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Comparative study on ChIP-seq data: normalization and binding pattern characterization

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Zeitschriftentitel: Bioinformatics
Personen und Körperschaften: Taslim, Cenny, Wu, Jiejun, Yan, Pearlly, Singer, Greg, Parvin, Jeffrey, Huang, Tim, Lin, Shili, Huang, Kun
In: Bioinformatics, 25, 2009, 18, S. 2334-2340
Format: E-Article
Sprache: Englisch
veröffentlicht:
Oxford University Press (OUP)
Schlagwörter:
Computational Mathematics
Computational Theory and Mathematics
Computer Science Applications
Molecular Biology
Biochemistry
Statistics and Probability
author_facet Taslim, Cenny
Wu, Jiejun
Yan, Pearlly
Singer, Greg
Parvin, Jeffrey
Huang, Tim
Lin, Shili
Huang, Kun
Taslim, Cenny
Wu, Jiejun
Yan, Pearlly
Singer, Greg
Parvin, Jeffrey
Huang, Tim
Lin, Shili
Huang, Kun
author Taslim, Cenny
Wu, Jiejun
Yan, Pearlly
Singer, Greg
Parvin, Jeffrey
Huang, Tim
Lin, Shili
Huang, Kun
spellingShingle Taslim, Cenny
Wu, Jiejun
Yan, Pearlly
Singer, Greg
Parvin, Jeffrey
Huang, Tim
Lin, Shili
Huang, Kun
Bioinformatics
Comparative study on ChIP-seq data: normalization and binding pattern characterization
Computational Mathematics
Computational Theory and Mathematics
Computer Science Applications
Molecular Biology
Biochemistry
Statistics and Probability
author_sort taslim, cenny
spelling Taslim, Cenny Wu, Jiejun Yan, Pearlly Singer, Greg Parvin, Jeffrey Huang, Tim Lin, Shili Huang, Kun 1367-4811 1367-4803 Oxford University Press (OUP) Computational Mathematics Computational Theory and Mathematics Computer Science Applications Molecular Biology Biochemistry Statistics and Probability http://dx.doi.org/10.1093/bioinformatics/btp384 <jats:title>Abstract</jats:title> <jats:p>Motivation: Antibody-based Chromatin Immunoprecipitation assay followed by high-throughput sequencing technology (ChIP-seq) is a relatively new method to study the binding patterns of specific protein molecules over the entire genome. ChIP-seq technology allows scientist to get more comprehensive results in shorter time. Here, we present a non-linear normalization algorithm and a mixture modeling method for comparing ChIP-seq data from multiple samples and characterizing genes based on their RNA polymerase II (Pol II) binding patterns.</jats:p> <jats:p>Results: We apply a two-step non-linear normalization method based on locally weighted regression (LOESS) approach to compare ChIP-seq data across multiple samples and model the difference using an Exponential-NormalK mixture model. Fitted model is used to identify genes associated with differential binding sites based on local false discovery rate (fdr). These genes are then standardized and hierarchically clustered to characterize their Pol II binding patterns. As a case study, we apply the analysis procedure comparing normal breast cancer (MCF7) to tamoxifen-resistant (OHT) cell line. We find enriched regions that are associated with cancer (P &amp;lt; 0.0001). Our findings also imply that there may be a dysregulation of cell cycle and gene expression control pathways in the tamoxifen-resistant cells. These results show that the non-linear normalization method can be used to analyze ChIP-seq data across multiple samples.</jats:p> <jats:p>Availability: Data are available at http://www.bmi.osu.edu/~khuang/Data/ChIP/RNAPII/</jats:p> <jats:p>Contact: taslim.2@osu.edu; khuang@bmi.osu.edu</jats:p> <jats:p>Supplementary information: Supplementary data are available at Bioinformatics online.</jats:p> Comparative study on ChIP-seq data: normalization and binding pattern characterization Bioinformatics
doi_str_mv 10.1093/bioinformatics/btp384
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title Comparative study on ChIP-seq data: normalization and binding pattern characterization
title_unstemmed Comparative study on ChIP-seq data: normalization and binding pattern characterization
title_full Comparative study on ChIP-seq data: normalization and binding pattern characterization
title_fullStr Comparative study on ChIP-seq data: normalization and binding pattern characterization
