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BMC Bioinformatics

Open Access

Computational analyses of eukaryotic promoters

  • Michael Q Zhang1
BMC Bioinformatics20078(Suppl 6):S3

https://doi.org/10.1186/1471-2105-8-S6-S3

Published: 27 September 2007

Abstract

Computational analysis of eukaryotic promoters is one of the most difficult problems in computational genomics and is essential for understanding gene expression profiles and reverse-engineering gene regulation network circuits. Here I give a basic introduction of the problem and recent update on both experimental and computational approaches. More details may be found in the extended references. This review is based on a summer lecture given at Max Planck Institute at Berlin in 2005.

Background

The promoter of a gene is defined as the cis-regulatory DNA region at a specific location (the transcription start site, or TSS) that can drive the transcription of its target gene in response to environmental signals. Computationally, it is often conveniently divided into three regions: the core-promoter (~80–100 bp surrounding the TSS), the proximal-promoter (~250–1000 bp upstream of the core-promoter) and the distal-promoter (further upstream, normally excluding enhancer or other regulatory regions whose influences are position/orientation independent). The core-promoter is minimally required for the assembly of the preinitiation complex (PIC) and can drive a reporter gene at a basal level from the TSS. The proximal-promoter often contains major cis-regulatory elements for driving activated reporter gene expression with some tissue-specificity. However, the distal-promoter together with distal enhancers/silencers and insulators are often necessary for accurately reproducing the endogenous gene expression patterns in vivo, especially for early developmental genes. Distal cis-regulatory elements also occur in the introns and the downstream regions, and therefore computational studies of these regions have been difficult and often limited to only the conserved sub-regions and/or regions in which functional cis-regulatory elements form clusters. Most of our work has been focused on 1 kb proximal-promoters (defined as -700 to +300 with respect to the TSS). We have shown that DNA motifs in this region can predict tissue-specific gene expression [1]. Computational promoter analyses usually face two related problems: the localization of the core-promoter (TSS prediction) and the identification of cis-regulatory elements (motif discovery). Basic computational methods have been reviewed previously [2], here I emphasize some recent developments.

Results

New experimental developments

One recent surprise, revealed after more detailed biochemistry studies of promoter activation, is that people have underestimated the diversity and complexity of core-promoter architecture and regulation. I refer readers to the recent comprehensive review on "the general transcription machinery and general cofactors" [3].

Although several core-promoter elements have been identified (Figure 1), with each element being short and degenerate and not every element occurring in a given core-promoter, the combinatorial regulatory code within core-promoters remains elusive. Their predictive value has also been very limited, despite some weak statistical correlations among certain subsets of the elements which were uncovered recently [4, 5]. Further biochemical characterization of core-promoter binding factors under various functional conditions is necessary before a reliable computational classification of core-promoters becomes possible. An example of the type of question that must be answered is how CK2 phosphorylation of TAF1 may switch TFIID binding specificity from a DCE to DPE function [6] (Figure 1).
Figure 1

Regulation of core-promoter elements by TFIID and TFIIB (adapted from Fig. 2 of Thomas & Chiang 2006 [3]).

The most significant advance comes from the new sequencing and microarray technologies that, for the first time, can provide ample and accurate 5'UTR sequence and core-promoter/TFBS location data. In particular, large-scale 5'RACE technology at Tokyo University and 5'CAGE tag technology at Riken have provided DBTSS (Database of Transcriptional Start Sites, mainly human) [7] and Fantom (Functional Annotation of Mouse) [8, 9] with an order of magnitude more promoter sequences derived from full-length 5'UTRs/cDNAs than were present in the traditional part of EPD (Eukaryotic Promoter Database) [10]. These sequences serve as the best training data for all current computational studies in promoter recognition. Many of the surprising new statistical features of the core-promoter have come from the recent analyses of such data (see [11] for a nice updated summary). One particularly interesting point made in this reference is that "Contrary to expectations, only a small fraction of RNAP II promoters appear to contain a TATA box. In contrast, a large proportion of RNAP II promoters in metazoan genomes appear to contain an INR element. Finally, about 25% of human promoters appear to lack known core promoter elements. This may point to the existence of additional core promoter sequence elements that remain to be identified and functionally characterized.". More mammalian promoter statistics are discussed in [12] which presents a comprehensive study of Fantom3 data.

