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  • HtSeq counts 0 features for mapped reads in sam fliles

    Hello,
    My input :
    50 bp RNA Seq single end illumina reads mapped to GRCh 38 from ensemble.
    Mapping was performed both by STAR and bowtie-2.For star almost 70% reads were uniquely mapped and I used HtSeq to count the features:
    python -m HTSeq.scripts.count -t exon -m union -s no 1.sam $ref > 1.sam_1.counts

    and for the gene "ENSG00000100219" I got a feature count of 0. I tried to see if there was any mapping for this gene

    samtools view 1.bam 22:28794555-28800597 | wc -l

    and found that there were 2899 reads mapped.

    I tried counting just the transcript and the gene to no avail.

    I am quite not sure why this would be the case? Please let me know if my understanding is right. Is there an issue with the alignment or htseq flags?

  • #2
    Originally posted by rags_brown View Post
    Hello,
    My input :
    50 bp RNA Seq single end illumina reads mapped to GRCh 38 from ensemble.
    Mapping was performed both by STAR and bowtie-2.For star almost 70% reads were uniquely mapped and I used HtSeq to count the features:
    python -m HTSeq.scripts.count -t exon -m union -s no 1.sam $ref > 1.sam_1.counts

    and for the gene "ENSG00000100219" I got a feature count of 0. I tried to see if there was any mapping for this gene

    samtools view 1.bam 22:28794555-28800597 | wc -l

    and found that there were 2899 reads mapped.

    I tried counting just the transcript and the gene to no avail.

    I am quite not sure why this would be the case? Please let me know if my understanding is right. Is there an issue with the alignment or htseq flags?
    Hi,

    I can think of a few of reasons HTseq might not count these reads: (i) they are multimappers, (ii) they are not overlapping exons of the gene (i.e. they are all intronic) (iii) they overlap exons of more than one gene.

    If you make a new BAM file with just the selected reads, using
    samtools view -b 1.bam 22:28794555-28800597 > 2.bam
    and then run HTseq with 2.bam, it will tell you how it classified the unassigned reads (after all the gene counts):
    __no_feature --> (ii)
    __ambiguous --> (iii)
    __too_low_aQual
    __not_aligned
    __alignment_not_unique -->(i)

    Also, you can check for (i) by looking at the NH:i:<Nmult> tag of the reads selected over the gene locus.
    To check for (ii) or (iii), you can load your alignments into a browser and see if any of them overlap exons of the gene.

    Cheers
    Alex

    Comment


    • #3
      Hi,

      next to Alex' suggestions, you might also check the chromosomes' name in your gtf-file. Sometimes they come as e.g. 'chr22' while your bam-file has a '22' instead.

      Cheers,
      Michael

      Comment

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