title_full_unstemmed Comparative study on ChIP-seq data: normalization and binding pattern characterization
title_short Comparative study on ChIP-seq data: normalization and binding pattern characterization
title_sort comparative study on chip-seq data: normalization and binding pattern characterization
topic Computational Mathematics
Computational Theory and Mathematics
Computer Science Applications
Molecular Biology
Biochemistry
Statistics and Probability
url http://dx.doi.org/10.1093/bioinformatics/btp384
publishDate 2009
physical 2334-2340
description <jats:title>Abstract</jats:title> <jats:p>Motivation: Antibody-based Chromatin Immunoprecipitation assay followed by high-throughput sequencing technology (ChIP-seq) is a relatively new method to study the binding patterns of specific protein molecules over the entire genome. ChIP-seq technology allows scientist to get more comprehensive results in shorter time. Here, we present a non-linear normalization algorithm and a mixture modeling method for comparing ChIP-seq data from multiple samples and characterizing genes based on their RNA polymerase II (Pol II) binding patterns.</jats:p> <jats:p>Results: We apply a two-step non-linear normalization method based on locally weighted regression (LOESS) approach to compare ChIP-seq data across multiple samples and model the difference using an Exponential-NormalK mixture model. Fitted model is used to identify genes associated with differential binding sites based on local false discovery rate (fdr). These genes are then standardized and hierarchically clustered to characterize their Pol II binding patterns. As a case study, we apply the analysis procedure comparing normal breast cancer (MCF7) to tamoxifen-resistant (OHT) cell line. We find enriched regions that are associated with cancer (P &amp;lt; 0.0001). Our findings also imply that there may be a dysregulation of cell cycle and gene expression control pathways in the tamoxifen-resistant cells. These results show that the non-linear normalization method can be used to analyze ChIP-seq data across multiple samples.</jats:p> <jats:p>Availability: Data are available at http://www.bmi.osu.edu/~khuang/Data/ChIP/RNAPII/</jats:p> <jats:p>Contact:  taslim.2@osu.edu; khuang@bmi.osu.edu</jats:p> <jats:p>Supplementary information:  Supplementary data are available at Bioinformatics online.</jats:p>
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author Taslim, Cenny, Wu, Jiejun, Yan, Pearlly, Singer, Greg, Parvin, Jeffrey, Huang, Tim, Lin, Shili, Huang, Kun
author_facet Taslim, Cenny, Wu, Jiejun, Yan, Pearlly, Singer, Greg, Parvin, Jeffrey, Huang, Tim, Lin, Shili, Huang, Kun, Taslim, Cenny, Wu, Jiejun, Yan, Pearlly, Singer, Greg, Parvin, Jeffrey, Huang, Tim, Lin, Shili, Huang, Kun
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description <jats:title>Abstract</jats:title> <jats:p>Motivation: Antibody-based Chromatin Immunoprecipitation assay followed by high-throughput sequencing technology (ChIP-seq) is a relatively new method to study the binding patterns of specific protein molecules over the entire genome. ChIP-seq technology allows scientist to get more comprehensive results in shorter time. Here, we present a non-linear normalization algorithm and a mixture modeling method for comparing ChIP-seq data from multiple samples and characterizing genes based on their RNA polymerase II (Pol II) binding patterns.</jats:p> <jats:p>Results: We apply a two-step non-linear normalization method based on locally weighted regression (LOESS) approach to compare ChIP-seq data across multiple samples and model the difference using an Exponential-NormalK mixture model. Fitted model is used to identify genes associated with differential binding sites based on local false discovery rate (fdr). These genes are then standardized and hierarchically clustered to characterize their Pol II binding patterns. As a case study, we apply the analysis procedure comparing normal breast cancer (MCF7) to tamoxifen-resistant (OHT) cell line. We find enriched regions that are associated with cancer (P &amp;lt; 0.0001). Our findings also imply that there may be a dysregulation of cell cycle and gene expression control pathways in the tamoxifen-resistant cells. These results show that the non-linear normalization method can be used to analyze ChIP-seq data across multiple samples.</jats:p> <jats:p>Availability: Data are available at http://www.bmi.osu.edu/~khuang/Data/ChIP/RNAPII/</jats:p> <jats:p>Contact:  taslim.2@osu.edu; khuang@bmi.osu.edu</jats:p> <jats:p>Supplementary information:  Supplementary data are available at Bioinformatics online.</jats:p>