In addition to sequence data, ChIP-chip technologies (e.g. see review [13]) provide genome-wide in vivo mapping of protein-DNA binding regions which provide the best experimental data for all current computational studies in cis-regulatory motif discovery. Most of the important data for promoter prediction has come from the ChIP-chip localization of PIC at active core-promoters in the whole genome at sub-100 bp resolution [14]. When more such data are produced for different tissues/cells and development stages, it will transform the field of computational promoter prediction and genome regulation networks (further discussed below).

Advances in motif discovery

The traditional approach for finding cis-elements is to collect a set of (target gene) promoter sequences believed to be enriched by some common TFBS motifs. They may either be collected from the literature or from systematic experiments (such as SELEX, etc.). There are many de novo TFBS motif finding algorithms available. For a recent review on computational TFBS finding methods, see e.g., [15]. For a recent benchmark of some popular motif finders, see [16]. In addition to the classical alignment-based motif finding algorithms, such as CONSENSUS [17], EM [18]/MEME [19] and the Gibbs sampler [20] which have been reviewed previously [21], most modern approaches have tried to extend either to the discovery of motif combinations (called cis-regulatory modules or CRMs), the use of evolutionary conservation information (with either phylogenetic footprinting or shadowing approaches), or a combination of both approaches. One can also increase specificity by incorporating structural information, for example, if the protein binds as a homodimer, one could restrict the search to only the palindromic motifs.

More powerful and flexible motif finders can take the advantage of a separate sequence set called a background set, serving as a negative control. The goal is to search only for motifs that are most discriminating, i.e. only those enriched in the foreground set relative to the background set. Examples of such motif finders, called discriminant motif finders, include: ANN-Spec [22], DMOTIFS [23], DWE [24] and DME [25]. DME is particularly novel and powerful; it can enumerate all possible (discretized) weight matrices above user-defined minimum information content. A newer version (called DME-B [26]) of DME can optimize the classification ability of the identified motifs based on whether or not the sequence contains at least one occurrence of the motif. This technology has been used to systematically catalog of mammalian tissue-specific TFBS motifs [27, 28].

The most powerful generalization of this idea would be to turn motif finding into a feature selection problem in regression analysis by asking what is the set of features X(some functions of the motifs or CRMs) that can best explain the microarray data Y (e.g. expression scores). This is very similar to the general problem in genetics: Y represents the phenotype (mRNA expression) and X represents the genotype (promoter DNA elements). One would like to learn a model (function f) so that f( X ) can best predict Y. When "best" is measured by the average squared error based on the distribution Pr( X , Y), the solution is the conditional expectation (also known as the regression function, see, e.g. [29]): f( X ) = E (Y | X= x ). REDUCE was the first successful motif selection algorithm based on linear regression [30]. It has now been generalized to include cross-interaction terms [31], to use nucleotide weight matrices discovered by MDscan (Motif Regressor [32]), to apply logistic regression [33] and to a nonlinear model based on regression trees called MARSMotif [34, 35]. The matrix version of REDUCE (called MatrixREDUCE [36]) and of MARSMotif (called MARSMotif-M [37]) are becoming important motif discovery tools for mammalian promoter analyses. Almost all the tools developed for analyzing expression microarray data can also be easily applied to the analysis of localization data, such as ChIP-chip data. Although ChIP-chip is a global measurement for in vivo binding of proteins to chromatin DNA and hence is potentially capable of revealing direct target genes (most targets identified in expression arrays are not direct targets); due to the current resolution and to non-specific or non-functional cross-links, not all putative targets are functional or possess functional cis-elements. ChIP-chip data have also been used to further refine motifs found by expression data (e.g. using a boosting approach [38]).

