Design and validation issues in RNA-seq experiments

Briefings in Bioinformatics

Volumn 12, Issue 3, 2011, Pages 280-287

  Fang, Zhide ^a   Cui, Xiangqin ^b  

^a LOUISIANA STATE UNIVERSITY HEALTH SCIENCES CENTER   (United States)

^b UNIVERSITY OF ALABAMA AT BIRMINGHAM   (United States)

Author keywords

Blocking; Experimental design; Next generation sequencing; Replicates; RNA seq; Sample size

Indexed keywords

RNA;

ARTICLE; CHEMISTRY; GENE EXPRESSION PROFILING; GENOME; METHODOLOGY; NUCLEOTIDE SEQUENCE; REPRODUCIBILITY; SEQUENCE ANALYSIS;

BASE SEQUENCE; GENE EXPRESSION PROFILING; GENOME; REPRODUCIBILITY OF RESULTS; RNA; SEQUENCE ANALYSIS, RNA;

EID: 79955756836     PISSN: 14675463     EISSN: 14774054     Source Type: Journal    
DOI: 10.1093/bib/bbr004     Document Type: Article

References (73)

1. Ansorge WJ. Next-generation DNA sequencing techniques. N Biotechnol 2009;25:195-203.
2. Schuster SC. Next-generation sequencing transforms today's biology. Nat Methods 2008;5:16-18.
3. Metzker ML. Sequencing technologies - the next generation. Nat Rev Genet 2010;11:31-46.
4. Hawkins RD, Hon GC, Ren B. Next-generation genomics: an integrative approach. Nat Rev Genet 2010;11:476-86.
5. Huber JA, Mark Welch DB, Morrison HG, et al. Microbial population structures in the deep marine biosphere. Science 2007;318:97-100.
6. Wheeler DA, Srinivasan M, Egholm M, et al. The complete genome of an individual by massively parallel DNA sequencing. Nature 2008;452:872-6.
7. Korbel JO, Urban AE, Affourtit JP, et al. Paired-end mapping reveals extensive structural variation in the human genome. Science 2007;318:420-6.
8. Alkan C, Kidd JM, Marques-Bonet T, et al. Personalized copy number and segmental duplication maps using next-generation sequencing. Nat Genet 2009;41:1061-7.
9. Johnson DS, Mortazavi A, Myers RM, et al. Genome-wide mapping of in vivo protein-DNA interactions. Science 2007; 316:1497-502.
10. Visel A, Blow MJ, Li Z, et al. ChIP-seq accurately predicts tissue-specific activity of enhancers. Nature 2009;457: 854-8.
11. Pan Q, Shai O, Lee LJ, et al. Deep surveying of alternative splicing complexity in the human transcriptome by high-throughput sequencing. Nat Genet 2008;40:1413-5.
12. Mortazavi A, Williams BA, McCue K, et al. Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nat Methods 2008;5:621-8.
13. Core LJ, Waterfall JJ, Lis JT. Nascent RNA sequencing reveals widespread pausing and divergent initiation at human promoters. Science 2008;322:1845-8.
14. Park PJ. Epigenetics meets next-generation sequencing. Epigenetics 2008;3:318-21.
15. Brunner AL, Johnson DS, Kim SW, et al. Distinct DNA methylation patterns characterize differentiated human embryonic stem cells and developing human fetal liver. Genome Res 2009;19:1044-56.
16. Lister R, O'Malley RC, Tonti-Filippini J, etal. Highly integrated single-base resolution maps of the epigenome in Arabidopsis. Cell 2008;133:523-36.
17. Auer PL, Doerge RW. Statistical design and analysis of RNA sequencing data. Genetics 2010;185:405-16.
18. Cloonan N, Forrest AR, Kolle G, et al. Stem cell transcriptome profiling via massive-scale mRNA sequencing. Nat Methods 2008;5:613-9.
19. Pickrell JK, Marioni JC, Pai AA, et al. Understanding mechanisms underlying human gene expression variation with RNA sequencing. Nature 2010;464:768-72.
20. Marioni JC, Mason CE, Mane SM, et al. RNA-seq: an assessment of technical reproducibility and comparison with gene expression arrays. Genome Res 2008;18:1509-17.
21. Sultan M, Schulz MH, Richard H, et al. A global view of gene activity and alternative splicing by deep sequencing of the human transcriptome. Science 2008;321:956-60.
22. Bainbridge MN, Warren RL, Hirst M, et al. Analysis of the prostate cancer cell line LNCaP transcriptome using a sequencing-by-synthesis approach. BMC Genomics 2006;7: 246.
