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Prioritizing transcriptomic and epigenomic experiments using an optimization strategy that leverages imputed data.

Jacob Schreiber1, Jeffrey Bilmes2, William Stafford Noble3

  • 1Paul G. Allen School of Computer Science and Engineering.

Bioinformatics (Oxford, England)
|September 23, 2020
PubMed
Summary
This summary is machine-generated.

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This study presents a computational method for selecting the most informative experiments for genomics and epigenomics research. The approach uses submodular optimization to identify experiments that maximize novel data generation, guiding consortia like ENCODE in prioritizing future research directions.

Area of Science:

  • Genomics and Epigenomics
  • Computational Biology
  • Bioinformatics

Background:

  • Scientific discovery relies on strategic experimental design.
  • Consortia like the National Institutes of Health ENCODE Consortium aim to comprehensively map human genome function.
  • Selecting the next experiments is crucial for maximizing novel insights.

Purpose of the Study:

  • To develop a method for selecting optimal experiments in genomics and epigenomics.
  • To guide large-scale research consortia in prioritizing future experimental endeavors.
  • To identify which experiments should be performed next to characterize functional elements in the human genome.

Main Methods:

  • Formulated the experiment selection task as a submodular optimization problem.

Related Experiment Videos

  • Utilized imputed data, rather than experimental data, for decision-making.
  • Applied the facility location function to maximize the diversity of chosen experiments.

    • Main Results:

    • The proposed method successfully selects panels of experiments that cover a diverse range of biochemical activities.
    • Demonstrated the effectiveness of submodular optimization for experimental design in large-scale genomics projects.
    • Developed modifications to the facility location function to incorporate domain knowledge and constraints.

    Conclusions:

    • Submodular optimization provides a powerful framework for selecting high-impact experiments in genomics and epigenomics.
    • The method offers a data-driven approach to guide research consortia in prioritizing experimental resources.
    • The developed Python package 'kiwano' facilitates the implementation of this strategy.