Open ben-silke opened 2 years ago
Running get_statistics.py Getting reference data for db_name='InterPro' len(high_counts)=1696 Getting reference data for db_name='UniProtKB/Swiss-Prot' len(high_counts)=3
Code Used
database = Database.objects.get(name=db_name)
references = DatabaseFeatureReference.objects.filter(database=database)
unique_db_xrefs = {reference.db_xref for reference in references}
counts = {
db_xref: references.filter(db_xref=db_xref).count()
for db_xref in unique_db_xrefs
}
high_counts = {
key: value
for key, value in counts.items() if value > 1
}
print(f'{len(high_counts)=}')
gonna use swissprot for now
So; I've been looking more through this data. The swissprot db_xrefs all come from two files. (NC_000913.3; NC_000964.3) the interpro references all come from one file (NC_0000964.3). Therefore I don't think that these databases are very good if we are looking for a more holistic view. I'll start querying the files to look at shared db_xrefs across different files. otherwise; we might need to look at using multiple databases to determine the orthology from other databases.
numbers are not meant to be perfect; just using ctrl f to get a sense
db_xref | occurs in no.files |
---|---|
swiss-prot | 2 |
interpro | 1 |
geneID | 7 |
RFAM | 550 |
taxon | 556 |
ASAP | 1 |
ecOCYC | 1 |
taxon = ncbi taxonomy browser
ASAP EcOCYC PDB ensemble
rfam generally used within rRNA
collect summary statistics from dataset.
Can be done easily with django methods