Differential Gene Expression Pipeline for Whole Transcriptome RNA-Seq Data using Personal Computer
Advances in the next generation sequencing (NGS) technologies, their cost effectiveness and well-developed pipelines using computational tools/softwares has allowed researchers to reveal ground-breaking discoveries in multi-omics data analysis. However, there is still uncertainty due to massive upsurge in parallel tools and difficulty in choosing best practiced pipeline for expression profiling of RNA sequenced (RNA-seq) data. Here, we detail the optimized pipeline that works at a fast pace with enhanced accuracy on personal computer rather than using cloud or high-performance computing clusters (HPC). The steps include quality check, base filtration, quasi-mapping, quantification of samples, estimation and counting of transcript/gene expression abundances, identification and clustering of differentially expressed features and visualization of the data. The tools FastQC, Trimmomatic, Salmon and some other scripts in Trinity toolkit were applied on two paired-end datasets. An extension of this pipeline may also be formulated in future for the gene ontology enrichment analysis and functional annotation of the differential expression matrix to make this data biologically more significant.