Differential analysis of RNA-seq incorporating quantification uncertainty

We describe sleuth (http://pachterlab.github.io/sleuth), a method for the differential analysis of gene expression data that utilizes bootstrapping in conjunction with response error linear modeling to decouple biological variance from inferential variance. sleuth is implemented in an interactive shiny app that utilizes kallisto quantifications and bootstraps for fast and accurate analysis of data from RNA-seq experiments.

[1]  A. Alavi,et al.  Opportunities and Challenges , 1998, In Vitro Diagnostic Industry in China.

[2]  W. Huber,et al.  Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2 , 2014, Genome Biology.

[3]  John P. Buonaccorsi,et al.  Measurement Error: Models, Methods, and Applications , 2010 .

[4]  Wei Shi,et al.  featureCounts: an efficient general purpose program for assigning sequence reads to genomic features , 2013, Bioinform..

[5]  J. Carpten,et al.  Translating RNA sequencing into clinical diagnostics: opportunities and challenges , 2016, Nature Reviews Genetics.

[6]  Wing Hung Wong,et al.  Statistical inferences for isoform expression in RNA-Seq , 2009, Bioinform..

[7]  Cole Trapnell,et al.  Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation. , 2010, Nature biotechnology.

[8]  Charity W. Law,et al.  voom: precision weights unlock linear model analysis tools for RNA-seq read counts , 2014, Genome Biology.

[9]  Giuseppe Testa,et al.  RNAontheBENCH: computational and empirical resources for benchmarking RNAseq quantification and differential expression methods , 2016, Nucleic acids research.

[10]  Daniel J. Gaffney,et al.  A survey of best practices for RNA-seq data analysis , 2016, Genome Biology.

[11]  Sven Rahmann,et al.  Snakemake--a scalable bioinformatics workflow engine. , 2012, Bioinformatics.

[12]  Cole Trapnell,et al.  Ultrafast and memory-efficient alignment of short DNA sequences to the human genome , 2009, Genome Biology.

[13]  Daniel Bottomly,et al.  Evaluating Gene Expression in C57BL/6J and DBA/2J Mouse Striatum Using RNA-Seq and Microarrays , 2011, PloS one.

[14]  David M. Rocke,et al.  Controlling False Positive Rates in Methods for Differential Gene Expression Analysis using RNA-Seq Data , 2015, bioRxiv.

[15]  Ernest Turro,et al.  Flexible analysis of RNA-seq data using mixed effects models , 2014, Bioinform..

[16]  Thomas J. Hardcastle,et al.  baySeq: Empirical Bayesian methods for identifying differential expression in sequence count data , 2010, BMC Bioinformatics.

[17]  Alyssa C. Frazee,et al.  Polyester: Simulating RNA-Seq Datasets With Differential Transcript Expression , 2014, bioRxiv.

[18]  W. Huber,et al.  Detecting differential usage of exons from RNA-seq data , 2012, Genome research.

[19]  Steven L Salzberg,et al.  HISAT: a fast spliced aligner with low memory requirements , 2015, Nature Methods.

[20]  David G Hendrickson,et al.  Differential analysis of gene regulation at transcript resolution with RNA-seq , 2012, Nature Biotechnology.

[21]  Antti Honkela,et al.  Identifying differentially expressed transcripts from RNA-seq data with biological variation , 2011, Bioinform..

[22]  L. Pachter.  Models for transcript quantification from RNA-Seq , 2011, 1104.3889.

[23]  Laura L. Elo,et al.  Comparison of software packages for detecting differential expression in RNA-seq studies , 2013, Briefings Bioinform..

[24]  R. Tibshirani,et al.  Normalization, testing, and false discovery rate estimation for RNA-sequencing data. , 2012, Biostatistics.

[25]  Mark D. Robinson,et al.  Moderated statistical tests for assessing differences in tag abundance , 2007, Bioinform..

[26]  Lior Pachter,et al.  Near-optimal probabilistic RNA-seq quantification , 2016, Nature Biotechnology.

[27]  Pedro G. Ferreira,et al.  Transcriptome and genome sequencing uncovers functional variation in humans , 2013, Nature.

[28]  Colin N. Dewey,et al.  RNA-Seq gene expression estimation with read mapping uncertainty , 2009, Bioinform..

[29]  Antti Honkela,et al.  Fast and accurate approximate inference of transcript expression from RNA-seq data , 2014, Bioinform..

[30]  Jeff H. Chang,et al.  The NBP Negative Binomial Model for Assessing Differential Gene Expression from RNA-Seq , 2011 .

[31]  M. Robinson,et al.  Small-sample estimation of negative binomial dispersion, with applications to SAGE data. , 2007, Biostatistics.

[32]  W. Huber,et al.  which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. MAnorm: a robust model for quantitative comparison of ChIP-Seq data sets , 2011 .

[33]  M. Robinson,et al.  Differential analyses for RNA-seq: transcript-level estimates improve gene-level inferences , 2015, F1000Research.

[34]  Ning Leng,et al.  EBSeq: an empirical Bayes hierarchical model for inference in RNA-seq experiments , 2013, Bioinform..

[35]  Robert Tibshirani,et al.  Finding consistent patterns: A nonparametric approach for identifying differential expression in RNA-Seq data , 2013, Statistical methods in medical research.

[36]  Dmitri D. Pervouchine,et al.  A benchmark for RNA-seq quantification pipelines , 2016, Genome Biology.