Empirical assessment of analysis workflows for differential expression analysis of human samples using RNA-Seq-Reference-Cited by-同舟云学术

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Empirical assessment of analysis workflows for differential expression analysis of human samples using RNA-Seq

Published:2017-01-17 Issue:1 Volume:18 Page:
ISSN:1471-2105
Container-title:BMC Bioinformatics
language:en
Short-container-title:BMC Bioinformatics

Author:

Williams Claire R.,Baccarella Alyssa,Parrish Jay Z.,Kim Charles C.

Funder

National Institute of Diabetes and Digestive and Kidney Diseases

University of California, San Francisco, Center for AIDS Research

National Institute of Allergy and Infectious Diseases

National Eye Institute

National Institute of Neurological Disorders and Stroke

University of Washington

National Science Foundation

University of California

Alameda-Contra Costa Medical Association

Publisher

Springer Science and Business Media LLC

Subject

Applied Mathematics,Computer Science Applications,Molecular Biology,Biochemistry,Structural Biology

Link

http://link.springer.com/content/pdf/10.1186/s12859-016-1457-z.pdf

Reference61 articles.

1. Robertson G, Schein J, Chiu R, Corbett R, Field M, Jackman SD, et al. De novo assembly and analysis of RNA-seq data. Nat Methods. 2010;7:909–12.

2. Grabherr MG, Haas BJ, Yassour M, Levin JZ, Thompson DA, Amit I, et al. Full-length transcriptome assembly from RNA-Seq data without a reference genome. Nat Biotechnol. 2011;29:644–52.

3. Seyednasrollah F, Laiho A, Elo LL. Comparison of software packages for detecting differential expression in RNA-seq studies. Brief Bioinform. 2015;16:59–70.

4. Pepke S, Wold B, Mortazavi A. Computation for ChIP-seq and RNA-seq studies. Nat Methods. 2009;6:S22–32.

5. Oshlack A, Robinson MD, Young MD. From RNA-seq reads to differential expression results. Genome Biol. 2010;11:220.

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