Better promoter prediction

A number of statistical and machine learning approaches that can discriminate between the known promoter and some non-promoter sequences have been applied to TSS prediction. In a recent large scale comparison [39], eight prediction algorithms were compared. Among the most successful algorithms were Eponine [40] (which trains Relevant Vector Machines to recognize a TATA-box motif in a G+C rich domain and uses Monte Carlo sampling), McPromoter [41] (based on Neural Networks, interpolated Markov models and physical properties of promoter regions), FirstEF [42] (based on quadratic discriminant analysis of promoters, first exons and the first donor site) and DragonGSF [43, 39] (based on artificial neural networks). However, DragonGSF is not publicly available and uses additional binding site information based on the TRANSFAC database [44], exploiting specific information that is typically not available for unknown promoters.

Two new de novo promoter prediction algorithms have emerged that further improve in accuracy. One is ARTS [45], which is based on Support Vector Machines with multiple sophisticated sequence kernels. It claims to find about 35% true positives at a false positive rate of 1/1000, where the above mentioned methods find only about half as many true positives (18%). ARTS uses only downstream genic sequences as the negative set (non-promoters), and therefore it may get more false-positives from upstream non-genic regions. Furthermore, ARTS does not distinquish if a promoter is CpG-island related or not and it is not clear how ARTS may peform on non-CpG-island related promoters. Another novel TSS prediction algorithm is CoreBoost [46] which is based on simple LogitBoosting with stumps. It has a false positive rate of 1/5000 at the same sensitivity level (Zhao, personal communication). CoreBoost uses both immediate upstream and downstream fragments as negative sets and trains separate classifiers for each before combining the two. The training sample is 300 bp fragments (-250, +50), hence it is more localized than ARTS which has training sample of 2 kb fragments (-1 kb, +1 kb). The ideal application of TSS prediction algorithms is to combine them with gene prediction algorithms [21] and/or with the ChIP-chip PIC mapping data [14].

Future direction: epigenetics and chromatin states

Although much progress has been made in promoter prediction and cis-regulatory motif discovery, false-positives are still the main problem when scanning through the whole genome. Fundamentally this is because the information about chromatin structure is still missing in all our models! Protein-DNA binding specificity is partly determined by the energetics and partly determined by "entropy", which depends on how much of the genome is accessible to the DNA binding protein [47] Without knowing which regions of chromatin are open or closed (and to what degree), researchers have to assume the whole genome is accessible for binding, which is obviously wrong and will lead to more false positives (and false negatives because of the extra noise). This is clearly shown by recent genome-wide ChIP-chip data as well as DNase I Hypersensitivity mapping data. There is a necessity for higher order prediction algorithms that are capable of predicting chromatin states based upon, perhaps, genome-wide epigenetic measurements, CpG-islands and repeat characteristics in addition to genomic sequences. It is fortunate that such kinds of data are rapidly being generated [4854] and the corresponding analysis tools [5557] are also coming along. The days of more realistic dynamic modeling of chromatin structure and its relation to expression and regulation are finally coming.

Declarations

Acknowledgements

I would like to thank Dr. Dustin Schones for his careful proof-reading of the manuscript. Work in my lab is partially supported by grants from NSF, NIH and Dart NeuroGenomics Alliance.

This article has been published as part of BMC Bioinformatics Volume 8 Supplement 6, 2007: Otto Warburg International Summer School and Workshop on Networks and Regulation. The full contents of the supplement are available online at http://www.biomedcentral.com/1471-2105/8?issue=S6

