DEgenes Hunter - A Self-customised Gene Expression Analysis Workflow for Non-model Organisms

Abstract

Data from high-throughput RNA sequencing require the development of more sophisticate bioinformatics tools to perform optimal gene expression analysis. Several R libraries are well considered for differential expression analyses but according to recent comparative studies, there is still an overall disagreement about which one is the most appropriate for each experiment. The applicable R libraries mainly depend on the presence or not of a reference genome and the number of replicates per condition. Here it is presented DEgenes Hunter, a RNA-seq analysis workflow for the detection of differentially expressed genes (DEGs) in organisms without genomic reference. The first advantage of DEgenes Hunter over other available solutions is that it is able to decide the most suitable algorithms to be employed according to the number of biological replicates provided in the sample. The different workflow branches allow its automatic self-customisation depending on the input data, when used by users without advanced statistical and programming skills. All applicable libraries served to obtain their respective DEGs and, as another advantage, genes marked as DEGs by all R packages employed are considered ‘common DEGs’, showing the lowest false discovery rate compared to the ‘complete DEGs’ group. A third advantage of DEgenes Hunter is that it comes with an integrated quality control module to discard or disregard low quality data before and after preprocessing. The ‘common DEGs’ are finally submitted to a functional gene set enrichment analysis (GSEA) and clustering. All results are provided as a PDF report.