Differential analysis of gene regulation at transcript resolution with RNA-seq
Cole Trapnell,David G. Hendrickson,David G. Hendrickson,Martin Sauvageau,Martin Sauvageau,Loyal A. Goff,Loyal A. Goff,John L. Rinn,John L. Rinn,Lior Pachter +9 more
Reads0
Chats0
TLDR
Cuffdiff 2, an algorithm that estimates expression at transcript-level resolution and controls for variability evident across replicate libraries, robustly identifies differentially expressed transcripts and genes and reveals differential splicing and promoter-preference changes.Abstract:
Differential analysis of gene and transcript expression using high-throughput RNA sequencing (RNA-seq) is complicated by several sources of measurement variability and poses numerous statistical challenges. We present Cuffdiff 2, an algorithm that estimates expression at transcript-level resolution and controls for variability evident across replicate libraries. Cuffdiff 2 robustly identifies differentially expressed transcripts and genes and reveals differential splicing and promoter-preference changes. We demonstrate the accuracy of our approach through differential analysis of lung fibroblasts in response to loss of the developmental transcription factor HOXA1, which we show is required for lung fibroblast and HeLa cell cycle progression. Loss of HOXA1 results in significant expression level changes in thousands of individual transcripts, along with isoform switching events in key regulators of the cell cycle. Cuffdiff 2 performs robust differential analysis in RNA-seq experiments at transcript resolution, revealing a layer of regulation not readily observable with other high-throughput technologies.read more
Citations
More filters
Journal ArticleDOI
Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2
TL;DR: This work presents DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates, which enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression.
Posted ContentDOI
Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2
TL;DR: This work presents DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates, which enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression.
Journal ArticleDOI
The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells
Cole Trapnell,Davide Cacchiarelli,Davide Cacchiarelli,Jonna Grimsby,Prapti Pokharel,Shuqiang Li,Michael A. Morse,Michael A. Morse,Niall J. Lennon,Kenneth J. Livak,Tarjei S. Mikkelsen,Tarjei S. Mikkelsen,John L. Rinn,John L. Rinn,John L. Rinn +14 more
TL;DR: Monocle is described, an unsupervised algorithm that increases the temporal resolution of transcriptome dynamics using single-cell RNA-Seq data collected at multiple time points that revealed switch-like changes in expression of key regulatory factors, sequential waves of gene regulation, and expression of regulators that were not known to act in differentiation.
Journal ArticleDOI
Transcript-level expression analysis of RNA-seq experiments with HISAT, StringTie and Ballgown
TL;DR: This protocol describes all the steps necessary to process a large set of raw sequencing reads and create lists of gene transcripts, expression levels, and differentially expressed genes and transcripts.
Journal ArticleDOI
Differential analyses for RNA-seq: transcript-level estimates improve gene-level inferences
TL;DR: It is illustrated that while the presence of differential isoform usage can lead to inflated false discovery rates in differential expression analyses on simple count matrices and transcript-level abundance estimates improve the performance in simulated data, the difference is relatively minor in several real data sets.
References
More filters
Journal ArticleDOI
Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles
Aravind Subramanian,Pablo Tamayo,Vamsi K. Mootha,Sayan Mukherjee,Benjamin L. Ebert,Michael A. Gillette,Amanda G. Paulovich,Scott L. Pomeroy,Todd R. Golub,Eric S. Lander,Jill P. Mesirov +10 more
TL;DR: The Gene Set Enrichment Analysis (GSEA) method as discussed by the authors focuses on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation.
Journal ArticleDOI
edgeR: a Bioconductor package for differential expression analysis of digital gene expression data.
TL;DR: EdgeR as mentioned in this paper is a Bioconductor software package for examining differential expression of replicated count data, which uses an overdispersed Poisson model to account for both biological and technical variability and empirical Bayes methods are used to moderate the degree of overdispersion across transcripts, improving the reliability of inference.
Journal ArticleDOI
Differential expression analysis for sequence count data.
Simon Anders,Wolfgang Huber +1 more
TL;DR: A method based on the negative binomial distribution, with variance and mean linked by local regression, is proposed and an implementation, DESeq, as an R/Bioconductor package is presented.
Journal ArticleDOI
Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation
Cole Trapnell,Cole Trapnell,Brian A. Williams,Geo Pertea,Ali Mortazavi,Gordon Kwan,Marijke J. van Baren,Steven L. Salzberg,Barbara J. Wold,Lior Pachter +9 more
TL;DR: The results suggest that Cufflinks can illuminate the substantial regulatory flexibility and complexity in even this well-studied model of muscle development and that it can improve transcriptome-based genome annotation.
Journal ArticleDOI
Mapping and quantifying mammalian transcriptomes by RNA-Seq.
TL;DR: Although >90% of uniquely mapped reads fell within known exons, the remaining data suggest new and revised gene models, including changed or additional promoters, exons and 3′ untranscribed regions, as well as new candidate microRNA precursors.