.Alphabet import generic_protein
rec1 = SeqRecord(Seq("MMYQQGCFAGGTVLRLAKDLAENNRGARVLVVCSEITAVTFRGPSETHLDSMVGQALFGD" \
+"GAGAVIVGSDPDLSVERPLYELVWTGATLLPDSEGAIDGHLREVGLTFHLLKDVPGLISK" \
+"NIEKSLKEAFTPLGISDWNSTFWIAHPGGPAILDQVEAKLGLKEEKMRATREVLSEYGNM" \
+"SSAC", generic_protein),
id="gi|14150838|gb|AAK54648.1|AF376133_1",
description="chalcone synthase [Cucumis sativus]")
rec2 = SeqRecord(Seq("YPDYYFRITNREHKAELKEKFQRMCDKSMIKKRYMYLTEEILKENPSMCEYMAPSLDARQ" \
+"DMVVVEIPKLGKEAAVKAIKEWGQ", generic_protein),
id="gi|13919613|gb|AAK33142.1|",
description="chalcone synthase [Fragaria vesca subsp. bracteata]")
rec3 = SeqRecord(Seq("MVTVEEFRRAQCAEGPATVMAIGTATPSNCVDQSTYPDYYFRITNSEHKVELKEKFKRMC" \
+"EKSMIKKRYMHLTEEILKENPNICAYMAPSLDARQDIVVVEVPKLGKEAAQKAIKEWGQP" \
+"KSKITHLVFCTTSGVDMPGCDYQLTKLLGLRPSVKRFMMYQQGCFAGGTVLRMAKDLAEN" \
+"NKGARVLVVCSEITAVTFRGPNDTHLDSLVGQALFGDGAAAVIIGSDPIPEVERPLFELV" \
+"SAAQTLLPDSEGAIDGHLREVGLTFHLLKDVPGLISKNIEKSLVEAFQPLGISDWNSLFW" \
+"IAHPGGPAILDQVELKLGLKQEKLKATRKVLSNYGNMSSACVLFILDEMRKASAKEGLGT" \
+"TGEGLEWGVLFGFGPGLTVETVVLHSVAT", generic_protein),
id="gi|13925890|gb|AAK49457.1|",
description="chalcone synthase [Nicotiana tabacum]")
my_records = [rec1, rec2, rec3]
Now we have a list of SeqRecord objects, we’ll write them to a FASTA format file:
from Bio import SeqIO
handle = open("my_example.faa", "w")
SeqIO.write(my_records, handle, "fasta")
handle.close()
And if you open this file in your favourite text editor it should look like this:
>gi|14150838|gb|AAK54648.1|AF376133_1 chalcone synthase [Cucumis sativus] MMYQQGCFAGGTVLRLAKDLAENNRGARVLVVCSEITAVTFRGPSETHLDSMVGQALFGD GAGAVIVGSDPDLSVERPLYELVWTGATLLPDSEGAIDGHLREVGLTFHLLKDVPGLISK NIEKSLKEAFTPLGISDWNSTFWIAHPGGPAILDQVEAKLGLKEEKMRATREVLSEYGNM SSAC >gi|13919613|gb|AAK33142.1| chalcone synthase [Fragaria vesca subsp. bracteata] YPDYYFRITNREHKAELKEKFQRMCDKSMIKKRYMYLTEEILKENPSMCEYMAPSLDARQ DMVVVEIPKLGKEAAVKAIKEWGQ >gi|13925890|gb|AAK49457.1| chalcone synthase [Nicotiana tabacum] MVTVEEFRRAQCAEGPATVMAIGTATPSNCVDQSTYPDYYFRITNSEHKVELKEKFKRMC EKSMIKKRYMHLTEEILKENPNICAYMAPSLDARQDIVVVEVPKLGKEAAQKAIKEWGQP KSKITHLVFCTTSGVDMPGCDYQLTKLLGLRPSVKRFMMYQQGCFAGGTVLRMAKDLAEN NKGARVLVVCSEITAVTFRGPNDTHLDSLVGQALFGDGAAAVIIGSDPIPEVERPLFELV SAAQTLLPDSEGAIDGHLREVGLTFHLLKDVPGLISKNIEKSLVEAFQPLGISDWNSLFW IAHPGGPAILDQVELKLGLKQEKLKATRKVLSNYGNMSSACVLFILDEMRKASAKEGLGT TGEGLEWGVLFGFGPGLTVETVVLHSVAT
In previous example we used a list of SeqRecord objects as input to the Bio.SeqIO.write() function, but it will also accept a SeqRecord interator like we get from Bio.SeqIO.parse() – this lets us do file conversion very succinctly. For this example we’ll read in the GenBank format file ls_orchid.gbk and write it out in FASTA format:
from Bio import SeqIO
in_handle = open("ls_orchid.gbk", "r")
out_handle = open("my_example.fasta", "w")
SeqIO.write(SeqIO.parse(in_handle, "genbank"), out_handle, "fasta")
in_handle.close()
out_handle.close()
You can in fact do this in one line, by being lazy about closing the file handles. This is arguably bad style, but its very concise:
from Bio import SeqIO
SeqIO.write(SeqIO.parse(open("ls_orchid.gbk"), "genbank"), open("my_example.faa", "w"), "fasta")
Suppose you had a file of nucleotide sequences, and you wanted to turn it into a file containing their reverse complement sequences. This time a little bit of work is required to transform the SeqRecords we get from our input file into something suitable for saving to our output file.
To start with, we’ll use Bio.SeqIO.parse() to load some nucleotide
sequences from a file, then print out their reverse complements using
the Seq object’s built in .reverse_complement() method:
from Bio import SeqIO
in_handle = open("ls_orchid.gbk")
for record in SeqIO.parse(in_handle, "genbank") :
print record.id
print record.seq.reverse_complement().tostring()
in_handle.close()
Now, if we want to save these reverse complements to a file, we’ll need to make SeqRecord objects. For this I think its most elegant to write our own function, where we can decide how to name our new records:
from Bio.SeqRecord import SeqRecord
def make_rc_record(record) :
"""Returns a new SeqRecord with the reverse complement sequence"""
rc_rec = SeqRecord(seq = record.seq.reverse_complement(), \
id = "rc_" + record.id, \
name = "rc_" + record.name, \
description = "reverse complement")
return rc_rec
We can then use this to turn the input records into reverse complement records ready for output. If you don’t mind about having all the records in memory at once, then the python map() function is a very intuitive way of doing this:
from Bio import SeqIO
in_handle = open("ls_orchid.fasta", "r")
records = map(make_rc_record, SeqIO.parse(in_handle, "fasta"))
in_handle.close()
out_handle = open("rev_comp.fasta", "w")
SeqIO.write(records, out_handle, "fasta")
out_handle.close()
This is an excellent place to demonstrate the power of list comprehensions (added to Python 2.0) which in their simplest are a long-winded equivalent to using map(), like this:
records = [make_rc_record(rec) for rec in SeqIO.parse(in_handle, "fasta")]
Now list comprehensions have a nice trick up their sleaves, you can add a conditional statement:
records = [make_rc_record(rec) for rec in SeqIO.parse(in_handle, "fasta") if len(rec.seq) < 700]
That would create an in memory list of reverse complement SeqRecords where the sequence length was under 700 base pairs. However, if you are using Python 2.4 or later, we can do exactly the same with a generator expression - but with the advantage that this does not create a list of all the records in memory at once:
records = (make_rc_record(rec) for rec in SeqIO.parse(in_handle, "fasta") if len(rec.seq) < 700)
If you like compact code, and don’t mind being lax about closing file handles, we can reduce this to one long line:
from Bio import SeqIO
SeqIO.write((make_rc_record(rec) for rec in \
SeqIO.parse(open("ls_orchid.fasta", "r"), "fasta") if len(rec.seq) < 700), \
open("rev_comp.fasta", "w"), "fasta")
Personally, I think the above snippet of code is a little too compact, and I find the following much easier to read:
from Bio import SeqIO
records = (make_rc_record(rec) for rec in \
SeqIO.parse(open("ls_orchid.fasta", "r"), "fasta") \
if len(rec.seq) < 700)
SeqIO.write(records, open("rev_comp.fasta", "w"), "fasta")
or, for Python 2.3 or older,
from Bio import SeqIO
records = [make_rc_record(rec) for rec in \
SeqIO.parse(open("ls_orchid.fasta", "r"), "fasta") \
if len(rec.seq) < 700]
SeqIO.write(records, open("rev_comp.fasta", "w"), "fasta")
Hey, everybody loves BLAST right? I mean, geez, how can get it get any easier to do comparisons between one of your sequences and every other sequence in the known world? Heck, if I was writing the code to do that it would probably take about a day and a half to complete, and the results still wouldn’t be as good. But, of course, this section isn’t about how cool BLAST is, since we already know that. It is about the problem with BLAST – it can be really difficult to deal with the volume of data generated by large runs, and to automate BLAST runs in general.
Fortunately, the Biopython folks know this only too well, so they’ve developed lots of tools for dealing with BLAST and making things much easier. This section details how to use these tools and do useful things with ’em.
Running BLAST locally (as opposed to over the internet, see Section 5.2) has two advantages:
Dealing with proprietary or unpublished sequence data can be another reason to run BLAST locally. You may not be allowed to redistribute the sequences, so submitting them to the NCBI as a BLAST query would not be an option.
Biopython provides lots of nice code to enable you to call local BLAST executables from your scripts, and have full access to the many command line options that these executables provide. You can obtain local BLAST precompiled for a number of platforms at ftp://ftp.ncbi.nlm.nih.gov/blast/executables/, or can compile it yourself in the NCBI toolbox (ftp://ftp.ncbi.nlm.nih.gov/toolbox/).
The code for dealing with local BLAST is found in Bio.Blast.NCBIStandalone, specifically in the functions blastall, blastpgp and rpsblast, which correspond with the BLAST executables that their names imply.
Let’s use these functions to run a blastall against a local database and return the results. First, we want to set up the paths to everything that we’ll need to do the BLAST. What we need to know is the path to the database (which should have been prepared using formatdb, see ftp://ftp.ncbi.nlm.nih.gov/blast/documents/formatdb.html) to search against, the path to the file we want to search, and the path to the blastall executable.
On Linux or Mac OS X your paths might look like this:
>>> my_blast_db = "/home/mdehoon/Data/Genomes/Databases/bsubtilis" # I used formatdb to create a BLAST database named bsubtilis # (for Bacillus subtilis) consisting of the following three files: # /home/mdehoon/Data/Genomes/Databases/bsubtilis.nhr # /home/mdehoon/Data/Genomes/Databases/bsubtilis.nin # /home/mdehoon/Data/Genomes/Databases/bsubtilis.nsq >>> my_blast_file = "m_cold.fasta" # A FASTA file with the sequence I want to BLAST >>> my_blast_exe = "/usr/local/blast/bin/blastall" # The name of my BLAST executable
while on Windows you might have something like this:
>>> my_blast_db = r"C:\Blast\Data\bsubtilis" # Assuming you used formatdb to create a BLAST database named bsubtilis # (for Bacillus subtilis) consisting of the following three files: # C:\Blast\Data\bsubtilis\bsubtilis.nhr # C:\Blast\Data\bsubtilis\bsubtilis.nin # C:\Blast\Data\bsubtilis\bsubtilis.nsq >>> my_blast_file = "m_cold.fasta" >>> my_blast_exe =r"C:\Blast\bin\blastall.exe"
The FASTA file used in this example is available here as well as online.
Now that we’ve got that all set, we are ready to run the BLAST and collect the results. We can do this with two lines:
>>> from Bio.Blast import NCBIStandalone
>>> result_handle, error_handle = NCBIStandalone.blastall(my_blast_exe, "blastn",
my_blast_db, my_blast_file)
Note that the Biopython interfaces to local blast programs returns two values. The first is a handle to the blast output, which is ready to either be saved or passed to a parser. The second is the possible error output generated by the blast command. See Section 9.1 for more about handles.
The error info can be hard to deal with, because if you try to do a error_handle.read() and there was no error info returned, then the read() call will block and not return, locking your script. In my opinion, the best way to deal with the error is only to print it out if you are not getting result_handle results to be parsed, but otherwise to leave it alone.
This command will generate BLAST output in XML format, as that is the format expected by the XML parser, described in Section 5.4. For plain text output, use the align_view='0' keyword. To parse text output instead of XML output, see the Section 5.6 below. However, parsing text output is not recommended, as the BLAST plain text output changes frequently, breaking our parsers.
If you are interested in saving your results to a file before parsing them, see Section 5.3. To find out how to parse the BLAST results, go to Section 5.4
The first step in automating BLASTing is to make everything accessible from Python scripts. So, Biopython contains code that allows you to run the WWW version of BLAST (http://www.ncbi.nlm.nih.gov/BLAST/) directly from your Python scripts. This is very nice, especially since BLAST can be a real pain to deal with from scripts, especially with the whole BLAST queue thing and the separate results page. Keeping the Biopython code up to date with all of the changes at NCBI is hard enough!
The code to deal with the WWW version of BLAST is found in the
Bio.Blast.NCBIWWW module, and the qblast function. Let’s
say we want to BLAST info we have in a FASTA formatted file against
the database. First, we need to get the info in the FASTA file. The
easiest way to do this is to use the Bio.SeqIO module.
In this example, we’ll use Bio.SeqIO.parse to parse the FASTA file
and store the first FASTA record in the file in a SeqRecord object
(section 2.4.1 explains Bio.SeqIO.parse in
more detail).
>>> from Bio import SeqIO
>>> file_handle = open("m_cold.fasta")
>>> records = SeqIO.parse(file_handle, format="fasta")
>>> record = records.next()
Now we take the sequence as a plain string from the SeqRecord:
>>> sequence = record.seq.data
and run BLAST on it. The code to do the simplest possible BLAST (a simple blastn of the FASTA file against all of the non-redundant databases) is:
>>> from Bio.Blast import NCBIWWW
>>> result_handle = NCBIWWW.qblast("blastn", "nr", sequence)
The qblast function also take a number of other option arguments
which are basically analogous to the different parameters you can set
on the basic BLAST page
(http://www.ncbi.nlm.nih.gov/blast/blast.cgi?Jform=0),
but for this I’ll just talk about the first few arguments, which are
the most important. The first three are non-optional.
qblast only works with blastn and blastp as program
arguments — let us known if you want to use one of the other blast
programs instead.
qblast function can return the BLAST results in various
formats, which you can choose with the optional format_type keyword:
"HTML", "Text", "ASN.1", or "XML".
The default is "XML", as that is the format expected by the parser,
described in section 5.4 below.
