KOBAS annotate and identify 3.0.3


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.

Quick Start

Test Data


Test data for this app appears directly in the Discovery Environment in the Data window under Community Data -> iplantcollaborative -> example_data -> KOBAS -> annotate_and_identify

Input File(s)

Use CFLO_1.fa from the directory above as test input.

Parameters Used in App

Use the these parameters with test data:

select protein FASTA as the input file type

use 'dme' as the species code (Species codes can be looked up here: https://www.kegg.jp/kegg/catalog/org_list.html)

Output File(s)

seq_pep folder --contains the BLAST database files that were used

  • dme.pep.fasta (and corresponding .phr, pin, pog, psd, psi, psq files)

sqlite3–contains the database files used to annotate your data

  • dme.db
  • organism.db

dme.tsv–tabular BLAST output

out_annotate.txt–KOBAS annotate output

out_identify.txt–KOBAS identify output

Tool Source for App