Parallel comparison of Illumina RNA-Seq and Affymetrix microarray platforms on transcriptomic profiles generated from 5-aza-deoxy-cytidine treated HT-29 colon cancer cells and simulated datasets

BMC Bioinformatics

Volumn 14, Issue SUPPL9, 2013, Pages

  Xu, Xiao ^a   Zhang, Yuanhao ^c   Williams, Jennie ^a   Antoniou, Eric ^b   McCombie, W R ^b   Wu, Song ^c   Zhu, Wei ^c   Davidson, Nicholas O ^d   Denoya, Paula ^a   Li, Ellen ^a  

^a STONY BROOK UNIVERSITY   (United States)

^b Howard Hughes Medical Institute Cold Spring Harbor Laboratory Cold Spring Harbor   (United States)

^c Stony Brook University   (United States)

^d WASHINGTON UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

AFFYMETRIX MICROARRAYS; DIFFERENTIALLY EXPRESSED GENE; EXPRESSION MEASUREMENTS; FALSE DISCOVERY RATE; GENE EXPRESSION PROFILES; GENERAL CORRELATIONS; PARALLEL COMPARISONS; SPEARMAN CORRELATION COEFFICIENTS;

ALGORITHMS; CELLS; GENE EXPRESSION; MATHEMATICAL MODELS; POLYMERASE CHAIN REACTION; REGRESSION ANALYSIS; RNA; STATISTICAL TESTS;

FIXED PLATFORMS;

AZACITIDINE; RNA;

ALGORITHM; ARTICLE; CELL STRAIN HT29; COLON TUMOR; COMPARATIVE STUDY; DNA MICROARRAY; GENE EXPRESSION PROFILING; GENETICS; HUMAN; METHODOLOGY; REGRESSION ANALYSIS; SENSITIVITY AND SPECIFICITY; SEQUENCE ANALYSIS; HT 29 CELL LINE; PROCEDURES;

ALGORITHMS; AZACITIDINE; COLONIC NEOPLASMS; GENE EXPRESSION PROFILING; HT29 CELLS; HUMANS; OLIGONUCLEOTIDE ARRAY SEQUENCE ANALYSIS; REGRESSION ANALYSIS; RNA, NEOPLASM; SENSITIVITY AND SPECIFICITY; SEQUENCE ANALYSIS, RNA;

EID: 84887156607     PISSN: None     EISSN: 14712105     Source Type: Journal    
DOI: 10.1186/1471-2105-14-S9-S1     Document Type: Article

References (43)

