the --fraction option of featureCounts
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12 weeks ago
Apex92 ▴ 60

Hi all,

I have a very specific question for the -fraction option of the featureCounts tool. I am analyzing single-cell data and only focusing on unique mapping reads. What I am aiming is that to weigh the counts for reads if they map to two different annotations that have an overlap.

So for example imagine a read gets mapped to two different annotations that have an overlap, then the total count for that read should be 1 (0.5 each) not 2 - is this what the -fraction option does?

Or is there a better solution for this?

Another thing about the -fraction is that it should be used with the -M (for multi-mapping reads) - while I have only allowed for unique mapping reads in my mapping step using STAR - can this cause a problem?

RNA-Seq featureCounts rna-seq genome sequencing • 375 views
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Entering edit mode
12 weeks ago

What version of featureCounts are you using ? In version 2.0 --fraction can be used with either the -O or -M option (or both). With the -O option, -M is not needed, and if a read overlaps two feature, 0.5 will be assigned to each of them. Here is the relevant documentation:

  --fraction          Assign fractional counts to features. This option must
be used together with '-M' or '-O' or both. When '-M' is
specified, each reported alignment from a multi-mapping
read (identified via 'NH' tag) will carry a fractional
count of 1/x, instead of 1 (one), where x is the total
number of alignments reported for the same read. When '-O'
is specified, each overlapping feature will receive a
fractional count of 1/y, where y is the total number of
features overlapping with the read. When both '-M' and
'-O' are specified, each alignment will carry a fractional
count of 1/(x*y).

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Dear Carlo thank you for your input - I do use featureCounts from the subread/2.0.0 package. So based on what you have mentioned, then -O seems what I want. But the question is -O also weighs the counts for overlapping features if only unique mapping reads are considered (I excluded all the multi-mapping reads and working only with unique mapping reads)?

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Yes yes, it is not a problem. A read can be uniquely mapped (one genomic position) but overlap multiple features.

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great help - thank you so much :)