Tweet
What's the meaning of "not_alignment" from the output of HTseq-count? The manual says it is the "reads in the Sam/Bam file without alignment". Does this mean the reads that are not mapped to the reference? or anything else? Thanks!
-
-
Yeah, these are just reads that didn't align. Whether the count for this is 0 or not will depend on the aligner that you're using (some don't put unaligned reads in the output SAM/BAM file, others do). -
Assuming that the file I used for input of HTseq-count includes the unaligned reads in the output BAM file, But the number of "no_alignment" reads doesn't match the number of reads not mapped to the reference. Any idea about this? Thanks!Comment
-
it should match. Maybe it's paired-end? (htseq-count counts read pairs, not reads)Comment
-
@flyingoyster: Try using the -o option to see what's going on in your case.Comment
-
@Simon Anders, Yes, My data is paired-end. If HTseq-count counts read pairs, not reads, that makes sense. Thanks a lot!
@dpryan, I used Galaxy server, so not be able to choose options.
If I want to calculate the number of reads within genes, is it right to substract the number of no_feature reads from the total number of mapped reads? Thanks!Comment
-
Data variability is still an issue in sequencing technologies despite the advances in reproducibility and accuracy of these platforms. But the problem does not originate in the sequencing itself, but in the previous steps, before the sample reaches the sequencer.
The first step is collection, followed by preservation and sample preparation for analysis. Most scientists overlook those steps, but not being careful might just be skewing the experiment’s results.
...06-02-2026, 10:05 AM -
With the launch of new single-cell sequencing platforms in 2026, the field stands at an exciting inflection point. This article surveys the most impactful advances in the field and discusses how they’re reshaping research in cancer, immunology, and beyond.
Introduction
Single-cell sequencing technologies have undergone remarkable advances over the past decade, transitioning from low-throughput experimental approaches to highly scalable platforms capable of...05-22-2026, 06:42 AM
Comment