RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome View Full Text


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Article Info

DATE

2011-12

AUTHORS

Bo Li, Colin N Dewey

ABSTRACT

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. More... »

PAGES

323

References to SciGraph publications

  • 2010-10. Differential expression analysis for sequence count data in GENOME BIOLOGY
  • 2009-01. RNA-Seq: a revolutionary tool for transcriptomics in NATURE REVIEWS GENETICS
  • 2010-05. Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation in NATURE BIOTECHNOLOGY
  • 2011-07. Full-length transcriptome assembly from RNA-Seq data without a reference genome in NATURE BIOTECHNOLOGY
  • 2010. Estimation of Alternative Splicing isoform Frequencies from RNA-Seq Data in ALGORITHMS IN BIOINFORMATICS
  • 2008-11. Alternative isoform regulation in human tissue transcriptomes in NATURE
  • 2010-12. Analysis and design of RNA sequencing experiments for identifying isoform regulation in NATURE METHODS
  • 2010-05. Modeling non-uniformity in short-read rates in RNA-Seq data in GENOME BIOLOGY
  • 2011-09. Improving RNA-Seq expression estimates by correcting for fragment bias in GENOME BIOLOGY
  • 2008-07. Mapping and quantifying mammalian transcriptomes by RNA-Seq in NATURE METHODS
  • 2010-05. Ab initio reconstruction of cell type–specific transcriptomes in mouse reveals the conserved multi-exonic structure of lincRNAs in NATURE BIOTECHNOLOGY
  • 2006-09. The MicroArray Quality Control (MAQC) project shows inter- and intraplatform reproducibility of gene expression measurements in NATURE BIOTECHNOLOGY
  • 2010-12. Evaluation of statistical methods for normalization and differential expression in mRNA-Seq experiments in BMC BIOINFORMATICS
  • 2009-03. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome in GENOME BIOLOGY
  • 2010-11. De novo assembly and analysis of RNA-seq data in NATURE METHODS
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1186/1471-2105-12-323

    DOI

    http://dx.doi.org/10.1186/1471-2105-12-323

    DIMENSIONS

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    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/21816040


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    56 schema:description 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.
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