23. Hashimoto S, Qu W, Ahsan B, et al. High-resolution analysis of the 50-end transcriptome using a next generation DNA sequencer. PLoS One 2009;4:e4108.
24. Bashir A, Bansal V, Bafna V. Designing deep sequencing experiments: structural variation, haplotype assembly, and transcript abundance. BMC Genomics 2010;11:385.
25. Fisher RA. The arrangement of field experiments. J Minist Agric Great Britain 1926;33:503-13.
26. Kuehl RO. Design of Experiments: Statistical Principles of Research Design and Analysis. Pacific Grove, California, USA: Duxbury Press, 2000.
27. Simon R, Korn EL, McShane LM, et al. Design and Analysis of DNA Microarray Investigations. New York, USA: Springer, 2003;11-35.
28. Cui X. Experimental designs on high-throughput biological experiments. In: Lee JK, (ed). Statistical Bioinformatics. Hoboken, New Jersey: Wiley-Blackwell, 2010;201-17.
29. Fisher RA. The Design of Experiments. Edinburgh: Oliver and Boyd, 1935.
30. Churchill GA. Fundamentals of experimental design for cDNA microarrays. Nat Genet 2002;32(Suppl 2):490-5.
31. Yang YH, Speed T. Design issues for cDNA microarray experiments. Nat Rev Genet 2002;3:579-88.
32. Nagalakshmi U, Wang Z, Waern K, et al. The transcriptional landscape of the yeast genome defined by RNA sequencing. Science 2008;320:1344-9.
33. Oliver HF, Orsi RH, Ponnala L, et al. Deep RNA sequencing of L. monocytogenes reveals overlapping and extensive stationary phase and sigma B-dependent transcriptomes, including multiple highly transcribed noncoding RNAs. BMC Genomics 2009;10:641.
34. Lu T, Lu G, Fan D, et al. Function annotation of rice transcriptome at single nucleotide resolution by RNA-seq. Genome Res 2010;20:1238-49.
35. Martin J, Zhu W, Passalacqua KD, et al. Bacillus anthracis genome organization in light of whole transcriptome sequencing. BMC Bioinformatics 2010;11(Suppl 3):S10.
36. Wang Z, Gerstein M, Snyder M. RNA-Seq: a revolutionary tool for transcriptomics. Nat Rev Genet 2009;10:57-63.
37. Degner JF, Marioni JC, Pai AA, etal. Effect of read-mapping biases on detecting allele-specific expression from RNA-sequencing data. Bioinformatics 2009;25:3207-12.
38. Robinson MD, Smyth GK. Small-sample estimation of negative binomial dispersion, with applications to SAGE data. Biostatistics 2008;9:321-32.
39. Anders S, Huber W. Differential expression analysis for sequence count data. Genome Biol 2010;11:R106.
40. Bullard JH, Purdom E, Hansen KD, et al. Evaluation of statistical methods for normalization and differential expression in mRNA-Seq experiments. BMC Bioinformatics 2010; 11:94.
41. Balwierz PJ, Carninci P, Daub CO, et al. Methods for analyzing deep sequencing expression data: constructing the human and mouse promoterome with deepCAGE data. Genome Biol 2009;10:R79.
42. Willenbrock H, Salomon J, Sokilde R, et al. Quantitative miRNA expression analysis: comparing microarrays with next-generation sequencing. RNA 2009;15:2028-34.
43. Furusawa C, Kaneko K. Zipf's law in gene expression. Phys Rev Lett 2003;90:088102.
44. Ueda HR, Hayashi S, Matsuyama S, et al. Universality and flexibility in gene expression from bacteria to human. Proc Natl Acad SciUSA 2004;101:3765-9.
45. Heap GA, Yang JH, Downes K, etal. Genome-wide analysis of allelic expression imbalance in human primary cells by high-throughput transcriptome resequencing. Hum Mol Genet 2010;19:122-34.
46. Serre D, Gurd S, Ge B, et al. Differential allelic expression in the human genome: a robust approach to identify genetic and epigenetic cis-acting mechanisms regulating gene expression. PLoS Genet 2008;4:e1000006.
47. Wang X, Sun Q, McGrath SD, et al. Transcriptome-wide identification of novel imprinted genes in neonatal mouse brain. PLoS One 2008;3:e3839.
48. Ni T, Corcoran DL, Rach EA, et al. A paired-end sequencing strategy to map the complex landscape of transcription initiation. Nat Methods 2010;7:521-7.
