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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Bibliographic Details
Published in:Nature methods Vol. 8; no. 6; pp. 469 - 477
Main Authors: Garber, Manuel, Grabherr, Manfred G, Guttman, Mitchell, Trapnell, Cole
Format: Journal Article
Language:English
Published: New York Nature Publishing Group US 01.06.2011
Nature Publishing Group
Subjects:
631/114/2184
631/1647/514/1949
631/208/212/2019
Animals
Bioinformatics
Biological Microscopy
Biological Techniques
Biomedical and Life Sciences
Biomedical Engineering/Biotechnology
Cellular biology
Computational Biology - methods
DNA sequencing
Gene Expression Profiling - statistics & numerical data
Genomics
Genomics - statistics & numerical data
High-Throughput Nucleotide Sequencing - statistics & numerical data
Humans
Life Sciences
Nucleotide sequencing
Physiological aspects
Proteomics
review-article
Ribonucleic acid
RNA
Sequence Alignment - statistics & numerical data
Sequence Analysis, RNA - statistics & numerical data
United States
ISSN:1548-7091, 1548-7105, 1548-7105
Online Access:Get full text
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Description
Summary: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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ISSN:1548-7091
1548-7105
1548-7105
DOI:10.1038/nmeth.1613