Using CisGenome to Analyze ChIP‐chip and ChIP‐seq Data

Hongkai Ji1, Hui Jiang2, Wenxiu Ma2, Wing Hung Wong2

1 The Johns Hopkins University, Baltimore, Maryland, 2 Stanford University, Stanford, California
Publication Name:  Current Protocols in Bioinformatics
Unit Number:  Unit 2.13
DOI:  10.1002/0471250953.bi0213s33
Online Posting Date:  March, 2011
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Chromatin immunoprecipitation (ChIP) coupled with genome tiling array hybridization (ChIP‐chip) and ChIP followed by massively parallel sequencing (ChIP‐seq) are high‐throughput approaches to profiling genome‐wide protein‐DNA interactions. Both technologies are increasingly used to study transcription‐factor binding sites and chromatin modifications. CisGenome is an integrated software system for analyzing ChIP‐chip and ChIP‐seq data. This unit describes basic functions of CisGenome and how to use them to find genomic regions with protein‐DNA interactions, visualize binding signals, associate binding regions with nearby genes, search for novel transcription‐factor binding motifs, and map existing DNA sequence motifs to user‐supplied genomic regions to define their exact locations.Curr. Protoc. Bioinform. 33:2.13.1‐2.13.45. © 2011 by John Wiley & Sons, Inc.

Keywords: transcription factor; chromatin immunoprecipitation; tiling array; next generation sequencing; motif; gene regulation

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Table of Contents

  • Introduction
  • Basic Protocol 1: ChIP‐chip Peak Calling for Affymetrix Tiling Array Data
  • Basic Protocol 2: Visualization
  • Basic Protocol 3: Peak‐Gene Association
  • Basic Protocol 4: DNA Sequence Retrieval
  • Basic Protocol 5: De Novo Motif Discovery
  • Basic Protocol 6: Motif Mapping
  • Basic Protocol 7: ChIP‐chip Peak Calling for Other Tiling Array Platforms
  • Basic Protocol 8: ChIP‐seq Peak Calling (One‐Sample Analysis)
  • Basic Protocol 9: ChIP‐seq Peak Calling (Two‐Sample Analysis)
  • Support Protocol 1: Installing CisGenome
  • Support Protocol 2: Installing Genome Databases
  • Guidelines for Understanding Results
  • Commentary
  • Literature Cited
  • Figures
  • Tables
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Literature Cited

Literature Cited
   Barski, A., Cuddapah, S., Cui, K., Roh, T.Y., Schones, D.E., Wang, Z., Wei, G., Chepelev, I., and Zhao, K. 2007. High‐resolution profiling of histone methylations in the human genome. Cell 129:823‐837.
   Bolstad, B.M., Irizarry, R.A., Astrand, M., and Speed, T.P. 2003. A comparison of normalization methods for high density oligonucleotide array data based on variance and bias. Bioinformatics 19:185‐193.
   Cawley, S., Bekiranov, S., Ng, H.H., Kapranov, P., Sekinger, E.A., Kampa, D., Piccolboni, A., Sementchenko, V., Cheng, J., Williams, A.J., Wheeler, R., Wong, B., Drenkow, J., Yamanaka, M., Patel, S., Brubaker, S., Tammana, H., Helt, G., Struhl, K., and Gingeras, T.R. 2004. Unbiased mapping of transcription factor binding sites along human chromosomes 21 and 22 points to widespread regulation of noncoding RNAs. Cell 116:499‐509.
   Crooks, G.E., Hon, G., Chandonia, J.M., and Brenner, S.E. 2004. WebLogo: A sequence logo generator. Genome Res. 14:1188‐1190.
   The Gene Ontology Consortium. 2000. Gene ontology: Tool for the unification of biology. Nat. Genet. 25:25‐29.
   Jensen, S.T., Liu, X.S., Zhou, Q., and Liu, J.S. 2004. Computational discovery of gene regulatory binding motifs: A Bayesian perspective. Stat. Sci. 19:188‐204.
   Ji, H. and Wong, W.H. 2005. TileMap: Create chromosomal map of tiling array hybridizations. Bioinformatics 21:3629‐3636.
   Ji, H., Jiang, H., Ma, W., Johnson, D.S., Myers, R.M., and Wong, W.H. 2008. An integrated software system for analyzing ChIP‐chip and ChIP‐seq data. Nat. Biotechnol. 26:1293‐1300.
   Johnson, D.S., Mortazavi, A., Myers, R.M., and Wold, B. 2007. Genome‐wide mapping of in vivo protein‐DNA interactions. Science 316:1497‐1502.
   Liu, J.S., Neuwald, A.F., and Lawrence, C.E. 1995. Bayesian models for multiple local sequence alignment and Gibbs sampling strategies. J. Amer. Statist. Assoc. 90:1156‐1170.
   Liu, X.S., Brutlag, D.L., and Liu, J.S. 2002. An algorithm for finding protein‐DNA binding sites with applications to chromatin‐immunoprecipitation microarray experiments. Nat. Biotechnol. 20:835‐839.
   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. and Bernstein, B.E. 2007. Genome‐wide maps of chromatin state in pluripotent and lineage‐committed cells. Nature 448:553‐560.
   Ren, B., Robert, F., Wyrick, J.J., Aparicio, O., Jennings, E.G., Simon, I., Zeitlinger, J., Schreiber, J., Hannett, N., Kanin, E., Volkert, T.L., Wilson, C.J., Bell, S.P., and Young, R.A. 2000. Genome‐wide location and function of DNA binding proteins. Science 290:2306‐2309.
   Robertson, G., Hirst, M., Bainbridge, M., Bilenky, M., Zhao, Y., Zeng, T., Euskirchen, G., Bernier, B., Varhol, R., Delaney, A., Thiessen, N., Griffith, O.L., He, A., Marra, M., Snyder, M., and Jones, S. 2007. Genome‐wide profiles of STAT1 DNA association using chromatin immunoprecipitation and massively parallel sequencing. Nat. Methods 4:651‐657.
   Siepel, A., Bejerano, G., Pedersen, J.S., Hinrichs, A.S., Hou, M., Rosenbloom, K., Clawson, H., Spieth, J., Hillier, L.W., Richards, S., Weinstock, G.M., Wilson, R.K., Gibbs, R.A., Kent, W.J., Miller, W., and Haussler, D. 2005. Evolutionarily conserved elements in vertebrate, insect, worm, and yeast genomes. Genome Res. 15:1034‐1050.
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