# How to best detect the “peaks” in RNA-seq data that are not assigned to any gene?

I encountered that many reads from single-cell RNA seq data were lost in the analysis because not assigned to any gene (genome: galgal6). I am trying to find an approach than could give me all the "peaks" in this data, that is to say regions where there is a high density of reads. Once I get there, I would extract "peaks" that are not assigned to any gene, and get the distance to the closest genes.

I am thinking of using first bedtools genomecov to get the "peaks", and then bedtools intersect to get all reads assigned to a gene. Finally, I extract reads that are not in the output of bedtools intersect to get a new file with all my reads from the peak, that are not assigned to any gene.

Does it make sense this way ? Can you see any other way of doing ?

Here are some tiny data:

> cat reads.bed
chr9    505479  505498
chr9    508014  508037
chr9    514603  514633
chr9    529519  529540
chr9    529519  529540
chr9    529519  529540

> cat tiny.galGal6.chrom.sizes
chr9    24153086

> bedtools genomecov -bg -split -i reads.bed -g tiny.galGal6.chrom.sizes
chr9    505479  505498  1
chr9    508014  508037  1
chr9    514603  514633  1
chr9    529519  529540  3


The last line would correspond to a peak. However, another difficulty is how should I define the thresholds ?

Any help would be more than welcome.