Abstract
Chromatin immunoprecipitation followed by high throughput sequencing (ChIP-seq) experiments are routinely utilized for studying epigenomics of transcriptional regulation. We review some of the important statistical issues in the analysis of these experiments and extend our previous model for the analysis of ChIP-seq data of transcription factors, named MOSAiCS, with a hidden Markov model architecture (MOSAiCS-HMM). MOSAiCS-HMM provides a model-based approach for modeling read counts in histone modification ChIP-seq experiments and accounts for the spatial dependence in their ChIP-seq profiles. In addition, its R package implementation provides many functionality for summarizing these data and generating files that can be directly uploaded to the UCSC genome browser.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Bailey, T., Krajewski, P., Ladunga, I., Lefebvre, C., Li, Q., Liu, T., Madrigal, P., Taslim, C., Zhang, J.: Practical guidelines for the comprehensive analysis of ChIP-seq data. PLoS Computat. Biol. 9(11), e1003,326 (2013)
Barski, A., Cuddapah, S., Cui, K., Roh, T.Y., Schones, D.E., Wang, Z., Wei, G., Chepelev, I., Zhao, K.: High-resolution profiling of histone methylations in the human genome. Cell 129(4), 823–837 (2007)
Benjamini, Y., Speed, T.P.: Summarizing and correcting the GC content bias in high-throughput sequencing. Nucleic Acids Res. 40, e72 (2012)
Bernstein, B.E., Stamatoyannopoulos, J.A., Costello, J.F., Ren, B., Milosavljevic, A., Meissner, A., Kellis, M., Marra, M.A., Beaudet, A.L., Ecker, J.R., Farnham, P.J., Hirst, M., Lander, E.S., Mikkelsen, T.S., Thomson, J.A.: The NIH Roadmap Epigenomics Mapping Consortium. Nat. Biotechnol. 28(10), 1045–1048 (2010)
Buck, M.J., Lieb, J.D.: ChIP-chip: considerations for the design, analysis, and application of genome-wide chromatin immunoprecipitation experiments. Genomics 84, 349–360 (2004)
Chung, D., Kuan, P.F., Li, B., Sanalkumar, R., Liang, K., Bresnick, E.H., Dewey, C., Keleş, S.: Discovering transcription factor binding sites in highly repetitive regions of genomes with multi-read analysis of ChIP-seq data. PLoS Computat. Biol. 7, e1002,111 (2011)
Chung, D., Park, D., Myers, K., Grass, J., Kiley, P., Landick, R., Keleş, S.: dPeak: High resolution identification of transcription factor binding sites from PET and SET ChIP-seq data. PLoS Computat. Biol. 9(10), e1003,246 (2013)
Dohm, J., Lottaz, C., Borodina, T., Himmelbauer, H.: Substantial biases in ultra-short read data sets from high-throughput DNA sequencing. Nucleic Acids Res. 36(16), e105 (2008)
Durbin, R., Eddy, S., Krogh, A., Mitchison, G.: Biological sequence analysis: Probabilistic models of proteins and nucleic acids. Cambridge University press, Cambridge (1998)
ENCODE Project Consortium, Bernstein, B.E., Birney, E., Dunham, I., Green, E.D., Gunter, C., Snyder, M.: An integrated encyclopedia of DNA elements in the human genome. Nature 489(7414), 57–74 (2012)
Ernst, J., Kellis, M.: Discovery and characterization of chromatin states for systematic annotation of the human genome. Nat. Biotechnol. 28, 817–25 (2010)
Gentleman, R.C., Carey, V.J., Bates, D.M., others: Bioconductor: Open software development for computational biology and bioinformatics. Genome Biol. 5, R80 (2004)
