Improved regulatory element prediction based on tissue-specific local epigenomic signatures

Proceedings of the National Academy of Sciences of the United States of America

Volumn 114, Issue 9, 2017, Pages E1633-E1640

  He, Yupeng ^a,b   Gorkin, David U ^b   Dickel, Diane E ^c   Nery, Joseph R ^a   Castanon, Rosa G ^a   Lee, Ah Young ^b   Shen, Yin ^d,e   Visel, Axel ^c,f,g   Pennacchio, Len A ^c,g   Ren, Bing ^b,h   Ecker, Joseph R ^a,i  

^a SALK INSTITUTE FOR BIOLOGICAL STUDIES   (United States)

^b UNIVERSITY OF CALIFORNIA   (United States)

^c LAWRENCE BERKELEY NATIONAL LABORATORY   (United States)

^d UNIVERSITY OF CALIFORNIA   (United States)

^e UNIVERSITY OF CALIFORNIA   (United States)

^f DOE JOINT GENOME INSTITUTE   (United States)

^g UNIVERSITY OF CALIFORNIA   (United States)

^h UNIVERSITY OF CALIFORNIA   (United States)

ⁱ HOWARD HUGHES MEDICAL INSTITUTE   (United States)

Author keywords

Bioinformatics; DNA methylation; Enhancer prediction; Epigenetics; Gene regulation

Indexed keywords

CYTOSINE; HISTONE;

ANIMAL CELL; ANIMAL TISSUE; ARTICLE; DNA METHYLATION; EMBRYO; ENHANCER REGION; EPIGENETICS; GENE LOCATION; GENETIC ALGORITHM; GENOME; HISTONE MODIFICATION; HUMAN; HUMAN CELL; HUMAN TISSUE; MOUSE; NONHUMAN; PRIORITY JOURNAL; REGULATORY ELEMENT PREDICTION BASED ON TISSUE-SPECIFIC LOCAL EPIGENETIC MARK; ALGORITHM; BIOLOGY; GENE EXPRESSION REGULATION; GENETIC EPIGENESIS; GENETIC TRANSCRIPTION; GENETICS; GENOMICS; HISTONE CODE; PROCEDURES;

ALGORITHMS; COMPUTATIONAL BIOLOGY; DNA METHYLATION; ENHANCER ELEMENTS, GENETIC; EPIGENESIS, GENETIC; EPIGENOMICS; GENE EXPRESSION REGULATION; GENOMICS; HISTONE CODE; HISTONES; HUMANS; TRANSCRIPTION, GENETIC;

EID: 85014282428     PISSN: 00278424     EISSN: 10916490     Source Type: Journal    
DOI: 10.1073/pnas.1618353114     Document Type: Article

References (69)

