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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Transcriptome Analysis of Pseudomonas syringae Identifies New Genes, Noncoding RNAs, and Antisense Activity
Melanie J. Filiatrault,Melanie J. Filiatrault,Paul Stodghill,Philip A. Bronstein,Philip A. Bronstein,Simon Moll,Magdalen Lindeberg,George Grills,Peter A. Schweitzer,Wei Wang,Gary P. Schroth,Shujun Luo,Irina Khrebtukova,Yong Yang,Theodore W. Thannhauser,Bronwyn G. Butcher,Samuel W. Cartinhour,Samuel W. Cartinhour,David J. Schneider,David J. Schneider +19 more
TL;DR: A strand-specific method was applied to sequence bacterial transcripts using Illumina's high-throughput sequencing technology and identified and confirmed transcriptional activity in areas of the genome inconsistent with the annotation and in unannotated regions, suggesting a role for RpoN-dependent promoter sequences upstream of several noncoding RNAs.
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An investigation of biomarkers derived from legacy microarray data for their utility in the RNA-seq era
Zhenqiang Su,Zhenqiang Su,Hong Fang,Huixiao Hong,Leming Shi,Wenqian Zhang,Wenwei Zhang,Yanyan Zhang,Zirui Dong,Lee Lancashire,Marina Bessarabova,Xi Yang,Baitang Ning,Binsheng Gong,Joe Meehan,Joshua Xu,Weigong Ge,Roger Perkins,Matthias Fischer,Weida Tong +19 more
TL;DR: Signature genes of predictive models are reciprocally transferable between microarray andRNA-seq data for model development, and microarray-based models can accurately predict RNA-seq-profiled samples; while RNA-sequencing-based model are less accurate in predicting micro array-Profiled samples and are affected both by the choice of modeling algorithm and the gene mapping complexity.
Journal ArticleDOI
powsimR: power analysis for bulk and single cell RNA-seq experiments
TL;DR: PowsimR is a flexible tool to simulate and evaluate differential expression from bulk and especially single‐cell RNA‐seq data making it suitable for a priori and posterior power analyses.
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Glycosylation of Human Milk Lactoferrin Exhibits Dynamic Changes During Early Lactation Enhancing Its Role in Pathogenic Bacteria-Host Interactions
Mariana Barboza,Janneth Pinzon,Saumiya Wickramasinghe,John W. Froehlich,Isabelle Moeller,Jennifer T. Smilowitz,L. Renee Ruhaak,Jincui Huang,Bo Lönnerdal,J. Bruce German,Juan F. Medrano,Bart C. Weimer,Carlito B. Lebrilla +12 more
TL;DR: It was found that fucosyltransferase expression increased during entire period, whereas expression of genes for the oligosaccharyl transferase complex decreased in the second week, and that glyco-variation is involved in modulating pathogen association.
Journal ArticleDOI
Insights into hepatopancreatic functions for nutrition metabolism and ovarian development in the crab Portunus trituberculatus: gene discovery in the comparative transcriptome of different hepatopancreas stages.
TL;DR: The results not only enhance the understanding of crustacean hepatopancreatic functions during growth and ovarian development, but also represent a basis for further research on new genes and functional genomics of P. trituberculatus or closely related species.
References
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Journal ArticleDOI
Highly Integrated Single-Base Resolution Maps of the Epigenome in Arabidopsis
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TL;DR: Three potentially functional classes of RNAs have been identified, two of which are syntenically conserved and correlate with the expression state of protein-coding genes and support a highly interleaved organization of the human transcriptome.
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