After you have set the search options, you are all ready to BLAST. Biopython takes care of worrying about when the results are available, and will pause until it can get the results and return them.
Before parsing the results, it is often useful to save them into a file so that you can use them later without having to go back and re-blast everything. I find this especially useful when debugging my code that extracts info from the BLAST files, but it could also be useful just for making backups of things you’ve done.
If you don’t want to save the BLAST output, you can skip to section 5.4. If you do, read on.
We need to be a bit careful since we can use result_handle.read() to
read the BLAST output only once – calling result_handle.read() again
returns an empty string. First, we use read() and store all of
the information from the handle into a string:
>>> blast_results = result_handle.read()
Next, we save this string in a file:
>>> save_file = open("my_blast.xml", "w")
>>> save_file.write(blast_results)
>>> save_file.close()
After doing this, the results are in the file my_blast.xml and the
variable blast_results contains the BLAST results in a string
form. However, the parse function of the BLAST parser (described
in 5.4) takes a file-handle-like object, not a
plain string. To get a handle, there are two things you can do:
cStringIO. The
following code will turn the plain string into a handle, which we can
feed directly into the BLAST parser:
>>> import cStringIO >>> result_handle = cStringIO.StringIO(blast_results)
>>> result_handle = open("my_blast.xml")
Now that we’ve got the BLAST results, we are ready to do something with them, so this leads us right into the parsing section.
As mentioned above, BLAST can generate output in various formats, such as XML, HTML, and plain text. Originally, Biopython had a parser for BLAST plain text and HTML output, as these were the output formats supported by BLAST. Unfortunately, the BLAST output in these formats kept changing, each time breaking the Biopython parsers. As keeping up with changes in BLAST became a hopeless endeavor, especially with users running different BLAST versions, we now recommend to parse the output in XML format, which can be generated by recent versions of BLAST. Not only is the XML output more stable than the plain text and HTML output, it is also much easier to parse automatically, making Biopython a whole lot more stable.
Though deprecated, the parsers for BLAST output in plain text or HTML output are still available in Biopython (see Section 5.6). Use them at your own risk: they may or may not work, depending on which BLAST version you’re using.
You can get BLAST output in XML format in various ways. For the parser, it doesn’t matter how the output was generated, as long as it is in the XML format.
The important point is that you do not have to use Biopython scripts to fetch the data in order to be able to parse it.
Doing things in one of these ways, you then need to get a handle
to the results. In Python, a handle is just a nice general way of
describing input to any info source so that the info can be retrieved
using read() and readline() functions. This is the type
of input the BLAST parser (and the other Biopython parsers take).
If you followed the code above for interacting with BLAST through a
script, then you already have result_handle, the handle to the
BLAST results. If instead you ran BLAST some other way, and have the
BLAST output (in XML format) in the file my_blast.xml, all you
need to do is to open the file for reading:
>>> result_handle = open("my_blast.xml")
Now that we’ve got a handle, we are ready to parse the output. The code to parse it is really quite small:
>>> from Bio.Blast import NCBIXML >>> blast_records = NCBIXML.parse(result_handle)
To understand what NCBIXML.parse returns, there are two things
that you need to keep in mind:
To be able to handle these situations, NCBIXML.parse returns an
iterator (just like Bio.SeqIO.parse). In plain English, an iterator
allows you to step through the BLAST output, retrieving BLAST records one
by one for each BLAST search:
>>> blast_record = blast_records.next() # ... do something with blast_record >>> blast_record = blast_records.next() # ... do something with blast_record >>> blast_record = blast_records.next() # ... do something with blast_record >>> blast_record = blast_records.next() Traceback (most recent call last): File "<stdin>", line 1, in <module> StopIteration # No further records
Or, you can use a for-loop:
>>> for blast_record in blast_records: ... # Do something with blast_record
Note though that you can step through the BLAST records only once. Usually, from each BLAST record you would save the information that you are interested in. If you want to save all returned BLAST records, you can convert the iterator into a list:
>>> blast_records = list(blast_records)
Now you can access each BLAST record in the list with an index as usual. If your BLAST file is huge though, you may run into problems trying to save them all in a list.
Usually, you’ll be running one BLAST search at a time. Then, all you need
to do is to pick up the first (and only) BLAST record in blast_records:
>>> blast_record = blast_records.next()
I guess by now you’re wondering what is in a BLAST record.
A BLAST Record contains everything you might ever want to extract from the BLAST output. Right now we’ll just show an example of how to get some info out of the BLAST report, but if you want something in particular that is not described here, look at the info on the record class in detail, and take a gander into the code or automatically generated documentation – the docstrings have lots of good info about what is stored in each piece of information.
To continue with our example, let’s just print out some summary info about all hits in our blast report greater than a particular threshold. The following code does this:
>>> E_VALUE_THRESH = 0.04 >>> for alignment in blast_record.alignments: ... for hsp in alignment.hsps: ... if hsp.expect < E_VALUE_THRESH: ... print '****Alignment****' ... print 'sequence:', alignment.title ... print 'length:', alignment.length ... print 'e value:', hsp.expect ... print hsp.query[0:75] + '...' ... print hsp.match[0:75] + '...' ... print hsp.sbjct[0:75] + '...'
This will print out summary reports like the following:
****Alignment**** sequence: >gb|AF283004.1|AF283004 Arabidopsis thaliana cold acclimation protein WCOR413-like protein alpha form mRNA, complete cds length: 783 e value: 0.034 tacttgttgatattggatcgaacaaactggagaaccaacatgctcacgtcacttttagtcccttacatattcctc... ||||||||| | ||||||||||| || |||| || || |||||||| |||||| | | |||||||| ||| ||... tacttgttggtgttggatcgaaccaattggaagacgaatatgctcacatcacttctcattccttacatcttcttc...
Basically, you can do anything you want to with the info in the BLAST report once you have parsed it. This will, of course, depend on what you want to use it for, but hopefully this helps you get started on doing what you need to do!
An important consideration for extracting information from a BLAST report is the type of objects that the information is stored in. In Biopython, the parsers return Record objects, either Blast or PSIBlast depending on what you are parsing. These objects are defined in Bio.Blast.Record and are quite complete.
Here are my attempts at UML class diagrams for the Blast and PSIBlast record classes. If you are good at UML and see mistakes/improvements that can be made, please let me know. The Blast class diagram is shown in Figure 5.5.
The PSIBlast record object is similar, but has support for the rounds that are used in the iteration steps of PSIBlast. The class diagram for PSIBlast is shown in Figure 5.5.
Older versions of Biopython had parsers for BLAST output in plain text or HTML format. Over the years, we discovered that it is very hard to maintain these parsers in working order. Basically, any small change to the BLAST output in newly released BLAST versions tends to cause the plain text and HTML parsers to break. We therefore recommend parsing BLAST output in XML format, as described in section 5.4. Whereas the plain text and HTML parsers are still available in Biopython, use them at your own risk. They may or may not work, depending on which BLAST versions you’re using.
The plain text BLAST parser is located in Bio.Blast.NCBIStandalone.
As with the XML parser, we need to have a handle object that we can pass to the parser. The handle must implement the readline() method and do this properly. The common ways to get such a handle are to either use the provided blastall or blastpgp functions to run the local blast, or to run a local blast via the command line, and then do something like the following:
>>> result_handle = open("my_file_of_blast_output.txt")
Well, now that we’ve got a handle (which we’ll call result_handle),
we are ready to parse it. This can be done with the following code:
>>> from Bio.Blast import NCBIStandalone >>> blast_parser = NCBIStandalone.BlastParser() >>> blast_record = blast_parser.parse(result_handle)
This will parse the BLAST report into a Blast Record class (either a Blast or a PSIBlast record, depending on what you are parsing) so that you can extract the information from it. In our case, let’s just use print out a quick summary of all of the alignments greater than some threshold value.
>>> E_VALUE_THRESH = 0.04 >>> for alignment in b_record.alignments: ... for hsp in alignment.hsps: ... if hsp.expect < E_VALUE_THRESH: ... print '****Alignment****' ... print 'sequence:', alignment.title ... print 'length:', alignment.length ... print 'e value:', hsp.expect ... print hsp.query[0:75] + '...' ... print hsp.match[0:75] + '...' ... print hsp.sbjct[0:75] + '...'
If you also read the section 5.4 on parsing BLAST XML output, you’ll notice that the above code is identical to what is found in that section. Once you parse something into a record class you can deal with it independent of the format of the original BLAST info you were parsing. Pretty snazzy!
Sure, parsing one record is great, but I’ve got a BLAST file with tons of records – how can I parse them all? Well, fear not, the answer lies in the very next section.
Of course, local blast is cool because you can run a whole bunch of sequences against a database and get back a nice report on all of it. So, Biopython definitely has facilities to make it easy to parse humongous files without memory problems.
We can do this using the blast iterator. To set up an iterator, we first set up a parser, to parse our blast reports in Blast Record objects:
>>> from Bio.Blast import NCBIStandalone >>> blast_parser = NCBIStandalone.BlastParser()
Then we will assume we have a handle to a bunch of blast records, which we’ll call result_handle. Getting a handle is described in full detail above in the blast parsing sections.
Now that we’ve got a parser and a handle, we are ready to set up the iterator with the following command:
>>> blast_iterator = NCBIStandalone.Iterator(blast_handle, blast_parser)
The second option, the parser, is optional. If we don’t supply a parser, then the iterator will just return the raw BLAST reports one at a time.
Now that we’ve got an iterator, we start retrieving blast records (generated by our parser) using next():
>>> blast_record = blast_iterator.next()
Each call to next will return a new record that we can deal with. Now we can iterate through this records and generate our old favorite, a nice little blast report:
>>> while 1:
... b_record = b_iterator.next()
... if b_record is None:
... break
... E_VALUE_THRESH = 0.04
... for alignment in b_record.alignments:
... for hsp in alignment.hsps:
... if hsp.expect < E_VALUE_THRESH:
... print '****Alignment****'
... print 'sequence:', alignment.title
... print 'length:', alignment.length
... print 'e value:', hsp.expect
... if len(hsp.query) > 75:
... dots = '...'
... else:
... dots = ''
... print hsp.query[0:75] + dots
... print hsp.match[0:75] + dots
... print hsp.sbjct[0:75] + dots
Notice that b_iterator.next() will return None when it runs out of records to parse, so it is easy to iterate through the entire file with a while loop that checks for the existence of a record.
The iterator allows you to deal with huge blast records without any memory problems, since things are read in one at a time. I have parsed tremendously huge files without any problems using this.
One really ugly problem that happens to me is that I’ll be parsing a huge blast file for a while, and the parser will bomb out with a SyntaxError. This is a serious problem, since you can’t tell if the SyntaxError is due to a parser problem, or a problem with the BLAST. To make it even worse, you have no idea where the parse failed, so you can’t just ignore the error, since this could be ignoring an important data point.
We used to have to make a little script to get around this problem, but the Bio.Blast module now includes a BlastErrorParser which really helps make this easier. The BlastErrorParser works very similar to the regular BlastParser, but it adds an extra layer of work by catching SyntaxErrors that are generated by the parser, and attempting to diagnose the errors.
Let’s take a look at using this parser – first we define the file we are going to parse and the file to write the problem reports to:
>>> import os >>> blast_file = os.path.join(os.getcwd(), "blast_out", "big_blast.out") >>> error_file = os.path.join(os.getcwd(), "blast_out", "big_blast.problems")
Now we want to get a BlastErrorParser:
>>> from Bio.Blast import NCBIStandalone >>> error_handle = open(error_file, "w") >>> blast_error_parser = NCBIStandalone.BlastErrorParser(error_handle)
Notice that the parser take an optional argument of a handle. If a handle is passed, then the parser will write any blast records which generate a SyntaxError to this handle. Otherwise, these records will not be recorded.
Now we can use the BlastErrorParser just like a regular blast parser. Specifically, we might want to make an iterator that goes through our blast records one at a time and parses them with the error parser:
>>> result_handle = open(blast_file) >>> iterator = NCBIStandalone.Iterator(result_handle, blast_error_parser)
Just like before, we can read these records one a time with, but now we can catch and deal with errors that are due to problems with Blast (and not with the parser itself):
>>> try: ... next_record = iterator.next() ... except NCBIStandalone.LowQualityBlastError, info: ... print "LowQualityBlastError detected in id %s" % info[1]
Right now the BlastErrorParser can generate the following errors:
SyntaxError – This is the same error generated by the regular BlastParser, and is due to the parser not being able to parse a specific file. This is normally either due to a bug in the parser, or some kind of discrepancy between the version of BLAST you are using and the versions the parser is able to handle.LowQualityBlastError – When BLASTing a sequence that is of really bad quality (for example, a short sequence that is basically a stretch of one nucleotide), it seems that Blast ends up masking out the entire sequence and ending up with nothing to parse. In this case it will produce a truncated report that causes the parser to generate a SyntaxError. LowQualityBlastError is reported in these cases. This error returns an info item with the following information:
item[0] – The error message
item[1] – The id of the input record that caused the error. This is really useful if you want to record all of the records that are causing problems.
As mentioned, with each error generated, the BlastErrorParser will write the offending record to the specified error_handle. You can then go ahead and look and these and deal with them as you see fit. Either you will be able to debug the parser with a single blast report, or will find out problems in your blast runs. Either way, it will definitely be a useful experience!
Hopefully the BlastErrorParser will make it much easier to debug and deal with large Blast files.
We should write some stuff to make it easier to deal directly with PSIBlast from scripts (i. e. output the align file in the proper format from an alignment). I need to look at PSIBlast more and come up with some good ways of going this...
SwissProt (http://www.expasy.org/sprot/sprot-top.html) is a hand-curated database of protein sequences. Let’s look at how we connect with it from a script and parse SwissProt formatted results. We touched on this briefly in Section 4.1.3.
First, we need to retrieve some results to parse. Let’s say we are looking at chalcone synthases for Orchids (see section 2.3 for some justification for looking for interesting things about orchids). Chalcone synthase is involved in flavanoid biosynthesis in plants, and flavanoids make lots of cool things like pigment colors and UV protectants.
If you do a search on SwissProt, you can find three orchid proteins for Chalcone Synthase, id numbers O23729, O23730, O23731. Now, let’s write a script which grabs these, and parses out some interesting information.
