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mistakenly ran featureCounts in paired-end mode on single-read data
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13 months ago
clboozy • 0

I am looking through an old pipeline that was run over a year ago in preparation for submitting data to GEO. I discovered that although the sequencing in this experiment was single-read (vs. paired-end), I had run featureCounts in paired-end mode (with a parameter of -p). According to the featureCounts documentation, the -p flag has the following definition: "If specified, fragments (or templates) will be counted instead of reads. This option is only applicable for paired-end reads." Did adding this parameter by mistake affect the run at all? Or did it not matter as all samples were single-read anyway?

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As far as I know, it doesn't effect results if you use -p on SE data. But you could quickly check it by running on any bam you have (with and without -p)

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Indeed, the scientist within you should run it with and without, and then cross-compare results.

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Agreed! That's what I would have done if I still had access to the bam files... unfortunately, I do not.

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Cool. I would have hoped that featureCounts issued a warning message, at least (?). Keep in mind that these counting methods are fairly rudimentary - one can perform read count abundance using BEDTools or custom scripts, if one wishes.

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except that featureCounts is blazingly fast and comes with tons of options

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Thanks! Unfortunately, I don't have access to any SE bam files currently.

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13 months ago
h.mon 25k
Brazil

As geek_y and Kevin Blighe said, test for yourself.

Even if do not have access to the original bams, you can easily grab single-end fastq (in which case you will have to align) and / or bam files, and run featureCounts twice, withand without -p. The result of this test will tell you if your original counts are correct or not.

You don't want to submit potentially bogus results to GEO (with your name on it) based on "but this internet guy told me my counts were fine", do you?

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15 months ago
swbarnes2 5.7k
United States

I doubt it will matter. When you have paired end reads, you need the software to understand that if it sees the same read name twice; one read1 and on read2, aligning to the same gene twice, it has to not count those as two separate reads, since they came from one fragment.

That won't be a problem with a single end dataset.

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