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.
Test data for this app appears directly in the Discovery Environment in the Data window under Community Data -> iplantcollaborative -> example_data -> KOBAS -> identify
Use from the directory above as test input.
use kobas_annoate_out.txt as the input file
Parameters Used in App
Use the these parameters with test data:
use 'dme' as the background (Species codes can be looked up here: https://www.kegg.jp/kegg/catalog/org_list.html)
sqlite3–contains the database files used to annotate your data
KOBAS identify output.txt