JAMIE: Joint analysis of multiple ChIP-chip experiments

Hao Wu, Hong Kai Ji

Research output: Contribution to journalArticle

Abstract

Motivation: Chromatin immunoprecipitation followed by genome tiling array hybridization (ChIP-chip) is a powerful approach to identify transcription factor binding sites (TFBSs) in target genomes. When multiple related ChIP-chip datasets are available, analyzing them jointly allows one to borrow information across datasets to improve peak detection. This is particularly useful for analyzing noisy datasets. Results: We propose a hierarchical mixture model and develop an R package JAMIE to perform the joint analysis. The genome is assumed to consist of background and potential binding regions (PBRs). PBRs have context-dependent probabilities to become bona fide binding sites in individual datasets. This model captures the correlation among datasets, which provides basis for sharing information across experiments. Real data tests illustrate the advantage of JAMIE over a strategy that analyzes individual datasets separately.

Original languageEnglish (US)
Article numberbtq314
Pages (from-to)1864-1870
Number of pages7
JournalBioinformatics
Volume26
Issue number15
DOIs
StatePublished - Jun 15 2010

Fingerprint

Chip
Genes
Binding sites
Genome
Binding Sites
Experiment
Transcription factors
Experiments
Chromatin
Information Sharing
Transcription Factors
Hierarchical Model
Transcription Factor
Tiling
Mixture Model
Information Dissemination
Chromatin Immunoprecipitation
Target
Datasets
Dependent

ASJC Scopus subject areas

  • Biochemistry
  • Molecular Biology
  • Computational Theory and Mathematics
  • Computer Science Applications
  • Computational Mathematics
  • Statistics and Probability
  • Medicine(all)

Cite this

JAMIE : Joint analysis of multiple ChIP-chip experiments. / Wu, Hao; Ji, Hong Kai.

In: Bioinformatics, Vol. 26, No. 15, btq314, 15.06.2010, p. 1864-1870.

Research output: Contribution to journalArticle

Wu, Hao ; Ji, Hong Kai. / JAMIE : Joint analysis of multiple ChIP-chip experiments. In: Bioinformatics. 2010 ; Vol. 26, No. 15. pp. 1864-1870.
@article{8997a5ad97ee4a6cad43035fa5f56b23,
title = "JAMIE: Joint analysis of multiple ChIP-chip experiments",
abstract = "Motivation: Chromatin immunoprecipitation followed by genome tiling array hybridization (ChIP-chip) is a powerful approach to identify transcription factor binding sites (TFBSs) in target genomes. When multiple related ChIP-chip datasets are available, analyzing them jointly allows one to borrow information across datasets to improve peak detection. This is particularly useful for analyzing noisy datasets. Results: We propose a hierarchical mixture model and develop an R package JAMIE to perform the joint analysis. The genome is assumed to consist of background and potential binding regions (PBRs). PBRs have context-dependent probabilities to become bona fide binding sites in individual datasets. This model captures the correlation among datasets, which provides basis for sharing information across experiments. Real data tests illustrate the advantage of JAMIE over a strategy that analyzes individual datasets separately.",
author = "Hao Wu and Ji, {Hong Kai}",
year = "2010",
month = "6",
day = "15",
doi = "10.1093/bioinformatics/btq314",
language = "English (US)",
volume = "26",
pages = "1864--1870",
journal = "Bioinformatics",
issn = "1367-4803",
publisher = "Oxford University Press",
number = "15",

}

TY - JOUR

T1 - JAMIE

T2 - Joint analysis of multiple ChIP-chip experiments

AU - Wu, Hao

AU - Ji, Hong Kai

PY - 2010/6/15

Y1 - 2010/6/15

N2 - Motivation: Chromatin immunoprecipitation followed by genome tiling array hybridization (ChIP-chip) is a powerful approach to identify transcription factor binding sites (TFBSs) in target genomes. When multiple related ChIP-chip datasets are available, analyzing them jointly allows one to borrow information across datasets to improve peak detection. This is particularly useful for analyzing noisy datasets. Results: We propose a hierarchical mixture model and develop an R package JAMIE to perform the joint analysis. The genome is assumed to consist of background and potential binding regions (PBRs). PBRs have context-dependent probabilities to become bona fide binding sites in individual datasets. This model captures the correlation among datasets, which provides basis for sharing information across experiments. Real data tests illustrate the advantage of JAMIE over a strategy that analyzes individual datasets separately.

AB - Motivation: Chromatin immunoprecipitation followed by genome tiling array hybridization (ChIP-chip) is a powerful approach to identify transcription factor binding sites (TFBSs) in target genomes. When multiple related ChIP-chip datasets are available, analyzing them jointly allows one to borrow information across datasets to improve peak detection. This is particularly useful for analyzing noisy datasets. Results: We propose a hierarchical mixture model and develop an R package JAMIE to perform the joint analysis. The genome is assumed to consist of background and potential binding regions (PBRs). PBRs have context-dependent probabilities to become bona fide binding sites in individual datasets. This model captures the correlation among datasets, which provides basis for sharing information across experiments. Real data tests illustrate the advantage of JAMIE over a strategy that analyzes individual datasets separately.

UR - http://www.scopus.com/inward/record.url?scp=77955016494&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=77955016494&partnerID=8YFLogxK

U2 - 10.1093/bioinformatics/btq314

DO - 10.1093/bioinformatics/btq314

M3 - Article

C2 - 20551135

AN - SCOPUS:77955016494

VL - 26

SP - 1864

EP - 1870

JO - Bioinformatics

JF - Bioinformatics

SN - 1367-4803

IS - 15

M1 - btq314

ER -