Integrative analyses for omics data: a Bayesian mixture model to assess the concordance of ChIP-chip and ChIP-seq measurements.

2.50
Hdl Handle:
http://hdl.handle.net/10033/239672
Title:
Integrative analyses for omics data: a Bayesian mixture model to assess the concordance of ChIP-chip and ChIP-seq measurements.
Authors:
Schäfer, Martin; Lkhagvasuren, Otgonzul; Klein, Hans-Ulrich; Elling, Christian; Wüstefeld, Torsten; Müller-Tidow, Carsten; Zender, Lars; Koschmieder, Steffen; Dugas, Martin; Ickstadt, Katja
Abstract:
The analysis of different variations in genomics, transcriptomics, epigenomics, and proteomics has increased considerably in recent years. This is especially due to the success of microarray and, more recently, sequencing technology. Apart from understanding mechanisms of disease pathogenesis on a molecular basis, for example in cancer research, the challenge of analyzing such different data types in an integrated way has become increasingly important also for the validation of new sequencing technologies with maximum resolution. For this purpose, a methodological framework for their comparison with microarray techniques in the context of smallest sample sizes, which result from the high costs of experiments, is proposed in this contribution. Based on an adaptation of the externally centered correlation coefficient ( Schäfer et al. 2009 ), it is demonstrated how a Bayesian mixture model can be applied to compare and classify measurements of histone acetylation that stem from chromatin immunoprecipitation combined with either microarray (ChIP-chip) or sequencing techniques (ChIP-seq) for the identification of DNA fragments. Here, the murine hematopoietic cell line 32D, which was transduced with the oncogene BCR-ABL, the hallmark of chronic myeloid leukemia, was characterized. Cells were compared to mock-transduced cells as control. Activation or inhibition of other genes by histone modifications induced by the oncogene is considered critical in such a context for the understanding of the disease.
Affiliation:
Department of Statistics, TU Dortmund University, Dortmund, Germany. martin.schaefer@tu-dortmund.de
Citation:
Integrative analyses for omics data: a Bayesian mixture model to assess the concordance of ChIP-chip and ChIP-seq measurements. 2012, 75 (8-10):461-70 J. Toxicol. Environ. Health Part A
Journal:
Journal of toxicology and environmental health. Part A
Issue Date:
2012
URI:
http://hdl.handle.net/10033/239672
DOI:
10.1080/15287394.2012.674914
PubMed ID:
22686305
Type:
Article
Language:
en
ISSN:
1528-7394
Appears in Collections:
Publications of NG Chronische Infektionen und Krebs CHIK

Full metadata record

DC FieldValue Language
dc.contributor.authorSchäfer, Martinen_GB
dc.contributor.authorLkhagvasuren, Otgonzulen_GB
dc.contributor.authorKlein, Hans-Ulrichen_GB
dc.contributor.authorElling, Christianen_GB
dc.contributor.authorWüstefeld, Torstenen_GB
dc.contributor.authorMüller-Tidow, Carstenen_GB
dc.contributor.authorZender, Larsen_GB
dc.contributor.authorKoschmieder, Steffenen_GB
dc.contributor.authorDugas, Martinen_GB
dc.contributor.authorIckstadt, Katjaen_GB
dc.date.accessioned2012-08-23T09:41:27Z-
dc.date.available2012-08-23T09:41:27Z-
dc.date.issued2012-
dc.identifier.citationIntegrative analyses for omics data: a Bayesian mixture model to assess the concordance of ChIP-chip and ChIP-seq measurements. 2012, 75 (8-10):461-70 J. Toxicol. Environ. Health Part Aen_GB
dc.identifier.issn1528-7394-
dc.identifier.pmid22686305-
dc.identifier.doi10.1080/15287394.2012.674914-
dc.identifier.urihttp://hdl.handle.net/10033/239672-
dc.description.abstractThe analysis of different variations in genomics, transcriptomics, epigenomics, and proteomics has increased considerably in recent years. This is especially due to the success of microarray and, more recently, sequencing technology. Apart from understanding mechanisms of disease pathogenesis on a molecular basis, for example in cancer research, the challenge of analyzing such different data types in an integrated way has become increasingly important also for the validation of new sequencing technologies with maximum resolution. For this purpose, a methodological framework for their comparison with microarray techniques in the context of smallest sample sizes, which result from the high costs of experiments, is proposed in this contribution. Based on an adaptation of the externally centered correlation coefficient ( Schäfer et al. 2009 ), it is demonstrated how a Bayesian mixture model can be applied to compare and classify measurements of histone acetylation that stem from chromatin immunoprecipitation combined with either microarray (ChIP-chip) or sequencing techniques (ChIP-seq) for the identification of DNA fragments. Here, the murine hematopoietic cell line 32D, which was transduced with the oncogene BCR-ABL, the hallmark of chronic myeloid leukemia, was characterized. Cells were compared to mock-transduced cells as control. Activation or inhibition of other genes by histone modifications induced by the oncogene is considered critical in such a context for the understanding of the disease.en_GB
dc.language.isoenen
dc.rightsArchived with thanks to Journal of toxicology and environmental health. Part Aen_GB
dc.subject.meshAlgorithmsen_GB
dc.subject.meshAnimalsen_GB
dc.subject.meshBayes Theoremen_GB
dc.subject.meshCapillary Electrochromatographyen_GB
dc.subject.meshChromatin Immunoprecipitationen_GB
dc.subject.meshDNAen_GB
dc.subject.meshData Interpretation, Statisticalen_GB
dc.subject.meshEpigenomicsen_GB
dc.subject.meshFusion Proteins, bcr-ablen_GB
dc.subject.meshGenomicsen_GB
dc.subject.meshHematopoietic Stem Cellsen_GB
dc.subject.meshHistonesen_GB
dc.subject.meshLeukemia, Myelogenous, Chronic, BCR-ABL Positiveen_GB
dc.subject.meshMarkov Chainsen_GB
dc.subject.meshMiceen_GB
dc.subject.meshMicroarray Analysisen_GB
dc.subject.meshModels, Statisticalen_GB
dc.subject.meshMonte Carlo Methoden_GB
dc.subject.meshOncogenesen_GB
dc.subject.meshProteomicsen_GB
dc.subject.meshSample Sizeen_GB
dc.subject.meshSequence Analysis, DNAen_GB
dc.subject.meshTransduction, Geneticen_GB
dc.titleIntegrative analyses for omics data: a Bayesian mixture model to assess the concordance of ChIP-chip and ChIP-seq measurements.en
dc.typeArticleen
dc.contributor.departmentDepartment of Statistics, TU Dortmund University, Dortmund, Germany. martin.schaefer@tu-dortmund.deen_GB
dc.identifier.journalJournal of toxicology and environmental health. Part Aen_GB

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