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DESeq2 hence offers to practitioners a wide set of features with state-of-the-art inferential power. Its use cases are not limited to RNA-seq data or other transcriptomics assays; rather, many kinds of high-throughput count data can be used. Other areas for which DESeq or DESeq2 have been used include chromatin immunoprecipitation sequencing assays (e.g., [40]; see also the DiffBind package [41],[42]), barcode-based assays (e.g., [43]), metagenomics data (e.g., [44]), ribosome profiling [45] and CRISPR/Cas-library assays [46]. Finally, the DESeq2 package is integrated well in the Bioconductor infrastructure [11] and comes with extensive documentation, including a vignette that demonstrates a complete analysis step by step and discusses advanced use cases.




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Sweave vignettes for reproducing all figures and tables in this paper, including data objects for the experiments mentioned, and code for aligning reads and for benchmarking, can be found in a package DESeq2paper [66].


RNA-seq was used to explore the developmental transcriptome of D. melanogaster[45]. Mapped read counts are available from the ReCount project [62]. Specifically the pooled version of the modencodefly dataset from the ReCount website [63] provides read counts summarized by Ensembl 61 gene IDs for 30 whole-animal biological samples. We discarded the larval, pupal and adult stages and kept only the 12 embryonic samples. Genes were retained in the analysis if they achieved cpm >1 for any embryonic stage. Effective library sizes were estimated by TMM scale-normalization [54] using edgeR software [12] prior to the voom analysis.


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