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Prediction and Quantification of Splice Events from RNA-Seq Data

Goldstein, Leonard D ; Cao, Yi ; Pau, Gregoire ; Lawrence, Michael ; Wu, Thomas D ; Seshagiri, Somasekar ; Gentleman, Robert Xing, Yi

PloS one, 2016-05, Vol.11 (5), p.e0156132-e0156132 [Periódico revisado por pares]

United States: Public Library of Science

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  • Título:
    Prediction and Quantification of Splice Events from RNA-Seq Data
  • Autor: Goldstein, Leonard D ; Cao, Yi ; Pau, Gregoire ; Lawrence, Michael ; Wu, Thomas D ; Seshagiri, Somasekar ; Gentleman, Robert
  • Xing, Yi
  • Assuntos: Acids ; Algorithms ; Alternative Splicing ; Analysis ; Bioinformatics ; Biology and Life Sciences ; Computational Biology - methods ; Computer and Information Sciences ; Data analysis ; Data processing ; Exons ; Gene expression ; Genomes ; Genomics ; Human tissues ; Humans ; Methods ; Molecular biology ; Polymerase chain reaction ; Predictions ; Research and Analysis Methods ; Ribonucleic acid ; RNA ; RNA - genetics ; RNA sequencing ; RNA Splicing ; Sequence Analysis, RNA - methods ; Simulation ; Software ; Splice junctions ; Tissues
  • É parte de: PloS one, 2016-05, Vol.11 (5), p.e0156132-e0156132
  • Notas: ObjectType-Article-1
    SourceType-Scholarly Journals-1
    ObjectType-Feature-2
    content type line 23
    Competing Interests: The authors of this manuscript have read the journal’s policy and have the following competing interests: All authors are or have been employees of Genentech Inc. and some hold shares in Roche. RG is an employee of 23AndMe Inc. This does not alter the authors’ adherence to PLOS ONE policies on sharing data and materials.
    Current address: 23andMe Inc., Mountain View, CA, United States of America
    Conceived and designed the experiments: LDG RG. Analyzed the data: LDG RG. Wrote the paper: LDG RG. Provided advice on method and software development: GP ML TDW. Performed RT-PCR experiments: YC. Provided advice and oversaw sequencing experiments: SS.
  • Descrição: Analysis of splice variants from short read RNA-seq data remains a challenging problem. Here we present a novel method for the genome-guided prediction and quantification of splice events from RNA-seq data, which enables the analysis of unannotated and complex splice events. Splice junctions and exons are predicted from reads mapped to a reference genome and are assembled into a genome-wide splice graph. Splice events are identified recursively from the graph and are quantified locally based on reads extending across the start or end of each splice variant. We assess prediction accuracy based on simulated and real RNA-seq data, and illustrate how different read aligners (GSNAP, HISAT2, STAR, TopHat2) affect prediction results. We validate our approach for quantification based on simulated data, and compare local estimates of relative splice variant usage with those from other methods (MISO, Cufflinks) based on simulated and real RNA-seq data. In a proof-of-concept study of splice variants in 16 normal human tissues (Illumina Body Map 2.0) we identify 249 internal exons that belong to known genes but are not related to annotated exons. Using independent RNA samples from 14 matched normal human tissues, we validate 9/9 of these exons by RT-PCR and 216/249 by paired-end RNA-seq (2 x 250 bp). These results indicate that de novo prediction of splice variants remains beneficial even in well-studied systems. An implementation of our method is freely available as an R/Bioconductor package [Formula: see text].
  • Editor: United States: Public Library of Science
  • Idioma: Inglês
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  • Realização: ABCD-USP USP   Logos de Redes Sociais: Freepik

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