First, we grab the records, using the get_sprot_raw() function of Bio.WWW.ExPASy. This function is very nice since you can feed it an id and get back a raw text record (no html to mess with!). As we get the three records we are interested in, we’ll just put them together into one big string, which we’ll then use for parsing. The following code accomplishes what I just wrote:
from Bio.WWW import ExPASy
ids = ["O23729", "O23730", "O23731"]
all_results = ""
for eid in ids:
results = ExPASy.get_sprot_raw(eid)
all_results = all_results + results.read()
Now that we’ve got the results, we are ready to parse them into interesting information. As with most parsers, we set up an iterator and parser. The parser we use here parses the SwissProt files into a Record object where the interesting features are attributes:
from Bio.SwissProt import SProt from Bio import File s_parser = SProt.RecordParser() s_iterator = SProt.Iterator(File.StringHandle(all_results), s_parser)
Note that we convert all_results, which is a string, into a handle before passing it. The iterator requires a handle to be passed so that it can read in everything one line at a time. The Bio.File module has a nice StringHandle, which conveniently will convert a string into a handle. Very nice! Now we are ready to start extracting information.
To get out the information, we’ll go through everything record by record using the iterator. For each record, we’ll just print out some summary information:
while 1:
cur_record = s_iterator.next()
if cur_record is None:
break
print "description:", cur_record.description
for ref in cur_record.references:
print "authors:", ref.authors
print "title:", ref.title
print "classification:", cur_record.organism_classification
print
This prints out a summary like the following:
description: CHALCONE SYNTHASE 8 (EC 2.3.1.74) (NARINGENIN-CHALCONE SYNTHASE 8) authors: Liew C.F., Lim S.H., Loh C.S., Goh C.J.; title: "Molecular cloning and sequence analysis of chalcone synthase cDNAs of Bromheadia finlaysoniana."; classification: ['Eukaryota', 'Viridiplantae', 'Embryophyta', 'Tracheophyta', 'Spermatophyta', 'Magnoliophyta', 'Liliopsida', 'Asparagales', 'Orchidaceae', 'Bromheadia']
Since Python 2.2, we can simplify the loop:
for cur_record in s_iterator:
print "description:", cur_record.description
for ref in cur_record.references:
print "authors:", ref.authors
print "title:", ref.title
print "classification:", cur_record.organism_classification
print
It is equally easy to extract any kind of information you’d like from SwissProt records.
Now, you may remark that I knew the records’ accession numbers
beforehand. Indeed, get_sprot_raw() needs either the entry name
or an accession number. When you don’t have them handy, you can use
one of the sprot_search_de() or sprot_search_ful()
functions.
sprot_search_de() searches in the ID, DE, GN, OS and OG lines;
sprot_search_ful() searches in (nearly) all the fields. They
are detailed on
http://www.expasy.org/cgi-bin/sprot-search-de and
http://www.expasy.org/cgi-bin/sprot-search-ful
respectively. Note that thet don’t search in TrEMBL by default
(argument trembl). Note also that they return html pages;
however, accession numbers are quite easily extractable:
from Bio.WWW import ExPASy
import re
handle = ExPASy.sprot_search_de("Orchid Chalcone Synthase")
# or:
# handle = ExPASy.sprot_search_ful("Orchid and {Chalcone Synthase}")
html_results = handle.read()
if "Number of sequences found" in html_results:
ids = re.findall(r'HREF="/uniprot/(\w+)"', html_results)
else:
ids = re.findall(r'href="/cgi-bin/niceprot\.pl\?(\w+)"', html_results)
If you are in the Medical field or interested in human issues (and many times even if you are not!), PubMed (http://www.ncbi.nlm.nih.gov/PubMed/) is an excellent source of all kinds of goodies. So like other things, we’d like to be able to grab information from it and use it in python scripts.
Querying PubMed using Biopython is extremely painless. To get all of the article ids for articles having to do with orchids (see section 2.3 for our motivation), we only need the following three lines of code:
from Bio import PubMed search_term = 'orchid' orchid_ids = PubMed.search_for(search_term)
This returns a python list containing all of the orchid ids
['11070358', '11064040', '11028023', '10947239', '10938351', '10936520', '10905611', '10899814', '10856762', '10854740', '10758893', '10716342', ...
With this list of ids we are ready to start retrieving the records, so follow on ahead to the next section.
The previous section described how to get a bunch of article ids. Now that we’ve got them, we obviously want to get the corresponding Medline records and extract the information from them.
The interface for retrieving records from PubMed should be very intuitive to python programmers – it models a python dictionary. To set up this interface, we need to set up a parser that will parse the results that we retrieve. The following lines of code get everything set up:
from Bio import PubMed from Bio import Medline rec_parser = Medline.RecordParser() medline_dict = PubMed.Dictionary(parser = rec_parser)
What we’ve done is create a dictionary like object medline_dict. To get an article we access it like medline_dict[id_to_get]. What this does is connect with PubMed, get the article you ask for, parse it into a record object, and return it. Very cool!
Now let’s look at how to use this nice dictionary to print out some information about some ids. We just need to loop through our ids (orchid_ids from the previous section) and print out the information we are interested in:
for oid in orchid_ids[0:5]:
cur_record = medline_dict[oid]
print 'title:', cur_record.title.rstrip()
print 'authors:', cur_record.authors
print 'source:', cur_record.source.strip()
print
The output for this looks like:
title: Sex pheromone mimicry in the early spider orchid (ophrys sphegodes): patterns of hydrocarbons as the key mechanism for pollination by sexual deception [In Process Citation] authors: ['Schiestl FP', 'Ayasse M', 'Paulus HF', 'Lofstedt C', 'Hansson BS', 'Ibarra F', 'Francke W'] source: J Comp Physiol [A] 2000 Jun;186(6):567-74
Especially interesting to note is the list of authors, which is returned as a standard python list. This makes it easy to manipulate and search using standard python tools. For instance, we could loop through a whole bunch of entries searching for a particular author with code like the following:
search_author = 'Waits T'
for our_id in our_id_list:
cur_record = medline_dict[our_id]
if search_author in cur_record.authors:
print "Author %s found: %s" % (search_author,
cur_record.source.strip())
The PubMed and Medline interfaces are very mature and nice to work with – hopefully this section gave you an idea of the power of the interfaces and how they can be used.
The GenBank record format is a very popular method of holding information about sequences, sequence features, and other associated sequence information. The format is a good way to get information from the NCBI databases at http://www.ncbi.nlm.nih.gov/.
One very nice feature of the GenBank libraries is the ability to automate retrieval of entries from GenBank. This is very convenient for creating scripts that automate a lot of your daily work. In this example we’ll show how to query the NCBI databases, and to retrieve the records from the query - something touched on in Section 4.1.2.
First, we want to make a query and find out the ids of the records to retrieve. Here we’ll do a quick search for Opuntia, my favorite organism (since I work on it). We can do quick search and get back the GIs (GenBank identifiers) for all of the corresponding records:
from Bio import GenBank
gi_list = GenBank.search_for("Opuntia AND rpl16")
gi_list will be a list of all of the GenBank identifiers that match our query:
['6273291', '6273290', '6273289', '6273287', '6273286', '6273285', '6273284']
Now that we’ve got the GIs, we can use these to access the NCBI database through a dictionary interface. For instance, to retrieve the information for the first GI, we’ll first have to create a dictionary that accesses NCBI:
ncbi_dict = GenBank.NCBIDictionary("nucleotide", "genbank")
Now that we’ve got this, we do the retrieval:
gb_record = ncbi_dict[gi_list[0]]
In this case, gb_record will be GenBank formatted record:
LOCUS AF191665 902 bp DNA PLN 07-NOV-1999
DEFINITION Opuntia marenae rpl16 gene; chloroplast gene for chloroplast
product, partial intron sequence.
ACCESSION AF191665
VERSION AF191665.1 GI:6273291
...
In this case, we are just getting the raw records. We can also pass these records directly into a parser and return the parsed record. For instance, if we wanted to get back SeqRecord objects with the GenBank file parsed into SeqFeature objects we would need to create the dictionary with the GenBank FeatureParser:
record_parser = GenBank.FeatureParser()
ncbi_dict = GenBank.NCBIDictionary("nucleotide", "genbank",
parser = record_parser)
Now retrieving a record will give you a SeqRecord object instead of the raw record:
>>> gb_seqrecord = ncbi_dict[gi_list[0]] >>> print gb_seqrecord <Bio.SeqRecord.SeqRecord instance at 0x102f9404>
For more information of formats you can parse GenBank records into, please see section 6.3.2.
Using these automated query retrieval functionality is a big plus over doing things by hand. Additionally, the retrieval has nice built in features like a time-delay, which will prevent NCBI from getting mad at you and blocking your access.
While GenBank files are nice and have lots of information, at the same time you probably only want to extract a small amount of that information at a time. The key to doing this is parsing out the information. Biopython provides GenBank parsers which help you accomplish this task. Right now the GenBank module provides the following parsers:
Depending on the type of GenBank files you are interested in, they will either contain a single record, or multiple records. Each record will start with a LOCUS line, various other header lines, a list of features, and finally the sequence data, ending with a // line.
Dealing with a GenBank file containing a single record is very easy. For example, let’s use a small bacterial genome, Nanoarchaeum equitans Kin4-M (RefSeq NC_005213, GenBank AE017199) which can be downloaded from the NCBI here (only 1.15 MB):
from Bio import GenBank feature_parser = GenBank.FeatureParser() gb_file = "AE017199.gbk" gb_record = feature_parser.parse(open(gb_file,"r")) # now do something with the record print "Name %s, %i features" % (gb_record.name, len(gb_record.features)) print gb_record.seq
This gives the following output:
Name AE017199, 1107 features
Seq('TCTCGCAGAGTTCTTTTTTGTATTAACAAACCCAAAACCCATAGAATTTAATGAACCCAA ...', IUPACAmbiguousDNA())
For multi-record GenBank files, the most common usage will be creating an iterator, and parsing through the file record by record. Doing this is very similar to how things are done in other formats, as the following code demonstrates, using an example file cor6_6.gb which is included in the BioPython source code under the Tests/GenBank/ directory:
from Bio import GenBank
gb_file = "cor6_6.gb"
gb_handle = open(gb_file, "r")
feature_parser = GenBank.FeatureParser()
gb_iterator = GenBank.Iterator(gb_handle, feature_parser)
while True:
cur_record = gb_iterator.next()
if cur_record is None:
break
# now do something with the record
print "Name %s, %i features" % (cur_record.name, len(cur_record.features))
print cur_record.seq
This just iterates over a GenBank file, parsing it into SeqRecord and SeqFeature objects, and prints out the Seq objects representing the sequences in the record.
As with other formats, you have lots of tools for dealing with GenBank records. This should make it possible to do whatever you need to with GenBank.
One very cool thing that you can do is set up your own personal GenBank database and access it like a dictionary (this can be extra cool because you can also allow access to these local databases over a network using BioCorba – see the BioCorba documentation for more information).
Note - this is only worth doing if your GenBank file contains more than one record.
Making a local database first involves creating an index file, which will allow quick access to any record in the file. To do this, we use the index file function.
Again, this example uses the file cor6_6.gb which is included in the BioPython source code under the Tests/GenBank/ directory:
>>> from Bio import GenBank >>> dict_file = 'cor6_6.gb' >>> index_file = 'cor6_6.idx' >>> GenBank.index_file(dict_file, index_file)
This will create a directory called cor6_6.idx containing the index files. Now, we can use this index to create a dictionary object that allows individual access to every record. Like the Iterator and NCBIDictionary interfaces, we can either get back raw records, or we can pass the dictionary a parser that will parse the records before returning them. In this case, we pass a FeatureParser so that when we get a record, then we retrieve a SeqRecord object.
Setting up the dictionary is as easy as one line:
>>> gb_dict = GenBank.Dictionary(index_file, GenBank.FeatureParser())
Now we can deal with this like a dictionary. For instance:
>>> len(gb_dict) 6 >>> gb_dict.keys() ['L31939', 'AJ237582', 'X62281', 'AF297471', 'M81224', 'X55053']
Finally, we retrieve objects using subscripting:
>>> gb_dict['AJ237582'] <Bio.SeqRecord.SeqRecord instance at 0x102fdd8c> >>> print len(gb_dict['X55053'].features) 3
It is often very useful to be able to align particular sequences. I do this quite often to get a quick and dirty idea of relationships between sequences. Consequently, it is very nice to be able to quickly write up a python script that does an alignment and gives you back objects that are easy to work with. The alignment related code in Biopython is meant to allow python-level access to alignment programs so that you can run alignments quickly from within scripts.
Clustalx (http://www-igbmc.u-strasbg.fr/BioInfo/ClustalX/Top.html) is a very nice program for doing multiple alignments. Biopython offers access to alignments in clustal format (these normally have a *.aln extension) that are produced by Clustalx. It also offers access to clustalw, which the is command line version of clustalx.
We’ll need some sequences to align, such as opuntia.fasta (also available online here) which is a small FASTA file containing seven orchid gene DNA sequences, which you can also from Doc/examples/ in the Biopython source distribution.
The first step in interacting with clustalw is to set up a command line you want to pass to the program. Clustalw has a ton of command line options, and if you set a lot of parameters, you can end up typing in a huge ol’ command line quite a bit. This command line class models the command line by making all of the options be attributes of the class that can be set. A few convenience functions also exist to set certain parameters, so that some error checking on the parameters can be done.
To create a command line object to do a clustalw multiple alignment we do the following:
import os
from Bio.Clustalw import MultipleAlignCL
cline = MultipleAlignCL(os.path.join(os.curdir, "opuntia.fasta"))
cline.set_output("test.aln")
First we import the MultipleAlignCL object, which models running a multiple alignment from clustalw. We then initialize the command line, with a single argument of the fasta file that we are going to be using for the alignment. The initialization function also takes an optional second argument which specifies the location of the clustalw executable. By default, the commandline will just be invoked with ’clustalw,’ assuming that you’ve got it somewhere on your PATH.
The second argument sets the output to go to the file test.aln. The MultipleAlignCL object also has numerous other parameters to specify things like output format, gap costs, etc.
We can look at the command line we have generated by invoking the __str__ member attribute of the MultipleAlignCL class. This is done by calling str(cline) or simple by printing out the command line with print cline. In this case, doing this would give the following output:
clustalw ./opuntia.fasta -OUTFILE=test.aln
Now that we’ve set up a simple command line, we now want to run the commandline and collect the results so we can deal with them. This can be done using the do_alignment function of Clustalw as follows:
from Bio import Clustalw alignment = Clustalw.do_alignment(cline)
What happens when you run this if that Biopython executes your command line and runs clustalw with the given parameters. It then grabs the output, and if it is in a format that Biopython can parse (currently only clustal format), then it will parse the results and return them as an alignment object of the appropriate type. So in this case since we are getting results in the default clustal format, the returned alignment object will be a ClustalAlignment type.