1. Lettice LA, et al. (2003) A long-range Shh enhancer regulates expression in the developing limb and fin and is associated with preaxial polydactyly. Hum Mol Genet 12(14):1725-1735.
2. Sagai T, Hosoya M, Mizushina Y, Tamura M, Shiroishi T (2005) Elimination of a longrange cis-regulatory module causes complete loss of limb-specific Shh expression and truncation of the mouse limb. Development 132(4):797-803.
3. PomerantzMM, et al. (2009) The 8q24 cancer risk variant rs6983267 shows long-range interaction with MYC in colorectal cancer. Nat Genet 41(8):882-884.
4. Harismendy O, et al. (2011) 9p21 DNA variants associated with coronary artery disease impair interferon-γ signalling response. Nature 470(7333):264-268.
5. Kleinjan DA, van Heyningen V (2005) Long-range control of gene expression: Emerging mechanisms and disruption in disease. Am J Hum Genet 76(1):8-32.
6. Sakabe NJ, Savic D, Nobrega MA (2012) Transcriptional enhancers in development and disease. Genome Biol 13(1):238.
7. Maurano MT, Humbert R, Rynes E, et al. (2012) Systematic localization of common disease-associated variation in regulatory DNA. Science 337:1190-1195.
8. Tak YG, Farnham PJ (2015) Making sense of GWAS: Using epigenomics and genome engineering to understand the functional relevance of SNPs in non-coding regions of the human genome. Epigenetics Chromatin 8:57.
9. MerikaM, Williams AJ, Chen G, Collins T, Thanos D (1998) Recruitment of CBP/p300 by the IFN β enhanceosome is required for synergistic activation of transcription. Mol Cell 1(2):277-287.
10. Heintzman ND, et al. (2009) Histone modifications at human enhancers reflect global cell-type-specific gene expression. Nature 459(7243):108-112.
11. Heintzman ND, et al. (2007) Distinct and predictive chromatin signatures of transcriptional promoters and enhancers in the human genome. Nat Genet 39(3):311-318.
12. Creyghton MP, et al. (2010) Histone H3K27ac separates active from poised enhancers and predicts developmental state. Proc Natl Acad Sci USA 107(50):21931-21936.
13. Kleftogiannis D, Kalnis P, Bajic VB (2015) Progress and challenges in bioinformatics approaches for enhancer identification. Brief Bioinform 17(6):967-979.
14. Bird A (2002) DNA methylation patterns and epigenetic memory. Genes Dev 16(1): 6-21.
15. Law JA, Jacobsen SE (2010) Establishing, maintaining and modifying DNA methylation patterns in plants and animals. Nat Rev Genet 11(3):204-220.
16. Jones PA (2012) Functions of DNA methylation: Islands, start sites, gene bodies and beyond. Nat Rev Genet 13(7):484-492.
17. Smith ZD, Meissner A (2013) DNA methylation: Roles in mammalian development. Nat Rev Genet 14(3):204-220.
18. Lister R, et al. (2009) Human DNA methylomes at base resolution show widespread epigenomic differences. Nature 462(7271):315-322.
19. Schultz MD, et al. (2015) Human body epigenome maps reveal noncanonical DNA methylation variation. Nature 523(7559):212-216.
20. Ziller MJ, et al. (2013) Charting a dynamic DNA methylation landscape of the human genome. Nature 500(7463):477-481.
21. Varley KE, et al. (2013) Dynamic DNA methylation across diverse human cell lines and tissues. Genome Res 23(3):555-567.
22. He Y, Ecker JR (2015) Non-CG methylation in the human genome. Annu Rev Genomics Hum Genet 16:55-77.
23. Stadler MB, et al. (2012) DNA-binding factors shape the mouse methylome at distal regulatory regions. Nature 484:550.
24. Sayeed SK, Zhao J, Sathyanarayana BK, Golla JP, Vinson C (2015) C/EBPβ (CEBPB) protein binding to the C/EBPjCRE DNA 8-mer TTGCjGTCA is inhibited by 5hmC and enhanced by 5mC, 5fC, and 5caC in the CG dinucleotide. Biochim Biophys Acta 1849(6):583-589.
25. O'Malley RC, et al. (2016) Cistrome and epicistrome features shape the regulatory DNA landscape. Cell 165(5):1280-1292.
26. Stephens DC, Poon GMK (2016) Differential sensitivity to methylated DNA by ETSfamily transcription factors is intrinsically encoded in their DNA-binding domains. Nucleic Acids Res 44(18):8671-8681.
27. Xu T, et al. (2015) Base-resolution methylation patterns accurately predict transcription factor bindings in vivo. Nucleic Acids Res 43(5):2757-2766.
28. Hwang W, Oliver VF, Merbs SL, Zhu H, Qian J (2015) Prediction of promoters and enhancers using multiple DNA methylation-associated features. BMC Genomics 16(Suppl 7):S11.
29. Park PJ (2009) ChIP-seq: Advantages and challenges of a maturing technology. Nat Rev Genet 10(10):669-680.
30. Hon GC, et al. (2013) Epigenetic memory at embryonic enhancers identified in DNA methylation maps from adult mouse tissues. Nat Genet 45(10):1198-1206.
31. Visel A, Minovitsky S, Dubchak I, Pennacchio LA (2007) VISTA Enhancer Browser-a database of tissue-specific human enhancers. Nucleic Acids Res 35(Database issue): D88-D92.
32. Yue F, et al.; Mouse ENCODE Consortium (2014) A comparative encyclopedia of DNA elements in the mouse genome. Nature 515(7527):355-364.
33. Breiman L (2001) Random forests. Mach Learn 45:5-32.
34. Liu F, Li H, Ren C, Bo X, Shu W (2016) PEDLA: Predicting enhancers with a deep learning-based algorithmic framework. Sci Rep 6:28517.
