Statistical Methods For The Analysis Of ChlP-chip Data

Sunduz Keles
University Of Wisconsin Madison Suite 6401 Madison, Wi 537151218

Grant 1R01HG003747-01A2 from National Human Genome Research Institute, IRG: GCAT

Abstract: With many genome-sequencing projects coming to an end, the biggest remaining challenge is to comprehend the information encoded in these sequences. Identifying interactions between transcription factors (TFs) and their DMA binding sites is an integral part of this challenge. These interactions control critical steps in cell functions, and their dysfunction can significantly contribute to the progression of various diseases. ChlP-chip experiments that couple chromatin immunoprecipitation with DMA microarray analysis have become powerful tools for the genome-wide identification and characterization of transcription factor binding sites. These experiments produce massive amounts of noisy data with small number of replicates and therefore require innovative robust statistical analysis methods. The objectives of this proposal are to develop, evaluate and disseminate statistical methods for analyzing data from ChlP-chip experiments. These objectives will be accomplished through four specific aims (1) Development of robust probabilistic methods for detecting TF bound regions. These methods will utilize the information common across probes on tiling arrays to increase power in small sample sizes. (2) Extension of the methods in Aim-1 to deal with array designs where probe sequences overlap and observations from nearby probes exhibit long-range spatial dependencies. As a result, we will develop rigorous statistical inference procedures for general tiling array designs. (3) Development of an adaptive framework for incorporating quantitative information from ChlP-chip experiments into motif finding. This will connect the first stage of the ChlP-chip data analysis, namely identification of the bound regions, with the downstream sequence analysis thereby boosting the sensitivity and specificity of the motif finding task. (4) Implementation of the statistical methods developed as part of this research in statistical packages. The resulting packages will be available to the scientific community both in stand-alone versions and as part of the Bioconductor Project which is an open source and development software project for the analysis of the genomic data. Successful completion of the proposed research will result in substantially improved statistical methods for the analysis of ChlP-chip experiments.

Keywords: genome, transcription factor, DNA, Drosophilidae, base, binding site, cell, cell line, chromatin immunoprecipitation, community, comprehension, computer program /software, computer system design /evaluation, conditioning, density, element, gait, gene, human, human genetic material tag, microarray technology, model, oligonucleotide, play, role, training

Project start date: 2007-04-26

Project end date: 2011-03-31

1R01HG003747-01A2 (2007): $282445

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Statistical Methods For The Analysis Of ChlP-chip Data

Sunduz Keles
University Of Wisconsin Madison Suite 6401 Madison, Wi 537151218

Grant 1R01HG003747-01A2 from National Human Genome Research Institute, IRG: GCAT

Abstract: With many genome-sequencing projects coming to an end, the biggest remaining challenge is to comprehend the information encoded in these sequences. Identifying interactions between transcription factors (TFs) and their DMA binding sites is an integral part of this challenge. These interactions control critical steps in cell functions, and their dysfunction can significantly contribute to the progression of various diseases. ChlP-chip experiments that couple chromatin immunoprecipitation with DMA microarray analysis have become powerful tools for the genome-wide identification and characterization of transcription factor binding sites. These experiments produce massive amounts of noisy data with small number of replicates and therefore require innovative robust statistical analysis methods. The objectives of this proposal are to develop, evaluate and disseminate statistical methods for analyzing data from ChlP-chip experiments. These objectives will be accomplished through four specific aims (1) Development of robust probabilistic methods for detecting TF bound regions. These methods will utilize the information common across probes on tiling arrays to increase power in small sample sizes. (2) Extension of the methods in Aim-1 to deal with array designs where probe sequences overlap and observations from nearby probes exhibit long-range spatial dependencies. As a result, we will develop rigorous statistical inference procedures for general tiling array designs. (3) Development of an adaptive framework for incorporating quantitative information from ChlP-chip experiments into motif finding. This will connect the first stage of the ChlP-chip data analysis, namely identification of the bound regions, with the downstream sequence analysis thereby boosting the sensitivity and specificity of the motif finding task. (4) Implementation of the statistical methods developed as part of this research in statistical packages. The resulting packages will be available to the scientific community both in stand-alone versions and as part of the Bioconductor Project which is an open source and development software project for the analysis of the genomic data. Successful completion of the proposed research will result in substantially improved statistical methods for the analysis of ChlP-chip experiments.

Keywords: genome, transcription factor, DNA, Drosophilidae, base, binding site, cell, cell line, chromatin immunoprecipitation, community, comprehension, computer program /software, computer system design /evaluation, conditioning, density, element, gait, gene, human, human genetic material tag, microarray technology, model, oligonucleotide, play, role, training