Once we’ve got this alignment, we can do some interesting things with it such as get seq_record objects for all of the sequences involved in the alignment:
all_records = alignment.get_all_seqs() print 'description:', all_records[0].description print 'sequence:', all_records[0].seq
This prints out the description and sequence object for the first sequence in the alignment:
description: gi|6273285|gb|AF191659.1|AF191
sequence: Seq('TATACATTAAAGAAGGGGGATGCGGATAAATGGAAAGGCGAAAGAAAGAAAAAAATGAAT
...', IUPACAmbiguousDNA())
You can also calculate the maximum length of the alignment with:
length = alignment.get_alignment_length()
Finally, to write out the alignment object in the original format, we just need to access the __str__ function. So doing a print alignment gives:
CLUSTAL X (1.81) multiple sequence alignment gi|6273285|gb|AF191659.1|AF191 TATACATTAAAGAAGGGGGATGCGGATAAATGGAAAGGCGAAAGAAAGAA gi|6273284|gb|AF191658.1|AF191 TATACATTAAAGAAGGGGGATGCGGATAAATGGAAAGGCGAAAGAAAGAA ...
This makes it easy to write your alignment back into a file with all of the original info intact.
If you want to do more interesting things with an alignment, the best thing to do is to pass the alignment to an alignment information generating object, such as the SummaryInfo object, described in section 6.4.2.
Once you have an alignment, you are very likely going to want to find out information about it. Instead of trying to have all of the functions that can generate information about an alignment in the alignment object itself, we’ve tried to separate out the functionality into separate classes, which act on the alignment.
Getting ready to calculate summary information about an object is quick to do. Let’s say we’ve got an alignment object called alignment. All we need to do to get an object that will calculate summary information is:
from Bio.Align import AlignInfo summary_align = AlignInfo.SummaryInfo(alignment)
The summary_align object is very useful, and will do the following neat things for you:
The SummaryInfo object, described in section 6.4.2, provides functionality to calculate a quick consensus of an alignment. Assuming we’ve got a SummaryInfo object called summary_align we can calculate a consensus by doing:
consensus = summary_align.dumb_consensus()
As the name suggests, this is a really simple consensus calculator, and will just add up all of the residues at each point in the consensus, and if the most common value is higher than some threshold value (the default is .3) will add the common residue to the consensus. If it doesn’t reach the threshold, it adds an ambiguity character to the consensus. The returned consensus object is Seq object whose alphabet is inferred from the alphabets of the sequences making up the consensus. So doing a print consensus would give:
consensus Seq('TATACATNAAAGNAGGGGGATGCGGATAAATGGAAAGGCGAAAGAAAGAAAAAAATGAAT
...', IUPACAmbiguousDNA())
You can adjust how dumb_consensus works by passing optional parameters:
Position specific score matrices (PSSMs) summarize the alignment information in a different way than a consensus, and may be useful for different tasks. Basically, a PSSM is a count matrix. For each column in the alignment, the number of each alphabet letters is counted and totaled. The totals are displayed relative to some representative sequence along the left axis. This sequence may be the consesus sequence, but can also be any sequence in the alignment. For instance for the alignment:
GTATC AT--C CTGTC
the PSSM for this alignment is:
G A T C
G 1 1 0 1
T 0 0 3 0
A 1 1 0 0
T 0 0 2 0
C 0 0 0 3
Let’s assume we’ve got an alignment object called c_align. To get a PSSM with the consensus sequence along the side we first get a summary object and calculate the consensus sequence:
summary_align = AlignInfo.SummaryInfo(c_align) consensus = summary_align.dumb_consensus()
Now, we want to make the PSSM, but ignore any N ambiguity residues when calculating this:
my_pssm = summary_align.pos_specific_score_matrix(consensus,
chars_to_ignore = ['N'])
Two notes should be made about this:
second_seq = alignment.get_seq_by_num(1)
my_pssm = summary_align.pos_specific_score_matrix(second_seq
chars_to_ignore = ['N'])
The command above returns a PSSM object. To print out the PSSM as we showed above, we simply need to do a print my_pssm, which gives:
A C G T T 0.0 0.0 0.0 7.0 A 7.0 0.0 0.0 0.0 T 0.0 0.0 0.0 7.0 A 7.0 0.0 0.0 0.0 C 0.0 7.0 0.0 0.0 A 7.0 0.0 0.0 0.0 T 0.0 0.0 0.0 7.0 T 1.0 0.0 0.0 6.0 ...
You can access any element of the PSSM by subscripting like your_pssm[sequence_number][residue_count_name]. For instance, to get the counts for the ’A’ residue in the second element of the above PSSM you would do:
>>> print my_pssm[1]['A'] 7.0
The structure of the PSSM class hopefully makes it easy both to access elements and to pretty print the matrix.
A potentially useful measure of evolutionary conservation is the information ceontent of a sequence.
A useful introduction to information theory targetted towards molecular biologists can be found at http://www.lecb.ncifcrf.gov/~toms/paper/primer/. For our purposes, we will be looking at the information content of a consesus sequence, or a portion of a consensus sequence. We calculate information content at a particular column in a multiple sequence alignment using the following formula:
| ICj = |
| Pij * log( |
| ) |
where:
Well, now that we have an idea what information content is being calculated in Biopython, let’s look at how to get it for a particular region of the alignment.
First, we need to use our alignment to get a alignment summary object, which we’ll assume is called summary_align (see section 6.4.2) for instructions on how to get this. Once we’ve got this object, calculating the information content for a region is as easy as:
info_content = summary_align.information_content(5, 30,
chars_to_ignore = ['N'])
Wow, that was much easier then the formula above made it look! The variable info_content now contains a float value specifying the information content over the specified region (from 5 to 30 of the alignment). We specifically ignore the ambiguity residue ’N’ when calculating the information content, since this value is not included in our alphabet (so we shouldn’t be interested in looking at it!).
As mentioned above, we can also calculate relative information content by supplying the expected frequencies:
expect_freq = {
'A' : .3,
'G' : .2,
'T' : .3,
'C' : .2}
The expected should not be passed as a raw dictionary, but instead by passed as a SubsMat.FreqTable object (see section 7.4.2 for more information about FreqTables). The FreqTable object provides a standard for associating the dictionary with an Alphabet, similar to how the Biopython Seq class works.
To create a FreqTable object, from the frequency dictionary you just need to do:
from Bio.Alphabet import IUPAC
from Bio.SubsMat import FreqTable
e_freq_table = FreqTable.FreqTable(expect_freq, FreqTable.FREQ,
IUPAC.unambiguous_dna)
Now that we’ve got that, calculating the relative information content for our region of the alignment is as simple as:
info_content = summary_align.information_content(5, 30,
e_freq_table = e_freq_table,
chars_to_ignore = ['N'])
Now, info_content will contain the relative information content over the region in relation to the expected frequencies.
The value return is calculated using base 2 as the logarithm base in the formula above. You can modify this by passing the parameter log_base as the base you want:
info_content = summary_align.information_content(5, 30, log_base = 10
chars_to_ignore = ['N'])
Well, now you are ready to calculate information content. If you want to try applying this to some real life problems, it would probably be best to dig into the literature on information content to get an idea of how it is used. Hopefully your digging won’t reveal any mistakes made in coding this function!
One thing that you always end up having to do is convert between different formats. Biopython does this using a FormatConverter class for alignment objects. First, let’s say we have just parsed an alignment from clustal format into a ClustalAlignment object:
import os from Bio import Clustalw alignment = Clustalw.parse_file(os.path.join(os.curdir, "test.aln"))
Now, let’s convert this alignment into FASTA format. First, we create a converter object:
from Bio.Align.FormatConvert import FormatConverter converter = FormatConverter(alignment)
We pass the converter the alignment that we want to convert. Now, to get this in FASTA alignment format, we simply do the following:
fasta_align = converter.to_fasta()
Looking at the newly created fasta_align object using print fasta_align gives:
>gi|6273285|gb|AF191659.1|AF191 TATACATTAAAGAAGGGGGATGCGGATAAATGGAAAGGCGAAAGAAAGAATATATA---- ------ATATATTTCAAATTTCCTTATATACCCAAATATAAAAATATCTAATAAATTAGA ...
The conversion process will, of course, lose information specific to a particular alignment format. Howerver, most of the basic information about the alignment will be retained.
As more formats are added the converter will be beefed up to read and write all of these different formats.
Substitution matrices are an extremely important part of everyday bioinformatics work. They provide the scoring terms for classifying how likely two different residues are to substitute for each other. This is essential in doing sequence comparisons. The book “Biological Sequence Analysis” by Durbin et al. provides a really nice introduction to Substitution Matrices and their uses. Some famous substitution matrices are the PAM and BLOSUM series of matrices.
Biopython provides a ton of common substitution matrices, and also provides functionality for creating your own substitution matrices.
A very cool thing that you can do easily with the substitution matrix classes is to create your own substitution matrix from an alignment. In practice, this is normally done with protein alignments. In this example, we’ll first get a biopython alignment object and then get a summary object to calculate info about the alignment. The file containing protein.aln (also available online here) contains the Clustalw alignment output.
from Bio import Clustalw
from Bio.Alphabet import IUPAC
from Bio.Align import AlignInfo
# get an alignment object from a Clustalw alignment output
c_align = Clustalw.parse_file("protein.aln", IUPAC.protein)
summary_align = AlignInfo.SummaryInfo(c_align)
Sections 6.4.1 and 6.4.2 contain more information on doing this.
Now that we’ve got our summary_align object, we want to use it
to find out the number of times different residues substitute for each
other. To make the example more readable, we’ll focus on only amino
acids with polar charged side chains. Luckily, this can be done easily
when generating a replacement dictionary, by passing in all of the
characters that should be ignored. Thus we’ll create a dictionary of
replacements for only charged polar amino acids using:
replace_info = summary_align.replacement_dictionary(["G", "A", "V", "L", "I",
"M", "P", "F", "W", "S",
"T", "N", "Q", "Y", "C"])
This information about amino acid replacements is represented as a python dictionary which will look something like:
{('R', 'R'): 2079.0, ('R', 'H'): 17.0, ('R', 'K'): 103.0, ('R', 'E'): 2.0,
('R', 'D'): 2.0, ('H', 'R'): 0, ('D', 'H'): 15.0, ('K', 'K'): 3218.0,
('K', 'H'): 24.0, ('H', 'K'): 8.0, ('E', 'H'): 15.0, ('H', 'H'): 1235.0,
('H', 'E'): 18.0, ('H', 'D'): 0, ('K', 'D'): 0, ('K', 'E'): 9.0,
('D', 'R'): 48.0, ('E', 'R'): 2.0, ('D', 'K'): 1.0, ('E', 'K'): 45.0,
('K', 'R'): 130.0, ('E', 'D'): 241.0, ('E', 'E'): 3305.0,
('D', 'E'): 270.0, ('D', 'D'): 2360.0}
This information gives us our accepted number of replacements, or how often we expect different things to substitute for each other. It turns out, amazingly enough, that this is all of the information we need to go ahead and create a substitution matrix. First, we use the replacement dictionary information to create an Accepted Replacement Matrix (ARM):
from Bio import SubsMat my_arm = SubsMat.SeqMat(replace_info)
With this accepted replacement matrix, we can go right ahead and create our log odds matrix (i. e. a standard type Substitution Matrix):
my_lom = SubsMat.make_log_odds_matrix(my_arm)
The log odds matrix you create is customizable with the following optional arguments:
exp_freq_table – You can pass a table of expected
frequencies for each alphabet. If supplied, this will be used
instead of the passed accepted replacement matrix when calculate
expected replacments.logbase - The base of the logarithm taken to create the
log odd matrix. Defaults to base 10.factor - The factor to multiply each matrix entry
by. This defaults to 10, which normally makes the matrix numbers
easy to work with.round_digit - The digit to round to in the matrix. This
defaults to 0 (i. e. no digits).Once you’ve got your log odds matrix, you can display it prettily
using the function print_mat. Doing this on our created matrix
gives:
>>> my_lom.print_mat() D 6 E -5 5 H -15 -13 10 K -31 -15 -13 6 R -13 -25 -14 -7 7 D E H K R
Very nice. Now we’ve got our very own substitution matrix to play with!
You read all about the basic Biopython sequence class in section 2.2, which described how to do many useful things with just the sequence. Howerver, many times sequences have important additional properties associated with them. This section described how Biopython handles these higher level descriptions of a sequence.
Immediately above the Sequence class is the SeqRecord class, defined in the Bio.SeqRecord module. This class allows higher level features such as ids and features to be associated with the sequence. The class itself is very simple, and offers the following information as attributes:
Using a SeqRecord class is not very complicated, since all of the information is stored as attributes of the class. Initializing the class just involves passing a Seq object to the SeqRecord:
>>> from Bio.Seq import Seq
>>> simple_seq = Seq("GATC")
>>> from Bio.SeqRecord import SeqRecord
>>> simple_seq_r = SeqRecord(simple_seq)
Additionally, you can also pass the id, name and description to the initialization function, but if not they will be set as strings indicating they are unknown, and can be modified subsequently:
>>> simple_seq_r.id
'<unknown id>'
>>> simple_seq_r.id = 'AC12345'
>>> simple_seq_r.description = 'My little made up sequence I wish I could
write a paper about and submit to GenBank'
>>> print simple_seq_r.description
My little made up sequence I wish I could write a paper about and submit
to GenBank
>>> simple_seq_r.seq
Seq('GATC', Alphabet())
Adding annotations is almost as easy, and just involves dealing directly with the annotation dictionary:
>>> simple_seq_r.annotations['evidence'] = 'None. I just made it up.'
>>> print simple_seq_r.annotations
{'evidence': 'None. I just made it up.'}
That’s just about all there is to it! Next, you may want to learn about SeqFeatures, which offer an additional structured way to represent information about a sequence.
Sequence features are an essential part of describing a sequence. Once you get beyond the sequence itself, you need some way to organize and easily get at the more “abstract” information that is known about the sequence. While it is probably impossible to develop a general sequence feature class that will cover everything, the Biopython SeqFeature class attempts to encapsulate as much of the information about the sequence as possible. The design is heavily based on the GenBank/EMBL feature tables, so if you understand how they look, you’ll probably have an easier time grasping the structure of the Biopython classes.
The first level of dealing with Sequence features is the SeqFeature class itself. This class has a number of attributes, so first we’ll list them and there general features, and then work through an example to show how this applies to a real life example, a GenBank feature table. The attributes of a SeqFeature are:
ref to provide a cross sequence reference. If there is a reference, ref_db will be set as None if the reference is in the same database, and will be set to the name of the database otherwise.sub_features underneath it. This allows nesting of features, and helps us to deal with things such as the GenBank/EMBL feature lines in a (we hope) intuitive way.
