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Related Experiment Videos

Differential analysis for high density tiling microarray data.

Srinka Ghosh1, Heather A Hirsch, Edward A Sekinger

  • 1Affymetrix Inc,, Santa Clara, CA 95051, USA. srinka_ghosh@affymetrix.com

BMC Bioinformatics
|September 26, 2007
PubMed
Summary
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This study introduces a novel method to analyze genome-wide differential gene expression, identifying coordinated changes across coding and non-coding regions. The approach reveals discordant patterns between exons and introns and significant co-localization of RNA Polymerase II and histone modifications.

Area of Science:

  • Genomics
  • Molecular Biology
  • Bioinformatics

Background:

  • High-density oligonucleotide tiling arrays offer unbiased, genome-wide analysis of transcription, chromatin structure, and transcription factor binding.
  • Understanding differential gene expression is crucial for deciphering cellular developmental programs.
  • Current methods often focus on a gene-centric approach, potentially missing broader genomic regulatory patterns.

Purpose of the Study:

  • To develop and apply a novel computational approach for analyzing genome-wide differential gene expression.
  • To investigate coordinated differential responses in both annotated and unannotated genomic regions during induced cellular development.
  • To move beyond the gene-centric paradigm to identify complex regulatory patterns.

Main Methods:

Related Experiment Videos

  • Utilized high-density oligonucleotide tiling arrays for genome-wide data acquisition.
  • Developed a novel piece-wise function-based approach for analyzing differential genomic response.
  • Employed an algorithm framework similar to Significance Analysis of Microarrays (SAM) for pattern identification.

Main Results:

  • Identified discordant differential expression patterns between exons and introns with a False Discovery Rate (FDR) < 12%.
  • Quantified co-localization of RNA Polymerase II and tetra-acetylated histone modifications.
  • Observed significant co-localization at the 5' end of genes (p-value < 10-13), with overall significance at p-value < 0.003.

Conclusions:

  • The developed algorithm effectively defines regions and patterns of coordinated differential genomic change.
  • The study highlights the importance of non-gene-centric analysis for uncovering complex regulatory relationships.
  • Findings provide insights into the coordinated regulation of transcription and chromatin structure during development.