DEGseq: an R package for identifying differentially expressed genes from RNA-seq data
Top Cited Papers
- 24 October 2009
- journal article
- Published by Oxford University Press (OUP) in Bioinformatics
- Vol. 26 (1), 136-138
- https://doi.org/10.1093/bioinformatics/btp612
Abstract
Summary: High-throughput RNA sequencing (RNA-seq) is rapidly emerging as a major quantitative transcriptome profiling platform. Here, we present DEGseq, an R package to identify differentially expressed genes or isoforms for RNA-seq data from different samples. In this package, we integrated three existing methods, and introduced two novel methods based on MA-plot to detect and visualize gene expression difference. Availability: The R package and a quick-start vignette is available at http://bioinfo.au.tsinghua.edu.cn/software/degseq Contact: xwwang@tsinghua.edu.cn; zhangxg@tsinghua.edu.cn Supplementary information: Supplementary data are available at Bioinformatics online.Keywords
This publication has 11 references indexed in Scilit:
- Measuring differential gene expression by short read sequencing: quantitative comparison to 2-channel gene expression microarraysBMC Genomics, 2009
- mRNA-Seq whole-transcriptome analysis of a single cellNature Methods, 2009
- Statistical inferences for isoform expression in RNA-SeqBioinformatics, 2009
- RNA-Seq: a revolutionary tool for transcriptomicsNature Reviews Genetics, 2009
- RNA-seq: An assessment of technical reproducibility and comparison with gene expression arraysGenome Research, 2008
- Mapping and quantifying mammalian transcriptomes by RNA-SeqNature Methods, 2008
- Moderated statistical tests for assessing differences in tag abundanceBioinformatics, 2007
- Statistical significance for genomewide studiesProceedings of the National Academy of Sciences, 2003
- Significance analysis of microarrays applied to the ionizing radiation responseProceedings of the National Academy of Sciences, 2001
- Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple TestingJournal of the Royal Statistical Society Series B: Statistical Methodology, 1995