Open Access
Analysis and design of RNA sequencing experiments for identifying isoform regulation
Yarden Katz,Eric T. Wang,Edoardo M. Airoldi,Christopher B. Burge +3 more
- 01 Nov 2010
1K
TL;DR: The mixture-of-isoforms (MISO) model is developed, a statistical model that estimates expression of alternatively spliced exons and isoforms and assesses confidence in these estimates, providing a probabilistic framework for RNA-seq analysis and functional insights into pre-mRNA processing.
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Abstract: Through alternative splicing, most human genes express multiple isoforms that often differ in function To infer isoform regulation from high-throughput sequencing of cDNA fragments (RNA-seq), we developed the mixture-of-isoforms (MISO) model, a statistical model that estimates expression of alternatively spliced exons and isoforms and assesses confidence in these estimates Incorporation of mRNA fragment length distribution in paired-end RNA-seq greatly improved estimation of alternative-splicing levels MISO also detects differentially regulated exons or isoforms Application of MISO implicated the RNA splicing factor hnRNP H1 in the regulation of alternative cleavage and polyadenylation, a role that was supported by UV cross-linking-immunoprecipitation sequencing (CLIP-seq) analysis in human cells Our results provide a probabilistic framework for RNA-seq analysis, give functional insights into pre-mRNA processing and yield guidelines for the optimal design of RNA-seq experiments for studies of gene and isoform expression
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Citations
RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome
Bo Li,Colin N. Dewey +1 more
TL;DR: It is shown that accurate gene-level abundance estimates are best obtained with large numbers of short single-end reads, and estimates of the relative frequencies of isoforms within single genes may be improved through the use of paired- end reads, depending on the number of possible splice forms for each gene.
Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks
Cole Trapnell,Adam Roberts,Loyal A. Goff,Loyal A. Goff,Loyal A. Goff,Geo Pertea,Daehwan Kim,Daehwan Kim,David R. Kelley,David R. Kelley,Harold Pimentel,Steven L. Salzberg,John L. Rinn,John L. Rinn,Lior Pachter +14 more
TL;DR: This 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, which takes less than 1 d of computer time for typical experiments and ∼1 h of hands-on time.
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.
6.1K
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
TL;DR: 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.
A survey of best practices for RNA-seq data analysis
Ana Conesa,Pedro Madrigal,Pedro Madrigal,Sonia Tarazona,David Gomez-Cabrero,Alejandra Cervera,Andrew McPherson,Michał Wojciech Szcześniak,Daniel J. Gaffney,Laura L. Elo,Xuegong Zhang,Ali Mortazavi +11 more
TL;DR: All of the major steps in RNA-seq data analysis are reviewed, including experimental design, quality control, read alignment, quantification of gene and transcript levels, visualization, differential gene expression, alternative splicing, functional analysis, gene fusion detection and eQTL mapping.
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Journal Article
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