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Choosing panels of genomics assays using submodular optimization.

Kai Wei1, Maxwell W Libbrecht2, Jeffrey A Bilmes1

  • 1Department of Electrical Engineering, University of Washington, Seattle, WA, USA.

Genome Biology
|November 17, 2016
PubMed
Summary

Choosing the right epigenomic assays is crucial due to high costs. Submodular selection of assays (SSA) uses optimization to select a diverse panel of genomic assays for better cell type characterization.

Keywords:
Discrete optimizationGenomics assaysSubmodularity

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Area of Science:

  • Genomics
  • Computational Biology
  • Epigenetics

Background:

  • Epigenomic characterization of cell types relies on sequencing-based assays like ChIP-seq and DNase-seq.
  • The high cost of these assays necessitates careful selection of a limited panel for each study.

Purpose of the Study:

  • To introduce a novel method, submodular selection of assays (SSA), for optimizing the selection of genomic assay panels.
  • To provide a framework for selecting diverse and informative assay panels in epigenomic studies.

Main Methods:

  • The study presents the submodular selection of assays (SSA) method.
  • SSA leverages principles from submodular optimization to select assay panels.

Main Results:

  • SSA enables the selection of a diverse panel of genomic assays.
  • This method addresses the critical challenge of assay selection in epigenomic studies.

Conclusions:

  • The submodular selection of assays (SSA) offers an effective approach to choosing genomic assays.
  • This work demonstrates the broader applicability of submodular optimization to discrete biological problems.