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I have whole genome data in paired end fastq files. Before going for its alignment, I checked some of the samples for quality using FastQC but I'm getting strange results as seen in the attachments. Shall I move ahead with such data or need to trim these sequences or discard the data? I've majority of samples like this!
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Thanks, -
Hi,
I would suggest you trim the leftmost part of the sequences. Given that you still have some high quality bases at the 3'-end, it will be good to set a Q-score threshold during trimming. By doing this you will end up with reads of varying lengths.
http://www.youtube.com/watch?v=bz93ReOv87Y
HTH -
Thanks, what about the sequences which ll be trimmed? Doesn't it has any impact on the results?Thanks,Comment
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Which ever method you use should enable you retain the high quality 5'-ends of trimmed reads.Comment
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I am not sure how much trimming will help you as your quality declines pretty rapidly and not very far into the reads. Try it, but I would also point this out to who ever did the sequencing (assuming it wasn't you).Comment
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Your Kmer plot suggests that a reasonable proportion of your library contains very short inserts (we can't really tell what proportion from the graph - you'd need the accompanying table for that).
Is it possible you've just made a library with extremely short inserts and that this is why the quality is dropping so quickly? Given the adapter contamination you probably need to adapter trim your data anyway for it to be of any use, so you might as well quality trim while you're at it.Comment
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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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