Abstract
BackgroundOnce bulk RNA-seq data has been processed, i.e. aligned and then expression and differential tables generated, there remains the essential process where the biology is explored, visualized and interpreted. Without the use of a visualisation and interpretation pipeline this step can be time consuming and laborious, and is often completed using R. Though commercial visualisation and interpretation pipelines are comprehensive, freely available pipelines are currently more limited.ResultsHere we demonstrate Searchlight, a freely available bulk RNA-seq visualisation and interpretation pipeline. Searchlight provides: a comprehensive statistical and visual analysis, focusing on the global, pathway and single gene levels; compatibility with most differential experimental designs irrespective of organism or experimental complexity, via three workflows; reports; and support for downstream user modification of plots via user-friendly R-scripts and a Shiny app. We show that Searchlight offers greater automation than current best tools (VIPER and BioJupies). We demonstrate in a timed re-analysis study, that alongside a standard bulk RNA-seq processing pipeline, Searchlight can be used to complete bulk RNA-seq projects up to the point of manuscript quality figures, in under 3 h.ConclusionsCompared to a manual R based analysis or current best freely available pipelines (VIPER and BioJupies), Searchlight can reduce the time and effort needed to complete bulk RNA-seq projects to manuscript level. Searchlight is suitable for bioinformaticians, service providers and bench scientists. https://github.com/Searchlight2/Searchlight2.
Highlights
Once bulk RNA-seq data has been processed, i.e. aligned and expression and differential tables generated, there remains the essential process where the biology is explored, visualized and interpreted
Re-analysis dataset 1 [20] (GEO ID: GSE97358) explored the effect of TGFB1 on primary cardiac fibroblasts and had two sample groups
Showed a clear split between control and TGFB1 treated, which was confirmed by the volcano plot (Fig. 5b), showing 737 differentially expressed genes
Summary
Once bulk RNA-seq data has been processed, i.e. aligned and expression and differential tables generated, there remains the essential process where the biology is explored, visualized and interpreted. Once bulk RNA-seq data has been processed, i.e. aligned and expression and differential tables generated [1], there remains the essential process where the biology is explored, visualized and interpreted ( known as EVI). Due to improved tools for quality control (QC) and alignment (e.g. FastP [2], STAR [3] and Kallisto [4]) and the use of automated pipelines the processing stage is largely trivial, typically taking bioinformaticians only a handful of hours to complete. Whilst providing convenient means for users to modify plots They can reduce the time needed to perform the EVI stage to only a few hours, and so typically trivialize much of the EVI stage
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