To show an example of SeqFeatures in action, let’s take a look at the following feature from a GenBank feature table:
mRNA complement(join(<49223..49300,49780..>50208))
/gene="F28B23.12"
To look at the easiest attributes of the SeqFeature first, if you got a SeqFeature object for this it would have it type of ’mRNA’, a strand of -1 (due to the ’complement’), and would have None for the ref and ref_db since there are no references to external databases. The qualifiers for this SeqFeature would be a python dictionarary that looked like {'gene' : 'F28B23.12'}.
Now let’s look at the more tricky part, how the ’join’ in the location
line is handled. First, the location for the top level SeqFeature (the
one we are dealing with right now) is set as going from
'<49223' to '>50208' (see section 6.6.2.2 for
the nitty gritty on how fuzzy locations like this are handled).
So the location of the top level object is the entire span of the
feature. So, how do you get at the information in the ’join?’
Well, that’s where the sub_features go in.
The sub_features attribute will have a list with two SeqFeature
objects in it, and these contain the information in the join. Let’s
look at top_level_feature.sub_features[0]; the first
sub_feature). This object is a SeqFeature object with a
type of ’mRNA_join,’ a strand of -1 (inherited
from the parent SeqFeature) and a location going from
'<49223' to '49300'.
So, the sub_features allow you to get at the internal information if you want it (i. e. if you were trying to get only the exons out of a genomic sequence), or just to deal with the broad picture (i. e. you just want to know that the coding sequence for a gene lies in a region). Hopefully this structuring makes it easy and intuitive to get at the sometimes complex information that can be contained in a SeqFeature.
In the section on SeqFeatures above, we skipped over one of the more difficult parts of Features, dealing with the locations. The reason this can be difficult is because of fuzziness of the positions in locations. Before we get into all of this, let’s just define the vocabulary we’ll use to talk about this. Basically there are two terms we’ll use:
<100 and
3^5 are all positions.I just mention this because sometimes I get confused between the two.
The complication in dealing with locations comes in the positions themselves. In biology many times things aren’t entirely certain (as much as us wet lab biologists try to make them certain!). For instance, you might do a dinucleotide priming experiment and discover that the start of mRNA transcript starts at one of two sites. This is very useful information, but the complication comes in how to represent this as a position. To help us deal with this, we have the concept of fuzzy positions. Basically there are five types of fuzzy positions, so we have five classes do deal with them:
position attribute of the object.'<13', signifying that
the real position is located somewhere less then 13. To get
the specified upper boundary, look at the position
attribute of the object.BeforePosition, this
class represents a position that occurs after some specified site.
This is represented in GenBank as '>13', and like
BeforePosition, you get the boundary number by looking
at the position attribute of the object.position attribute specifies the lower boundary of the range we are looking at, so in our example case this would be one. The extension attribute specifies the range to the higher boundary, so in this case it would be 4. So object.position is the lower boundary and object.position + object.extension is the upper boundary.'2^3', which indicates that
the real position happens between position 2 and 3. Getting
this information from the object is very similar to
WithinPosition, the position attribute specifies
the lower boundary (2, in this case) and the extension
indicates the range to the higher boundary (1 in this case).
Now that we’ve got all of the types of fuzzy positions we can have taken care of, we are ready to actually specify a location on a sequence. This is handled by the FeatureLocation class. An object of this type basically just holds the potentially fuzzy start and end positions of a feature. You can create a FeatureLocation object by creating the positions and passing them in:
>>> from Bio import SeqFeature >>> start_pos = SeqFeature.AfterPosition(5) >>> end_pos = SeqFeature.BetweenPosition(8, 1) >>> my_location = SeqFeature.FeatureLocation(start_pos, end_pos)
If you print out a FeatureLocation object, you can get a nice representation of the information:
>>> print my_location [>5:(8^9)]
We can access the fuzzy start and end positions using the start and end attributes of the location:
>>> my_location.start <Bio.SeqFeature.AfterPosition instance at 0x101d7164> >>> print my_location.start >5 >>> print my_location.end (8^9)
If you don’t want to deal with fuzzy positions and just want numbers, you just need to ask for the nofuzzy_start and nofuzzy_end attributes of the location:
>>> my_location.nofuzzy_start 5 >>> my_location.nofuzzy_end 8
Notice that this just gives you back the position attributes of the fuzzy locations.
Similary, to make it easy to create a position without worrying about fuzzy positions, you can just pass in numbers to the FeaturePosition constructors, and you’ll get back out ExactPosition objects:
>>> exact_location = SeqFeature.FeatureLocation(5, 8) >>> print exact_location [5:8] >>> exact_location.start <Bio.SeqFeature.ExactPosition instance at 0x101dcab4>
That is all of the nitty gritty about dealing with fuzzy positions in Biopython. It has been designed so that dealing with fuzziness is not that much more complicated than dealing with exact positions, and hopefully you find that true!
Another common annotation related to a sequence is a reference to a journal or other published work dealing with the sequence. We have a fairly simple way of representing a Reference in Biopython – we have a Bio.SeqFeature.Reference class that stores the relevant information about a reference as attributes of an object.
The attributes include things that you would expect to see in a reference like journal, title and authors. Additionally, it also can hold the medline_id and pubmed_id and a comment about the reference. These are all accessed simply as attributes of the object.
A reference also has a location object so that it can specify a particular location on the sequence that the reference refers to. For instance, you might have a journal that is dealing with a particular gene located on a BAC, and want to specify that it only refers to this position exactly. The location is a potentially fuzzy location, as described in section 6.6.2.2.
That’s all there is too it. References are meant to be easy to deal with, and hopefully general enough to cover lots of usage cases.
A consistently annoying problem in bioinformatics is easily finding a sequence and making it available to your program. Sequences are available from a ton of standard locations like NCBI and EMBL. as well as from non-standard locations such as local databases or web servers. To make this problem easier, Biopython (as well as the other open-bio projects) is working towards a standard mechanism to allow specification of the locations of resources. Once locations are specified, your code using Biopython can readily retrieve sequences without having to worry about the specifics of where the sequence came from.
This transparency of retrieval has a number of advantages for your code. If a single web service is down (ie. NCBI is too busy and is refusing connections), backup locations can be tried without having any effect on the code that you wrote. Similary, you can have local repositories of sequences that you use often, and then if these repositories are off-line, switch to a web based service. Third, it keeps the details of retrieval out of your code, allowing you to focus on your biological problem, instead of focusing on boring retrieval details. Finally, it’s just a very cool idea.
This section deals with the specifics of setting up and using this system of automatically retrieving sequences. The first section deals with the interoperable configuration file method, while the second talks about a similar Biopython-specific method. The configuration file method is definately the way to go, unless you have specific needs it won’t give you.
Biopython has also developed a proprietary mechanism for retrieval that is Biopython only. This is only a good choice to use if the standard configuration file system doesn’t give you everything you want, since this method is not compatible with other open-bio projects.
By default, Biopython is configured to allow retrieval of sequences from a number of standard locations. This makes it useable immediately without knowing much about the system itself. To retrieve a Registry of databases, all you need to do is:
>>> from Bio import db
You can readily view all of the different databases that retrieval is possible be either printing the object and examining them, or programmatically through the keys() function of object:
>>> print db DBRegistry, exporting 'embl', 'embl-dbfetch-cgi', 'embl-ebi-cgi', 'embl-fast', 'embl-xembl-cgi', 'interpro-ebi-cgi', 'nucleotide-dbfetch-cgi', 'nucleotide-genbank-cgi', 'pdb', 'pdb-ebi-cgi', 'pdb-rcsb-cgi', 'prodoc-expasy-cgi', 'prosite-expasy-cgi', 'protein-genbank-cgi', 'swissprot', 'swissprot-expasy-cgi' >>> db.keys() ['embl-dbfetch-cgi', 'embl-fast', 'embl', 'prosite-expasy-cgi', 'swissprot-expasy-cgi', 'nucleotide-genbank-cgi', 'pdb-ebi-cgi', 'interpro-ebi-cgi', 'embl-ebi-cgi', 'embl-xembl-cgi', 'protein-genbank-cgi', 'pdb', 'prodoc-expasy-cgi', 'nucleotide-dbfetch-cgi', 'swissprot', 'pdb-rcsb-cgi']
Now, let’s say we want to retrieve a swissprot record for one of our orchid chalcone synthases. First, we get the swissprot connection, then we retrieve an record of interest:
>>> sp = db["swissprot"] >>> sp <Bio.DBRegistry.DBGroup instance at 0x82fdb2c> record_handle = sp['O23729'] >>> print record_handle.read()[:200] ID CHS3_BROFI STANDARD; PRT; 394 AA. AC O23729; DT 15-JUL-1999 (Rel. 38, Created) DT 15-JUL-1999 (Rel. 38, Last sequence update) DT 15-JUL-1999 (Rel. 38, Last annotation update)
This retrieval method is nice for a number of reasons. First, we didn’t have to worry about where exactly swissprot records were being retrieved from – we only ask for an object that will give us any swissprot record we can get. Secondly, once we get the swissprot object, we don’t need to worry about how we are getting our sequence – we just ask for it by id and don’t worry about the implementation details.
The default biopython database registry object can be used similarly to retrieve sequences from EMBL, prosite, PDB, interpro, GenBank and XEMBL.
The basic registry objects are nice in that they provide basic functionality, but if you have a more advanced system it is nice to be able to specify new databases. This is a more advanced topic, but is very possible with the current system.
This example describes adding a local CGI script serving out GenBank (ie. if you had something like a local mirror of GenBank), and then registering this and the normal NCBI GenBank as a single group to retrieve from. This would allow you to normally get things from a local mirror and then switch over to the main GenBank server if your server goes down, all without adjusting your retrieval code.
First, we need to describe the CGI script to retrieve from. This example uses a CGI script, but we eventually hope to handle other sources such as Applications, databases, or CORBA servers (XXX, should have an example once this is in place). We describe the CGI script as follows:
from Bio.sources import CGI
local_cgi = CGI(name = "local_cgi",
delay = 0.0,
cgi = "http://www.myserver.org/cgi-bin/my_local.cgi",
url = "http://www.myserver.org/cgi_documentation.html",
doc = "Query a local databases",
failure_cases = [])
Now that we have specified the details for connecting to the CGI script, we are ready to register this CGI script. We just need one more detail – specifying what the script returns upon failure to find a sequence. We do this using Martel regular expressions:
import Martel
my_failures = [
(Martel.Str("Sequence not available"), "No sequence found")]
Now we’ve got everything we need, and can register the database:
from Bio import register_db
register_db(name = "nucleotide-genbank-local",
key = "uid",
source = local_cgi,
failure = my_failures)
This makes the database available as before, so if we print the keys of
the database, we’ll see "nucleotide-genbank-local" available. Now that
we’ve got it registered, we’d like to link all of the genbank databases
together. We do this, using a group_db command. First, we need to
create a group named "genbank" to retrieve things from the database:
register_db(name = "genbank", behavior = "concurrent")
The behavior argument specifies how the group will try to
retrieve things from the various databases registered with it.
concurrent tells it to try to retrieve from all databases at
once, and then just take whatever sequence record comes back first. You
can also specify serial behavior, in which the retriever will
connect to one database at a time until something gets retrieved.
Now that we’ve got the group, we want to register our local GenBank and the NCBI GenBank with this command:
group_db("genbank", "nucleotide-genbank-local")
group_db("genbank", "nucleotide-genbank-cgi")
Now we’ve got our database access set up, and the database registry contains our genbank and nucleotide-genbank-local entries:
['embl-dbfetch-cgi', 'embl-fast', 'embl', 'prosite-expasy-cgi', 'swissprot-expasy-cgi', 'nucleotide-genbank-cgi', 'pdb-ebi-cgi', 'genbank', 'nucleotide-genbank-local', 'interpro-ebi-cgi', 'embl-ebi-cgi', 'embl-xembl-cgi', 'protein-genbank-cgi', 'pdb', 'prodoc-expasy-cgi', 'nucleotide-dbfetch-cgi', 'swissprot', 'pdb-rcsb-cgi']
Cool, now we can add our own databases to the registry and make use of the simplified retrieval scheme!
Biocorba does some cool stuff with CORBA. Basically, it allows you to easily interact with code written in other languages, including Perl and Java. This is all done through an interface which is very similar to the standard biopython interface. Much work has been done to make it easy to use knowing only very little about CORBA. You should check out the biocorba specific documentation, which describes in detail how to use it.
Biopython also allows you to explore the extensive realm of macromolecular structure. Biopython comes with a PDBParser class that produces a Structure object. The Structure object can be used to access the atomic data in the file in a convenient manner.
A macromolecular structure is represented using a structure, model chain, residue, atom (or SMCRA) hierarchy. Fig. 6.10.1 shows a UML class diagram of the SMCRA data structure. Such a data structure is not necessarily best suited for the representation of the macromolecular content of a structure, but it is absolutely necessary for a good interpretation of the data present in a file that describes the structure (typically a PDB or MMCIF file). If this hierarchy cannot represent the contents of a structure file, it is fairly certain that the file contains an error or at least does not describe the structure unambiguously. If a SMCRA data structure cannot be generated, there is reason to suspect a problem. Parsing a PDB file can thus be used to detect likely problems. We will give several examples of this in section 6.10.5.1.
Structure, Model, Chain and Residue are all subclasses of the Entity base class. The Atom class only (partly) implements the Entity interface (because an Atom does not have children).
For each Entity subclass, you can extract a child by using a unique id for that child as a key (e.g. you can extract an Atom object from a Residue object by using an atom name string as a key, you can extract a Chain object from a Model object by using its chain identifier as a key).
Disordered atoms and residues are represented by DisorderedAtom and DisorderedResidue classes, which are both subclasses of the DisorderedEntityWrapper base class. They hide the complexity associated with disorder and behave exactly as Atom and Residue objects.
In general, a child Entity object (i.e. Atom, Residue, Chain, Model) can be extracted from its parent (i.e. Residue, Chain, Model, Structure, respectively) by using an id as a key.
child_entity=parent_entity[child_id]
You can also get a list of all child Entities of a parent Entity object. Note that this list is sorted in a specific way (e.g. according to chain identifier for Chain objects in a Model object).
child_list=parent_entity.get_list()
You can also get the parent from a child.
parent_entity=child_entity.get_parent()
At all levels of the SMCRA hierarchy, you can also extract a full id. The full id is a tuple containing all id’s starting from the top object (Structure) down to the current object. A full id for a Residue object e.g. is something like:
full_id=residue.get_full_id()
print full_id
("1abc", 0, "A", ("", 10, "A"))
This corresponds to:
The Residue id indicates that the residue is not a hetero-residue (nor a water) because it has a blanc hetero field, that its sequence identifier is 10 and that its insertion code is "A".
