We want to split out sra_metadata.py so that it actually is a standalone python script that will create a metadata.yaml sheet from querying SRA, for qcdb.db_load to parse and load. It will take as input a path to a data folder, and an output path where metadata.yaml will go. The data folder should have either SRR ids as generated from sra-tools/fasts-dump, or SRX_SRS ids as generated from bioflows.
For a sheet that's based on data from SRA, db_id should be filled out as an SRS_SRX id, and it should also have fields sample_id: SRS and experiment_id: SRX.
See #41 for some context.
We want to split out
sra_metadata.py
so that it actually is a standalone python script that will create ametadata.yaml
sheet from querying SRA, forqcdb.db_load
to parse and load. It will take as input a path to a data folder, and an output path wheremetadata.yaml
will go. The data folder should have eitherSRR
ids as generated fromsra-tools
/fasts-dump
, orSRX_SRS
ids as generated frombioflows
.