- Download https://applbio.biologie.uni-frankfurt.de/download/QFO/qfo20_fas.tar.gz and unpack it. You will find:
- a qfo20_anno_dir folder containing the feature annotations for 79 taxa in the QfO reference set
- a nr_pairs.txt file containing non-redundant pairwise orthologs between 17 different prediction tools (from QfO20)
- a qfo20_fas_subset.json file containing the calculated FAS scores for all pairwise orthologs between human, mouse, rat, yeast, A. thaliana and E. coli
- Run
parse_op.py for a pairwise ortholog tab-delimited file from a submitteed approach to get list of ortholog pairs that are specific for that approach (an example output for this command can be found at test_data/test_pairs.txt.mapped
python parse_op.py -i test_data/test_pairs.txt -a qfo20_anno_dir --nrPairs nr_pairs.txt --cpus 8
- Install FAS tool and run
fas.setup (the installation of the annotation tools can be skipped with the option --noAnno)
- Calculate FAS scores for those approach specific ortholog pairs
fas.runMultiTaxa --input test_data/test_pairs.txt.mapped -a qfo20_anno_dir -o <output_path> --oldJson qfo20_fas_subset.json --bidirectional --tsv --domain --no_config --json --mergeJson --outName <output_filename> --max_cardinality 100 --pairLimit 30000 --cpus 32
- Merge the result from step 4
<output_path>/<output_filename>.json with qfo20_fas_subset.json either manually or using this script
# first, copy or link qfo20_fas_subset.json to <output_path>
# then, run
fas.mergeJson -i <output_path> -n qfo20_fas_merged
- and get all FAS scores for a prediction tool
python get_fas.py -i inparanoid.txt -f qfo20_fas_merged.json -o inparanoid_fas