Predictive modelling of gene expression from transcriptional regulatory elements

Briefings in Bioinformatics

Volumn 16, Issue 4, 2014, Pages 616-628

  Budden, David M ^a   Hurley, Daniel G ^a   Crampin, Edmund J ^a,b,c  

^a The University of Melbourne   (Australia)

^b UNIVERSITY OF MELBOURNE   (Australia)

^c UNIVERSITY OF MELBOURNE   (Australia)

Author keywords

Gene expression; Histone modifications; Predictive modelling; Transcription factors; Transcriptional regulation

Indexed keywords

ALGORITHM; ANIMAL; BIOLOGICAL MODEL; CHROMATIN IMMUNOPRECIPITATION; GENE EXPRESSION REGULATION; GENETIC TRANSCRIPTION; HUMAN; MOUSE; REGULATORY SEQUENCE;

ALGORITHMS; ANIMALS; CHROMATIN IMMUNOPRECIPITATION; GENE EXPRESSION REGULATION; HUMANS; MICE; MODELS, GENETIC; REGULATORY SEQUENCES, NUCLEIC ACID; TRANSCRIPTION, GENETIC;

EID: 84934981753     PISSN: 14675463     EISSN: 14774054     Source Type: Journal    
DOI: 10.1093/bib/bbu034     Document Type: Article

References (72)