Some other useful methods:
# get the entity's id entity.get_id() # check if there is a child with a given id entity.has_id(entity_id) # get number of children nr_children=len(entity)
It is possible to delete, rename, add, etc. child entities from a parent entity, but this does not include any sanity checks (e.g. it is possible to add two residues with the same id to one chain). This really should be done via a nice Decorator class that includes integrity checking, but you can take a look at the code (Entity.py) if you want to use the raw interface.
The Structure object is at the top of the hierarchy. Its id is a user given string. The Structure contains a number of Model children. Most crystal structures (but not all) contain a single model, while NMR structures typically consist of several models. Disorder in crystal structures of large parts of molecules can also result in several models.
A Structure object is produced by a PDBParser object:
from Bio.PDB.PDBParser import PDBParser p=PDBParser(PERMISSIVE=1) structure_id="1fat" filename="pdb1fat.ent" s=p.get_structure(structure_id, filename)
The PERMISSIVE flag indicates that a number of common problems (see 6.10.5.1) associated with PDB files will be ignored (but note that some atoms and/or residues will be missing). If the flag is not present a PDBConstructionException will be generated during the parse operation.
You can extract the header and trailer (simple lists of strings) of the PDB file from the PDBParser object with the get_header and get_trailer methods.
The id of the Model object is an integer, which is derived from the position of the model in the parsed file (they are automatically numbered starting from 0). The Model object stores a list of Chain children.
Get the first model from a Structure object.
first_model=structure[0]
The id of a Chain object is derived from the chain identifier in the structure file, and can be any string. Each Chain in a Model object has a unique id. The Chain object stores a list of Residue children.
Get the Chain object with identifier “A” from a Model object.
chain_A=model["A"]
Unsurprisingly, a Residue object stores a set of Atom children. In addition, it also contains a string that specifies the residue name (e.g. “ASN”) and the segment identifier of the residue (well known to X-PLOR users, but not used in the construction of the SMCRA data structure).
The id of a Residue object is composed of three parts: the hetero field (hetfield), the sequence identifier (resseq) and the insertion code (icode).
The hetero field is a string : it is “W” for waters, “H_” followed by the residue name (e.g. “H_FUC”) for other hetero residues and blank for standard amino and nucleic acids. This scheme is adopted for reasons described in section 6.10.3.1.
The second field in the Residue id is the sequence identifier, an integer describing the position of the residue in the chain.
The third field is a string, consisting of the insertion code. The insertion code is sometimes used to preserve a certain desirable residue numbering scheme. A Ser 80 insertion mutant (inserted e.g. between a Thr 80 and an Asn 81 residue) could e.g. have sequence identifiers and insertion codes as followed: Thr 80 A, Ser 80 B, Asn 81. In this way the residue numbering scheme stays in tune with that of the wild type structure.
Let’s give some examples. Asn 10 with a blank insertion code would have residue id (’’ ’’, 10, ’’ ’’). Water 10 would have residue id (‘‘W‘‘, 10, ‘‘ ‘‘). A glucose molecule (a hetero residue with residue name GLC) with sequence identifier 10 would have residue id (’’H_GLC’’, 10, ’’ ’’). In this way, the three residues (with the same insertion code and sequence identifier) can be part of the same chain because their residue id’s are distinct.
In most cases, the hetflag and insertion code fields will be blank, e.g. (’’ ’’, 10, ’’ ’’). In these cases, the sequence identifier can be used as a shortcut for the full id:
# use full id
res10=chain[("", 10, "")]
# use shortcut
res10=chain[10]
Each Residue object in a Chain object should have a unique id. However, disordered residues are dealt with in a special way, as described in section 6.10.2.3.2.
A Residue object has a number of additional methods:
r.get_resname() # return residue name, e.g. "ASN" r.get_segid() # return the SEGID, e.g. "CHN1"
The Atom object stores the data associated with an atom, and has no children. The id of an atom is its atom name (e.g. “OG” for the side chain oxygen of a Ser residue). An Atom id needs to be unique in a Residue. Again, an exception is made for disordered atoms, as described in section 6.10.2.2.
In a PDB file, an atom name consists of 4 chars, typically with leading and trailing spaces. Often these spaces can be removed for ease of use (e.g. an amino acid C α atom is labeled “.CA.” in a PDB file, where the dots represent spaces). To generate an atom name (and thus an atom id) the spaces are removed, unless this would result in a name collision in a Residue (i.e. two Atom objects with the same atom name and id). In the latter case, the atom name including spaces is tried. This situation can e.g. happen when one residue contains atoms with names “.CA.” and “CA..”, although this is not very likely.
The atomic data stored includes the atom name, the atomic coordinates (including standard deviation if present), the B factor (including anisotropic B factors and standard deviation if present), the altloc specifier and the full atom name including spaces. Less used items like the atom element number or the atomic charge sometimes specified in a PDB file are not stored.
An Atom object has the following additional methods:
a.get_name() # atom name (spaces stripped, e.g. "CA") a.get_id() # id (equals atom name) a.get_coord() # atomic coordinates a.get_bfactor() # B factor a.get_occupancy() # occupancy a.get_altloc() # alternative location specifie a.get_sigatm() # std. dev. of atomic parameters a.get_siguij() # std. dev. of anisotropic B factor a.get_anisou() # anisotropic B factor a.get_fullname() # atom name (with spaces, e.g. ".CA.")
To represent the atom coordinates, siguij, anisotropic B factor and sigatm Numpy arrays are used.
Disorder should be dealt with from two points of view: the atom and the residue points of view. In general, we have tried to encapsulate all the complexity that arises from disorder. If you just want to loop over all C α atoms, you do not care that some residues have a disordered side chain. On the other hand it should also be possible to represent disorder completely in the data structure. Therefore, disordered atoms or residues are stored in special objects that behave as if there is no disorder. This is done by only representing a subset of the disordered atoms or residues. Which subset is picked (e.g. which of the two disordered OG side chain atom positions of a Ser residue is used) can be specified by the user.
Disordered atoms are represented by ordinary Atom objects, but all Atom objects that represent the same physical atom are stored in a DisorderedAtom object. Each Atom object in a DisorderedAtom object can be uniquely indexed using its altloc specifier. The DisorderedAtom object forwards all uncaught method calls to the selected Atom object, by default the one that represents the atom with with the highest occupancy. The user can of course change the selected Atom object, making use of its altloc specifier. In this way atom disorder is represented correctly without much additional complexity. In other words, if you are not interested in atom disorder, you will not be bothered by it.
Each disordered atom has a characteristic altloc identifier. You can specify that a DisorderedAtom object should behave like the Atom object associated with a specific altloc identifier:
atom.disordered\_select("A") # select altloc A atom
print atom.get_altloc()
"A"
atom.disordered_select("B") # select altloc B atom
print atom.get_altloc()
"B"
The most common case is a residue that contains one or more disordered atoms. This is evidently solved by using DisorderedAtom objects to represent the disordered atoms, and storing the DisorderedAtom object in a Residue object just like ordinary Atom objects. The DisorderedAtom will behave exactly like an ordinary atom (in fact the atom with the highest occupancy) by forwarding all uncaught method calls to one of the Atom objects (the selected Atom object) it contains.
A special case arises when disorder is due to a point mutation, i.e. when two or more point mutants of a polypeptide are present in the crystal. An example of this can be found in PDB structure 1EN2.
Since these residues belong to a different residue type (e.g. let’s say Ser 60 and Cys 60) they should not be stored in a single Residue object as in the common case. In this case, each residue is represented by one Residue object, and both Residue objects are stored in a DisorderedResidue object.
The DisorderedResidue object forwards all uncaught methods to the selected Residue object (by default the last Residue object added), and thus behaves like an ordinary residue. Each Residue object in a DisorderedResidue object can be uniquely identified by its residue name. In the above example, residue Ser 60 would have id “SER” in the DisorderedResidue object, while residue Cys 60 would have id “CYS”. The user can select the active Residue object in a DisorderedResidue object via this id.
A common problem with hetero residues is that several hetero and non-hetero residues present in the same chain share the same sequence identifier (and insertion code). Therefore, to generate a unique id for each hetero residue, waters and other hetero residues are treated in a different way.
Remember that Residue object have the tuple (hetfield, resseq, icode) as id. The hetfield is blank (“ “) for amino and nucleic acids, and a string for waters and other hetero residues. The content of the hetfield is explained below.
The hetfield string of a water residue consists of the letter “W”. So a typical residue id for a water is (“W”, 1, “ “).
The hetfield string for other hetero residues starts with “H_” followed by the residue name. A glucose molecule e.g. with residue name “GLC” would have hetfield “H_GLC”. It’s residue id could e.g. be (“H_GLC”, 1, “ “).
Parse a PDB file, and extract some Model, Chain, Residue and Atom objects.
from PDBParser import PDBParser
parser=PDBParser()
structure=parser.get_structure("test", "1fat.pdb")
model=structure[0]
chain=model["A"]
residue=chain[1]
atom=residue["CA"]
Extract a hetero residue from a chain (e.g. a glucose (GLC) moiety with resseq 10).
residue_id=("H_GLC", 10, " ")
residue=chain[residue_id]
Print all hetero residues in chain.
for residue in chain.get_list(): residue_id=residue.get_id() hetfield=residue_id[0] if hetfield[0]=="H": print residue_id
Print out the coordinates of all CA atoms in a structure with B factor greater than 50.
for model in structure.get_list():
for chain in model.get_list():
for residue in chain.get_list():
if residue.has_id("CA"):
ca=residue["CA"]
if ca.get_bfactor()>50.0:
print ca.get_coord()
Print out all the residues that contain disordered atoms.
for model in structure.get_list()
for chain in model.get_list():
for residue in chain.get_list():
if residue.is_disordered():
resseq=residue.get_id()[1]
resname=residue.get_resname()
model_id=model.get_id()
chain_id=chain.get_id()
print model_id, chain_id, resname, resseq
Loop over all disordered atoms, and select all atoms with altloc A (if present). This will make sure that the SMCRA data structure will behave as if only the atoms with altloc A are present.
for model in structure.get_list()
for chain in model.get_list():
for residue in chain.get_list():
if residue.is_disordered():
for atom in residue.get_list():
if atom.is_disordered():
if atom.disordered_has_id("A"):
atom.disordered_select("A")
Suppose that a chain has a point mutation at position 10, consisting of a Ser and a Cys residue. Make sure that residue 10 of this chain behaves as the Cys residue.
residue=chain[10]
residue.disordered_select("CYS")
The PDBParser/Structure class was tested on about 800 structures (each belonging to a unique SCOP superfamily). This takes about 20 minutes, or on average 1.5 seconds per structure. Parsing the structure of the large ribosomal subunit (1FKK), which contains about 64000 atoms, takes 10 seconds on a 1000 MHz PC.
Three exceptions were generated in cases where an unambiguous data structure could not be built. In all three cases, the likely cause is an error in the PDB file that should be corrected. Generating an exception in these cases is much better than running the chance of incorrectly describing the structure in a data structure.
One structure contains two amino acid residues in one chain with the same sequence identifier (resseq 3) and icode. Upon inspection it was found that this chain contains the residues Thr A3, …, Gly A202, Leu A3, Glu A204. Clearly, Leu A3 should be Leu A203. A couple of similar situations exist for structure 1FFK (which e.g. contains Gly B64, Met B65, Glu B65, Thr B67, i.e. residue Glu B65 should be Glu B66).
Structure 1EJG contains a Ser/Pro point mutation in chain A at position 22. In turn, Ser 22 contains some disordered atoms. As expected, all atoms belonging to Ser 22 have a non-blank altloc specifier (B or C). All atoms of Pro 22 have altloc A, except the N atom which has a blank altloc. This generates an exception, because all atoms belonging to two residues at a point mutation should have non-blank altloc. It turns out that this atom is probably shared by Ser and Pro 22, as Ser 22 misses the N atom. Again, this points to a problem in the file: the N atom should be present in both the Ser and the Pro residue, in both cases associated with a suitable altloc identifier.
Some errors are quite common and can be easily corrected without much risk of making a wrong interpretation. These cases are listed below.
Normally each disordered atom should have a non-blanc altloc identifier. However, there are many structures that do not follow this convention, and have a blank and a non-blank identifier for two disordered positions of the same atom. This is automatically interpreted in the right way.
Sometimes a structure contains a list of residues belonging to chain A, followed by residues belonging to chain B, and again followed by residues belonging to chain A, i.e. the chains are “broken”. This is correctly interpreted.
Sometimes a PDB file cannot be unambiguously interpreted. Rather than guessing and risking a mistake, an exception is generated, and the user is expected to correct the PDB file. These cases are listed below.
All residues in a chain should have a unique id. This id is generated based on:
If this does not lead to a unique id something is quite likely wrong, and an exception is generated.
All atoms in a residue should have a unique id. This id is generated based on:
If this does not lead to a unique id something is quite likely wrong, and an exception is generated.
There are also some tools to analyze a crystal structure. Tools exist to superimpose two coordinate sets (SVDSuperimposer), to extract polypeptides from a structure (Polypeptide), to perform neighbor lookup (NeighborSearch) and to write out PDB files (PDBIO). The neighbor lookup is done using a KD tree module written in C++. It is very fast and also includes a fast method to find all point pairs within a certain distance of each other.
A Polypeptide object is simply a UserList of Residue objects. You can construct a list of Polypeptide objects from a Structure object as follows:
model_nr=1
polypeptide_list=build_peptides(structure, model_nr)
for polypeptide in polypeptide_list:
print polypeptide
The Polypeptide objects are always created from a single Model (in this case model 1).
Bio.PopGen is a new Biopython module supporting population genetics, available in Biopython 1.44 onwards.
The medium term objective for the module is to support widely used data formats, applications and databases. This module is currently under intense development and support for new features should appear at a rather fast pace. Unfortunately this might also entail some instability on the API, especially if you are using a CVS version. APIs that are made available on public versions should be much stabler.
GenePop (http://genepop.curtin.edu.au/) is a popular population genetics software package supporting Hardy-Weinberg tests, linkage desiquilibrium, population diferentiation, basic statistics, Fst and migration estimates, among others. GenePop does not supply sequence based statistics as it doesn’t handle sequence data. The GenePop file format is supported by a wide range of other population genetic software applications, thus making it a relevant format in the population genetics field.
