Display Settings:

Format

Send to:

Choose Destination
See comment in PubMed Commons below
Bioinformatics. 2010 Aug 1;26(15):1864-70. doi: 10.1093/bioinformatics/btq314. Epub 2010 Jun 15.

JAMIE: joint analysis of multiple ChIP-chip experiments.

Author information

  • 1Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, 615 North Wolfe Street, Baltimore, MD 21205, USA.

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.

AVAILABILITY:

JAMIE is freely available from http://www.biostat.jhsph.edu/~hji/jamie

PMID:
20551135
[PubMed - indexed for MEDLINE]
PMCID:
PMC2905557
Free PMC Article

Images from this publication.See all images (4)Free text

Fig. 1.
Fig. 2.
Fig. 3.
Fig. 4.
PubMed Commons home

PubMed Commons

0 comments
How to join PubMed Commons

    Supplemental Content

    Full text links

    Icon for HighWire Icon for PubMed Central
    Loading ...
    Write to the Help Desk