Authors’ Affiliations

(1)
Cold Spring Harbor Laboratory

References

  1. Smith AD, Sumazin P, Xuan Z, Zhang MQ: DNA motifs in human and mouse proximal promoters predict tissue-specific expression. Proc Natl Acad Sci USA 2006, 103: 6275–6280. 10.1073/pnas.0508169103PubMed CentralView ArticlePubMedGoogle Scholar
  2. Zhang MQ: Computational Methods for Promoter Recognition. Edited by: Jiang T, Xu Y, Zhang MQ. MIT Press, Cambridge, Massachusetts; 249–268.Google Scholar
  3. Thomas MC, Chiang CM: The general transcription machinery and general cofactors. Crit Rev Biochem Mol Biol 2006, 41: 105–78. 10.1080/10409230600648736View ArticlePubMedGoogle Scholar
  4. Jin VX, Singer GA, Agosto-Perez FJ, Liyanarachchi S, Davuluri RV: Genome-wide analysis of core promoter elements from conserved human and mouse orthologous pairs. BMC Bioinformatics 2006, 7: 114. 10.1186/1471-2105-7-114PubMed CentralView ArticlePubMedGoogle Scholar
  5. Gershenzon NI, Trifonov EN, Ioshikhes IP: The features of Drosophila core promoters revealed by statistical analysis. BMC Genomics 2006, 7: 161. 10.1186/1471-2164-7-161PubMed CentralView ArticlePubMedGoogle Scholar
  6. Lewis BA, Sims RJ 3rd, Lane WS, Reinberg D: Functional characterization of core promoter elements: DPE-specific transcription requires the protein kinase CK2 and the PC4 coactivator. Mol Cell 2005, 18: 471–481. 10.1016/j.molcel.2005.04.005View ArticlePubMedGoogle Scholar
  7. Suzuki Y, Yamashita R, Sugano S, Nakai K: DBTSS, DataBase of Transcriptional Start Sites: Progress Report 2004. Nucleic Acids Res 2004, 32: D78–81. 10.1093/nar/gkh076PubMed CentralView ArticlePubMedGoogle Scholar
  8. Maeda N, Kasukawa T, Oyama R, Gough J, Frith M, Engstrom PG, Lenhard B, Aturaliya RN, Batalov S, Beisel KW, Bult CJ, Fletcher CF, Forrest AR, Furuno M, Hill D, Itoh M, Kanamori-Katayama M, Katayama S, Katoh M, Kawashima T, Quackenbush J, Ravasi T, Ring BZ, Shibata K, Sugiura K, Takenaka Y, Teasdale RD, Wells CA, Zhu Y, Kai C, Kawai J, Hume DA, Carninci P, Hayashizaki Y: Transcript annotation in FANTOM3: mouse gene catalog based on physical cDNAs. PLoS Genet 2006, 2: e62. 10.1371/journal.pgen.0020062PubMed CentralView ArticlePubMedGoogle Scholar
  9. Carninci P, Sandelin A, Lenhard B, Katayama S, Shimokawa K, Ponjavic J, Semple CA, Taylor MS, Engstrom PG, Frith MC, Forrest AR, Alkema WB, Tan SL, Plessy C, Kodzius R, Ravasi T, Kasukawa T, Fukuda S, Kanamori-Katayama M, Kitazume Y, Kawaji H, Kai C, Nakamura M, Konno H, Nakano K, Mottagui-Tabar S, Arner P, Chesi A, Gustincich S, Persichetti F, Suzuki H, Grimmond SM, Wells CA, Orlando V, Wahlestedt C, Liu ET, Harbers M, Kawai J, Bajic VB, Hume DA, Hayashizaki Y: Genomewide analysis of mammalian promoter architecture and evolution. Nat Genet 2006, 38: 626–635. 10.1038/ng1789View ArticlePubMedGoogle Scholar
  10. Schmid CD, Perier R, Praz V, Bucher P: EPD in its twentieth year: towards complete promoter coverage of selected model organisms. Nucleic Acids Res 2006, 34: D82–5. 10.1093/nar/gkj146PubMed CentralView ArticlePubMedGoogle Scholar
  11. Gross P, Oelgeschlager T: Core promoter-selective RNA polymerase II transcription. Biochem Soc Symp 2006, 73: 225–36.View ArticlePubMedGoogle Scholar
  12. Bajic VB, Tan SL, Christoffels A, Schonbach C, Lipovich L, Yang L, Hofmann O, Kruger A, Hide W, Kai C, Kawai J, Hume DA, Carninci P, Hayashizaki Y: Mice and men: their promoter properties. PLoS Genet 2006, 2: e54. 10.1371/journal.pgen.0020054PubMed CentralView ArticlePubMedGoogle Scholar
