Bad quality fastq files for analysis
2 answers
You can simply make simulated files/data with any features you like. Use randomreads.sh from BBTools or a similar tool.
Illumina quality parameters:
maxq=36 Upper bound of quality values.
midq=28 Approximate average of quality values.
minq=20 Lower bound of quality values.
q= Sets maxq, midq, and minq to the same value.
adderrors=t Add substitution errors based on quality values,
after mutations.
qv=4 Vary the base quality of reads by up to this much
to simulate tile effects.
You can run "seqkit stats" (https://bioinf.shenwei.me/seqkit/usage/#stats) for all these files. And, then, classify them into "bad" and "good" based on, for example, Q30.
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Bad in what way?
Based on fatsqc scores. The boxplots produced by fastqc display quality scores on all bases. Usually, a score of 30 and above is considered good quality. Is there a way to extract multiple files (~100) that don't pass this threshold?
Do you already have data and want to identify bad ones or do you need to download files that are bad, e.g. from GEO? I do not really get that.
I don't have the data available. I want to identify such datasets. The overall aim is to determine what factors influence fastq data quality. For that, I already have a set of features available. All I need is labeled measurements from 100s-1000s of fatsq files.