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@Phil: Is a better fix coming along for this? That kind of seems like a hacky solution.
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Hello,
Does any one know where I can find average Q score on miseq raw reads fastQC report? I know fastQC gives per sequence Q scores. But it doesn't tell the average Q scores of the raw reads though. Thank you very much in advance.Comment
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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?
PhilComment
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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 PostComment
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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!!Comment
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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.Comment
You'll have to ask Simon about that..
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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...06-18-2026, 07:11 AM -
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
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