  13. Kim TH, Ren B: Genome-Wide Analysis of Protein-DNA Interactions. Annu Rev Genomics Hum Genet 2006, 7: 81–102. 10.1146/annurev.genom.7.080505.115634View ArticlePubMedGoogle Scholar
  14. Kim TH, Barrera LO, Zheng M, Qu C, Singer MA, Richmond TA, Wu Y, Green RD, Ren B: A high-resolution map of active promoters in the human genome. Nature 2005, 436: 876–80. 10.1038/nature03877PubMed CentralView ArticlePubMedGoogle Scholar
  15. Wasserman WW, Sandelin A: Applied bioinformatics for the identification of regulatory elements. Nat Rev Genet 2004, 5: 276–87. 10.1038/nrg1315View ArticlePubMedGoogle Scholar
  16. Tompa M, Li N, Bailey TL, Church GM, De Moor B, Eskin E, Favorov AV, Frith MC, Fu Y, Kent WJ, Makeev VJ, Mironov AA, Noble WS, Pavesi G, Pesole G, Regnier M, Simonis N, Sinha S, Thijs G, van Helden J, Vandenbogaert M, Weng Z, Workman C, Ye C, Zhu Z: Assessing computational tools for the discovery of transcription factor binding sites. Nat Biotechnol 2005, 23: 137–44. 10.1038/nbt1053View ArticlePubMedGoogle Scholar
  17. Hertz GZ, Hartzell GW 3rd, Stormo GD: Identification of consensus patterns in unaligned DNA sequences known to be functionally related. Comput Appl Biosci 1990, 6: 81–92.PubMedGoogle Scholar
  18. Lawrence CE, Reilly AA: An expectation maximization (EM) algorithm for the identification and characterization of common sites in unaligned biopolymer sequences. Proteins 1990, 7: 41–51. 10.1002/prot.340070105View ArticlePubMedGoogle Scholar
  19. Bailey TL, Elkan C: The value of prior knowledge in discovering motifs with MEME. Proceedings of the International Conference on Intelligent Systems for Molecular Biology 1995, 3: 21–9.Google Scholar
  20. Lawrence CE, Altschul SF, Boguski MS, Liu JS, Neuwald AF, Wootton JC: Detecting subtle sequence signals: A Gibbs sampling strategy for multiple alignment. Science 1993, 262: 208–14. 10.1126/science.8211139View ArticlePubMedGoogle Scholar
  21. Zhang MQ: Computational Prediction of Eukaryotic Protein-Coding Genes. Nat Rev Genet 2002, 3(9):698–709. 10.1038/nrg890View ArticlePubMedGoogle Scholar
  22. Workman CT, Stormo GD: ANN-Spec: A method for discovering transcription factor binding sites with improved specificity. Pacific Symposium on Biocomputing 2002, 467–78.Google Scholar
  23. Sinha S: Discriminative motifs. J Comput Biol 2003, 10: 599–615. 10.1089/10665270360688219View ArticlePubMedGoogle Scholar
  24. Sumazin P, Chen G, Hata N, Smith AD, Zhang T, Zhang MQ: DWE: Discriminating word enumerator. Bioinformatics 2005, 21: 31–8. 10.1093/bioinformatics/bth471View ArticlePubMedGoogle Scholar
  25. Smith AD, Sumazin P, Zhang MQ: Identifying tissue-selective transcription factor binding sites in vertebrate promoters. Proc Natl Acad Sci USA 2005, 102: 1560–5. 10.1073/pnas.0406123102PubMed CentralView ArticlePubMedGoogle Scholar
  26. Smith AD, Sumazin P, Das D, Zhang MQ: Mining ChIP-chip data for transcription factor and cofactor binding sites. Bioinformatics 2005, 21(Suppl 1):i403–12. 10.1093/bioinformatics/bti1043View ArticlePubMedGoogle Scholar
  27. Martinez MJ, Smith AD, Li B, Zhang MQ, Harrod KS: Computational prediction of novel components of lung transcriptional networks. Bioinformatics 2007, 23: 21–29. 10.1093/bioinformatics/btl531View ArticlePubMedGoogle Scholar
