• Publications
  • Influence
Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2
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 experimentalExpand
  • 16,736
  • 2719
  • PDF
Differential expression analysis for sequence count data
High-throughput sequencing assays such as RNA-Seq, ChIP-Seq or barcode counting provide quantitative readouts in the form of count data. To infer differential signal in such data correctly and withExpand
  • 7,869
  • 1181
  • PDF
HTSeq—a Python framework to work with high-throughput sequencing data
TLDR
We present HTSeq, a Python library to facilitate the rapid development of scripts for processing and analysing HTS data. Expand
  • 9,117
  • 925
  • PDF
Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2
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 experimentalExpand
  • 3,133
  • 690
Orchestrating high-throughput genomic analysis with Bioconductor
TLDR
Bioconductor is an open-source, open-development software project for the analysis and comprehension of high-throughput data in genomics and molecular biology with 934 interoperable packages contributed by a large, diverse community of scientists. Expand
  • 1,705
  • 173
  • PDF
Detecting differential usage of exons from RNA-seq data.
RNA-seq is a powerful tool for the study of alternative splicing and other forms of alternative isoform expression. Understanding the regulation of these processes requires sensitive and specificExpand
  • 742
  • 129
  • PDF
Count-based differential expression analysis of RNA sequencing data using R and Bioconductor
RNA sequencing (RNA-seq) has been rapidly adopted for the profiling of transcriptomes in many areas of biology, including studies into gene regulation, development and disease. Of particular interestExpand
  • 828
  • 71
  • PDF
Accounting for technical noise in single-cell RNA-seq experiments
Single-cell RNA-seq can yield valuable insights about the variability within a population of seemingly homogeneous cells. We developed a quantitative statistical method to distinguish true biologicalExpand
  • 690
  • 40
  • PDF
Differential expression of RNA-Seq data at the gene level – the DESeq package
A basic task in the analysis of count data from RNA-Seq is the detection of differentially expressed genes. The count data are presented as a table which reports, for each sample, the number of readsExpand
  • 254
  • 39
  • PDF
Differential analysis of count data – the DESeq 2 package
TLDR
A basic task in the analysis of count data from RNA-Seq is the detection of di↵erentially expressed genes. Expand
  • 168
  • 31
  • PDF