1. Vaquerizas JM, Kummerfeld SK, Teichmann SA, et al. A census of human transcription factors: function, expression and evolution. Nat Rev Genet 2009;10(4):252-63.
2. Portela A, Esteller M. Epigenetic modifications and human disease. Nat Biotechnol 2010;28(10):1057-68.
3. Maston GA, Evans SK, Green MR. Transcriptional regulatory elements in the human genome. Annu Rev Genomics Hum Genet 2006;7:29-59.
4. Cheng C, Yan KK, Yip KY, et al. A statistical framework for modeling gene expression using chromatin features and application to modENCODE datasets. Genome Biol 2011; 12(2):R15.
5. Cheng C, Gerstein M. Modeling the relative relationship of transcription factor binding and histone modifications to gene expression levels in mouse embryonic stem cells. Nucleic Acids Res 2012;40(2):553-68.
6. McLeay RC, Lesluyes T, Partida GC, Bailey TL. Genomewide in silico prediction of gene expression. Bioinformatics 2012;28(21):2789-96.
7. Ouyang Z, Zhou Q, Wong WH. ChIP-Seq of transcription factors predicts absolute and differential gene expression in embryonic stem cells. Proc Natl Acad Sci USA 2009;106(51): 21521-6.
8. Karlić R, Chung HR, Lasserre J, et al. Histone modification levels are predictive for gene expression. Proc Natl Acad Sci USA 2010;107(7):2926-31.
9. Li B, Carey M, Workman JL. The role of chromatin during transcription. Cell 2007;128(4):707-19.
10. Farnham PJ. Insights from genomic profiling of transcription factors. Nat Rev Genet 2009;10(9):605-16.
11. Chen X, Xu H, Yuan P, et al. Integration of external signaling pathways with the core transcriptional network in embryonic stem cells. Cell 2008;133(6):1106-17.
12. Jimenez-Sanchez G, Childs B, Valle D. Human disease genes. Nature 2001;409(6822):853-55.
13. Luger K, Mäder AW, Richmond RK, et al. Crystal structure of the nucleosome core particle at 2.8 å resolution. Nature 1997;389(6648):251-60.
14. Tazi J, Bird A. Alternative chromatin structure at CpG islands. Cell 1990;60(6):909-20.
15. Berger SL. The complex language of chromatin regulation during transcription. Nature 2007;447(7143):407-12.
16. Bernstein BE, Meissner A, Lander ES. The mammalian epigenome. Cell 2007;128(4):669-81.
17. Kouzarides T. Chromatin modifications and their function. Cell 2007;128(4):693-705.
18. Pekowska A, Benoukraf T, Ferrier P, Spicuglia S. A unique H3K4me2 profile marks tissue-specific gene regulation. Genome Res 2010;20(11):1493-502.
19. Fraga MF, Ballestar E, Villar-Garea A, et al. Loss of acetylation at Lys16 and trimethylation at Lys20 of histone H4 is a common hallmark of human cancer. Nat Genet 2005;37(4): 391-400.
20. Zhu P, Martin E, Mengwasser J, et al. Induction of HDAC2 expression upon loss of APC in colorectal tumorigenesis. Cancer Cell 2004;5(5):455-63.
21. Ropero S, Fraga MF, Ballestar E, et al. A truncating mutation of HDAC2 in human cancers confers resistance to histone deacetylase inhibition. Nat Genet 2006;38(5):566-9.
22. Hamamoto R, Furukawa Y, Morita M, et al. SMYD3 encodes a histone methyltransferase involved in the proliferation of cancer cells. Nat Cell Biol 2004;6(8):731-40.
23. Kondo Y, Shen L, Suzuki S, et al. Alterations of DNA methylation and histone modifications contribute to gene silencing in hepatocellular carcinomas. Hepatol Res 2007; 37(11):974-83.
24. Viré E, Brenner C, Deplus R, et al. The Polycomb group protein EZH2 directly controls DNA methylation. Nature 2006;439(7078):871-4.
25. Krivtsov AV, Feng Z, Lemieux ME, et al. H3K79 methylation profiles define murine and human MLL-AF4 leukemias. Cancer Cell 2008;14(5):355-68.
26. Wang P, Lin C, Smith ER, et al. Global analysis of H3K4 methylation defines MLL family member targets and points to a role for MLL1-mediated H3K4 methylation in the regulation of transcriptional initiation by RNA polymerase II. Mol Cell Biol 2009;29(22):6074-85.
27. Shi Y. Histone lysine demethylases: emerging roles in development, physiology and disease. Nat Rev Genet 2007; 8(11):829-33.
28. Cuellar-Partida G, Buske FA, McLeay RC, et al. Epigenetic priors for identifying active transcription factor binding sites. Bioinformatics 2012;28(1):56-62.
29. Whitington T, Perkins AC, Bailey TL. High-throughput chromatin information enables accurate tissue-specific prediction of transcription factor binding sites. NucleicAcidsRes 2009;37(1):14-25.
30. Barski A, Cuddapah S, Cui K, et al. High-resolution profiling of histone methylations in the human genome. Cell 2007;129(4):823-37.
31. Johnson DS, Mortazavi A, Myers RM, Wold B. Genomewide mapping of in vivo protein-DNA interactions. Science 2007;316(5830):1497-502.
32. Meissner A, Mikkelsen TS, Gu H, et al. Genome-scale DNA methylation maps of pluripotent and differentiated cells. Nature 2008;454(7205):766-70.
33. Crawford GE, Holt IE, Whittle J, et al. Genome-wide mapping of DNase hypersensitive sites using massively parallel signature sequencing (MPSS). Genome Res 2006;16(1): 123-31.
34. Mortazavi A, Williams BA, McCue K, et al. Mapping and quantifying mammalian transcriptomes by RNA-Seq. NatMethods 2008;5(7):621-28.
35. Trapnell C, Williams BA, Pertea G, et al. Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation. Nat Biotechnol 2010;28(5):511-5.
36. Conlon EM, Liu XS, Lieb JD, Liu JS. Integrating regulatory motif discovery and genome-wide expression analysis. ProcNatl Acad SciUSA 2003;100(6):3339-44.