1. Marioni JC, Mason CE, Mane SM, Stephens M, Gilad Y. RNA-seq: an assessment of technical reproducibility and comparison with gene expression arrays. Genome research 2008, 18(9):1509-1517. 10.1101/gr.079558.108, 2527709, 18550803.
2. Oshlack A, Robinson MD, Young MD. From RNA-seq reads to differential expression results. Genome biology 2010, 11(12):220. 10.1186/gb-2010-11-12-220, 3046478, 21176179.
3. Ren S, Peng Z, Mao JH, Yu Y, Yin C, Gao X, Cui Z, Zhang J, Yi K, Xu W, et al. RNA-seq analysis of prostate cancer in the Chinese population identifies recurrent gene fusions, cancer-associated long noncoding RNAs and aberrant alternative splicings. Cell research 2012, 22(5):806-821. 10.1038/cr.2012.30, 3343650, 22349460.
4. Courtney E, Kornfeld S, Janitz K, Janitz M. Transcriptome profiling in neurodegenerative disease. Journal of neuroscience methods 2010, 193(2):189-202. 10.1016/j.jneumeth.2010.08.018, 20800617.
5. Farkas MH, Grant GR, Pierce EA. Transcriptome analyses to investigate the pathogenesis of RNA splicing factor retinitis pigmentosa. Advances in experimental medicine and biology 2012, 723:519-525. 10.1007/978-1-4614-0631-0_65, 22183372.
6. Castellarin M, Warren RL, Freeman JD, Dreolini L, Krzywinski M, Strauss J, Barnes R, Watson P, Allen-Vercoe E, Moore RA, et al. Fusobacterium nucleatum infection is prevalent in human colorectal carcinoma. Genome research 2012, 22(2):299-306. 10.1101/gr.126516.111, 3266037, 22009989.
7. Fu X, Fu N, Guo S, Yan Z, Xu Y, Hu H, Menzel C, Chen W, Li Y, Zeng R, et al. Estimating accuracy of RNA-Seq and microarrays with proteomics. BMC genomics 2009, 10:161. 10.1186/1471-2164-10-161, 2676304, 19371429.
8. Bradford JR, Hey Y, Yates T, Li Y, Pepper SD, Miller CJ. A comparison of massively parallel nucleotide sequencing with oligonucleotide microarrays for global transcription profiling. BMC genomics 2010, 11:282. 10.1186/1471-2164-11-282, 2877694, 20444259.
9. Mokry M, Hatzis P, Schuijers J, Lansu N, Ruzius FP, Clevers H, Cuppen E. Integrated genome-wide analysis of transcription factor occupancy, RNA polymerase II binding and steady-state RNA levels identify differentially regulated functional gene classes. Nucleic acids research 2012, 40(1):148-158. 10.1093/nar/gkr720, 3245935, 21914722.
10. Bottomly D, Walter NA, Hunter JE, Darakjian P, Kawane S, Buck KJ, Searles RP, Mooney M, McWeeney SK, Hitzemann R. Evaluating gene expression in C57BL/6J and DBA/2J mouse striatum using RNA-Seq and microarrays. PloS one 2011, 6(3):e17820. 10.1371/journal.pone.0017820, 3063777, 21455293.
11. Su Z, Li Z, Chen T, Li QZ, Fang H, Ding D, Ge W, Ning B, Hong H, Perkins RG, et al. Comparing next-generation sequencing and microarray technologies in a toxicological study of the effects of aristolochic acid on rat kidneys. Chemical research in toxicology 2011, 24(9):1486-1493. 10.1021/tx200103b, 21834575.
12. Lahiry P, Lee LJ, Frey BJ, Rupar CA, Siu VM, Blencowe BJ, Hegele RA. Transcriptional profiling of endocrine cerebro-osteodysplasia using microarray and next-generation sequencing. PloS one 2011, 6(9):e25400. 10.1371/journal.pone.0025400, 3181319, 21980446.
13. Liu S, Lin L, Jiang P, Wang D, Xing Y. A comparison of RNA-Seq and high-density exon array for detecting differential gene expression between closely related species. Nucleic acids research 2011, 39(2):578-588. 10.1093/nar/gkq817, 3025565, 20864445.
14. Lancaste T. A Note on an Errors in Variables Model. J Am Stat Assoc 1966, 61(313):128-&.
15. Linnet K. Evaluation of Regression Procedures for Methods Comparison Studies. Clin Chem 1993, 39(3):424-432.
16. Cheetham S, Tang MJ, Mesak F, Kennecke H, Owen D, Tai IT. SPARC promoter hypermethylation in colorectal cancers can be reversed by 5-Aza-2'deoxycytidine to increase SPARC expression and improve therapy response. British journal of cancer 2008, 98(11):1810-1819. 10.1038/sj.bjc.6604377, 2410109, 18458674.
17. 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(2):185-193. 10.1093/bioinformatics/19.2.185, 12538238.
18. FASTX-Toolkit. , http://hannonlab.cshl.edu/fastx_toolkit/
19. Trapnell C, Pachter L, Salzberg SL. TopHat: discovering splice junctions with RNA-Seq. Bioinformatics 2009, 25(9):1105-1111. 10.1093/bioinformatics/btp120, 2672628, 19289445.
20. Trapnell C, Williams BA, Pertea G, Mortazavi A, Kwan G, van Baren MJ, Salzberg SL, Wold BJ, Pachter L. Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation. Nature biotechnology 2010, 28(5):511-515. 10.1038/nbt.1621, 3146043, 20436464.
21. HTSeq-count. , http://www-huber.embl.de/users/anders/HTSeq/doc/count.html
