RNA-seq data science: From raw data to effective interpretation

TL;DR

This paper reviews the evolution of RNA-seq technology and computational tools, highlighting their role in advancing biological and clinical research through systematic analysis of available software from 2008 to 2020.

Contribution

It provides a comprehensive overview of RNA-seq technology and catalogs 235 computational tools, emphasizing interdisciplinary bioinformatics developments over time.

Findings

Reviewed 235 RNA-seq tools from 2008-2020

Highlighted technological challenges and solutions in RNA-seq analysis

Mapped the evolution of computational methods in transcriptomics

Abstract

RNA-sequencing (RNA-seq) has become an exemplar technology in modern biology and clinical applications over the past decade. It has gained immense popularity in the recent years driven by continuous efforts of the bioinformatics community to develop accurate and scalable computational tools. RNA-seq is a method of analyzing the RNA content of a sample using the modern sequencing platforms. It generates enormous amounts of transcriptomic data in the form of nucleotide sequences, known as reads. RNA-seq analysis enables the probing of genes and corresponding transcripts which is essential for answering important biological questions, such as detecting novel exons, transcripts, gene expressions, and studying alternative splicing structure. However, obtaining meaningful biological signals from raw data using computational methods is challenging due to the limitations of modern sequencing technologies. The need to leverage these technological challenges have pushed the rapid development of many novel computational tools which have evolved and diversified in accordance with technological advancements, leading to the current myriad population of RNA-seq tools. Our review provides a systemic overview of RNA-seq technology and 235 available RNA-seq tools across various domains published from 2008 to 2020, discussing the interdisciplinary nature of bioinformatics involved in RNA sequencing, analysis, and software development.