Journal ArticleDOI
Mapping and quantifying mammalian transcriptomes by RNA-Seq.
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TLDR
Although >90% of uniquely mapped reads fell within known exons, the remaining data suggest new and revised gene models, including changed or additional promoters, exons and 3′ untranscribed regions, as well as new candidate microRNA precursors.Abstract:
We have mapped and quantified mouse transcriptomes by deeply sequencing them and recording how frequently each gene is represented in the sequence sample (RNA-Seq). This provides a digital measure of the presence and prevalence of transcripts from known and previously unknown genes. We report reference measurements composed of 41–52 million mapped 25-base-pair reads for poly(A)-selected RNA from adult mouse brain, liver and skeletal muscle tissues. We used RNA standards to quantify transcript prevalence and to test the linear range of transcript detection, which spanned five orders of magnitude. Although >90% of uniquely mapped reads fell within known exons, the remaining data suggest new and revised gene models, including changed or additional promoters, exons and 3′ untranscribed regions, as well as new candidate microRNA precursors. RNA splice events, which are not readily measured by standard gene expression microarray or serial analysis of gene expression methods, were detected directly by mapping splice-crossing sequence reads. We observed 1.45 × 10 5 distinct splices, and alternative splices were prominent, with 3,500 different genes expressing one or more alternate internal splices. The mRNA population specifies a cell’s identity and helps to govern its present and future activities. This has made transcriptome analysis a general phenotyping method, with expression microarrays of many kinds in routine use. Here we explore the possibility that transcriptome analysis, transcript discovery and transcript refinement can be done effectively in large and complex mammalian genomes by ultra-high-throughput sequencing. Expression microarrays are currently the most widely used methodology for transcriptome analysis, although some limitations persist. These include hybridization and cross-hybridization artifacts 1–3 , dye-based detection issues and design constraints that preclude or seriously limit the detection of RNA splice patterns and previously unmapped genes. These issues have made it difficult for standard array designs to provide full sequence comprehensiveness (coverage of all possible genes, including unknown ones, in large genomes) or transcriptome comprehensiveness (reliable detection of all RNAs of all prevalence classes, including the least abundant ones that are physiologically relevant). Otherread more
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Journal ArticleDOI
SOAPdenovo-Trans: de novo transcriptome assembly with short RNA-Seq reads
Yinlong Xie,Yinlong Xie,Gengxiong Wu,Jingbo Tang,Ruibang Luo,Jordan Patterson,Shanlin Liu,Weihua Huang,Guangzhu He,Shengchang Gu,Shengkang Li,Xin Zhou,Tak-Wah Lam,Yingrui Li,Xun Xu,Gane Ka-Shu Wong,Jun Wang +16 more
TL;DR: The conclusion is that SOAPdenovo-Trans provides higher contiguity, lower redundancy and faster execution, compared with two other popular transcriptome assemblers.
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Exploiting the mutanome for tumor vaccination
John C. Castle,Sebastian Kreiter,Jan Diekmann,Martin Löwer,Niels van de Roemer,Jos de Graaf,Abderraouf Selmi,Mustafa Diken,Sebastian Boegel,Claudia Paret,Michael Koslowski,Andreas Kuhn,Cedrik M. Britten,Christoph Huber,Özlem Türeci,Ugur Sahin +15 more
TL;DR: The findings provide a comprehensive picture of the mutanome of B16F10 melanoma and offer insight into the extent of the immunogenicity of nonsynonymous base substitution mutations, and argue that the use of deep sequencing to systematically analyze immunogenic mutations may pave the way for individualized immunotherapy of cancer patients.
Journal ArticleDOI
Biases in Illumina transcriptome sequencing caused by random hexamer priming
TL;DR: A read count reweighting scheme, based on the nucleotide frequencies of the reads, that mitigates the impact of the bias in nucleotide composition at the beginning of transcriptome sequencing reads from the Illumina Genome Analyzer.
Journal ArticleDOI
Comprehensive comparative analysis of strand-specific RNA sequencing methods
Joshua Z. Levin,Moran Yassour,Moran Yassour,Xian Adiconis,Chad Nusbaum,Dawn Thompson,Nir Friedman,Andreas Gnirke,Aviv Regev +8 more
TL;DR: In this paper, the authors developed a comprehensive computational pipeline to compare library quality metrics from any RNA-seq method, using the well-annotated Saccharomyces cerevisiae transcriptome as a benchmark.
Journal ArticleDOI
GC-content normalization for RNA-Seq data.
TL;DR: The authors' within-lane normalization procedures, followed by between-lanenormalization, reduce GC-content bias and lead to more accurate estimates of expression fold-changes and tests of differential expression.
References
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Highly Integrated Single-Base Resolution Maps of the Epigenome in Arabidopsis
Ryan Lister,Ronan C. O'Malley,Julian Tonti-Filippini,Brian D. Gregory,Charles C. Berry,A. Harvey Millar,Joseph R. Ecker +6 more
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