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- Command line interface
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- GNU Lesser General Public License version 3.0
- S4Vectors, IRanges, GenomicRanges, SummarizedExperiment
- Michael Love <>
- Simon Anders <>
An implementation for detection of differential translated genes using Ribo-seq is available at https://github.com/SGDDNB/DTG-detection
No open topic.
(Love et al., 2014)
Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2.
PMID: 25516281 DOI: 10.1186/s13059-014-0550-8
(Anders and Huber, 2010)
Differential expression analysis for sequence count data.
(Chothani et al., 2017)
Reliable detection of translational regulation with Ribo-seq.
Department of Biostatistics and Computational Biology, Dana Farber Cancer Institute, Boston, MA, USA; Department of Biostatistics, Harvard School of Public Health, Boston, MA, USA; Genome Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany; Department of Computational Molecular Biology, Max Planck Institute for Molecular Genetics, Berlin, Germany
Supported by a stipend from the International Max Planck Research School for Computational Biology and Scientific Computing, a grant from the National Institutes of Health (5T32CA009337-33), and the European Union’s 7th Framework Programme (Health) via Project Radiant.
4 user reviews
4 user reviews
DEseq2 is a very robust and fast package for differential gene expression analysis of RNA-seq data. It has a very intuitive pipeline to get most out of your data with few lines of code. The documentation is also pretty straightforward, I would only include more Case Studies, since the technique is used with many diverse experimental designs. On the other hand, Bioconductor mailing list and forums frequented by the author(s) help circuvemting this. In summary, is a must tool for a bioinformatician toolkit.
A very robust algorithm and streamlined package for that final step in RNA-seq pipelines--differential expression analyses. Note the current iteration, DESeq2 provides improved considerations for normalization and transformation of feature counts. Several tutorials exist on how to get started using the package for bulk RNA-seq, but have personally found some disagreement on best practices for its use with single cell RNA-seq.
Deseq2 is a fast and powerful R package for differential gene expression analysis using raw read counts.
Very powerful R package for differential expression analyses. The new implementation, DESeq2, appears to be one of the most relevant approach to identify differentially expressed genes. Cons: As always it requires to use eSet-like classes