fastp VS MOSCA

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.

MOSCA (https://github.com/iquasere/MOSCA) performs all major steps of RNA-Seq analysis in a fully automated workflow, meaning you only have to input your data and it will take care of the rest for you!

MOSCA (https://github.com/iquasere/MOSCA) performs integrated analysis of all of those

I developed a script for this type of analysis, it takes as input a count matrix and the underlying conditions (replicates) information, hope it helps

I have developed MOSCA (https://github.com/iquasere/MOSCA) which performs exactly this type of analysis. You can read more about it in the Wiki (https://github.com/iquasere/MOSCA/wiki)

You probably should use some pipeline such as MOSCA (https://github.com/iquasere/MOSCA) and then check on the annotations provided to see which genes contain the same names/functions

Perhaps this script may help you? https://github.com/iquasere/MOSCA/blob/master/workflow/scripts/de_analysis.R

You may run MOSCA (https://github.com/iquasere/MOSCA), it performs all major steps of metagenomics analysis. It includes that functional classification you are looking for, since with UPIMAPI (https://github.com/iquasere/UPIMAPI) it annotates with UniProt DB as reference, and obtains information including taxonomy, EC numbers, and even those GOs, and reCOGnizer (https://github.com/iquasere/reCOGnizer), which annotates with CDD DB as reference, and obtains orthologous groups information (COG, Pfam, etc).

I also developed a neet preprocessing script, which uses FastQC reports to inform Trimmomatic on what parameters it should use, seems like exactly what you want. Its use is detailed in MOSCA's wiki, and you can obtain the script directly from GitHub here.

With MOSCA, would I essentially be following all of the steps here for each of my samples? https://github.com/iquasere/MOSCA/wiki/Partial-runs I have some metagenomes that my lab previously obtained from the same site, but am unsure how I'd integrate them here.

I was recently given six RNA-Seq datasets to identify significantly expressed genes in a specific condition - butyrate degradation in the presence of activated carbon. I already did the major steps of metatranscriptomics analysis using MOSCA (https://github.com/iquasere/MOSCA), and was now wondering how can I determine which proteins have an interesting level of expression.