@Phil: Is a better fix coming along for this? That kind of seems like a hacky solution.
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Hi @fnn4,
Do you mean a single mean quality score for the entire FastQ file? The Per Sequence Quality Scores plot shows the distribution of average Q scores across all reads in the file. I don't think that FastQC reports an average for the entire file, but it should be simple enough to do using the raw data reported in fastqc_data.txt - just sum the (Q score * read count) from the per-sequence section and divide by the sum of the read count.
What's your reason for wanting this statistic, out of interest?
Phil
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Hi Phil,
Thank you for your reply. Yes, that is what I was asking for. When we submit samples, we have to pass a certain threshold for average Q score.
Originally posted by ewels View PostHi @fnn4,
Do you mean a single mean quality score for the entire FastQ file? The Per Sequence Quality Scores plot shows the distribution of average Q scores across all reads in the file. I don't think that FastQC reports an average for the entire file, but it should be simple enough to do using the raw data reported in fastqc_data.txt - just sum the (Q score * read count) from the per-sequence section and divide by the sum of the read count.
What's your reason for wanting this statistic, out of interest?
Phil
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FastQC weird Kmer count in GBS data
Hi @simonandrews and everybody elese, I'm using FastQC to check the quality of my GBS reads, focusing in th R1 sequences, and I'm getting weird results in the Kmer content. After trimming and removing adapters and barcodes, I got a first Kmer profile showing a higher than expected count for several 6-mers, like this:
I thought it may be the adapters of the other side of the read for the sequencing of the R2, so I trimmed all the reads longer than 130 pb to 130 pb. And strangely, I got new Kmer peaks, not shown before:
Does anybody know why and what are these new peaks, or what it's the origin of them? May be related with FastQC algorithms? Somebody found similar problems? Thanks a lot guys!!
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I most want to say that you are doing a great job.Last edited by hiretabletsae; 02-06-2019, 10:07 PM.
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by GATTACATLove this - good data definitely starts from good input, and poor input can only give relatively poor data. I particularly like the mention of Nanodrop/absorbance based methods for quantification. It's such a toss up if you'll get an accurate reading or what amounts to a randomly generated number, and a lot of library/sequencing related issues can be traced back to poor quant.
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Channel: Articles
07-01-2026, 11:43 AM -
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by SEQadmin2
I’m not a sequencing expert. I’m a purification scientist who uses NGS to evaluate workflows my group develops. With this perspective, we think about the sample first and the NGS workflow second. The sequencer is an exceptionally honest reporter, but it can only report on what you give it, so whether you get clean, interpretable data from an NGS workflow is largely determined before you begin.
Here are nine questions we think about, in roughly the order they matter, before...-
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You'll have to ask Simon about that..
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