Bio.PopGen provides a parser and generator of GenePop file format. Utilities to manipulate the content of a record are also provided. Here is an example on how to read a GenePop file (you can find example GenePop data files in the Test/PopGen directory of Biopython):
from Bio.PopGen import GenePop
handle = open("example.gen")
rec = GenePop.parse(handle)
handle.close()
This will read a file called example.gen and parse it. If you do print rec, the record will be output again, in GenePop format.
The most important information in rec will be the loci names and population information (but there is more – use help(GenePop.Record) to check the API documentation). Loci names can be found on rec.loci_list. Population information can be found on rec.populations. Populations is a list with one element per population. Each element is itself a list of individuals, each individual is a pair composed by individual name and a list of alleles (2 per marker), here is an example for rec.populations:
[
[
('Ind1', [(1, 2), (3, 3), (200, 201)],
('Ind2', [(2, None), (3, 3), (None, None)],
],
[
('Other1', [(1, 1), (4, 3), (200, 200)],
]
]
So we have two populations, the first with two individuals, the second with only one. The first individual of the first population is called Ind1, allelic information for each of the 3 loci follows. Please note that for any locus, information might be missing (see as an example, Ind2 above).
A few utility functions to manipulate GenePop records are made available, here is an example:
from Bio.PopGen import GenePop #Imagine that you have loaded rec, as per the code snippet above... rec.remove_population(pos) #Removes a population from a record, pos is the population position in # rec.populations, remember that it starts on position 0. # rec is altered. rec.remove_locus_by_position(pos) #Removes a locus by its position, pos is the locus position in # rec.loci_list, remember that it starts on position 0. # rec is altered. rec.remove_locus_by_name(name) #Removes a locus by its name, name is the locus name as in # rec.loci_list. If the name doesn't exist the function fails # silently. # rec is altered. rec_loci = rec.split_in_loci() #Splits a record in loci, that is, for each loci, it creates a new # record, with a single loci and all populations. # The result is returned in a dictionary, being each key the locus name. # The value is the GenePop record. # rec is not altered. rec_pops = rec.split_in_pops(pop_names) #Splits a record in populations, that is, for each population, it creates # a new record, with a single population and all loci. # The result is returned in a dictionary, being each key # the population name. As population names are not available in GenePop, # they are passed in array (pop_names). # The value of each dictionary entry is the GenePop record. # rec is not altered.
GenePop does not support population names, a limitation which can be cumbersome at times. Functionality to enable population names is currently being planned for Biopython. These extensions won’t break compatibility in any way with the standard format. In the medium term, we would also like to support the GenePop web service.
Here we discuss interfaces and utilities to deal with population genetics’ applications which arguably have a smaller user base.
FDist is a selection detection application suite based on computing (i.e. simulating) a “neutral” confidence interval based on Fst and heterozygosity. Markers (which can be SNPs, microsatellites, AFLPs among others) which lie outside the “neutral” interval are to be considered as possible candidates for being under selection.
FDist is mainly used when the number of markers is considered enough to estimate an average Fst, but not enough to either have outliers calculated from the dataset directly or, with even more markers for which the relative positions in the genome are known, to use approaches based on, e.g., Extended Haplotype Heterozygosity (EHH).
The typical usage pattern for FDist is as follows:
We will now discuss each step with illustrating example code (for this example to work FDist binaries have to be on the executable PATH).
The FDist data format is application specific and is not used at all by other applications, as such you will probably have to convert your data for use with FDist. Biopython can help you do this. Here is an example converting from GenePop format to FDist format (along with imports that will be needed on examples further below):
from Bio.PopGen import GenePop
from Bio.PopGen import FDist
from Bio.PopGen.FDist import Controller
from Bio.PopGen.FDist.Utils import convert_genepop_to_fdist
gp_rec = GenePop.parse(open("example.gen"))
fd_rec = convert_genepop_to_fdist(gp_rec)
in_file = open("infile", "w")
in_file.write(str(fd_rec))
in_file.close()
In this code we simply parse a GenePop file and convert it to a FDist record.
Printing an FDist record will generate a string that can be directly saved to a file and supplied to FDist. FDist requires the input file to be called infile, therefore we save the record on a file with that name.
The most important fields on a FDist record are: num_pops, the number of populations; num_loci, the number of loci and loci_data with the marker data itself. Most probably the details of the record are of no interest to the user, as the record only purpose is to be passed to FDist.
The next step is to calculate the average Fst of the dataset (along with the sample size):
ctrl = Controller.FDistController() fst, samp_size = ctrl.run_datacal()
On the first line we create an object to control the call of FDist suite, this object will be used further on in order to call other suite applications.
On the second line we call the datacal application which computes the average Fst and the sample size. It is worth noting that the Fst computed by datacal is a variation of Weir and Cockerham’s θ.
We can now call the main fdist application in order to simulate neutral markers.
sim_fst = ctrl.run_fdist(npops = 15, nsamples = fd_rec.num_pops, fst = fst,
sample_size = samp_size, mut = 0, num_sims = 40000)
The confusion in wording between number of samples and sample size stems from the original application.
A file named out.dat will be created with the simulated heterozygosities and Fsts, it will have as many lines as the number of simulations requested.
Note that fdist returns the average Fst that it was capable of simulating, for more details about this issue please read below the paragraph on approximating the desired average Fst.
The next (optional) step is to calculate the confidence interval:
cpl_interval = ctrl.run_cplot(ci=0.99)
You can only call cplot after having run fdist.
This will calculate the confidence intervals (99% in this case) for a previous fdist run. A list of quadruples is returned. The first element represents the heterozygosity, the second the lower bound of Fst confidence interval for that heterozygosity, the third the average and the fourth the upper bound. This can be used to trace the confidence interval contour. This list is also written to a file, out.cpl.
The main purpose of this step is return a set of points which can be easily used to plot a confidence interval. It can be skipped if the objective is only to assess the status of each marker against the simulation, which is the next step...
pv_data = ctrl.run_pv()
You can only call cplot after having run datacal and fdist.
This will use the simulated markers to assess the status of each individual real marker. A list, in the same order than the loci_list that is on the FDist record (which is in the same order that the GenePop record) is returned. Each element in the list is a quadruple, the fundamental member of each quadruple is the last element (regarding the other elements, please refer to the pv documentation – for the sake of simplicity we will not discuss them here) which returns the probability of the simulated Fst being lower than the marker Fst. Higher values would indicate a stronger candidate for positive selection, lower values a candidate for balancing selection, and intermediate values a possible neutral marker. What is “higher”, “lower” or “intermediate” is really a subjective issue, but taking a “confidence interval” approach and considering a 95% confidence interval, “higher” would be between 0.95 and 1.0, “lower” between 0.0 and 0.05 and “intermediate” between 0.05 and 0.95.
Fdist tries to approximate the desired average Fst by doing a coalescent simulation using migration rates based on the formula
PLEASE FIX THIS FORMULA!! USE ASCII ONLY!!
This formula assumes a few premises like an infinite number of populations.
In practice, when the number of populations is low, the mutation model is stepwise and the sample size increases, fdist will not be able to simulate an acceptable approximate average Fst.
To address that, a function is provided to iteratively approach the desired value by running several fdists in sequence. This approach is computationally more intensive than running a single fdist run, but yields good results. The following code runs fdist approximating the desired Fst:
sim_fst = ctrl.run_fdist_force_fst(npops = 15, nsamples = fd_rec.num_pops,
fst = fst, sample_size = samp_size, mut = 0, num_sims = 40000,
limit = 0.05)
The only new optional parameter, when comparing with run_fdist, is limit which is the desired maximum error. run_fdist can (and probably should) be safely replaced with run_fdist_force_fst.
The process to determine the average Fst can be more sophisticated than the one presented here. For more information we refer you to the FDist README file. Biopython’s code can be used to implement more sophisticated approaches.
The most desired future developments would be the ones you add yourself ;) .
That being said, already existing fully functional code is currently being incorporated in Bio.PopGen, that code covers the applications FDist and SimCoal2, the HapMap and UCSC Table Browser databases and some simple statistics like Fst, or allele counts.
Biopython has a regression testing framework originally written by Andrew Dalke and ported to PyUnit by Brad Chapman which helps us make sure the code is as bug-free as possible before going out.
Every module that goes into Biopython should have a test (and should also have documentation!). Let’s say you’ve written a new module called Biospam – here is what you should do to make a regression test:
test_Biospam.pypython test_Biospam.py > test_Biospam which would write the output to the file test_Biospam.test_Biospam to make sure the output is correct. When you are sure it is all right and there are no bugs, you need to quickly edit the test_Biospam file so that the first line is: ‘test_Biospam’ (no quotes).test_Biospam file to the directory Tests/outputpython run_tests.py -g test_Biospam.py. The
regression testing framework is nifty enough that it’ll put
the output in the right place in just the way it likes it. Tests/output/test_Biospam) and double check the output to make sure it is all correct. python run_tests.py. This will run all of the tests, and
you should see your test run (and pass!).Many of the Biopython parsers are built around an event-oriented design that includes Scanner and Consumer objects.
Scanners take input from a data source and analyze it line by line,
sending off an event whenever it recognizes some information in the
data. For example, if the data includes information about an organism
name, the scanner may generate an organism_name event whenever it
encounters a line containing the name.
Consumers are objects that receive the events generated by Scanners.
Following the previous example, the consumer receives the
organism_name event, and the processes it in whatever manner
necessary in the current application.
There are two types of events: info events that tag the location of information within a data stream, and section events that mark sections within a stream. Info events are associated with specific lines within the data, while section events are not.
Section event names must be in the format start_EVENTNAME and
end_EVENTNAME where EVENTNAME is the name of the event.
For example, a FASTA-formatted sequence scanner may generate the following events:
EVENT NAME ORIGINAL INPUT begin_sequence title >gi|132871|sp|P19947|RL30_BACSU 50S RIBOSOMAL PROTEIN L30 (BL27 sequence MAKLEITLKRSVIGRPEDQRVTVRTLGLKKTNQTVVHEDNAAIRGMINKVSHLVSVKEQ end_sequence begin_sequence title >gi|132679|sp|P19946|RL15_BACSU 50S RIBOSOMAL PROTEIN L15 sequence MKLHELKPSEGSRKTRNRVGRGIGSGNGKTAGKGHKGQNARSGGGVRPGFEGGQMPLFQRLPK sequence RKEYAVVNLDKLNGFAEGTEVTPELLLETGVISKLNAGVKILGNGKLEKKLTVKANKFSASAK sequence GTAEVI end_sequence [...]
(I cut the lines shorter so they’d look nicer in my editor).
The FASTA scanner generated the following events: title, sequence,
begin_sequence, and end_sequence. Note that the begin_sequence
and end_sequence events are not associated with any line in the
original input. They are used to delineate separate sequences within
the file.
The events a scanner can send must be specifically defined for each data format.
A data file can contain lines that have no meaningful information, such as blank lines. By convention, a scanner should generate the "noevent" event for these lines.
class Scanner:
def feed(self, handle, consumer):
# Implementation
Scanners should implement a method named ’feed’ that takes a file handle and a consumer. The scanner should read data from the file handle and generate appropriate events for the consumer.
class Consumer:
# event handlers
Consumers contain methods that handle events. The name of the method is the event that it handles. Info events are passed the line of the data containing the information, and section events are passed nothing.
You are free to ignore events that are not interesting for your application. You should just not implement methods for those events.
All consumers should be derived from the base Consumer class.
An example:
class FASTAConsumer(Consumer):
def title(self, line):
# do something with the title
def sequence(self, line):
# do something with the sequence
def begin_sequence(self):
# a new sequence starts
def end_sequence(self):
# a sequence ends
BLAST Scanners produce the following events:
header
version
reference
query_info
database_info
descriptions
description_header
round psi blast
model_sequences psi blast
nonmodel_sequences psi blast
converged psi blast
description
no_hits
alignment
multalign master-slave
title pairwise
length pairwise
hsp
score pairwise
identities pairwise
strand pairwise, blastn
frame pairwise, blastx, tblastn, tblastx
query pairwise
align pairwise
sbjct pairwise
database_report
database
posted_date
num_letters_in_database
num_sequences_in_database
num_letters_searched RESERVED. Currently unused. I've never
num_sequences_searched RESERVED. seen it, but it's in blastool.c..
ka_params
gapped not blastp
ka_params_gap gapped mode (not tblastx)
parameters
matrix
gap_penalties gapped mode (not tblastx)
num_hits
num_sequences
num_extends
num_good_extends
num_seqs_better_e
hsps_no_gap gapped (not tblastx) and not blastn
hsps_prelim_gapped gapped (not tblastx) and not blastn
hsps_prelim_gap_attempted gapped (not tblastx) and not blastn
hsps_gapped gapped (not tblastx) and not blastn
query_length
database_length
effective_hsp_length
effective_query_length
effective_database_length
effective_search_space
effective_search_space_used
frameshift blastx or tblastn or tblastx
threshold
window_size
dropoff_1st_pass
gap_x_dropoff
gap_x_dropoff_final gapped (not tblastx) and not blastn
gap_trigger
blast_cutoff
The Enzyme.py module works with the enzyme.dat file included with the Enzyme distribution. The Enzyme Scanner produces the following events:
record
identification
description
alternate_name
catalytic_activity
cofactor
comment
disease
prosite_reference
databank_reference
terminator
The Bio.KEGG.Enzyme module works with the ’enzyme’ file from the Ligand database, which can be obtained from the KEGG project. (http://www.genome.ad.jp/kegg).
The Bio.KEGG.Enzyme.Record contains all the information stored in a KEGG/Enzyme record. Its string representation also is a valid KEGG record, but it is NOT guaranteed to be equivalent to the record from which it was produced.
The Bio.KEGG.Enzyme.Scanner produces the following events:
entry name classname sysname reaction substrate product inhibitor cofactor effector comment pathway_db pathway_id pathway_desc organism gene_id disease_db disease_id disease_desc motif_db motif_id motif structure_db structure_id dblinks_db dblinks_id record_end
The Bio.KEGG.Compound module works with the ’compound’ file from the Ligand database, which can be obtained from the KEGG project. (http://www.genome.ad.jp/kegg).
The Bio.KEGG.Compound.Record contains all the information stored in a KEGG/Compound record. Its string representation also is a valid KEGG record, but it is NOT guaranteed to be equivalent to the record from which it was produced.