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spelling Taslim, Cenny Wu, Jiejun Yan, Pearlly Singer, Greg Parvin, Jeffrey Huang, Tim Lin, Shili Huang, Kun 1367-4811 1367-4803 Oxford University Press (OUP) Computational Mathematics Computational Theory and Mathematics Computer Science Applications Molecular Biology Biochemistry Statistics and Probability http://dx.doi.org/10.1093/bioinformatics/btp384 <jats:title>Abstract</jats:title> <jats:p>Motivation: Antibody-based Chromatin Immunoprecipitation assay followed by high-throughput sequencing technology (ChIP-seq) is a relatively new method to study the binding patterns of specific protein molecules over the entire genome. ChIP-seq technology allows scientist to get more comprehensive results in shorter time. Here, we present a non-linear normalization algorithm and a mixture modeling method for comparing ChIP-seq data from multiple samples and characterizing genes based on their RNA polymerase II (Pol II) binding patterns.</jats:p> <jats:p>Results: We apply a two-step non-linear normalization method based on locally weighted regression (LOESS) approach to compare ChIP-seq data across multiple samples and model the difference using an Exponential-NormalK mixture model. Fitted model is used to identify genes associated with differential binding sites based on local false discovery rate (fdr). These genes are then standardized and hierarchically clustered to characterize their Pol II binding patterns. As a case study, we apply the analysis procedure comparing normal breast cancer (MCF7) to tamoxifen-resistant (OHT) cell line. We find enriched regions that are associated with cancer (P &amp;lt; 0.0001). Our findings also imply that there may be a dysregulation of cell cycle and gene expression control pathways in the tamoxifen-resistant cells. These results show that the non-linear normalization method can be used to analyze ChIP-seq data across multiple samples.</jats:p> <jats:p>Availability: Data are available at http://www.bmi.osu.edu/~khuang/Data/ChIP/RNAPII/</jats:p> <jats:p>Contact: taslim.2@osu.edu; khuang@bmi.osu.edu</jats:p> <jats:p>Supplementary information: Supplementary data are available at Bioinformatics online.</jats:p> Comparative study on ChIP-seq data: normalization and binding pattern characterization Bioinformatics
spellingShingle Taslim, Cenny, Wu, Jiejun, Yan, Pearlly, Singer, Greg, Parvin, Jeffrey, Huang, Tim, Lin, Shili, Huang, Kun, Bioinformatics, Comparative study on ChIP-seq data: normalization and binding pattern characterization, Computational Mathematics, Computational Theory and Mathematics, Computer Science Applications, Molecular Biology, Biochemistry, Statistics and Probability
title Comparative study on ChIP-seq data: normalization and binding pattern characterization
title_full Comparative study on ChIP-seq data: normalization and binding pattern characterization
title_fullStr Comparative study on ChIP-seq data: normalization and binding pattern characterization
title_full_unstemmed Comparative study on ChIP-seq data: normalization and binding pattern characterization
title_short Comparative study on ChIP-seq data: normalization and binding pattern characterization
title_sort comparative study on chip-seq data: normalization and binding pattern characterization
title_unstemmed Comparative study on ChIP-seq data: normalization and binding pattern characterization
topic Computational Mathematics, Computational Theory and Mathematics, Computer Science Applications, Molecular Biology, Biochemistry, Statistics and Probability
url http://dx.doi.org/10.1093/bioinformatics/btp384