35. Rajagopal N, et al. (2013) RFECS: A random-forest based algorithm for enhancer identification from chromatin state. PLoS Comput Biol 9(3):e1002968.
36. Lu Y, Qu W, Shan G, Zhang C (2015) DELTA: A distal enhancer locating tool based on AdaBoost algorithm and shape features of chromatin modifications. PLoS One 10(6): e0130622.
37. Firpi HA, Ucar D, Tan K (2010) Discover regulatory DNA elements using chromatin signatures and artificial neural network. Bioinformatics 26(13):1579-1586.
38. Rajagopal N, et al. (2016) High-throughput mapping of regulatory DNA. Nat Biotechnol 34(2):167-174.
39. Boyle AP, et al. (2008) High-resolution mapping and characterization of open chromatin across the genome. Cell 132(2):311-322.
40. Buenrostro JD, Giresi PG, Zaba LC, Chang HY, Greenleaf WJ (2013) Transposition of native chromatin for fast and sensitive epigenomic profiling of open chromatin, DNAbinding proteins and nucleosome position. Nat Methods 10(12):1213-1218.
41. Yao L, Shen H, Laird PW, Farnham PJ, Berman BP (2015) Inferring regulatory element landscapes and transcription factor networks from cancer methylomes. Genome Biol 16:105.
42. Rhie SK, et al. (2016) Identification of activated enhancers and linked transcription factors in breast, prostate, and kidney tumors by tracing enhancer networks using epigenetic traits. Epigenetics Chromatin 9:50.
43. Erwin GD, et al. (2014) Integrating diverse datasets improves developmental enhancer prediction. PLoS Comput Biol 10(6):e1003677.
44. Birney E, et al.; ENCODE Project Consortium; NISC Comparative Sequencing Program; Baylor College of Medicine Human Genome Sequencing Center; Washington University Genome Sequencing Center; Broad Institute; Children's Hospital Oakland Research Institute (2007) Identification and analysis of functional elements in 1% of the human genome by the ENCODE pilot project. Nature 447(7146):799-816.
45. Consortium EP, et al. (2012) An integrated encyclopedia of DNA elements in the human genome. Nature 489:57-74.
46. Kundaje A, et al.; Roadmap Epigenomics Consortium (2015) Integrative analysis of 111 reference human epigenomes. Nature 518(7539):317-330.
47. Xie W, et al. (2013) Epigenomic analysis of multilineage differentiation of human embryonic stem cells. Cell 153(5):1134-1148.
48. Leung D, et al. (2015) Integrative analysis of haplotype-resolved epigenomes across human tissues. Nature 518(7539):350-354.
49. Narlikar L, et al. (2010) Genome-wide discovery of human heart enhancers. Genome Res 20(3):381-392.
50. Won K-J, Chepelev I, Ren B, Wang W (2008) Prediction of regulatory elements in mammalian genomes using chromatin signatures. BMC Bioinformatics 9:547.
51. Robinson MD, et al. (2014) Statistical methods for detecting differentially methylated loci and regions. Front Genet 5:324.
52. Ma H, et al. (2014) Abnormalities in human pluripotent cells due to reprogramming mechanisms. Nature 511(7508):177-183.
53. Kent WJ, et al. (2002) The human genome browser at UCSC. Genome Res 12(6): 996-1006.
54. Schultz MD, Schmitz RJ, Ecker JR (2012) "Leveling" the playing field for analyses of single-base resolution DNA methylomes. Trends Genet 28(12):583-585.
55. Perkins W, TygertM, Ward R (2011) Computing the confidence levels for a root-meansquare test of goodness-of-fit. Appl Math Comput 217:9072-9084.
56. Bancroft T, Du C, Nettleton D (2013) Estimation of false discovery rate using sequential permutation p-values. Biometrics 69(1):1-7.
57. Li H, Durbin R (2009) Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25(14):1754-1760.
58. Quinlan AR, Hall IM (2010) BEDTools: A flexible suite of utilities for comparing genomic features. Bioinformatics 26(6):841-842.
59. Zhang Y, et al. (2008) Model-based analysis of ChIP-Seq (MACS). Genome Biol 9(9): R137.
60. Harrow J, et al. (2012) GENCODE: The reference human genome annotation for The ENCODE Project. Genome Res 22(9):1760-1774.
61. Ong C-T, Corces VG (2014) CTCF: An architectural protein bridging genome topology and function. Nat Rev Genet 15(4):234-246.
62. Neph S, et al. (2012) BEDOPS: High-performance genomic feature operations. Bioinformatics 28(14):1919-1920.
63. Freund Y, Schapire RE (1995) A decision-theoretic generalization of on-line learning and an application to boosting. European Conference on Computational Learning Theory (Springer, Berlin), pp 23-37.
64. Ernst J, Kellis M (2012) ChromHMM: Automating chromatin-state discovery and characterization. Nat Methods 9(3):215-216.
65. Hoffman MM, et al. (2012) Unsupervised pattern discovery in human chromatin structure through genomic segmentation. Nat Methods 9(5):473-476.
66. Pennacchio LA, et al. (2006) In vivo enhancer analysis of human conserved non-coding sequences. Nature 444(7118):499-502.
67. Kothary R, et al. (1989) Inducible expression of an hsp68-lacZ hybrid gene in transgenic mice. Development 105(4):707-714.
68. Heinz S, et al. (2010) Simple combinations of lineage-determining transcription factors prime cis-regulatory elements required for macrophage and B cell identities. Mol Cell 38(4):576-589.
69. Liaw A, Wiener M (2002) Classification and regression by randomForest. R News 2:18-22.