swMATH ID: 17160
Software Authors: Li B, Dewey CN
Description: RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome. Background: RNA-Seq is revolutionizing the way transcript abundances are measured. A key challenge in transcript quantification from RNA-Seq data is the handling of reads that map to multiple genes or isoforms. This issue is particularly important for quantification with de novo transcriptome assemblies in the absence of sequenced genomes, as it is difficult to determine which transcripts are isoforms of the same gene. A second significant issue is the design of RNA-Seq experiments, in terms of the number of reads, read length, and whether reads come from one or both ends of cDNA fragments. Results: We present RSEM, an user-friendly software package for quantifying gene and isoform abundances from single-end or paired-end RNA-Seq data. RSEM outputs abundance estimates, 95Conclusions. RSEM is an accurate and user-friendly software tool for quantifying transcript abundances from RNA-Seq data. As it does not rely on the existence of a reference genome, it is particularly useful for quantification with de novo transcriptome assemblies. In addition, RSEM has enabled valuable guidance for cost-efficient design of quantification experiments with RNA-Seq, which is currently relatively expensive.
Homepage: https://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-12-323
Related Software: edgeR; Bioconductor; TopHat; R; DEseq; Salmon; STAR; MapSplice; HTSeq; EBSeq; casper; SNVMix; VarScan; Voom; DESeq2; FSCseq; NB.MClust; iClusterPlus; zinbwave; TCGAbiolinks
Cited in: 12 Publications

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