Searchlight: automated bulk RNA-seq exploration and visualisation using dynamically generated R scripts

Cole, J. J., Faydaci, B. A., Mcguinness, D. , Shaw, R., Maciewicz, R. A., Robertson, N. A. and Goodyear, C. S. (2021) Searchlight: automated bulk RNA-seq exploration and visualisation using dynamically generated R scripts. BMC Bioinformatics, 22(1), 411. (doi: 10.1186/s12859-021-04321-2)

Abstract

Background: Once 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. Results: Here 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. Conclusions: Compared 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.

• Item Type: Articles
• Additional Information: This project was funded by the GLAZgo Discovery Centre.
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Cole, Mr John and Robertson, Mr Neil and Maciewicz, Dr Rose and Mcguinness, Dr David and Faydaci, Mr Bekir and Goodyear, Professor Carl
• Authors: Cole, J. J., Faydaci, B. A., Mcguinness, D., Shaw, R., Maciewicz, R. A., Robertson, N. A., and Goodyear, C. S.
• College/School: College of Medical Veterinary and Life Sciences; College of Medical Veterinary and Life Sciences > School of Health & Wellbeing > Mental Health and Wellbeing; College of Medical Veterinary and Life Sciences > School of Infection & Immunity
• Research Centre: College of Medical Veterinary and Life Sciences > School of Infection & Immunity > Centre for Immunobiology
• Journal Name: BMC Bioinformatics
• Publisher: Biomed Central
• ISSN: 1471-2105
• ISSN (Online): 1471-2105
• Copyright Holders: Copyright © 2021 The Authors
• First Published: First published in BMC Bioinformatics 22(1):411
• Publisher Policy: Reproduced under a Creative Commons License