Gaining comprehensive biological insight into the transcriptome by performing a broad-spectrum RNA-seq analysis

Abstract

RNA-sequencing (RNA-seq) is an essential technique for transcriptome studies, hundreds of analysis tools have been developed since it was debuted. Although recent efforts have attempted to assess the latest available tools, they have not evaluated the analysis workflows comprehensively to unleash the power within RNA-seq. Here we conduct an extensive study analysing a broad spectrum of RNA-seq workflows. Surpassing the expression analysis scope, our work also includes assessment of RNA variant-calling, RNA editing and RNA fusion detection techniques. Specifically, we examine both short- and long-read RNA-seq technologies, 39 analysis tools resulting in ~120 combinations, and ~490 analyses involving 15 samples with a variety of germline, cancer and stem cell data sets. We report the performance and propose a comprehensive RNA-seq analysis protocol, named RNACocktail, along with a computational pipeline achieving high accuracy. Validation on different samples reveals that our proposed protocol could help researchers extract more biologically relevant predictions by broad analysis of the transcriptome.

Microfluidic isoform sequencing shows widespread splicing coordination in the human transcriptome

“…Using SLR-RNA-seq, we demonstrated coordination of distant splicing events in the human brain . Oxford Nanopore has also been applied to isoform sequencing (Oikonomopoulos et al 2016), and combining different data types for comprehensive analysis is promising (Sahraeian et al 2017). Nevertheless, presently it appears unlikely that these methods could provide a full-length description of 10-100 million cellular RNA molecules-which is common in current short-read RNA-seq experiments.…”

Infection by the castrating parasitic nematode Sphaerularia bombi changes gene expression in Bombus terrestris bumblebee queens

“…terrestris Reference Sequence (RefSeq) transcript dataset using kallisto (v. 43.1; Bray et al ., ). We chose to use a pseudoaligner, such as kallisto , as they have greater accuracy and consistency in transcript quantification in comparison to traditional aligners (Sahraeian et al ., ). For each sample, we computed gene‐level count estimates using the R package tximport (v. 1.2.0; Soneson et al .…”

“…Using the raw sequences, we pseudoaligned each sample against the latest B. terrestris Reference Sequence (RefSeq) transcript dataset using KALLISTO (v. 43.1;Bray et al, 2016). We chose to use a pseudoaligner, such as KALLISTO, as they have greater accuracy and consistency in transcript quantification in comparison to traditional aligners (Sahraeian et al, 2017). For each sample, we computed gene-level count estimates using the R package tximport (v. 1.2.0;Soneson et al 2015) and loaded the count estimates into the R package DESeq2 (v. 1.14.1; Love et al 2014) to perform differential expression analysis.…”

Improved RNA‐seq Workflows Using CyVerse Cyberinfrastructure

“…The most commonly cited and widely used workflow is the Tuxedo protocol (Trapnell et al, 2012). Most of these tools have been benchmarked with human RNA-seq data or simulated datasets (Sahraeian et al, 2017). The updated Tuxedo protocol not only scales, but also more accurately detects differentially expressed genes.…”

“…Thus, alignment-free methods work much faster than alignment-based methods, but the former cannot be used to identify novel transcripts in an experiment. Most of these tools have been benchmarked with human RNA-seq data or simulated datasets (Sahraeian et al, 2017). In this unit, we will benchmark with actual plant RNA-seq data from a study that compared the drought response of two sorghum genotypes with different water use efficiencies (Fracasso, Trindade, & Amaducci, 2016).…”