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doepipeline: a systematic approach to optimizing multi-level and multi-step data processing workflows.

Daniel Svensson1, Rickard Sjögren1,2, David Sundell3

  • 1Department of Chemistry, Computational Life Science Cluster (CLiC), Umeå University, Umeå, Sweden.

BMC Bioinformatics
|October 17, 2019
PubMed
Summary
This summary is machine-generated.

Optimizing bioinformatic software parameters is complex. The new doepipeline approach uses Design of Experiments to systematically find optimal settings, improving results over defaults for various genomic analyses.

Keywords:
AssemblyClassificationDesign of ExperimentsMinIONNanoporeOptimizationScaffoldingSequencingVariant calling

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Selecting optimal parameter settings for bioinformatics software is challenging due to individual and interactive parameter effects.
  • Complexity increases in sequential pipelines, often leading to default settings or trial-and-error optimization.
  • A systematic approach is needed for reliable and efficient parameter selection in bioinformatics.

Purpose of the Study:

  • To introduce a novel, systematic approach for optimizing bioinformatic software parameters.
  • To develop a user-friendly implementation for accessible parameter optimization.
  • To demonstrate improved outcomes compared to default parameter settings.

Main Methods:

  • Utilized Design of Experiments (DOE) methodology and subset designs for efficient parameter space exploration.
  • Employed a two-phase approach: screening with subset designs and optimization with response surface designs and OLS modeling.
  • Applied the doepipeline approach to optimize parameters for de-novo assembly, genome scaffolding, k-mer classification, and variant calling.

Main Results:

  • The doepipeline approach successfully optimized parameters in four distinct bioinformatics use cases.
  • In all tested cases, doepipeline identified parameter settings that yielded superior outcomes compared to default values.
  • The approach demonstrated effectiveness across diverse genomic analysis tasks.

Conclusions:

  • The proposed methodology offers a systematic and robust framework for bioinformatic software parameter optimization.
  • The doepipeline Python package provides an accessible and user-friendly implementation of this methodology.
  • This approach automates optimization, overcoming limitations of manual parameter tweaking for various bioinformatic tools.