49. Bashir A, Volik S, Collins C, et al. Evaluation of paired-end sequencing strategies for detection of genome rearrangements in cancer. PLoS Comput Biol 2008;4:e1000051.
50. Dempsey MP, Nietfeldt J, Ravel J, et al. Paired-end sequence mapping detects extensive genomic rearrangement and translocation during divergence of Francisella tularensis subsp. tularensis and Francisella tularensis subsp. holarctica populations. J Bacteriol 2006;188:5904-14.
51. Au KF, Jiang H, Lin L, et al. Detection of splice junctions from paired-end RNA-seq data by Splice Map. Nucleic Acids Res 2010;38:4570-8.
52. Maher CA, Palanisamy N, Brenner JC, et al. Chimeric transcript discovery by paired-end transcriptome sequencing. Proc Natl Acad Sci USA 2009;106:12353-8.
53. Oshlack A, Wakefield MJ. Transcript length bias in RNA-seq data confounds systems biology. Biol Direct 2009;4:14.
54. Dohm JC, Lottaz C, Borodina T, et al. Substantial biases in ultra-short read data sets from high-throughput DNA sequencing. Nucleic Acids Res 2008;36:e105.
55. Hansen KD, Brenner SE, Dudoit S. Biases in Illumina transcriptome sequencing caused by random hexamer priming. Nucleic Acids Res 2010;38:e131.
56. Linsen SE, de Wit E, Janssens G, et al. Limitations and possibilities of small RNA digital gene expression profiling. Nat Methods 2009;6:474-6.
57. Mamanova L, Andrews RM, James KD, et al. FRT-seq: amplification-free, strand-specific transcriptome sequencing. Nat Methods 2010;7:130-2.
58. Thode HC Jr. Power and sample size requirements for tests of differences between two poisson rates. J Royal Stat Soc Series D 1997;46:227-30.
59. Ng HK, Tang ML. Testing the equality of two Poisson means using the rate ratio. Stat Med 2005;24:955-65.
60. Gu K, Ng HK, Tang ML, et al. Testing the ratio of two poisson rates. Biom J 2008;50:283-98.
61. Aban IB, Cutter GR, Mavinga N. Inferences and power analysis concerning two negative binomial distributions with an application to MRI lesion counts data. Comput Stat Data Anal 2008;53:820-33.
62. Lee ML, Whitmore GA. Power and sample size for DNA microarray studies. Stat Med 2002;21:3543-70.
63. Page GP, Edwards JW, Gadbury GL, et al. The PowerAtlas: a power and sample size atlas for microarray experimental design and research. BMC Bioinformatics 2006;7:84.
64. Rockett JC, Hellmann GM. Confirming microarray data -is it really necessary? Genomics 2004;83:541-9.
65. Allison DB, Cui X, Page GP, et al. Microarray data analysis: from disarray to consolidation and consensus. Nat Rev Genet 2006;7:55-65.
66. Camarena L, Bruno V, Euskirchen G, et al. Molecular mechanisms of ethanol-induced pathogenesis revealed by RNA-sequencing. PLoS Pathog 2010;6:e1000834.
67. Feng L, Liu H, Liu Y, et al. Power of deep sequencing and agilent microarray for gene expression profiling study. Mol Biotechnol 2010;45:101-10.
68. Ramskold D, Wang ET, Burge CB, et al. An abundance of ubiquitously expressed genes revealed by tissue transcriptome sequence data. PLoS Comput Biol 2009;5: e1000598.
69. Dallas P, Gottardo N, Firth M, et al. Gene expression levels assessed by oligonucleotide microarray analysis and quantitative real-time RT-PCR - how well do they correlate? BMC Genomics 2005;6:59.
70. Zheng S, Chen L. A hierarchical Bayesian model for comparing transcriptomes at the individual transcript isoform level. Nucleic Acids Res 2009;37:e75.
71. Robertson AG, Bilenky M, Tam A, et al. Genome-wide relationship between histone H3 lysine 4 mono- and tri-methylation and transcription factor binding. Genome Res 2008;18:1906-17.
72. Johnson DS, Li W, Gordon DB, et al. Systematic evaluation of variability in ChIP-chip experiments using predefined DNA targets. Genome Res 2008;18:393-403.
73. Tuteja G, White P, Schug J, et al. Extracting transcription factor targets from ChIP-Seq data. Nucleic Acids Res 2009;37: e113.