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Infrared: a declarative tree decomposition-powered framework for bioinformatics.

Hua-Ting Yao1,2,3, Bertrand Marchand4, Sarah J Berkemer4,5

  • 1LIX, CNRS UMR 7161, Ecole Polytechnique, Institut Polytechnique de Paris, Palaiseau, France. htyao@tbi.univie.ac.at.

Algorithms for Molecular Biology : AMB
|March 17, 2024
PubMed
Summary
This summary is machine-generated.

The Infrared framework simplifies complex bioinformatics problems by using declarative modeling and generic algorithms for exact optimization and controlled sampling. This enables efficient development of specialized applications without custom coding.

Keywords:
BioinformaticsBoltzmann samplingFixed-parameter tractable algorithmsNetwork phylogenyPseudoknotsRNA alignmentRNA sequence designTree decomposition

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Algorithm Development

Background:

  • Dynamic Programming (DP) offers exact solutions for bioinformatics but requires specialized, complex implementations.
  • Existing DP methods are often difficult to adapt to variations in problem settings.

Purpose of the Study:

  • Introduce the Infrared framework to generalize and simplify the application of DP in bioinformatics.
  • Enable efficient development of exact optimization and controlled sampling tools for diverse bioinformatics tasks.

Main Methods:

  • Infrared models problems as sparse feature networks, a generalization of constraint networks.
  • Utilizes generic cluster tree elimination algorithms on tree decompositions of feature networks.
  • Achieves complexities linear in the number of variables and exponential in treewidth.

Main Results:

  • The Infrared software facilitates rapid development of bioinformatics applications in Python.
  • Problems are declaratively modeled, and solved automatically by generic DP algorithms.
  • Demonstrated applications include RNA design, sequence-structure alignment, phylogenetic inference, and coding sequence design.

Conclusions:

  • Infrared provides a powerful and flexible framework for solving a wide range of bioinformatics problems.
  • Offers practical empirical efficiency comparable to specialized algorithms.
  • Lowers the barrier to entry for developing advanced computational biology tools.