22. Levene H, Olkin II, Hotelling H. Robust tests for equality of variances. 1960, Stanford University Press.
23. Barnett VD. Fitting Straight Lines-The Linear Functional Relationship with Replicated Observations. Journal of the Royal Statistical Society Series C (Applied Statistics) 1970, 135-144.
24. Benjamini Y, Hochberg Y. Controlling the False Discovery Rate - a Practical and Powerful Approach to Multiple Testing. J Roy Stat Soc B Met 1995, 57(1):289-300.
25. Tusher VG, Tibshirani R, Chu G. Significance analysis of microarrays applied to the ionizing radiation response (vol 98, pg 5116, 2001). Proceedings of the National Academy of Sciences of the United States of America 2001, 98(18):10515-10515.
26. Smyth GK. Linear models and empirical bayes methods for assessing differential expression in microarray experiments. Statistical applications in genetics and molecular biology 2004, 3:Article3.
27. Li J, Tibshirani R. Finding consistent patterns: A nonparametric approach for identifying differential expression in RNA-Seq data. Statistical methods in medical research 2011,
28. Anders S, Huber W. Differential expression analysis for sequence count data. Genome biology 2010, 11(10):R106. 10.1186/gb-2010-11-10-r106, 3218662, 20979621.
29. Hardcastle TJ, Kelly KA. baySeq: empirical Bayesian methods for identifying differential expression in sequence count data. BMC bioinformatics 2010, 11:422. 10.1186/1471-2105-11-422, 2928208, 20698981.
30. Tarazona S, Garcia-Alcalde F, Dopazo J, Ferrer A, Conesa A. Differential expression in RNA-seq: a matter of depth. Genome research 2011, 21(12):2213-2223. 10.1101/gr.124321.111, 3227109, 21903743.
31. Robinson MD, McCarthy DJ, Smyth GK. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 2010, 26(1):139-140. 10.1093/bioinformatics/btp616, 2796818, 19910308.
32. Rocke DM, Durbin B. A model for measurement error for gene expression arrays. Journal of computational biology: a journal of computational molecular cell biology 2001, 8(6):557-569. 10.1089/106652701753307485, 11747612.
33. Durbin BP, Hardin JS, Hawkins DM, Rocke DM. A variance-stabilizing transformation for gene-expression microarray data. Bioinformatics 2002, 18(Suppl 1):S105-110. 10.1093/bioinformatics/18.suppl_1.S105, 12169537.
34. RMAExpress. , http://rmaexpress.bmbolstad.com/
35. Kvam VM, Liu P, Si Y. A comparison of statistical methods for detecting differentially expressed genes from RNA-seq data. American journal of botany 2012, 99(2):248-256. 10.3732/ajb.1100340, 22268221.
36. Zhang T, DeSimone RA, Jiao X, Rohlf FJ, Zhu W, Gong QQ, Hunt SR, Dassopoulos T, Newberry RD, Sodergren E, et al. Host genes related to paneth cells and xenobiotic metabolism are associated with shifts in human ileum-associated microbial composition. PloS one 2012, 7(6):e30044. 10.1371/journal.pone.0030044, 3374611, 22719822.
37. Karpf AR, Peterson PW, Rawlins JT, Dalley BK, Yang Q, Albertsen H, Jones DA. Inhibition of DNA methyltransferase stimulates the expression of signal transducer and activator of transcription 1, 2, and 3 genes in colon tumor cells. Proceedings of the National Academy of Sciences of the United States of America 1999, 96(24):14007-14012. 10.1073/pnas.96.24.14007, 24181, 10570189.
38. Sultan M, Schulz MH, Richard H, Magen A, Klingenhoff A, Scherf M, Seifert M, Borodina T, Soldatov A, Parkhomchuk D, et al. A global view of gene activity and alternative splicing by deep sequencing of the human transcriptome. Science 2008, 321(5891):956-960. 10.1126/science.1160342, 18599741.
39. Wilhelm BT, Marguerat S, Watt S, Schubert F, Wood V, Goodhead I, Penkett CJ, Rogers J, Bahler J. Dynamic repertoire of a eukaryotic transcriptome surveyed at single-nucleotide resolution. Nature 2008, 453(7199):1239-U1239. 10.1038/nature07002, 18488015.
40. Davidson RM, Hansey CN, Gowda M, Childs KL, Lin H, Vaillancourt B, Sekhon RS, Natalia de L, Kaeppler SM, Jiang N, et al. Utility of RNA Sequencing for Analysis of Maize Reproductive Transcriptomes. The Plant Genome 2011, 4:191-203.
41. Jeanmougin M, de Reynies A, Marisa L, Paccard C, Nuel G, Guedj M. Should We Abandon the t-Test in the Analysis of Gene Expression Microarray Data: A Comparison of Variance Modeling Strategies. PloS one 2010, 5(9). 2933223, 20838429.
42. Hoyle DC, Rattray M, Jupp R, Brass A. Making sense of microarray data distributions. Bioinformatics 2002, 18(4):576-584. 10.1093/bioinformatics/18.4.576, 12016055.
43. Robinson MD, Smyth GK. Moderated statistical tests for assessing differences in tag abundance. Bioinformatics 2007, 23(21):2881-2887. 10.1093/bioinformatics/btm453, 17881408.