  28. Smith AD, Sumazin P, Zhang MQ: Tissue-specific regulatory elements in mammalian promoters. Mol Syst Biol 2007, 3: 73. 10.1038/msb4100114PubMed CentralPubMedGoogle Scholar
  29. Hastie T, Tibshirani R, Friedman J: The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer, New York; 2001.View ArticleGoogle Scholar
  30. Bussemaker HJ, Li H, Siggia ED: Regulatory element detection using correlation with expression. Nat Genet 2001, 27: 167–71. 10.1038/84792View ArticlePubMedGoogle Scholar
  31. Keles S, van der Laan M, Eisen MB: Identification of regulatory elements using a feature selection method. Bioinformatics 2002, 18: 1167–75. 10.1093/bioinformatics/18.9.1167View ArticlePubMedGoogle Scholar
  32. Conlon EM, XS Liu, JD Lieb, JS Liu: Integrating regulatory motif discovery and genome-wide expression analysis. Proc Natl Acad Sci USA 2003, 100: 3339–44. 10.1073/pnas.0630591100PubMed CentralView ArticlePubMedGoogle Scholar
  33. Keles S, van der Laan MJ, Vulpe C: Regulatory motif finding by logic regression. Bioinformatics 2004, 20: 2799–811. 10.1093/bioinformatics/bth333View ArticlePubMedGoogle Scholar
  34. Friedman J: Multivariate adaptive regression splines. Ann Stat 1991, 19: 1–141.View ArticleGoogle Scholar
  35. Das D, Banerjee N, Zhang MQ: Interacting models of cooperative gene regulation. Proc Natl Acad Sci USA 2004, 101: 16234–9. 10.1073/pnas.0407365101PubMed CentralView ArticlePubMedGoogle Scholar
  36. Foat BC, Morozov AV, Bussemaker HJ: Statistical mechanical modeling of genome-wide transcription factor occupancy data by MatrixREDUCE. Bioinformatics 2006, 22: e141–9. 10.1093/bioinformatics/btl223View ArticlePubMedGoogle Scholar
  37. Das D, Nahle Z, Zhang MQ: Adaptively inferring human transcriptional subnetworks. Mol Syst Biol 2006., 2: 2006.0029. Epub Jun 6 2006.0029. Epub Jun 6Google Scholar
  38. Hong P, Liu XS, Zhou Q, Lu X, Liu JS, Wong WH: A boosting approach for motif modeling using ChIP-chip data. Bioinformatics 2005, 21: 2636–43. 10.1093/bioinformatics/bti402View ArticlePubMedGoogle Scholar
  39. Bajic VB, Tan SL, Suzuki Y, Sugano S: Promoter prediction analysis on the whole human genome. Nat Biotechnol 2004, 22: 1467–73. 10.1038/nbt1032View ArticlePubMedGoogle Scholar
  40. Down TA, Hubbard TJ: Computational detection and location of transcription start sites in mammalian genomic DNA. Genome Res 2002, 12: 458–61. 10.1101/gr.216102PubMed CentralView ArticlePubMedGoogle Scholar
  41. Ohler U, Liao GC, Niemann H, Rubin GM: Computational analysis of core promoters in the Drosophila genome. Genome Biol 2002., 3: RESEARCH0087. Epub 2002 Dec 20 RESEARCH0087. Epub 2002 Dec 20Google Scholar
  42. Davuluri RV, Grosse I, Zhang MQ: Computational identification of promoters and first exons in the human genome. Nat Genet 2001, 29(4):412–417. Erratum: Nat Genet 2002, 32(3):459. Erratum: Nat Genet 2002, 32(3):459. 10.1038/ng780View ArticlePubMedGoogle Scholar
  43. Bajic VB, Seah SH: Dragon Gene Start Finder identifies approximate locations of the 5' ends of genes. Nucleic Acids Res 2003, 31: 3560–3. 10.1093/nar/gkg570PubMed CentralView ArticlePubMedGoogle Scholar
  44. Matys V, Kel-Margoulis OV, Fricke E, Liebich I, Land S, Barre-Dirrie A, Reuter I, Chekmenev D, Krull M, Hornischer K, Voss N, Stegmaier P, Lewicki-Potapov B, Saxel H, Kel AE, Wingender E: TRANSFAC and its module TRANSCompel: transcriptional gene regulation in eukaryotes. Nucleic Acids Res 2006, 34: D108–10. 10.1093/nar/gkj143PubMed CentralView ArticlePubMedGoogle Scholar
