Computational methods for transcriptome annotation and quantification using RNA-seq
High-throughput RNA sequencing (RNA-seq) promises a comprehensive picture of the transcriptome, allowing for the complete annotation and quantification of all genes and their isoforms across samples. Realizing this promise requires increasingly complex computational methods. These computational chal...
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| Vydáno v: | Nature methods Ročník 8; číslo 6; s. 469 - 477 |
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| Hlavní autoři: | , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
New York
Nature Publishing Group US
01.06.2011
Nature Publishing Group |
| Témata: | |
| ISSN: | 1548-7091, 1548-7105, 1548-7105 |
| On-line přístup: | Získat plný text |
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| Abstract | High-throughput RNA sequencing (RNA-seq) promises a comprehensive picture of the transcriptome, allowing for the complete annotation and quantification of all genes and their isoforms across samples. Realizing this promise requires increasingly complex computational methods. These computational challenges fall into three main categories: (i) read mapping, (ii) transcriptome reconstruction and (iii) expression quantification. Here we explain the major conceptual and practical challenges, and the general classes of solutions for each category. Finally, we highlight the interdependence between these categories and discuss the benefits for different biological applications. |
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| AbstractList | High-throughput RNA sequencing (RNA-seq) promises a comprehensive picture of the transcriptome, allowing for the complete annotation and quantification of all genes and their isoforms across samples. Realizing this promise requires increasingly complex computational methods. These computational challenges fall into three main categories: (i) read mapping, (ii) transcriptome reconstruction and (iii) expression quantification. Here we explain the major conceptual and practical challenges, and the general classes of solutions for each category. Finally, we highlight the interdependence between these categories and discuss the benefits for different biological applications. High-throughput RNA sequencing (RNA-seq) promises a comprehensive picture of the transcriptome, allowing for the complete annotation and quantification of all genes and their isoforms across samples. Realizing this promise requires increasingly complex computational methods. These computational challenges fall into three main categories: (i) read mapping, (ii) transcriptome reconstruction and (iii) expression quantification. Here we explain the major conceptual and practical challenges, and the general classes of solutions for each category. Finally, we highlight the interdependence between these categories and discuss the benefits for different biological applications.High-throughput RNA sequencing (RNA-seq) promises a comprehensive picture of the transcriptome, allowing for the complete annotation and quantification of all genes and their isoforms across samples. Realizing this promise requires increasingly complex computational methods. These computational challenges fall into three main categories: (i) read mapping, (ii) transcriptome reconstruction and (iii) expression quantification. Here we explain the major conceptual and practical challenges, and the general classes of solutions for each category. Finally, we highlight the interdependence between these categories and discuss the benefits for different biological applications. High-throughput RNA sequencing (RNA-seq) promises a comprehensive picture of the transcriptome, allowing for the complete annotation and quantification of all genes and their isoforms across samples. Realizing this promise requires increasingly complex computational methods. These computational challenges fall into three main categories: (i) read mapping, (ii) transcriptome reconstruction and (iii) expression quantification. Here we explain the major conceptual and practical challenges, and the general classes of solutions for each category. Finally, we highlight the interdependence between these categories and discuss the benefits for different biological applications. [PUBLICATION ABSTRACT] |
| Audience | Academic |
| Author | Garber, Manuel Guttman, Mitchell Grabherr, Manfred G Trapnell, Cole |
| Author_xml | – sequence: 1 givenname: Manuel surname: Garber fullname: Garber, Manuel email: mgarber@broadinstitute.org organization: Broad Institute of Massachusetts Institute of Technology and Harvard – sequence: 2 givenname: Manfred G surname: Grabherr fullname: Grabherr, Manfred G organization: Broad Institute of Massachusetts Institute of Technology and Harvard – sequence: 3 givenname: Mitchell surname: Guttman fullname: Guttman, Mitchell organization: Broad Institute of Massachusetts Institute of Technology and Harvard, Department of Biology, Massachusetts Institute of Technology – sequence: 4 givenname: Cole surname: Trapnell fullname: Trapnell, Cole organization: Broad Institute of Massachusetts Institute of Technology and Harvard, Department of Stem Cell and Regenerative Biology, Harvard University |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/21623353$$D View this record in MEDLINE/PubMed |
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| Title | Computational methods for transcriptome annotation and quantification using RNA-seq |
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