Orchestrating high-throughput genomic analysis with Bioconductor

Nature Methods

Volumn 12, Issue 2, 2015, Pages 115-121

  Huber, Wolfgang ^a   Carey, Vincent J ^b,c   Gentleman, Robert ^d   Anders, Simon ^a   Carlson, Marc ^e   Carvalho, Benilton S ^f   Bravo, Hector Corrada ^g   Davis, Sean ^h   Gatto, Laurent ⁱ   Girke, Thomas ^j   Gottardo, Raphael ^e   Hahne, Florian ^k   Hansen, Kasper D ^l,m   Irizarry, Rafael A ^c,n   Lawrence, Michael ^d   Love, Michael I ^c,n   MaCdonald, James ^o   Obenchain, Valerie ^e   Oles̈, Andrzej K ^a   Pagès, Hervé ^e   Reyes, Alejandro ^a   Shannon, Paul ^e   Smyth, Gordon K ^p,q   Tenenbaum, Dan ^e   Waldron, Levi ^r   Morgan, Martin ^e   more..

^a EUROPEAN MOLECULAR BIOLOGY LABORATORY   (Germany)

^b BRIGHAM AND WOMEN S HOSPITAL   (United States)

^c HARVARD SCHOOL OF PUBLIC HEALTH   (United States)

^d GENENTECH INC   (United States)

^e Fred Hutchinson Cancer Research Center   (United States)

^f UNIVERSITY OF CAMPINAS   (Brazil)

^g Bldg 142   (United States)

^h NATIONAL CANCER INSTITUTE   (United States)

ⁱ UNIVERSITY OF CAMBRIDGE   (United Kingdom)

^j UNIVERSITY OF CALIFORNIA   (United States)

^k NOVARTIS PHARMA AG   (Switzerland)

^l JOHNS HOPKINS UNIVERSITY   (United States)

^m JOHNS HOPKINS UNIVERSITY   (United States)

ⁿ DANA FARBER CANCER INSTITUTE   (United States)

^o UNIVERSITY OF WASHINGTON   (United States)

^p WALTER AND ELIZA HALL INSTITUTE OF MEDICAL RESEARCH   (Australia)

^q UNIVERSITY OF MELBOURNE   (Australia)

^r CITY UNIVERSITY OF NEW YORK   (United States)

more..

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHM; ARTICLE; BIOCONDUCTOR; COMPUTER LANGUAGE; COMPUTER PROGRAM; DATA ANALYSIS; FLOW CYTOMETRY; GENE EXPRESSION; GENOMICS; MASS SPECTROMETRY; NONHUMAN; PRIORITY JOURNAL; WORKFLOW; BIOLOGY; COMPUTER INTERFACE; GENE EXPRESSION PROFILING; HIGH THROUGHPUT SCREENING; PROCEDURES;

COMPUTATIONAL BIOLOGY; GENE EXPRESSION PROFILING; GENOMICS; HIGH-THROUGHPUT SCREENING ASSAYS; PROGRAMMING LANGUAGES; SOFTWARE; USER-COMPUTER INTERFACE;

EID: 84961289551     PISSN: 15487091     EISSN: 15487105     Source Type: Journal    
DOI: 10.1038/nmeth.3252     Document Type: Article

References (24)

1. Gentleman, R.C. et al. Bioconductor: open software development for computational biology and bioinformatics. Genome Biol. 5, R80 (2004
2. R Development Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2014
3. Hahne, F., Huber, W., Gentleman, R., & Falcon, S. Bioconductor Case Studies (Springer, 2008
4. Lawrence, M. et al. Software for computing and annotating genomic ranges. PLoS Comput. Biol. 9, e1003118 (2013
5. The ENCODE Project Consortium. An integrated encyclopedia of DNA elements in the human genome. Nature 489, 57-74 (2012
6. Ohnishi, Y. et al. Cell-to-cell expression variability followed by signal reinforcement progressively segregates early mouse lineages. Nat. Cell Biol. 16, 27-37 (2014
7. Finak, G. et al. OpenCyto: an open source infrastructure for scalable, robust, reproducible, and automated, end-to-end flow cytometry data analysis. PLoS Comput. Biol. 10, e1003806 (2014
8. Chelaru, F., Smith, L., Goldstein, N., & Corrada Bravo, H. Epiviz: interactive visual analytics for functional genomics data. Nat. Methods 11, 938-940 (2014
9. Gentleman, R. Reproducible research: a bioinformatics case study. Stat. Appl. Genet. Mol. Biol. 4, Article2 (2005
10. Anders, S., & Huber, W. Differential expression analysis for sequence count data. Genome Biol. 11, R106 (2010
11. Laufer, C., Fischer, B., Billmann, M., Huber, W., & Boutros, M. Mapping genetic interactions in human cancer cells with RNAi and multiparametric phenotyping. Nat. Methods 10, 427-431 (2013
12. Waldron, L. et al. Comparative meta-Analysis of prognostic gene signatures for late-stage ovarian cancer. J. Natl. Cancer Inst. 106, dju049 (2014
13. Riester, M. et al. Risk prediction for late-stage ovarian cancer by meta-Analysis of 1525 patient samples. J. Natl. Cancer Inst. 106, dju048 (2014
14. McMurdie, P.J., & Holmes, S. Waste not, want not: why rarefying microbiome data is inadmissible. PLoS Comput. Biol. 10, e1003531 (2014
15. Goecks, J., Nekrutenko, A., Taylor, J., & The Galaxy Team. Galaxy: a comprehensive approach for supporting accessible, reproducible, and transparent computational research in the life sciences. Genome Biol. 11, R86 (2010
16. Pérez, F., & Granger, B.E. IPython: a system for interactive scientific computing. Comput. Sci. Eng. 9, 21-29 (2007
17. Anonymous. Credit for code. Nat. Genet. 46, 1 (2014
18. Altschul, S. et al. The anatomy of successful computational biology software. Nat. Biotechnol. 31, 894-897 (2013
19. Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078-2079 (2009
20. Lawrence, M., & Morgan, M. Scalable genomics with R and Bioconductor. Stat. Sci. 29, 214-226 (2014
21. Brazma, A. et al. Minimum information about a microarray experiment (MIAME)-toward standards for microarray data. Nat. Genet. 29, 365-371 (2001
22. Cabezas-Wallscheid, N. et al. Identification of regulatory networks in HSCs and their immediate progeny via integrated proteome, transcriptome, and DNA methylome analysis. Cell Stem Cell 15, 507-522 (2014
23. Anders, S., Reyes, A., & Huber, W. Detecting differential usage of exons from RNA-seq data. Genome Res. 22, 2008-2017 (2012
24. Obenchain, V. et al. VariantAnnotation: a Bioconductor package for exploration and annotation of genetic variants. Bioinformatics 30, 2076 (2014).