Hello,
I'm performing guided de-novo annotation assembling via Stringtie. I've been wondering what filtering parameters should I set if I'm analyzing Small Upstream ORFs ? (Part of sample's have genes responsible for NMD knocked down)
Especially
-f <0.0-1.0> Sets the minimum isoform abundance of the predicted transcripts as a fraction of the most abundant transcript assembled at a given locus. Lower abundance transcripts are often artifacts of incompletely spliced precursors of processed transcripts. Default: 0.01
-c <float> Sets the minimum read coverage allowed for the predicted transcripts. A transcript with a lower coverage than this value is not shown in the output. Default: 1
-g <int> Minimum locus gap separation value. Reads that are mapped closer than this distance are merged together in the same processing bundle. Default: 50 (bp)
And on the next step, merging of annotation, I have the same question on the following parameters: -c <min_cov> minimum input transcript coverage to include in the merge (default: 0)
-F <min_fpkm> minimum input transcript FPKM to include in the merge (default: 0)
-T <min_tpm> minimum input transcript TPM to include in the merge (default: 0)
-f <min_iso> minimum isoform fraction (default: 0.01)
I suppose transcripts coming from Small Upstream ORFs will be lowly expressed and I'm afraid to filter them out accidentally (may be I'm mistaken here?)
If you have any suggestion on other parameters-I will be glad to know them.
Thank you in advance!
