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
Citations
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Book ChapterDOI
Quality control of RNA-seq experiments.
TL;DR: This chapter discusses the most widely used quality control metrics including sequence quality, sequencing depth, reads duplication rates (clonal reads), alignment quality, nucleotide composition bias, PCR bias, GC bias, rRNA and mitochondria contamination, coverage uniformity, etc.
Journal ArticleDOI
A benchmark for RNA-seq quantification pipelines
Mingxiang Teng,Mingxiang Teng,Michael I. Love,Carrie A. Davis,Sarah Djebali,Alexander Dobin,Brenton R. Graveley,Sheng Li,Christopher E. Mason,Sara Olson,Dmitri D. Pervouchine,Cricket A. Sloan,Xintao Wei,Lijun Zhan,Rafael A. Irizarry +14 more
TL;DR: A series of statistical summaries and plots are presented to evaluate the performance of RNA-seq methods in terms of specificity and sensitivity, available as a R/Bioconductor package.
Journal ArticleDOI
Targeted sequencing for gene discovery and quantification using RNA CaptureSeq
Tim R. Mercer,Michael B. Clark,Joanna Crawford,Marion E. G. Brunck,Daniel J. Gerhardt,Ryan J. Taft,Lars K. Nielsen,Marcel E. Dinger,John S. Mattick +8 more
TL;DR: A detailed protocol for all stages of RNA CaptureSeq is described, from initial probe design considerations and capture of targeted genes to final assembly and quantification of captured transcripts.
Journal ArticleDOI
Molecular Evolution of Early-Onset Prostate Cancer Identifies Molecular Risk Markers and Clinical Trajectories
Clarissa Gerhäuser,Francesco Favero,Thomas Risch,Ronald Simon,Lars Feuerbach,Yassen Assenov,Doreen Heckmann,Nikos Sidiropoulos,Sebastian M. Waszak,Daniel Hübschmann,Alfonso Urbanucci,Etsehiwot G. Girma,Vladimir Kuryshev,Leszek J. Klimczak,Natalie Saini,Adrian M. Stütz,Dieter Weichenhan,Lisa Marie Böttcher,Reka Toth,Josephine D. Hendriksen,Christina Koop,Pavlo Lutsik,Sören Matzk,Hans-Jörg Warnatz,Vyacheslav Amstislavskiy,Clarissa Feuerstein,Benjamin Raeder,Olga Bogatyrova,Eva Maria Schmitz,Claudia Hube-Magg,Martina Kluth,Hartwig Huland,Markus Graefen,Chris Lawerenz,Gervaise H. Henry,Takafumi N. Yamaguchi,Alicia Malewska,Jan Meiners,Daniela Schilling,Eva Reisinger,Roland Eils,Matthias Schlesner,Douglas W. Strand,Robert G. Bristow,Paul C. Boutros,Paul C. Boutros,Christof von Kalle,Dmitry A. Gordenin,Holger Sültmann,Benedikt Brors,Guido Sauter,Christoph Plass,Marie-Laure Yaspo,Jan O. Korbel,Thorsten Schlomm,Thorsten Schlomm,Joachim Weischenfeldt +56 more
TL;DR: Characterization across 292 prostate cancer genomes revealed age-related genomic alterations and a clock-like enzymatic-driven mutational process contributing to the earliest mutations in prostate cancer patients.
Journal ArticleDOI
Deep RNA sequencing of L. monocytogenes reveals overlapping and extensive stationary phase and sigma B-dependent transcriptomes, including multiple highly transcribed noncoding RNAs
Haley F. Oliver,Renato H. Orsi,Lalit Ponnala,Uri Keich,Uri Keich,Wei Wang,Qi Sun,Samuel W. Cartinhour,Samuel W. Cartinhour,Melanie J. Filiatrault,Melanie J. Filiatrault,Martin Wiedmann,Kathryn J. Boor +12 more
TL;DR: The results from these studies provide powerful evidence that RNA-Seq data combined with appropriate bioinformatics tools allow quantitative characterization of prokaryotic transcriptomes, thus providing exciting new strategies for exploring transcriptional regulatory networks in bacteria.
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