BABAR: An R package to simplify the normalisation of common reference design microarray-based transcriptomic datasets

BMC Bioinformatics

Volumn 11, Issue , 2010, Pages

  Alston, Mark J ^a,b   Seers, John ^a   Hinton, Jay C D ^a,c   Lucchini, Sacha ^a  

^a INSTITUTE OF FOOD RESEARCH   (United Kingdom)

^b EARLHAM INSTITUTE   (United Kingdom)

^c TRINITY COLLEGE DUBLIN   (Ireland)

Author keywords

[No Author keywords available]

Indexed keywords

DIFFERENTIALLY EXPRESSED GENE; DNA MICRO-ARRAY; EXPERIMENTAL GROUPS; HETEROGENEOUS DATASETS; IDENTIFICATION OF DIFFERENTIALLY EXPRESSED GENES; MICROARRAY DESIGNS; REFERENCE DESIGNS; SIMULATED DATASETS;

ALGORITHMS; BIOCHIPS; DESIGN; GENE EXPRESSION; MAMMALS; POLYMERASE CHAIN REACTION;

BIOASSAY;

MAMMALIA; SALMONELLA;

ARTICLE; BIOLOGY; COMPUTER PROGRAM; DNA MICROARRAY; GENE EXPRESSION PROFILING; METHODOLOGY; REVERSE TRANSCRIPTION POLYMERASE CHAIN REACTION;

COMPUTATIONAL BIOLOGY; GENE EXPRESSION PROFILING; OLIGONUCLEOTIDE ARRAY SEQUENCE ANALYSIS; REVERSE TRANSCRIPTASE POLYMERASE CHAIN REACTION; SOFTWARE;

EID: 77649095680     PISSN: None     EISSN: 14712105     Source Type: Journal    
DOI: 10.1186/1471-2105-11-73     Document Type: Article

References (32)