  45. Sonnenburg S, Zien A, Ratsch G: ARTS: accurate recognition of transcription starts in human. Bioinformatics 2006, 22: e472–80. 10.1093/bioinformatics/btl250View ArticlePubMedGoogle Scholar
  46. Zhao X, Xuan Z, Zhang MQ: Boosting with stumps for predicting transcription start sites. Genome Biol 2007, 8(2):R17. 10.1186/gb-2007-8-2-r17PubMed CentralView ArticlePubMedGoogle Scholar
  47. Buck MJ, Lieb JD: A chromatin-mediated mechanism for specification of conditional transcription factor targets. Nat Genet 2006, 38: 1446–51. 10.1038/ng1917PubMed CentralView ArticlePubMedGoogle Scholar
  48. Huebert DJ, Bernstein BE: Genomic views of chromatin. Curr Opin Genet Dev 2005, 15: 476–81. 10.1016/j.gde.2005.08.001View ArticlePubMedGoogle Scholar
  49. Yuan GC, Liu YJ, Dion MF, Slack MD, Wu LF, Altschuler SJ, Rando OJ: Genome-scale identification of nucleosome positions in S. cerevisiae. Science 2005, 309: 626–30. 10.1126/science.1112178View ArticlePubMedGoogle Scholar
  50. Rollins RA, Haghighi F, Edwards JR, Das R, Zhang MQ, Ju J, Bestor TH: Large-scale structure of genomic methylation patterns. Genome Res 2006, 16: 157–63. 10.1101/gr.4362006PubMed CentralView ArticlePubMedGoogle Scholar
  51. Schulze SR, Wallrath LL: Gene Regulation by Chromatin Structure: Paradigms Established in Drosophila melanogaster. Annu Rev Entomol 2007, 52: 171–92. 10.1146/annurev.ento.51.110104.151007View ArticlePubMedGoogle Scholar
  52. Cavalli G: Chromatin and epigenetics in development: blending cellular memory with cell fate plasticity. Development 2006, 133: 2089–94. 10.1242/dev.02402View ArticlePubMedGoogle Scholar
  53. Sabo PJ, Kuehn MS, Thurman R, Johnson BE, Johnson EM, Cao H, Yu M, Rosenzweig E, Goldy J, Haydock A, Weaver M, Shafer A, Lee K, Neri F, Humbert R, Singer MA, Richmond TA, Dorschner MO, McArthur M, Hawrylycz M, Green RD, Navas PA, Noble WS, Stamatoyannopoulos JA: Genome-scale mapping of DNase I sensitivity in vivo using tiling DNA microarrays. Nat Methods 2006, 3: 511–8. 10.1038/nmeth890View ArticlePubMedGoogle Scholar
  54. Heintzman ND, Stuart RK, Hon G, Fu Y, Ching CW, Hawkins RD, Barrera LO, Van Calcar S, Qu C, Ching KA, Wang W, Weng Z, Green RD, Crawford GE, Ren B: Distinct and predictive chromatin signatures of transcriptional promoters and enhancers in the human genome. Nat Genet 2007, 39: 311–318. 10.1038/ng1966View ArticlePubMedGoogle Scholar
  55. Bock C, Paulsen M, Tierling S, Mikeska T, Lengauer T, Walter J: CpG island methylation in human lymphocytes is highly correlated with DNA sequence, repeats, and predicted DNA structure. PLoS Genet 2006, 2: e26. 10.1371/journal.pgen.0020026PubMed CentralView ArticlePubMedGoogle Scholar
  56. Das R, Dimitrova N, Xuan Z, Rollins RA, Haghighi F, Edwards JR, Ju J, Bestor TH, Zhang MQ: Computational prediction of methylation status in human genomic sequences. Proc Natl Acad Sci USA 2006, 103: 10713–6. 10.1073/pnas.0602949103PubMed CentralView ArticlePubMedGoogle Scholar
  57. Segal E, Fondufe-Mittendorf Y, Chen L, Thastrom A, Field Y, Moore IK, Wang JP, Widom J: A genomic code for nucleosome positioning. Nature 2006, 442(7104):772–8. 10.1038/nature04979PubMed CentralView ArticlePubMedGoogle Scholar

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This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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