Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks

By Cole Trapnell, Adam Roberts, L. Goff, Geo Pertea, Daehwan Kim, David R. Kelley, Harold Pimentel, Steven L. Salzberg, Johannes Rinn, Lior S. Pachter. Nature Protocols, 2012

11,804 citations recorded by the source corpus.

Recent advances in high-throughput cDNA sequencing (RNA-seq) can reveal new genes and splice variants and quantify expression genome-wide in a single assay. The volume and complexity of data from RNA-seq experiments necessitate scalable, fast and mathematically principled analysis software. TopHat and Cufflinks are free, open-source software tools for gene discovery and comprehensive expression analysis of high-throughput mRNA sequencing (RNA-seq) data. Together, they allow biologists to identify new genes and new splice variants of known ones, as well as compare gene and transcript expression under two or more conditions. This protocol describes in detail how to use TopHat and Cufflinks to perform such analyses. It also covers several accessory tools and utilities that aid in managing data, including CummeRbund, a tool for visualizing RNA-seq analysis results. Although the procedure assumes basic informatics skills, these tools assume little to no background with RNA-seq analysis and are meant for novices and experts alike. The protocol begins with raw sequencing reads and produces a transcriptome assembly, lists of differentially expressed and regulated genes and transcripts, and publication-quality visualizations of analysis results. The protocol's execution time depends on the volume of transcriptome sequencing data and available computing resources but takes less than 1 d of computer time for typical experiments and ∼1 h of hands-on time.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/623811c52a3f09aeb03e56d4cafe32d6bec20ba5

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Citations in the corpus

In vitro generation of human pluripotent stem cell derived lung organoids (2015, methodology): “Cufflinks/CuffNorm (version 2.2.1) was used for expression quantitation and differential expression analysis (Trapnell et al., 2012), using UCSC hg19.fa as the reference genome sequence and UCSC hg19.gtf as the reference transcriptome annotation.”
Comparison of software packages for detecting differential expression in RNA-seq studies (2013, methodology): “Consequently, methods to deal with biological variability and overdispersion have been introduced, such as methods based on negative binomial and beta negative binomial models [4–6, 14].”
lncRNAdb v2.0: expanding the reference database for functional long noncoding RNAs (2014, methodology): “Data from the human body were generated via the Tuxedo suite (17) using the Gencode V15 Gene model and can be exported in XML format.”
Using intron position conservation for homology-based gene prediction (2016, methodology): “For each of the three mapping files, we assembled transcripts using Cufflinks (23) v2.”
Genome-wide changes in lncRNA, splicing, and regional gene expression patterns in autism (2016, neutral): “43 ) followed by multivariate analysis of transcript splicing, MATS 35 ) with PSI values obtained via another method (read alignment by OLego followed by PSI quantification with Quantas 15 ). g , Comparison of Δ PSI values between RT–PCR and RNA-seq for nine splicing events (Supplementary Table 3).…”
Intron-centric estimation of alternative splicing from RNA-seq data (2012, methodology): “It can be used as a consistent protocol for quantifying splice junctions from RNA-seq data because no such standard procedure currently exists.”