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https://github.com/biogo/biogo

biogo is a bioinformatics library for Go
https://github.com/biogo/biogo

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biogo is a bioinformatics library for Go

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README 

        
![bíogo](https://raw.githubusercontent.com/biogo/biogo/master/biogo.png)



# bíogo



[![GoDoc](https://godoc.org/github.com/biogo/biogo?status.png)](http://godoc.org/github.com/biogo/biogo)

[![Build Status](https://travis-ci.org/biogo/biogo.svg?branch=master)](https://travis-ci.org/biogo/biogo)



## Installation



        $ go get github.com/biogo/biogo/...



## Overview



bíogo is a bioinformatics library for the Go language.



## Getting help



Help or similar requests are preferred on the biogo-user Google Group.



https://groups.google.com/forum/#!forum/biogo-user



## Contributing



If you find any bugs, feel free to file an issue on the github issue tracker.

Pull requests are welcome, though if they involve changes to API or addition of features, please first open a discussion at the biogo-dev Google Group.



https://groups.google.com/forum/#!forum/biogo-dev



## Citing ##



If you use bíogo, please cite Kortschak, Snyder, Maragkakis and Adelson "bíogo: a simple high-performance bioinformatics toolkit for the Go language", doi:[10.21105/joss.00167](http://dx.doi.org/10.21105/joss.00167), and Kortschak and Adelson "bíogo: a simple high-performance bioinformatics toolkit for the Go language", doi:[10.1101/005033](http://biorxiv.org/content/early/2014/05/12/005033).



## The Purpose of bíogo



bíogo stems from the need to address the size and structure of modern genomic

and metagenomic data sets. These properties enforce requirements on the

libraries and languages used for analysis:



* speed - size of data sets

* concurrency - problems often embarrassingly parallelisable



In addition to the computational burden of massive data set sizes in modern

genomics there is an increasing need for complex pipelines to resolve questions

in tightening problem space and also a developing need to be able to develop

new algorithms to allow novel approaches to interesting questions. These issues

suggest the need for a simplicity in syntax to facilitate:



* ease of coding

* checking for correctness in development and particularly in peer review



Related to the second issue is the [reluctance of some researchers to release

code because of quality

concerns](http://www.nature.com/news/2010/101013/full/467753a.html "Publish

your computer code: it is good enough. Nature 2010.").



The issue of code release is the first of the principles formalised in the

[Science Code Manifesto](http://sciencecodemanifesto.org/).



    Code  All source code written specifically to process data for a published

          paper must be available to the reviewers and readers of the paper.



A language with a simple, yet expressive, syntax should facilitate development

of higher quality code and thus help reduce this barrier to research code

release.



## Articles ##



[bíogo: a simple high-performance bioinformatics toolkit for the Go language](http://biorxiv.org/content/early/2014/05/12/005033)



[Analysis of Illumina sequencing data using bíogo](http://talks.godoc.org/github.com/biogo/talks/illumination/illumina.article)



[Using and extending types in bíogo](http://talks.godoc.org/github.com/biogo/talks/types/types.article)



## Yet Another Bioinformatics Library



It seems that nearly every language has it own bioinformatics library, some of

which are very mature, for example [BioPerl](http://bioperl.org) and

[BioPython](http://biopython.org). Why add another one?



The different libraries excel in different fields, acting as scripting glue for

applications in a pipeline (much of [[1], [2], [3]]) and interacting with external hosts

[[1], [2], [4], [5]], wrapping lower level high performance languages with more user

friendly syntax [[1], [2], [3], [4]] or providing bioinformatics functions for high

performance languages [[5], [6]].



The intended niche for bíogo lies somewhere between the scripting libraries and

high performance language libraries in being easy to use for both small and

large projects while having reasonable performance with computationally

intensive tasks.



The intent is to reduce the level of investment required to develop new

research software for computationally intensive tasks.



[1]: http://bioperl.org/ "BioPerl"

[2]: http://biopython.org/ "BioPython"

[3]: http://bioruby.org/ "BioRuby"

[4]: http://pycogent.sourceforge.net/ "PyCogent"

[5]: http://biojava.org/ "BioJava"

[6]: http://www.seqan.de/ "SeqAn"



1. BioPerl

    http://genome.cshlp.org/content/12/10/1611.full

    http://www.springerlink.com/content/pp72033m171568p2



2. BioPython

    http://bioinformatics.oxfordjournals.org/content/25/11/1422



3. BioRuby

    http://bioinformatics.oxfordjournals.org/content/26/20/2617



4. PyCogent

    http://genomebiology.com/2007/8/8/R171



5. BioJava

    http://bioinformatics.oxfordjournals.org/content/24/18/2096



6. SeqAn

    http://www.biomedcentral.com/1471-2105/9/11



## Library Structure and Coding Style



The bíogo library structure is influenced both by the Go core library.



The coding style should be aligned with normal Go idioms as represented in the

Go core libraries.



## Quality Scores



Quality scores are supported for all sequence types, including protein. Phred

and Solexa scoring systems are able to be read from files, however internal

representation of quality scores is with Phred, so there will be precision loss

in conversion. A Solexa quality score type is provided for use where this will

be a problem.



## Copyright and License



Copyright ©2011-2013 The bíogo Authors except where otherwise noted. All rights

reserved. Use of this source code is governed by a BSD-style license that can be

found in the LICENSE file.



The bíogo logo is derived from Bitstream Charter, Copyright ©1989-1992

Bitstream Inc., Cambridge, MA.



BITSTREAM CHARTER is a registered trademark of Bitstream Inc.