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Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

TLDR
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.
Abstract
In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. The DESeq2 package is available at http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html .

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Citations
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Journal ArticleDOI

HTSeq—a Python framework to work with high-throughput sequencing data

TL;DR: This work presents HTSeq, a Python library to facilitate the rapid development of custom scripts for high-throughput sequencing data analysis, and presents htseq-count, a tool developed with HTSequ that preprocesses RNA-Seq data for differential expression analysis by counting the overlap of reads with genes.
Journal ArticleDOI

Comprehensive Integration of Single-Cell Data.

TL;DR: A strategy to "anchor" diverse datasets together, enabling us to integrate single-cell measurements not only across scRNA-seq technologies, but also across different modalities.
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.
References
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Journal ArticleDOI

The NBP Negative Binomial Model for Assessing Differential Gene Expression from RNA-Seq

TL;DR: This paper proposed a new statistical test for assessing differential gene expression using RNA sequencing (RNA-Seq) data, which is based on the NBP parameterization of the negative binomial distribution.
Journal ArticleDOI

The pea aphid genome sequence brings theories of insect defense into question

TL;DR: The genome sequence of the pea aphid is the first for a basal hemimetabolous insect and provides insights into developmental plasticity, symbiosis and insect immunity.
Journal ArticleDOI

ReCount: A multi-experiment resource of analysis-ready RNA-seq gene count datasets

TL;DR: ReCount is an online resource of RNA-seq gene count tables and auxilliary data built from raw RNA sequencing data from 18 different published studies comprising 475 samples and over 8 billion reads, allowing statistical and computational scientists to consider alternative parameter values.
Posted Content

Near-optimal RNA-Seq quantification

TL;DR: Software implementing the approach, called kallisto, can be used to analyze 30 million unaligned paired-end RNA-Seq reads in less than 5 minutes on a standard laptop computer while providing results as accurate as those of the best existing tools.
Journal ArticleDOI

Comparative ribosome profiling reveals extensive translational complexity in different Trypanosoma brucei life cycle stages

TL;DR: In this article, the absence of transcription initiation regulation for RNA polymerase II in the protozoan parasite Trypanosoma brucei greatly simplifies the task of elucidating the contribution of translation to global gene expression.
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