The Bio.KEGG.Enzyme.Scanner produces the following events:
entry name formula pathway_db pathway_id pathway_desc enzyme_id enzyme_role structure_db structure_id dblinks_db dblinks_id record_end
The Fasta.py module works with FASTA-formatted sequence data. The Fasta Scanner produces the following events:
sequence
title
sequence
The Online Services Reference Manual documents the MEDLINE format at: http://www.nlm.nih.gov/pubs/osrm_nlm.html
The Medline scanner produces the following events:
record
undefined
abstract_author
abstract
address
author
call_number
comments
class_update_date
country
entry_date
publication_date
english_abstract
entry_month
gene_symbol
identification
issue_part_supplement
issn
journal_title_code
language
special_list
last_revision_date
mesh_heading
mesh_tree_number
major_revision_date
no_author
substance_name
pagination
personal_name_as_subject
publication_type
number_of_references
cas_registry_number
record_originator
journal_subset
subheadings
secondary_source_id
source
title_abbreviation
title
transliterated_title
unique_identifier
volume_issue
year
pubmed_id
undefined is a special event that is called for every line with a qualifier not defined in the specification.
The Prosite scanner produces the following events:
copyrights
copyright
record
identification
accession
date
description
pattern
matrix
rule
numerical_results
comment
database_reference
pdb_reference
documentation
terminator
The PRODOC scanner produces the following events:
record
accession
prosite_reference
text
reference
The SProt.py module works with the sprotXX.dat file included with the SwissProt distribution. The SProt Scanner produces the following events:
record
identification
accession
date
description
gene_name
organism_species
organelle
organism_classification
reference_number
reference_position
reference_comment
reference_cross_reference
reference_author
reference_title
reference_location
comment
database_cross_reference
keyword
feature_table
sequence_header
sequence_data
terminator
The KeyWList.py modules works with the keywlist.txt file included with the SwissProt distribution. The KeyWList scanner produces the following events:
header
keywords
keyword
footer
copyright
The NBRF module works with NBRF-formatted sequence data. Data is available at: http://www-nbrf.georgetown.edu/pirwww/pirhome.shtml.
The NBRF Scanner produces the following events:
sequence_type
sequence_name
comment
sequence
The Ndb module works with Ndb-formatted sequence data. Data is available at: http://ndbserver.rutgers.edu/NDB/NDBATLAS/index.html.
The Ndb record contains the following items:
Id
Features
Name
Sequence
Citation
Space Group
Cell Constants
Crystallization Conditions
Refinement
Coordinates
Sequence is an instance of Crystal which is dictionary of Chain objects. Each chain is a sequence of PDB hetero items. Citation is a list of Reference objects. Crystal, Reference, Chain and Hetero are part of the biopython distribution.
The MetaTool parser works with MetaTool output files. MetaTool implements algorithms to decompose a biochemical pathway into a combination of simpler networks that are more accessible to analysis.
The MetaTool web page is http://pinguin.biologie.uni-jena.de/bioinformatik/networks/.
The MetaTool parser requires Numeric Python. Information is available at http://numpy.scipy.org/#older_array.
The Bio.MetaTool.Scanner produces the following events:
input_file_name num_int_metabolites num_reactions metabolite_line unbalanced_metabolite num_rows num_cols irreversible_vector branch_metabolite non_branch_metabolite stoichiometric_tag kernel_tag subsets_tag reduced_system_tag convex_basis_tag conservation_relations_tag elementary_modes_tag reaction enzyme matrix_row sum_is_constant_line end_stochiometric end_kernel end_subsets end_reduced_system end_convex_basis end_conservation_relations end_elementary_modes
This module provides a class and a few routines for generating substitution matrices, similar to BLOSUM or PAM matrices, but based on user-provided data.
Additionally, you may select a matrix from MatrixInfo.py, a collection of established substitution matrices.
class SeqMat(UserDict.UserDict)
self.data: a dictionary in the form of {(i1,j1):n1, (i1,j2):n2,...,(ik,jk):nk} where i, j are alphabet letters, and n is a value.self.alphabet: a class as defined in Bio.Alphabetself.ab_list: a list of the alphabet’s letters, sorted. Needed mainly for internal purposesself.sum_letters: a dictionary. {i1: s1, i2: s2,...,in:sn} where:
__init__(self,data=None,alphabet=None,
mat_type=NOTYPE,mat_name='',build_later=0):
data: can be either a dictionary, or another SeqMat instance.
alphabet: a Bio.Alphabet instance. If not provided, construct an alphabet from data.mat_type: type of matrix generated. One of the following:mat_type is provided automatically by some of SubsMat’s functions.
mat_name: matrix name, such as "BLOSUM62" or "PAM250"build_later: default false. If true, user may supply only alphabet and empty dictionary, if intending to build the matrix later. this skips the sanity check of alphabet size vs. matrix size. entropy(self,obs_freq_mat)
obs_freq_mat: an observed frequency matrix. Returns the matrix’s entropy, based on the frequency in obs_freq_mat. The matrix instance should be LO or SUBS.
letter_sum(self,letter)
Returns the sum of all values in the matrix, for the provided letter
all_letters_sum(self)
Fills the dictionary attribute self.sum_letters with the sum of values for each letter in the matrix’s alphabet.
print_mat(self,f,format="%4d",bottomformat="%4s",alphabet=None)
prints the matrix to file handle f. format is the format field for the matrix values; bottomformat is the format field for the bottom row, containing matrix letters. Example output for a 3-letter alphabet matrix:
A 23 B 12 34 C 7 22 27 A B C
The alphabet optional argument is a string of all characters in the alphabet. If supplied, the order of letters along the axes is taken from the string, rather than by alphabetical order.
The following section is layed out in the order by which most people wish to generate a log-odds matrix. Of course, interim matrices can be generated and investigated. Most people just want a log-odds matrix, that’s all.
Initially, you should generate an accepted replacement matrix (ARM) from your data. The values in ARM are the counted number of replacements according to your data. The data could be a set of pairs or multiple alignments. So for instance if Alanine was replaced by Cysteine 10 times, and Cysteine by Alanine 12 times, the corresponding ARM entries would be:
('A','C'): 10, ('C','A'): 12
as order doesn’t matter, user can already provide only one entry:
('A','C'): 22
A SeqMat instance may be initialized with either a full (first method of counting: 10, 12) or half (the latter method, 22) matrices. A full protein alphabet matrix would be of the size 20x20 = 400. A half matrix of that alphabet would be 20x20/2 + 20/2 = 210. That is because same-letter entries don’t change. (The matrix diagonal). Given an alphabet size of N:
The SeqMat constructor automatically generates a half-matrix, if a full matrix is passed. If a half matrix is passed, letters in the key should be provided in alphabetical order: (’A’,’C’) and not (’C’,A’).
At this point, if all you wish to do is generate a log-odds matrix, please go to the section titled Example of Use. The following text describes the nitty-gritty of internal functions, to be used by people who wish to investigate their nucleotide/amino-acid frequency data more thoroughly.
Use:
OFM = SubsMat._build_obs_freq_mat(ARM)
The OFM is generated from the ARM, only instead of replacement counts, it contains replacement frequencies.
Use:
EFM = SubsMat._build_exp_freq_mat(OFM,exp_freq_table)
exp_freq_table: should be a FreqTable instance. See section 7.4.2 for detailed information on FreqTable. Briefly, the expected frequency table has the frequencies of appearance for each member of the alphabet. It is
implemented as a dictionary with the alphabet letters as keys, and each letter’s frequency as a value. Values sum to 1.
The expected frequency table can (and generally should) be generated from the observed frequency matrix. So in most cases you will generate exp_freq_table using:
>>> exp_freq_table = SubsMat._exp_freq_table_from_obs_freq(OFM) >>> EFM = SubsMat._build_exp_freq_mat(OFM,exp_freq_table)
But you can supply your own exp_freq_table, if you wish
Use:
SFM = SubsMat._build_subs_mat(OFM,EFM)
Accepts an OFM, EFM. Provides the division product of the corresponding values.
Use:
LOM=SubsMat._build_log_odds_mat(SFM[,logbase=10,factor=10.0,round_digit=1])
logbase: base of the logarithm used to generate the log-odds values. factor: factor used to multiply the log-odds values. Each entry is generated by log(LOM[key])*factor And rounded to the round_digit place after the decimal point, if required.As most people would want to generate a log-odds matrix, with minimum hassle, SubsMat provides one function which does it all:
make_log_odds_matrix(acc_rep_mat,exp_freq_table=None,logbase=10,
factor=10.0,round_digit=0):
acc_rep_mat: user provided accepted replacements matrix
exp_freq_table: expected frequencies table. Used if provided, if not, generated from the acc_rep_mat.
logbase: base of logarithm for the log-odds matrix. Default base 10.
round_digit: number after decimal digit to which result should be rounded. Default zero.
FreqTable.FreqTable(UserDict.UserDict)
alphabet: A Bio.Alphabet instance.data: frequency dictionarycount: count dictionary (in case counts are provided).
read_count(f): read a count file from stream f. Then convert to frequenciesread_freq(f): read a frequency data file from stream f. Of course, we then don’t have the counts, but it is usually the letter frquencies which are interesting.The expected count of the residues in the database is sitting in a file, whitespace delimited, in the following format (example given for a 3-letter alphabet):
A 35 B 65 C 100
And will be read using the FreqTable.read_count(file_handle) function.
An equivalent frequency file:
A 0.175 B 0.325 C 0.5
Conversely, the residue frequencies or counts can be passed as a dictionary. Example of a count dictionary (3-letter alphabet):
{'A': 35, 'B': 65, 'C': 100}
Which means that an expected data count would give a 0.5 frequency for ’C’, a 0.325 probability of ’B’ and a 0.175 probability of ’A’ out of 200 total, sum of A, B and C)
A frequency dictionary for the same data would be:
{'A': 0.175, 'B': 0.325, 'C': 0.5}
Summing up to 1.
When passing a dictionary as an argument, you should indicate whether it is a count or a frequency dictionary. Therefore the FreqTable class constructor requires two arguments: the dictionary itself, and FreqTable.COUNT or FreqTable.FREQ indicating counts or frequencies, respectively.
Read expected counts. readCount will already generate the frequencies Any one of the following may be done to geerate the frequency table (ftab):
>>> from SubsMat import *
>>> ftab = FreqTable.FreqTable(my_frequency_dictionary,FreqTable.FREQ)
>>> ftab = FreqTable.FreqTable(my_count_dictionary,FreqTable.COUNT)
>>> ftab = FreqTable.read_count(open('myCountFile'))
>>> ftab = FreqTable.read_frequency(open('myFrequencyFile'))
We try to release Biopython to make it as easy to install as possible for users. Thus, we try to provide the Biopython libraries in as many install formats as we can. Doing this from release to release can be a lot of work for developers, and sometimes requires them to maintain packages they are not all that familiar with. This section is meant to provide tips to encourage other people besides developers to maintain platform builds.
In general, this is fairly easy – all you would need to do is produce the system specific package whenever we make a release. You should then check the package (of course!) to make sure it installs everything properly. Then you just send it to one of the main developers, they stick the package on the web site and just like that, you’ve contributed to Biopython! Snazzy.
Below are some tips for certain platforms to maybe get people started with helping out:
To make the RPM, you just need to do:
python setup.py bdist_rpm
This will create an RPM for your specific platform and a source RPM in the directory dist. This RPM should be good and ready to go, so this is all you need to do! Nice and easy.
You must first make sure you have a C compiler on your Windows computer, and that you can compile and install things (see the Biopython installation instructions for info on how to do this).
Once you are setup with a C compiler, making the installer just requires doing:
python setup.py bdist_wininst
Now you’ve got a Windows installer. Congrats!
Once you’ve got a package, please test it on your system to make sure it installs everything in a good way and seems to work properly. Once you feel good about it, send it off to one of the biopython developers (write to our main list serve at biopython@biopython.org if you’re not sure who to send it to) and you’ve done it. Thanks!
Getting feedback on the Biopython modules is very important to us. Open-source projects like this benefit greatly from feedback, bug-reports (and patches!) from a wide variety of contributors.
The main forums for discussing feature requests and potential bugs are the biopython development lists:
Additionally, if you think you’ve found a bug, you can submit it to our bug-tracking page at http://bugzilla.open-bio.org/. This way, it won’t get buried in anyone’s Inbox and forgotten about.
There are no barriers to joining biopython code development other than an interest in creating biology-related code in python. The best place to express an interest is on the biopython mailing lists – just let us know you are interested in coding and what kind of stuff you want to work on. Normally, we try to have some discussion on modules before coding them, since that helps generate good ideas – then just feel free to jump right in and start coding!
The main biopython release tries to be fairly uniform and interworkable, to make it easier for users. You can read about some of (fairly informal) coding style guidelines we try to use in biopython in the contributing documentation at http://biopython.org/wiki/Contributing. We also try to add code to the distribution along with tests (see section 7.2 for more info on the regression testing framework) and documentation, so that everything can stay as workable and well documented as possible. This is, of course, the most ideal situation, under many situations you’ll be able to find other people on the list who will be willing to help add documentation or more tests for your code once you make it available. So, to end this paragraph like the last, feel free to start working!
Additionally, if you have code that you don’t think fits in the distribution, but that you want to make available, we maintain Script Central (http://biopython.org/wiki/Scriptcentral) which has pointers to freely available code in python for bioinformatics.
Hopefully this documentation has got you excited enough about biopython to try it out (and most importantly, contribute!). Thanks for reading all the way through!
If you haven’t spent a lot of time programming in python, many questions and problems that come up in using Biopython are often related to python itself. This section tries to present some ideas and code that come up often (at least for us!) while using the Biopython libraries. If you have any suggestions for useful pointers that could go here, please contribute!
Handles are mentioned quite frequently throughout this documentation, and are also fairly confusing (at least to me!). Basically, you can think of a handle as being a “wrapper” around text information.
Handles provide (at least) two benefits over plain text information:
Handles can deal with text information that is being read (e. g. reading
from a file) or written (e. g. writing information to a file). In the
case of a “read” handle, commonly used functions are read(),
which reads the entire text information from the handle, and
readline(), which reads information one line at a time. For
“write” handles, the function write() is regularly used.
The most common usage for handles is reading information from a file,
which is done using the built-in python function open. Here, we open a
handle to the file m_cold.fasta
(also available online
here):
>>> handle = open("m_cold.fasta", "r")
>>> handle.readline()
">gi|8332116|gb|BE037100.1|BE037100 MP14H09 MP Mesembryanthemum ...\n"
Handles are regularly used in Biopython for passing information to parsers.
One useful thing is to be able to turn information contained in a
string into a handle. The following example shows how to do this using
cStringIO from the Python standard library:
>>> my_info = 'A string\n with multiple lines.' >>> print my_info A string with multiple lines. >>> import cStringIO >>> my_info_handle = cStringIO.StringIO(my_info) >>> first_line = my_info_handle.readline() >>> print first_line A string >>> second_line = my_info_handle.readline() >>> print second_line with multiple lines.
This document was translated from LATEX by HEVEA.