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PMID: 21623353 Published · ppublish English Journal Article Review

Computational methods for transcriptome annotation and quantification using RNA-seq.

Nature methods ·Vol. 8 ·No. 6 ·2011-06-00 ·Pages 469-77

Garber M, Grabherr MG, Guttman M, Trapnell C

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.

MeSH Terms
Animals Computational Biology/methods Gene Expression Profiling/statistics & numerical data Genomics/statistics & numerical data High-Throughput Nucleotide Sequencing/statistics & numerical data Humans Sequence Alignment/statistics & numerical data Sequence Analysis, RNA/statistics & numerical data
Authors & Affiliations
4 authors, click to expand affiliations / ORCID
Garber Manuel
Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, Massachusetts, USA. [email protected]
Grabherr Manfred G
Guttman Mitchell
Trapnell Cole
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Article Info
Journal
Nature methods
Abbr.
Nat Methods
ISSN
1548-7105
DOI
10.1038/nmeth.1613
Email
[email protected]
Published
2011-06-00
Epub
2011-00-27
Pages
469-77
Language
English
Region
United States
NLM ID
101215604
Subset
IM
Corrections
ErratumIn
-
External Links
PubMed source DOI: 10.1038/nmeth.1613 View journal details
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