MultiQC VS fastp

fastp + multiQC + Salmon + DESeq2 all some nextflow workflow. It is a good exercise (not complicated) to create the pipeline from scratch the first time to properly understand each tool.

I would recommend looking at the pages for FastQC and MultiQC. I run FastQC on my fastq files, then MultiQC on them to collect all that individual data into one report. You can also use MultiQC to analyze the quality of your alignments, at least after using STAR aligner (probably others too, I just have only used STAR aligned).

If that all checks out then I would have to see more of your files in order to help, sorry. Submitting the issue at https://github.com/ewels/MultiQC/issues would help you more

I have seen this Github thread, but it is not applicable for me because I am not running MultiQC natively, I am running a docker version of it in HPC.

fastp + multiQC + Salmon + DESeq2 all some nextflow workflow. It is a good exercise (not complicated) to create the pipeline from scratch the first time to properly understand each tool.

1) QC the data with fastp. This'll trim out adapters and toss reads that are poor quality.

fastp has an adjustable low complexity filter option.

- in terms of preprocessing and QC, I prefer fastp (https://github.com/OpenGene/fastp)

I generally use fastp as an all-in-one tool for short reads: https://github.com/OpenGene/fastp

I don't know if this fits exactly what you need, but I'm using fastp to check my fastq.gz files lately: https://github.com/OpenGene/fastp. You can install it via conda.

If you also need some other basic statistics or want to filter the reads you can try fastp (https://github.com/OpenGene/fastp). If only the basepair count is needed, awk might be the fastest solution as suggested before.