Summary: The Biopython project is a mature open source international collaboration of volunteer developers, providing Python libraries for a wide range of bioinformatics problems. Biopython includes modules for reading and writing different sequence file formats and multiple sequence alignments, dealing with 3D macro molecular structures, interacting with common tools such as BLAST, ClustalW and EMBOSS, accessing key online databases, as well as providing numerical methods for statistical learning. Availability: Biopython is freely available, with documentation and source code at www. Contact: All queries should be directed to the Biopython mailing lists, see www. Python www. Python is a very high-level programming language, in widespread commercial and academic use.
|Published (Last):||20 October 2009|
|PDF File Size:||16.74 Mb|
|ePub File Size:||10.4 Mb|
|Price:||Free* [*Free Regsitration Required]|
Biopython is the largest and most common bioinformatics package for Python. It contains a number of different sub-modules for common bioinformatics tasks. The Biopython code is distributed under the Biopython License. Data structures for biological sequences and features thereof, as well as a multitude of manipulation functions for performing common tasks, such as translation, transcription, weight computations, Functions for loading biological data into dict -like data structures.
Functions for data analysis , including basic data mining and machine learning algorithm: k -Nearest Neighbors, Naive Bayes, and Support Vector Machines. In order to use local services such as Blast , the latter must be properly installed and configured on the local machine. Please refer to the official documentation for further instructions. While Biopython is the main player in the field, it is not the only one. Other interesting packages are:. ETE and DendroPy , dedicated to computation and visualization of phylogenetic trees.
Sequences lay at the core of bioinformatics: although DNA, RNA and proteins are molecules with specific structures and dynamical behaviors, their basic building block is their sequence. Biopython encodes sequences using objects of type Seq , provided by the Bio. Seq sub-module. Take a look at their manual:. The official documentation of the Biopython Seq class can be found on the Biopython wiki.
Each Seq object is associated to a formal alphabet. The various alphabets supported by Biopython can be found in Bio. Alphabet , e. To create a generic sequence, i. To create a DNA sequence, specify the corresponding alphabet when creating the Seq object:. Seq objects act mostly like standard Python strings str.
For instance, you can write:. However, some operations are restricted to Seq objects with the same alphabet in order to prevent the user from, say, concatenating nucleotides and amino acids :.
To convert back a Seq object into a normal str object, just use the normal type-casting operator str :. Just like strings, Seq objects are immutable :. Thanks to their immutability, Seq objects can be used as keys in dictionaries like str objects do.
Biopython also provides a mutable sequence type MutableSeq. You can convert back and forth between Seq and MutableSeq :. Some operations are only available when specific alphabets are used.
Adapted from the Biopython tutorial. From the template strand instead we first have to compute the reverse complement, and then perform the character substitution:. These functions also take care of checking that the input alphabet is the right one e. DNA and apply the right alphabet to the result e. GenBank is a richer sequence format for genes, it includes fields for various kinds of annotations e.
The Bio. SeqIO module provides a functions to parse all of the above among others. In particular, the parse function takes a file handle and the format of the file as a string and returns an iterable of SeqRecord objects:.
An iterable is a function that allows to iterate over a collection of objects similar to a list. The iterable allows to iterate over a list of SeqRecord objects. The SeqRecord objects associate a Seq sequence with additional meta-data, such as human-readable descriptions, annotations, and sequence features:. Each SeqRecord object provides these attributes:. In order to read a SeqRecord object from a file:. Here record.
This way, you can almost forget about the format of the sequence data you are working with — all the parsing is done automatically by Biopython. Finally, the SeqIO. Biopython allows to manipulate polypeptide structures via the Bio. PDB module. The PDB is by far the largest protein structure resource available online. At the time of writing, it hosts more thank k distinct protein structures, including protein-protein, protein-DNA, protein-RNA complexes. For the documentation of the Bio.
PDB module, see:. In the following we will use the Zaire Ebolavirus glicoprotein structure 5JQ3 , available here:. The mmcif file format. The actual file extension is. See for instance:. The pdb file format, which is a specially formatted text file. This is the historical file format. It is mostly still distributed because of legacy tools that do not yet understand the newer and cleaner mmcif files. Some many? PDB files distributed by the Protein Data Bank contain formatting errors that make them ambiguous or difficult to parse.
PDB module attempts to deal with these errors automatically. Of course, Biopython is not perfect, and some formatting errors may still make it do the wrong thing, or raise an exception.
PDB module implements two different parsers, one for the pdb format and one for the mmcif format. To load a cif file, use the Bio. To load a pdb file, use the Bio. PDBParser module:. As you can see, both parsers give you the same kind of object, a Structure. The header of the structure is a dict which stores meta-data about the protein structure, including its resolution in Angstroms , name, author, release date, etc. The header dictionary may be an empty, depending on the structure and the parser.
Try it out with the mmcif parser! Note that the database takes quite a bit of disk space. The hierarchy is represented by Bio. More specifically, here are the related Bio. PDB classes:. A Structure describes one or more models , i.
The Structure. Here I turned the iterator into a proper list with the list function. A Structure also acts as a dict : given a model ID, it returns the corresponding Model object:. A Model describes exactly one 3D conformation. It contains one or more chains. The Model. A Model also acts as a dict mapping from chain IDs to Chain objects:. A Chain describes a proper polypeptide structure, i. The Chain. A Residue holds the atoms that belong to an amino acid. The Residue.
An Atom holds the 3D coordinate of an atom, as a Vector :. The x, y, z coordinates in a Vector can be accessed with:. This is a bit more complicated, due to the clumsy PDB format. A residue id is a tuple with three elements:.
The reason for the hetero-flag is that many, many PDB files use the same sequence identifier for an amino acid and a hetero-residue or a water, which would create obvious problems if the hetero-flag was not used. Given a Structure object, it is easy to write it to disk in pdb format:.
In order to be comparable, structures must first be aligned : superimposed on top of each other. Say you have a wildtype protein and an SNP mutant, and you would like to compare the structural consequences of the mutation. It also occurs in protein structure prediction: in order to evaluate the quality of a structural predictor a software that takes the sequence of a protein and attempts to figure out how the structure looks like , the predictions produced by the software must be compared to the real corresponding protein structures.
The same goes, of course, for dynamical molecular simulation software. The most basic superposition algorithms work by rototranslating the two proteins so to maximize their alignment. This is a least-squares minimization problem, and is solved using standard decomposition techniques.
Biopython Tutorial and Cookbook
New to Biopython? Check out the Getting Started page, or follow one of the links below. The Biopython Tutorial and Cookbook contains the bulk of Biopython documentation. It provides information to get you started with Biopython, in addition to specific documentation on a number of modules. API documentation for Biopython modules is generated directly from source code comments Sphinx autodoc:.
Biopython: freely available Python tools for computational molecular biology and bioinformatics
This aims to provide a simple interface for working with assorted sequence file formats in a uniform way. See also the Bio. The workhorse function Bio. This function expects two arguments:. There is an optional argument alphabet to specify the alphabet to be used. SeqIO will default to a generic alphabet. The Bio.
Python is an object oriented, interpreted, flexible language that is becoming increasingly popular for scientific computing. Basically, the goal of Biopython is to make it as easy as possible to use Python for bioinformatics by creating high-quality, reusable modules and classes. 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. All of the installation information for Biopython was separated from this document to make it easier to keep updated. This naming was used until June in the run-up to Biopython 1. If you still need to support old versions of Biopython, use these explicit forms to avoid problems. 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.