37. Sinha S. On counting position weight matrix matches in a sequence, with application to discriminative motif finding. Bioinformatics 2006;22(14):e454-63.
38. Mikkelsen TS, Ku M, Jaffe DB, et al. Genome-wide maps of chromatin state in pluripotent and lineage-committed cells. Nature 2007;448(7153):553-60.
39. Gerstein MB, Kundaje A, Hariharan M, et al. Architecture of the human regulatory network derived from ENCODE data. Nature 2012;489(7414):91-100.
40. Thurman RE, Rynes E, Humbert R, et al. The accessible chromatin landscape of the human genome. Nature 2012; 489(7414):75-82.
41. Shen Y, Yue F, McCleary DF, et al. A map of the cisregulatory sequences in the mouse genome. Nature 2012; 488(7409):116-20.
42. Kleinjan DA, vanHeyningen V. Long-range control of gene expression: emerging mechanisms and disruption in disease. AmJHum Genet 2005;76(1):8-32.
43. Dekker J, Marti-Renom MA, Mirny LA. Exploring the three-dimensional organization of genomes: interpreting chromatin interaction data. Nat Rev Genet 2013;14(6): 390-403.
44. Hu M, Deng K, Qin Z, et al. Bayesian inference of spatial organizations of chromosomes. PLoSComput Biol 2013;9(1): e1002893.
45. Tjong H, Gong K, Chen L, Alber F. Physical tethering and volume exclusion determine higher-order genome organization in budding yeast. Genome Res 2012;22(7): 1295-305.
46. Russell SJ, Norvig P. Artificial Intelligence: A Modern Approach, Vol. 3. Upper Saddle River, New Jersey 07458: Prentice Hall, 2010.
47. Drucker H, Burges CJ, Kaufman L, et al. Support vector regression machines. Adv Neural Inf Proc Syst 1997;9: 155-61.
48. Vapnik VN. The Nature of Statistical Learning Theory, Vol. 2. 175 Fifth Avenue, New York, NY: Springer, 1995.
49. Grant CE, Bailey TL, Noble WS. FIMO: scanning for occurrences of a given motif. Bioinformatics 2011;27(7): 1017-8.
50. ENCODE Project Consortium An integrated encyclopedia of DNA elements in the human genome. Nature 2012; 489(7414):57-74.
51. Flicek P, Amode MR, Barrell D, et al. Ensembl 2014. Nucleic Acids Res 2014;42(D1):D749-55.
52. Cloonan N, Forrest AR, Kolle G, et al. Stem cell transcriptome profiling via massive-scale mRNA sequencing. NatMethods 2008;5(7):613-9.
53. Berger MF, Bulyk ML. Universal protein-binding microarrays for the comprehensive characterization of the DNAbinding specificities of transcription factors. Nat Protocols 2009;4(3):393-411.
54. Portales-Casamar E, Thongjuea S, Kwon AT, et al. JASPAR 2010: the greatly expanded open-access database of transcription factor binding profiles. Nucleic Acids Res 2010; 38(Suppl 1):D105-10.
55. Matys V, Fricke E, Geffers R, et al. TRANSFAC®: transcriptional regulation, from patterns to profiles. NucleicAcids Res 2003;31(1):374-8.
56. Harel O. The estimation of r 2 and adjusted r 2 in incomplete data sets using multiple imputation. JAppl Stat 2009; 36(10):1109-18.
57. Gunawardena J. Models in biology: 'Accurate descriptions of our pathetic thinking'. BMC Biol 2014;12(1):29.
58. Alter O, Brown PO, Botstein D. Singular value decomposition for genome-wide expression data processing and modeling. Proc Natl Acad Sci USA 2000;97(18): 10101-6.
59. Holter NS, Mitra M, Maritan A, et al. Fundamental patterns underlying gene expression profiles: simplicity from complexity. Proc Natl Acad Sci USA 2000;97(15):8409-14.
60. Zhang ZD, Paccanaro A, Fu Y, et al. Statistical analysis of the genomic distribution and correlation of regulatory elements in the ENCODE regions. Genome Res 2007; 17(6):787-97.
61. Watkins DS. Fundamentals of Matrix Computations, Vol. 2. 605 Third Avenue, New York, NY: John Wiley & Sons, 2004.
62. Barski A, Jothi R, Cuddapah S, et al. Chromatin poises miRNA-and protein-coding genes for expression. Genome Res 2009;19(10):1742-51.
63. Wagner EJ, Carpenter PB. Understanding the language of Lys36 methylation at histone H3. Nat Rev Mol Cell Biol 2012;13(2):15-126.
64. Christophersen NS, Helin K. Epigenetic control of embryonic stem cell fate. J Exp Med 2010;207(11): 2287-95.
65. Meissner A. Epigenetic modifications in pluripotent and differentiated cells. Nat Biotechnol 2010;28(10):1079-88.
66. He X, Samee MAH, Blatti C, Sinha S. Thermodynamicsbased models of transcriptional regulation by enhancers: the roles of synergistic activation, cooperative binding and short-range repression. PLoS Comput Biol 2010;6(9): e1000935.
67. Mariani L, Löhning M, Radbruch A, Höfer T. Transcriptional control networks of cell differentiation: insights from helper T lymphocytes. Prog Biophys Mol Biol 2004;86(1):45-76.
68. Sherman MS, Cohen BA. Thermodynamic state ensemble models of cis-regulation. PLoS Comput Biol 2012;8(3): e1002407.
69. Marbach D, Costello JC, Küffner R, et al. Wisdom of crowds for robust gene network inference. Nat Methods 2012;9(8):796-804.
70. Maetschke SR, Madhamshettiwar PB, Davis MJ, Ragan MA. Supervised, semi-supervised and unsupervised inference of gene regulatory networks. Brief Bioinform 2014;15(2):195-211.
71. Hurley D, Araki H, Tamada Y, et al. Gene network inference and visualization tools for biologists: application to new human transcriptome datasets. Nucleic Acids Res 2012; 40(6):2377-98.
72. Begley CG, Ellis LM. Drug development: raise standards for preclinical cancer research. Nature 2012;483(7391):531-3.