DOI: 10.5281/zenodo.9885
Annotation-free estimation of percent spliced in of a junction. This will convert [RNA-STAR aligner](http://bioinformatics.oxfordjournals .org/content/29/1/15.long) "SJ.out.tab" files to "Percent spliced-in" (Psi) scores.
As described in Pervouchine et al, Bioinformatics (2013), we will take the approach of asking, how often is this donor site (5' splice site) used with this acceptor site (3' splice site), compared to ALL OTHER acceptors?
Same goes for acceptor sites. How often is this acceptor site, used with this donor site, compared to ALL OTHER donors?
To illustrate, check out this example. Each "-" represents 10 bp
Splice junction fig genome location number of reads
[ ]--------[ ] chr1:100-180 90
[ ]----------[ ] chr1:100-200 10
[ ]-------[ ] chr1:130-200 40
For the 5' splice site chr1:100, we have 90+10 = 100 total reads. Thus the "psi5" for chr1:100-180 is 90/100 = 0.9, and 0.1 for chr:100-200.
For the 3' splice site chr1:200, we have 10+40 = 50 total reads. Thus the "psi3" for chr1:100-200 is 10/50 = 0.2, and 0.8 for chr:130-200.
What's left is the uninteresting splice sites of chr1:180 and chr1:130, both of which didn't have any variance and were always used. Thus psi3 for chr1:180 is 1.0, and psi5 for chr1:130 is 1.0 as well.
>>> import pandas as pd
>>> data = {'chrom': ['chr1', 'chr1', 'chr1'],
... 'first_bp_intron':[100, 100, 130], 'last_bp_intron':[100, 200, 200],
... 'unique_junction_reads':[90, 10, 40],
... 'multimap_junction_reads':[0, 0, 0]}
>>> sj = pd.DataFrame(data)
>>> get_psis(sj)
chrom first_bp_intron last_bp_intron multimap_junction_reads \
0 chr1 100 100 0
1 chr1 100 200 0
2 chr1 130 200 0
<BLANKLINE>
unique_junction_reads multimap_junction_reads_filtered \
0 90 0
1 10 0
2 40 0
<BLANKLINE>
unique_junction_reads_filtered total_filtered_reads psi5 psi3
0 90 90 0.9 1.0
1 10 10 0.1 0.2
2 40 40 1.0 0.8
<BLANKLINE>
[3 rows x 10 columns]