Dear Community,
I'd like to clarify unexpected behaviour of featureCounts when counting on ONT cDNA bam files, generated with minimap2 with the splice alignment preset. FeatureCounts version: 2.1.1. When running
featureCounts -L -C -Q 10 --primary -M -O --fraction -t transcript
aired-end : no ||
|| Count read pairs : no ||
|| Annotation : genes.filtered.gtf (GTF) ||
|| Dir for temp files : /scratch/local/snakepipes.khA6aeMgrY ||
|| ||
|| Threads : 8 ||
|| Level : meta-feature level ||
|| Multimapping reads : counted (fractional) ||
|| Multiple alignments : primary alignment only ||
|| Multi-overlapping reads : counted ||
|| Min overlapping bases : 1 ||
|| Long read mode : yes ||
|| ||
\\============================================================================//
//================================= Running ==================================\\
|| ||
|| Load annotation file genes.filtered.gtf ... ||
|| Features : 267506 ||
|| Meta-features : 67928 ||
|| Chromosomes/contigs : 439 ||
|| ||
|| Process BAM file sorted.bam... ||
|| Single-end reads are included. ||
|| Total alignments : 9121703 ||
|| Successfully assigned alignments : 3639166 (39.9%) ||
|| Running time : 1.37 minutes ||
i.e. ~ 40% of the fragments are counted. In the summary file, I notice 4481575 fragments (~ 49%) are Unassigned:Mapping Quality.
Samtools stats run on the same bam file on the genic regions tells me that <12% of alignments have MAPQ<10.
What could be the reason for the discrepancy between low MAPQ fraction calculated by samtools stats and by featureCounts ?
Best wishes,
Katarzyna

FeatureCounts can behave differently with ONT cDNA BAM files due to long read splice patterns, and many users compare settings or presets much like gamers explore different modes with q789 game free Download this app. Alignment gaps, secondary reads, and strand settings often influence counting accuracy, so adjusting parameters is essential for reliable results.