RSEM

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, 95% credibility intervals, and visualization files and can also simulate RNA-Seq data. In contrast to other existing tools, the software does not require a reference genome. Thus, in combination with a de novo transcriptome assembler, RSEM enables accurate transcript quantification for species without sequenced genomes. On simulated and real data sets, RSEM has superior or comparable performance to quantification methods that rely on a reference genome. Taking advantage of RSEM’s ability to effectively use ambiguously-mapping reads, we show that accurate gene-level abundance estimates are best obtained with large numbers of short single-end reads. On the other hand, estimates of the relative frequencies of isoforms within single genes may be improved through the use of paired-end reads, depending on the number of possible splice forms for each gene. Conclusions. 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.


References in zbMATH (referenced in 11 articles )

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  1. Ni, Yang; Stingo, Francesco C.; Ha, Min Jin; Akbani, Rehan; Baladandayuthapani, Veerabhadran: Bayesian hierarchical varying-sparsity regression models with application to cancer proteogenomics (2019)
  2. Li, Wei Vivian; Zhao, Anqi; Zhang, Shihua; Li, Jingyi Jessica: MSIQ: joint modeling of multiple RNA-seq samples for accurate isoform quantification (2018)
  3. Segal, Brian D.; Braun, Thomas; Elliott, Michael R.; Jiang, Hui: Fast approximation of small (p)-values in permutation tests by partitioning the permutations (2018)
  4. Wolff, Alexander: Analysis of expression profile and gene variation via development of methods for next generation sequencing data (2018)
  5. Gunady, Mohamed K.; Cornwell, Steffen; Mount, Stephen M.; Bravo, Héctor Corrada: Yanagi: transcript segment library construction for RNA-seq quantification (2017)
  6. Mao, Shunfu; Mohajer, Soheil; Ramachandran, Kannan; Tse, David; Kannan, Sreeram: abSNP: RNA-Seq SNP calling in repetitive regions via abundance estimation (2017)
  7. Papastamoulis, Panagiotis; Rattray, Magnus: Bayesian estimation of differential transcript usage from RNA-seq data (2017)
  8. Kruppa, Jochen; Kramer, Frank; Beißbarth, Tim; Jung, Klaus: A simulation framework for correlated count data of features subsets in high-throughput sequencing or proteomics experiments (2016)
  9. Lin, Zhixiang; Li, Mingfeng; Sestan, Nenad; Zhao, Hongyu: A Markov random field-based approach for joint estimation of differentially expressed genes in mouse transcriptome data (2016)
  10. Gelfond, Jonathan A.; Ibrahim, Joseph G.; Chen, Ming-Hui; Sun, Wei; Lewis, Kaitlyn; Kinahan, Sean; Hibbs, Matthew; Buffenstein, Rochelle: Homology cluster differential expression analysis for interspecies mRNA-seq experiments (2015)
  11. Picardi, Ernesto (ed.): RNA bioinformatics (2015)