Which bioinformatics tools are written in python

Question: Which bioinformatics tools are written in python

which bioinformatics tools are written in python?

I ask this question because new bioinformatic programmers or new pythoners like me can read the source code to find out how python can be used to deal with complex bioinformatics problems besides the problems solved in related books such as "Beginning Python for Bioinformatics"

Thank you

python tool

created 9 months ago by bbsunchen 80•3

updated 9 months ago by Manu Prestat ♦

5 answers

There are so many! To get you started:

Biopython: set of freely available tools for biological computation
PyMOL: molecular visualization system
PyCogent is a software library for genomic biology
Galaxy: an open, web-based platform for data intensive biomedical research
pygr: sequence and comparative genomics analyses, even with extremely large multi-genome data sets
Biskit: facilitates the manipulation and analysis of macromolecular structures, protein complexes, and molecular dynamics trajectories
Ruffus: a lightweight python module for running computational pipelines
Pysam: for reading and manipulating Samfiles
msatcommander: locates microsatellite (SSR, VNTR, &c) repeats within fasta-formatted sequence or consensus files
glu-genetics: tools to store, clean, and analyze data generated by whole-genome or candidate gene association scans
PySCeS provides a variety of tools for the analysis of cellular systems
OpenAlea: odules to analyse, visualize and model the functioning and growth of plant architecture
ETE assists in the automated manipulation, analysis and visualization of phylogenetic and other type of trees
bx-python: allows for rapid implementation of genome scale analyses
RSeQC: comprehensively evaluate high throughput sequence data especially RNA-seq data
incf-omni: analysis and simulation construction of the nervous system
genetrack: storing, querying and visualizing genomic interval oriented data
chimerascan: detection of chimeric transcripts in high-throughput sequencing data

Since you're new to the field of bioinformatics, you might also be interested in:

ANGUS, a site built around the 2010 course on Analyzing Next-Generation Sequencing Data. It contains a number of detailed tutorials on mapping, assembly, mRNAseq, ChIP-seq, and resequencing analysis using Python.
this article by Peter Norvig on species barcoding

To give another example of the very valid point that Dk made: the company I work for (Applied Maths) sells a bioinformatics software suite called BioNumerics. The core of the program is written in C++, but Python is used to customize the software to specific clients' needs:

to create custom reports,
to import and export non-standard formats,
to automate series of actions that are executed repeatedly,
to perform custom calculations, etc.

created 9 months ago by Jeroen Van Goey ♦ 1,750•3•14

updated 9 months ago by Jeroen Van Goey ♦

Most commonly used tools are written in compiled languages like C or java simply because they run faster and the ability to access low level memory resources are crucial to analyzing large amounts of data. When python is used in these packages, it is usually in the form of 'pipeline glue'.

Tophat (http://tophat.cbcb.umd.edu/) is a perfect example of that. It consist of several smaller programs written in C. Python is then used to interpret user paramters and run the smaller programs in sequence.

Interpreted languages like python or perl are usually used for format conversions or statistics reporting.

Good place to start for real examples is to read up on BioPython (http://biopython.org/wiki/Biopython). Their tutorials have tons of real life examples. You can come up with small projects for yourself like writing a script that analyzes gc content of a fasta file, or a script that parses a blast output file and filter on various criteria.

created 9 months ago by Damian Kao ♦ 7,790•2•12

updated 9 months ago by Damian Kao ♦

I will chime in to say QIIME (http://www.qiime.org) is another example.

I believe you're right about the speed consideration, the ability of C or C++ to access low level RAM...etc lets one possibilities to tune a program as close to the hardware as possible (one can also try assembler), but I'm sure the way of coding to achieve a specific task is more critical. Look at, for instance, this biostar discussion. http://www.biostars.org/post/show/10353/how-to-efficiently-parse-a-huge-fastq-file/ (Leszek answer). For the thread interest I would say: Python is good, but use dict() and set() types instead of lists whenever you can.

My answer is malformated due to the transition of the website. If you read my answer together with reformated table in a separate answer, you will know a proper C/C++ implementation is 4-fold faster than Leszek's script. The C++ one is slow due to a stdio synchronization issue which I only know recently. Also, each data structure has its own use. It is just in that example dict() is better.

OK, I see. When I read that answer properly the last time, the best implementation race was not over yet :-) However, that still supports the fact the way of coding is very critical, whatever the programming language. That is a very good post, I like biostar especially for that kind of these. BTW, I'll compile right back Pierre's code.