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Open AccessJournal ArticleDOI

voom: precision weights unlock linear model analysis tools for RNA-seq read counts

TLDR
New normal linear modeling strategies are presented for analyzing read counts from RNA-seq experiments, and the voom method estimates the mean-variance relationship of the log-counts, generates a precision weight for each observation and enters these into the limma empirical Bayes analysis pipeline.
Abstract
New normal linear modeling strategies are presented for analyzing read counts from RNA-seq experiments. The voom method estimates the mean-variance relationship of the log-counts, generates a precision weight for each observation and enters these into the limma empirical Bayes analysis pipeline. This opens access for RNA-seq analysts to a large body of methodology developed for microarrays. Simulation studies show that voom performs as well or better than count-based RNA-seq methods even when the data are generated according to the assumptions of the earlier methods. Two case studies illustrate the use of linear modeling and gene set testing methods.

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Journal ArticleDOI

Necroptosis activation in Alzheimer's disease

TL;DR: It is found that necroptosis was activated in postmortem human AD brains, positively correlated with Braak stage, and inversely correlated with brain weight and cognitive scores, and the set of genes regulated by RIPK1 overlapped significantly with multiple independent AD transcriptomic signatures, indicating that RIPK 1 activity could explain a substantial portion of transcriptomic changes in AD.
Journal ArticleDOI

Linear models enable powerful differential activity analysis in massively parallel reporter assays.

TL;DR: The mpralm method is presented, which is shown to be calibrated and powerful, by analyzing its performance on multiple MPRA datasets and to show that it outperforms existing statistical methods for analysis of this data type, in the first comprehensive evaluation of statistical methods on several datasets.
Journal ArticleDOI

Genomic innovations, transcriptional plasticity and gene loss underlying the evolution and divergence of two highly polyphagous and invasive Helicoverpa pest species

Stephen L. Pearce, +72 more
- 31 Jul 2017 - 
TL;DR: The extreme polyphagy of the two heliothines is associated with extensive amplification and neofunctionalisation of genes involved in host finding and use, coupled with versatile transcriptional responses on different hosts.
Journal ArticleDOI

Analysis of intronic and exonic reads in RNA-seq data characterizes transcriptional and post-transcriptional regulation

TL;DR: A computational approach called exon-intron split analysis (EISA) that measures changes in mature RNA and pre-mRNA reads across different experimental conditions to quantify transcriptional and post-transcriptional regulation of gene expression is presented.
References
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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.
Book

Generalized Linear Models

TL;DR: In this paper, a generalization of the analysis of variance is given for these models using log- likelihoods, illustrated by examples relating to four distributions; the Normal, Binomial (probit analysis, etc.), Poisson (contingency tables), and gamma (variance components).
Journal ArticleDOI

featureCounts: an efficient general-purpose program for assigning sequence reads to genomic features

TL;DR: FeatureCounts as discussed by the authors is a read summarization program suitable for counting reads generated from either RNA or genomic DNA sequencing experiments, which implements highly efficient chromosome hashing and feature blocking techniques.
Journal ArticleDOI

Differential expression analysis for sequence count data.

Simon Anders, +1 more
- 27 Oct 2010 - 
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.
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