For RNAseq data analysis, a combination of robust tools across different stages of the workflow is essential. Initially, quality control can be performed using FastQC and MultiQC, followed by read trimming with tools like Trimmomaticor Cutadapt. For alignment, STAR and HISAT2 are widely used for their speed and accuracy. Transcript quantification can be achieved with featureCounts or pseudoalignment-based tools like Salmon and Kallisto. Differential expression analysis is commonly performed using DESeq2, edgeR, or limma-voom, depending on the experimental design. For functional enrichment analysis, clusterProfiler, DAVID, and g:Profiler are effective. Visualization and downstream interpretation can be facilitated through PCAtools, EnhancedVolcano, and ggplot2 in R. Integrating these tools within workflows using platforms like Galaxy, Nextflow, or Snakemake enhances reproducibility and scalability.

Dear Debajit Das

Thanks for this useful and practical information.

For the fastQC part, the ONLY secure and free web tool is right here:

https://genesyslifebio.com/fastq/

Dear Jorby Parker

Thank you for your response to this question. I checked the tool, and it only supports FASTQ files up to 50 MB in size, making it unsuitable for larger files. Since my data exceeds 1 GB, I am unable to perform quality assessments using this tool.

This is a link you can use for FastQC: https://www.bioinformatics.babraham.ac.uk/projects/download.html#fastqc

Make sure you have Java and Perl on your system, as these are the requirements for running FastQC. You also need to add the path to fastqc command and Java to your bash file if you want to run fastqc via terminal. I hope this helps.