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.
- KOBAS accepts several file formats: FASTA, tabular BLAST output, IDs list (see documentation for details)
- Resources: https://github.com/AgBase/kobas, https://agbase-docs.readthedocs.io/en/latest/
Test data for this app appears directly in the Discovery Environment in the Data window under Community Data -> iplantcollaborative -> example_data -> KOBAS -> annotate
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)
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.tsv–tabular BLAST output
kobas_annotate_out.txt–KOBAS annotate output (this can be used as input for KOBAS identify)