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/10033/595412
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:
Helmholtz Centre for infection research, Inhoffenstr. 7, D-38124 Braunschweig, Germany.
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/10033/595412
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
dc.contributor.authorLkhagvasuren, Otgonzulen
dc.contributor.authorKlein, Hans-Ulrichen
dc.contributor.authorElling, Christianen
dc.contributor.authorWüstefeld, Torstenen
dc.contributor.authorMüller-Tidow, Carstenen
dc.contributor.authorZender, Larsen
dc.contributor.authorKoschmieder, Steffenen
dc.contributor.authorDugas, Martinen
dc.contributor.authorIckstadt, Katjaen
dc.date.accessioned2016-02-02T15:52:19Zen
dc.date.available2016-02-02T15:52:19Zen
dc.date.issued2012en
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
dc.identifier.issn1528-7394en
dc.identifier.pmid22686305en
dc.identifier.doi10.1080/15287394.2012.674914en
dc.identifier.urihttp://hdl.handle.net/10033/595412en
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
dc.language.isoenen
dc.subject.meshAlgorithmsen
dc.subject.meshAnimalsen
dc.subject.meshBayes Theoremen
dc.subject.meshCapillary Electrochromatographyen
dc.subject.meshChromatin Immunoprecipitationen
dc.subject.meshDNAen
dc.subject.meshData Interpretation, Statisticalen
dc.subject.meshEpigenomicsen
dc.subject.meshFusion Proteins, bcr-ablen
dc.subject.meshGenomicsen
dc.subject.meshHematopoietic Stem Cellsen
dc.subject.meshHistonesen
dc.subject.meshLeukemia, Myelogenous, Chronic, BCR-ABL Positiveen
dc.subject.meshMarkov Chainsen
dc.subject.meshMiceen
dc.subject.meshMicroarray Analysisen
dc.subject.meshModels, Statisticalen
dc.subject.meshMonte Carlo Methoden
dc.subject.meshOncogenesen
dc.subject.meshProteomicsen
dc.subject.meshSample Sizeen
dc.subject.meshSequence Analysis, DNAen
dc.subject.meshTransduction, Geneticen
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.departmentHelmholtz Centre for infection research, Inhoffenstr. 7, D-38124 Braunschweig, Germany.en
dc.identifier.journalJournal of toxicology and environmental health. Part Aen

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