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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
- KOBAS KOBAS identify accepts several file formats: FASTA, tabular BLAST output, IDs list (see documentation for details) the output file from KOBAS annotate
- Resources: https://github.com/AgBase/kobas, https://agbase-docs.readthedocs.io/en/latest/
Test Data
| Test data for this app appears directly in the Discovery Environment in the Data window under Community Data -> iplantcollaborative -> example_data -> KOBAS -> annotateidentify |
Input File(s)
Use CFLO_1.fa 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:select protein FASTA as the input file type
use 'dme' as the species code background (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
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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 outputKOBAS identify output.txt