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DNA Microarrays02:34

DNA Microarrays

Microarrays are high-throughput and relatively inexpensive assays that can be automated to analyze large quantities of data at a time. They are used in genome-wide studies to compare gene or protein expression under two varied conditions, such as healthy and diseased states. Microarrays consist of glass or silica slides on which probe molecules are covalently attached through surface functionalization. Most commonly, the slides are prepared through the chemisorption of silanes to silica...

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Utilizing gene pair orientations for HMM-based analysis of promoter array ChIP-chip data.

Michael Seifert1, Jens Keilwagen, Marc Strickert

  • 1Leibniz Institute of Plant Genetics and Crop Plant Research, Data Inspection Group, Corrensstrasse 3, 06466 Gatersleben, Germany. seifert@ipk-gatersleben.de

Bioinformatics (Oxford, England)
|April 30, 2009
PubMed
Summary

A new Hidden Markov Model with scaled transition matrices (SHMM) improves transcription factor target gene identification from ChIP-chip data. This method outperforms standard log-fold-change and basic HMM approaches in yeast and Arabidopsis thaliana analyses.

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Area of Science:

  • Genomics
  • Molecular Biology
  • Bioinformatics

Background:

  • Chromatin immunoprecipitation followed by chip array (ChIP-chip) analysis is vital for identifying DNA-binding transcription factor targets.
  • Analyzing comprehensive promoter array ChIP-chip data to pinpoint these targets presents significant challenges.

Purpose of the Study:

  • To present and compare three distinct computational approaches for identifying transcription factor target genes from promoter array ChIP-chip data.
  • To introduce a novel Hidden Markov Model with scaled transition matrices (SHMM) that enhances target gene prediction.

Main Methods:

  • Comparison of a standard log-fold-change analysis (LFC).
  • Application of a basic Hidden Markov Model (HMM).
  • Implementation of a new HMM extension with scaled transition matrices (SHMM) incorporating adjacent gene pair orientation.

Main Results:

  • All three methods were applied to ChIP-chip datasets from Saccharomyces cerevisiae and Arabidopsis thaliana.
  • The novel SHMM approach demonstrated superior performance in identifying transcription factor target genes compared to LFC and HMM.
  • Validation in yeast cell cycle and Arabidopsis thaliana seed development contexts confirmed SHMM's improved accuracy.

Conclusions:

  • The SHMM method offers a significant advancement for identifying transcription factor target genes from promoter array ChIP-chip data.
  • This approach enhances the accuracy and reliability of genomic regulatory network analysis.
  • The developed software and datasets are publicly available for further research.