Analysis of RNA-Seq Data Using TopHat and Cufflinks

Abstract

The recent advances in high throughput RNA sequencing (RNA-Seq) have generated huge amounts of data in a very short span of time for a single sample. These data have required the parallel advancement of computing tools to organize and interpret them meaningfully in terms of biological implications, at the same time using minimum computing resources to reduce computation costs. Here we describe the method of analyzing RNA-seq data using the set of open source software programs of the Tuxedo suite: TopHat and Cufflinks. TopHat is designed to align RNA-seq reads to a reference genome, while Cufflinks assembles these mapped reads into possible transcripts and then generates a final transcriptome assembly. Cufflinks also includes Cuffdiff, which accepts the reads assembled from two or more biological conditions and analyzes their differential expression of genes and transcripts, thus aiding in the investigation of their transcriptional and post transcriptional regulation under different conditions. We also describe the use of an accessory tool called CummeRbund, which processes the output files of Cuffdiff and gives an output of publication quality plots and figures of the user's choice. We demonstrate the effectiveness of the Tuxedo suite by analyzing RNA-Seq datasets of Arabidopsis thaliana root subjected to two different conditions.

RNA Sequencing and Bioinformatics Analysis Implicate the Regulatory Role of a Long Noncoding RNA-mRNA Network in Hepatic Stellate Cell Activation

“…We determined differentially expressed genes and lncRNAs using edgeR analysis [13] for gene-expression profiling (http://bioconductor.org/packages/release/bioc/html/edgeR.html) and the Cufflinks algorithm [14] for identification of transcripts from RNA-Seq data. False-discovery rate (FDR) was used to determine the significance threshold of the p-value for multiple tests.…”

Discovery of differentially expressed genes in cashmere goat (Capra hircus) hair follicles by RNA sequencing

“…The analysis of large volumes of RNA-Seq data has revealed novel isoforms and splice variants, as well as unannotated transcripts, even in well-studied biological models (Trapnell et al, 2010). The transcriptome reveals a great deal about the functional aspects of the genome, as well as the different kinds of biomolecules present within the cell or tissue, so is very useful for studying the genetics behind growth, development, and disease (Ghosh and Chan, 2016). To understand the mechanisms of gene regulation in these two follicles, we studied microRNAs (miRNAs) and their targets, which play important roles in the activation of secondary follicles.…”

The design of experiments for the transcriptome studies by high-throughput sequencing methods

“…В настоящее время существует значительное число обзоров литературы, детально рассказывающих о техни-ческих особенностях считывания ридов при секвениро-ва нии транскриптомов на различных платформах (напри-мер, Ansorge, 2009;Mutz et al, 2013), а также о програм-мах и алгоритмах, которые могут быть использованы на вто рой стадии исследования -стадии сборки транс-криптов (например, Martin, Wang, 2011;Florea, Salzberg, 2013;Ghosh, Chan, 2016). Вместе с тем в литературе мало вни мания уделяется базовым правилам планирования экс периментов RNA-Seq и Ribo-Seq, а большинство ин-формации, посвященной данному вопросу, встречается в разрозненном виде в обзорах литературы, описывающих технологию полногеномного секвенирования (напри-мер, Sim et al, 2014).…”