1. Breitling R, Armengaud P, Amtmann A, Herzyk P. Rank products: a simple, yet powerful, new method to detect differentially regulated genes in replicated microarray experiments. FEBS Letters 2004, 573:83-92. 10.1016/j.febslet.2004.07.055, 15327980.
2. Smyth GK. Linear models and empirical Bayes methods for assessing differential expression in microarray experiments. Statistical Applications in Genetics and Molecular Biology 2004, 3:3.
3. Qlucore Gene Expression Explorer 1.1. , http://www.qlucore.com
4. Leung YF, Cavalieri D. Fundamentals of cDNA microarray data analysis. Trends in Genetics 2003, 19:649-659. 10.1016/j.tig.2003.09.015, 14585617.
5. Hughes TR, Marton MJ, Jones AR, Roberts CJ, Stoughton R, Armour CD, Bennett HA, Coffey E, Dai HY, He YDD, et al. Functional discovery via a compendium of expression profiles. Cell 2000, 102:109-126. 10.1016/S0092-8674(00)00015-5, 10929718.
6. Thompson A, Rowley G, Alston M, Danino V, Hinton JCD. Salmonella transcriptomics: relating regulons, stimulons and regulatory networks to the process of infection. Current Opinion in Microbiology 2006, 9:109-116. 10.1016/j.mib.2005.12.010, 16413221.
7. Yang YH, Speed T. Design issues for cDNA microarray experiments. Nature Reviews Genetics 2002, 3:579-588.
8. DeRisi JL, Iyer VR, Brown PO. Exploring the metabolic and genetic control of gene expression on a genomic scale. Science 1997, 278:680-686. 10.1126/science.278.5338.680, 9381177.
9. Gadgil M, Lian W, Gadgil C, Kapur V, Wu WS. An analysis of the use of genomic DNA as a universal reference in two channel DNA microarrays. BMC Genomics 2005, 6:66. 10.1186/1471-2164-6-66, 1142311, 15877823.
10. Faith JJ, Hayete B, Thaden JT, Mogno I, Wierzbowski J, Cottarel G, Kasif S, Collins JJ, Gardner TS. Large-scale mapping and validation of Escherichia coli transcriptional regulation from a compendium of expression profiles. PLoS Biology 2007, 5:54-66.
11. Dudoit S, Yang YH, Callow MJ, Speed TP. Statistical methods for identifying differentially expressed genes in replicated cDNA microarray experiments. Statistica Sinica 2002, 12:111-139.
12. Eriksson S, Lucchini S, Thompson A, Rhen M, Hinton JCD. Unravelling the biology of macrophage infection by gene expression profiling of intracellular Salmonella enterica. Molecular Microbiology 2003, 47:103-118. 10.1046/j.1365-2958.2003.03313.x, 12492857.
13. Hautefort I, Thompson A, Eriksson-Ygberg S, Parker ML, Lucchini S, Danino V, Bongaerts RJM, Ahmad N, Rhen M, Hinton JCD. During infection of epithelial cells Salmonella enterica serovar Typhimurium undergoes a time-dependent transcriptional adaptation that results in simultaneous expression of three type 3 secretion systems. Cellular Microbiology 2008, 10:958-984. 10.1111/j.1462-5822.2007.01099.x, 2343689, 18031307.
14. Grissom SF, Lobenhofer EK, Tucker CJ. A qualitative assessment of direct- labeled cDNA products prior to microarray analysis. BMC Genomics 2005, 6:36. 10.1186/1471-2164-6-36, 1079821, 15762992.
15. Bolstad BM, Irizarry RA, Astrand M, Speed TP. A comparison of normalization methods for high density oligonucleotide array data based on variance and bias. Bioinformatics 2003, 19:185-193. 10.1093/bioinformatics/19.2.185, 12538238.
16. Ballman KV, Grill DE, Oberg AL, Therneau TM. Faster cyclic loess: normalizing RNA arrays via linear models. Bioinformatics 2004, 20:2778-2786. 10.1093/bioinformatics/bth327, 15166021.
17. Gentleman R, Carey V, Bates D, Bolstad B, Dettling M, Dudoit S, Ellis B, Gautier L, Ge Y, Gentry J, et al. Bioconductor: open software development for computational biology and bioinformatics. Genome Biology 2004, 5:R80. 10.1186/gb-2004-5-10-r80, 545600, 15461798.
18. R Development Core Team R: a language and environment for statistical computing 2008, R Foundation for Statistical Computing, Vienna, Austria, R Development Core Team., http://www.r-project.org
19. Smyth GK. Limma: linear models for microarray data. Bioinformatics and computational biology solutions using R and Bioconductor 2005, 397-420. New York: Springer, Gentleman R, Carey V, Dudoit S, Irizarry R, Huber W.
20. Quackenbush J. Microarray data normalization and transformation. Nature Genetics Supplement 2002, 32:496-501.
21. Troyanskaya O, Cantor M, Sherlock G, Brown P, Hastie T, Tibshirani R, Botstein D, Altman RB. Missing value estimation methods for DNA microarrays. Bioinformatics 2001, 17:520-525. 10.1093/bioinformatics/17.6.520, 11395428.
22. Mangan MW, Lucchini S, Danino V, Croinin TO, Hinton JCD, Dorman CJ. The integration host factor (IHF) integrates stationary-phase and virulence gene expression in Salmonella enterica serovar Typhimurium. Molecular Microbiology 2006, 59:1831-1847. 10.1111/j.1365-2958.2006.05062.x, 16553887.
23. Albers CJ, Jansen RC, Kok J, Kuipers OP, van Hijum SAFT. SIMAGE: simulation of DNA-microarray gene expression data. BMC Bioinformatics 2006, 7:205. 10.1186/1471-2105-7-205, 1479841, 16613602.
24. Tusher VG, Tibshirani R, Chu G. Significance analysis of microarrays applied to the ionizing radiation response. Proceedings of the National Academy of Sciences of the United States of America 2001, 98:5116-5121. 10.1073/pnas.091062498, 33173, 11309499.
25. samr R package. , http://www-stat.stanford.edu/~tibs/SAM/
26. Jorstad TS, Langaas M, Bones AM. Understanding sample size: what determines the required number of microarrays for an experiment?. Trends in Plant Science 2007, 12:46-50. 10.1016/j.tplants.2007.01.001, 17229587.
27. Gilks WR, Tom BDM, Brazma A. Fusing microarray experiments with multivariate regression. Bioinformatics 2005, 21:137-143. 10.1093/bioinformatics/bti1123, 15310560.
28. Benito M, Parker J, Du Q, Wu JY, Xang D, Perou CM, Marron JS. Adjustment of systematic microarray data biases. Bioinformatics 2004, 20:105-114. 10.1093/bioinformatics/btg385, 14693816.
29. Kilpinen S, Autio R, Ojala K, Iljin K, Bucher E, Sara H, Pisto T, Saarela M, Skotheim RI, Bjorkman M, et al. Systematic bioinformatic analysis of expression levels of 17,330 human genes across 9,783 samples from 175 types of healthy and pathological tissues. Genome Biology 2008, 9:R139. 10.1186/gb-2008-9-9-r139, 2592717, 18803840.
30. Yoon HJ, McDermott JE, Porwollik S, McClelland M, Heffron F. Coordinated Regulation of Virulence during Systemic Infection of Salmonella enterica serovar Typhimurium. PLoS Pathogens 2009, 5(2):e1000306. 10.1371/journal.ppat.1000306, 2639726, 19229334.
31. MacLean D, Jones JDG, Studholme DJ. Application of 'next-generation' sequencing technologies to microbial genetics. Nature Reviews Microbiology 2009, 7:287-296.
32. Ledford H. The death of microarrays?. Nature 2008, 455:847-847. 10.1038/455847a, 18923476.