Project start date: 2007-04-26

Project end date: 2011-03-31

1R01HG003747-01A2 (2007): $282445

Statistical Methods For The Analysis Of ChlP-chip Data

Sunduz Keles
Biostatistics/med Informaticsuniversity Of Wisconsin Madison

Grant 5R01HG003747-02 from National Human Genome Research Institute, IRG: GCAT

Abstract: With many genome-sequencing projects coming to an end, the biggest remaining challenge is to comprehend the information encoded in these sequences. Identifying interactions between transcription factors (TFs) and their DMA binding sites is an integral part of this challenge. These interactions control critical steps in cell functions, and their dysfunction can significantly contribute to the progression of various diseases. ChlP-chip experiments that couple chromatin immunoprecipitation with DMA microarray analysis have become powerful tools for the genome-wide identification and characterization of transcription factor binding sites. These experiments produce massive amounts of noisy data with small number of replicates and therefore require innovative robust statistical analysis methods. The objectives of this proposal are to develop, evaluate and disseminate statistical methods for analyzing data from ChlP-chip experiments. These objectives will be accomplished through four specific aims (1) Development of robust probabilistic methods for detecting TF bound regions. These methods will utilize the information common across probes on tiling arrays to increase power in small sample sizes. (2) Extension of the methods in Aim-1 to deal with array designs where probe sequences overlap and observations from nearby probes exhibit long-range spatial dependencies. As a result, we will develop rigorous statistical inference procedures for general tiling array designs. (3) Development of an adaptive framework for incorporating quantitative information from ChlP-chip experiments into motif finding. This will connect the first stage of the ChlP-chip data analysis, namely identification of the bound regions, with the downstream sequence analysis thereby boosting the sensitivity and specificity of the motif finding task. (4) Implementation of the statistical methods developed as part of this research in statistical packages. The resulting packages will be available to the scientific community both in stand-alone versions and as part of the Bioconductor Project which is an open source and development software project for the analysis of the genomic data. Successful completion of the proposed research will result in substantially improved statistical methods for the analysis of ChlP-chip experiments

Keywords: genome, transcription factor DNA, Drosophilidae, base, binding site, cell, cell line, chromatin immunoprecipitation, community, comprehension, computer program /software, computer system design /evaluation, conditioning, density, element, gait, gene, human, human genetic material tag, microarray technology, model, oligonucleotide, play, role, training

Project start date: 2007-04-26

Project end date: 2011-03-31

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CMARRT: a tool for the analysis of ChIP-chip data from tiling arrays by incorporating the correlation structure. Pac Symp Biocomput. 2008: 515-26. PMID: 18229712

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Transcription of histone gene cluster by differential core-promoter factors. Genes Dev. 2007 Nov 15; 21( 22): 2936-49. Epub 2007 Oct 31. PMID: 17978101

Goff LA, Davila J, Jörnsten R, Keles S, Hart RP.

Bioinformatic analysis of neural stem cell differentiation. J Biomol Tech. 2007 Sep; 18( 4): 205-12. PMID: 17916793

Sweitzer NK, Shenoy M, Stein JH, Keles S, Palta M, LeCaire T, Mitchell GF.

Increases in central aortic impedance precede alterations in arterial stiffness measures in type 1 diabetes. Diabetes Care. 2007 Nov; 30( 11): 2886-91. Epub 2007 Aug 8. PMID: 17686834

Hopkins DR, Keles S, Greenspan DS.

The bone morphogenetic protein 1/Tolloid-like metalloproteinases. Matrix Biol. 2007 Sep; 26( 7): 508-23. Epub 2007 May 18. Review. PMID: 17560775

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Keleş S.

Mixture modeling for genome-wide localization of transcription factors. Biometrics. 2007 Mar; 63( 1): 10-21. PMID: 17447925

Bembom O, Keles S, van der Laan MJ.

Supervised detection of conserved motifs in DNA sequences with cosmo. Stat Appl Genet Mol Biol. 2007; 6: Article8. Epub 2007 Feb 23. PMID: 17402923

Isogai Y, Takada S, Tjian R, Keleş S.

Novel TRF1/BRF target genes revealed by genome-wide analysis of Drosophila Pol III transcription. EMBO J. 2007 Jan 10; 26( 1): 79-89. Epub 2006 Dec 14. PMID: 17170711

Keleş S, van der Laan MJ, Dudoit S, Cawley SE.

Multiple testing methods for ChIP-Chip high density oligonucleotide array data. J Comput Biol. 2006 Apr; 13( 3): 579-613. PMID: 16706714

van der Laan MJ, Dudoit S, Keles S.

Asymptotic optimality of likelihood-based cross-validation. Stat Appl Genet Mol Biol. 2004; 3: Article4. Epub 2004 Mar 22. PMID: 16646820 [PubMed]

Keles S, van der Laan MJ, Dudoit S, Xing B, Eisen MB.

Supervised detection of regulatory motifs in DNA sequences. Stat Appl Genet Mol Biol. 2003; 2: Article5. Epub 2003 Aug 25. PMID: 16646783 [PubMed]

Lu F, Keles S, Wright SJ, Wahba G.

Framework for kernel regularization with application to protein clustering. Proc Natl Acad Sci U S A. 2005 Aug 30; 102( 35): 12332-7. Epub 2005 Aug 18. PMID: 16109767

Cinar H, Keles S, Jin Y.

Expression profiling of GABAergic motor neurons in Caenorhabditis elegans. Curr Biol. 2005 Feb 22; 15( 4): 340-6. PMID: 15723795

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Regulatory motif finding by logic regression. Bioinformatics. 2004 Nov 1; 20( 16): 2799-811. Epub 2004 May 27. PMID: 15166027

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Exploratory and confirmatory gene expression profiling of mac1Delta. J Biol Chem. 2004 Feb 6; 279( 6): 4450-8. Epub 2003 Oct 8. PMID: 14534306

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