Guo, Y., Mahony, S., Gifford, D.K.: High resolution genome wide binding event finding and motif discovery reveals transcription factor spatial binding constraints. PLoS Computat. Biol. 8, e1002,638 (2012)
Jang, S.W., Srinivasan, R., Jones, E.A., Sun, G., Keles, S., Krueger, C., Chang, L.W., Nagarajan, R., Svaren, J.: Locus-wide identification of egr2/krox20 regulatory targets in myelin genes. J. Neurochem. 115(6), 1409–1420 (2010)
Johnson, D.S., Mortazavi, A., Myers, R.M., Wold, B.: Genome-wide mapping of in vivo protein-DNA interactions. Science 316(5830), 1497–1502 (2007)
Keleş, S.: Mixture modeling for genome-wide localization of transcription factors. Biometrics 63, 10–21 (2007)
Kharchenko, P.V., Tolstorukov, M., Park, P.J.: Design and analysis of ChIP-seq experiments for DNA-binding proteins. Nat. Biotechnol. 6, 1351–1359 (2008)
Kuan, P., Chung, D., Pan, G., Thomson, J., Stewart, R., Keleş, S.: A Statistical Framework for the Analysis of ChIP-seq data. J Am. Stat. Assoc. 106(459), 891–903 (2011)
Landt, S.G., Marinov, G.K., Kundaje, A., Kheradpour, P., Pauli, F., Batzoglou, S., Bernstein, B.E., Bickel, P., Brown, J.B., Cayting, P., Chen, Y., DeSalvo, G., Epstein, C., Fisher-Aylor, K.I., Euskirchen, G., Gerstein, M., Gertz, J., Hartemink, A.J., Hoffman, M.M., Iyer, V.R., Jung, Y.L., Karmakar, S., Kellis, M., Kharchenko, P.V., Li, Q., Liu, T., Liu, X.S., Ma, L., Milosavljevic, A., Myers, R.M., Park, P.J., Pazin, M.J., Perry, M.D., Raha, D., Reddy, T.E., Rozowsky, J., Shoresh, N., Sidow, A., Slattery, M., Stamatoyannopoulos, J.A., Tolstorukov, M.Y., White, K.P., Xi, S., Farnham, P.J., Lieb, J.D., Wold, B.J., Snyder, M.: ChIP-seq guidelines and practices of the ENCODE and modENCODE consortia. Genome Res. 22(9), 1813–1831 (2012)
Langmead, B., Trapnell, C., Pop, M., Salzberg, S.L.: Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol.10, R25 (2009)
Mikkelsen, T.S., Ku, M., Jaffe, D.B., Issac, B., Lieberman, E., Giannoukos, G., Alvarez, P., Brockman, W., Kim, T.K., Koche, R.P., Lee, W., Mendenhall, E., O’Donovan, A., Presser, A., Russ, C., Xie, X., Meissner, A., Wernig, M., Jaenisch, R., Nusbaum, C., Lander, E.S., Bernstein, B.E.: Genome-wide maps of chromatin state in pluripotent and lineage-committed cells. Nature 448, 553–560 (2007)
Myers, K.S., Yan, H., Ong, I.M., Chung, D., Liang, K., Tran, F., Kele, S., Landick, R., Kiley, P.J.: Genome-scale analysis of escherichia coli fnr reveals complex features of transcription factor binding. PLoS Genetics 9(6), e1003,565 (2013)
Nair, N.U., Sahu, A.D., Bucher, P., Moret, B.M.E.: ChIPnorm: A statistical method for normalizing and identifying differential regions in histone modification ChIP-seq libraries. PLoS ONE 7(8), e39,573 (2012)
Newton, M.A., Noueiry, A., Sarkar, D., Ahlquist, P.: Detecting differential gene expression with a semiparametric hierarchical mixture method. Biostatistics 5(2), 155–176 (2004)
Rabiner, L.R.: A tutorial on hidden Markov models and selected applications in speech recognition. Proc. IEEE 77(2), 257–286 (1989)
