KOBAS - KEGG Orthology Based Annotation System

1. What’s KOBAS?

KEGG Orthology Based Annotation System (KOBAS) is a standalone Python application in Bioinformatics. KOBAS can assign appropriate KO terms for queried sequences based on similarity search, and it can further discover enriched KO terms among the annotation results by frequency of pathways or statistical significance of pathways.

2. Installation

Please refer to install.txt under docs directory in KOBAS source

3. Usage

KOBAS composes of two major scripts, which are similar to usual command line tools: the behavor can be adjusted by swiches and configuration file. The whole work flow can be divided to two steps: sequence annotation and pathway discovery.

Sequence Annotation

Given a FASTA file of protein, you can annotate them by this command:

  blast2ko.py <FASTA FILE> 

All the result are printed default, you can redirect into a file. If the input are nuclear acid sequences, you can adjust blast2ko behavor by the command option “-p” as follows:

  blast2ko.py -p blastx <FASTA FILE>

Pathway Discovery

Once the annotation is complete, pathfind can be used to discover enriched pathways by two ways: frequency prior and statistally significance prior. You can find the most frequent pathways among the annotation result:

  pathfind.py <ANNOATION FILE>

Or, you can also find the most statistically enriched pathways:

  pathfind.py [-k b/c/h] <ANNOATION FILE> <BACKGROUND ORGANISM>

‘-k’ option specifies the statistical method to rank, ‘b’ represents binomial distribution, ‘c’ represents chisquare distribution and ‘h’ represents hypergeometry distrubtion. is three-charater abbreviation of the organism in KEGG GENES as backgroud distribution. Also, you can specifiy an data file as backround like this:

  pathfind.py [-k b/c/h] <ANNOATION FILE> <BACKGROUND FILE>

Please refer other details to online manpage by the command “ -h”.

4. Contact

Please, report bugs, comments and suggestions to the email below. Also, if you succeed in compiling this module under a platform other than Linux, please, send us a note.

Authors: Xizeng Mao, Jianmin Wu, Tao Cai, Chen Xie, Liping Wei

Email: kobas@mail.cbi.pku.edu.cn

Web: http://kobas.cbi.pku.edu.cn

Enjoy :)