RNA-Seq gene expression estimation with read mapping uncertainty

By Bo Li, Victor Ruotti, Ron M. Stewart, James A. Thomson, Colin N. Dewey. Bioinform., 2009

1,147 citations recorded by the source corpus.

Motivation: RNA-Seq is a promising new technology for accurately measuring gene expression levels. Expression estimation with RNA-Seq requires the mapping of relatively short sequencing reads to a reference genome or transcript set. Because reads are generally shorter than transcripts from which they are derived, a single read may map to multiple genes and isoforms, complicating expression analyses. Previous computational methods either discard reads that map to multiple locations or allocate them to genes heuristically. Results: We present a generative statistical model and associated inference methods that handle read mapping uncertainty in a principled manner. Through simulations parameterized by real RNA-Seq data, we show that our method is more accurate than previous methods. Our improved accuracy is the result of handling read mapping uncertainty with a statistical model and the estimation of gene expression levels as the sum of isoform expression levels. Unlike previous methods, our method is capable of modeling non-uniform read distributions. Simulations with our method indicate that a read length of 20–25 bases is optimal for gene-level expression estimation from mouse and maize RNA-Seq data when sequencing throughput is fixed. Availability: An initial C++ implementation of our method that was used for the results presented in this article is available at http://deweylab.biostat.wisc.edu/rsem. Contact: cdewey@biostat.wisc.edu Supplementary information: Supplementary data are available at Bioinformatics on

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/50992d36648c4b4f9da1b3c6aae11463c053f428

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Citations in the corpus

Applications of next-generation sequencing to the study of biological invasions (2015, background): “For example, the giant panda became the first mammal to be sequenced using solely NGS platforms (Li et al., 2010b), and the ambitious Genome 10K The steps of the pathway are shown in red and the processes in blue.”
Computational methods for transcriptome annotation and quantification using RNA-seq (2011, methodology): “…function of the mean counts using various different parametric approaches (such as the normal and negative binomial distributions) 19,29,65–67, It is important to note although these approaches can assign significance to differential expression, the biological conclusions must be…”
Revealing the vectors of cellular identity with single-cell genomics (2016, background): “…are uniform across libraries and to scale read counts accordingly with size factors 52 or relative expression units, such as RPKM 53 (reads per kilobase of exon per million mapped reads), FPKM 54 (fragments per kilobase of exon per million mapped fragments), or TPM 55,56 (transcripts per million).”
Unprecedented genomic diversity of RNA viruses in arthropods reveals the ancestry of negative-sense RNA viruses (2015, methodology): “The remaining reads from each library were then mapped on to the assembled transcripts and analyzed with RSEM (Li et al., 2010), using the run_RSEM_align_n_estimate.pl scripts implemented in the Trinity program (Grabherr et al., 2011).”
From RNA-seq reads to differential expression results (2010, neutral): “In the past year, several methods have emerged that use RNA-seq data for abundance estimation [8,9], detection of alternative splicing [10-12], RNA editing [13] and novel transcripts [11,14].”