Rozowsky, J., Euskirchen, G., Auerbach, R., Zhang, D., Gibson, T., Bjornson, R., Carriero, N., Snyder, M., Gerstein, M.: PeakSeq enables systematic scoring of ChIP-Seq experiments relative to controls. Nat. Biotechnol. 27(1), 66–75 (2009)
Seo, Y.K., Chong, H.K., Infante, A.M., In, S.S., Xie, X., Osborne, T.F.: Genome-wide analysis of SREBP-1 binding in mouse liver chromatin reveals a preference for promoter proximal binding to a new motif. PNAS 106(33), 13,765–13,769 (2009)
Song, Q., Smith, A.D.: Identifying dispersed epigenomic domains from ChIP-Seq data. Bioinformatics 27(6), 870–871 (2011)
Srinivasan, R., Sun, G., Keles, S., Jones, E.A., Jang, S.W., Krueger, C., Moran, J.J., Svaren, J.: Genome-wide analysis of egr2/sox10 binding in myelinating peripheral nerve. Nucleic Acids Res. 40(14), 6449–6460 (2012)
Strahl, B.D., Allis, C.D.: The language of covalent histone modifications. Nature 403(6765), 41–45 (2000)
Sun, G., Chung, D., Liang, K., Keleş, S.: Statistical analysis of ChIP-seq data with MOSAiCS In: Shomron, N. (ed.) Deep Sequencing Data Analysis. Methods in Molecular Biology, vol. 1038, pp. 193–212. Humana Press, New york (2013)
Taslim, C., Huang, T., Lin, S.: DIME: R-package for identifying differential ChIP-seq based on an ensemble of mixture models. Bioinformatics 27(11), 1569–70 (2011)
Xing, H., Mo, Y., Liao, W., Zhang, M.Q.: Genome-wide localization of protein-DNA binding and histone modification by a Bayesian change-point method with ChIP-seq data. PLoS Computat. Biol. 8(7), e1002613 (2012)
Xu, H., Wei, C.L., Lin, F., Sung, W.K.: An HMM approach to genome-wide identification of differential histone modification sites form chip-seq data. Bioinformatics 24(20), 2344–2349 (2008)
Zeng, X., Sanalkumar, R., Bresnick, E.H., Li, H., Chang, Q., Keleş, S.: jMOSAiCS: Joint analysis of multiple ChIP-seq datasets. Genome Biol. 14, R38 (2013)
Zhang, X., Robertson, G., Krzywinski, M., Ning, K., Droit, A., Jones, S., Gottardo, R.: PICS: probabilistic inference for ChIP-seq. Biometrics 67(1), 151–163 (2011)
Zhang, Y., Liu, T., Meyer, C.A., Eeckhoute, J., Johnson, D.S., Bernstein, B.E., Nussbaum, C., Myers, R.M., Brown, M., Li, W., Liu, X.S.: Model-based analysis of ChIP-Seq (MACS). Genome Biol. 9(9), R137 (2008)
Zhang, Z.D., Rozowsky, J., Snyder, M., Chang, J., Gerstein, M.: Modeling ChIP sequencing in silico with applications. PLoS Computat. Biol. 4(8), e1000,158 (2008)
Acknowledgements
We thank Professor Colin Dewey of University of Wisconsin, Madison, for providing us with a RSEM-processed version of ENCODE GM12878 RNA-seq data. This research was supported by National Institutes of Health Grants HG007019 and HG003747 to S.K.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this chapter
Cite this chapter
Chung, D., Zhang, Q., Keleş, S. (2014). MOSAiCS-HMM: A Model-Based Approach for Detecting Regions of Histone Modifications from ChIP-Seq Data. In: Datta, S., Nettleton, D. (eds) Statistical Analysis of Next Generation Sequencing Data. Frontiers in Probability and the Statistical Sciences. Springer, Cham. https://doi.org/10.1007/978-3-319-07212-8_14
Download citation
DOI: https://doi.org/10.1007/978-3-319-07212-8_14
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-07211-1
Online ISBN: 978-3-319-07212-8
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)