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Related Experiment Video

Updated: Jan 26, 2026

Determination of the Mating Efficiency of Haploids in Saccharomyces cerevisiae
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An average-case sublinear forward algorithm for the haploid Li and Stephens model.

Yohei M Rosen1,2, Benedict J Paten2

  • 11UCSC Genomics Institute, 1156 High St, Santa Cruz, CA 95064 USA.

Algorithms for Molecular Biology : AMB
|April 17, 2019
PubMed
Summary
This summary is machine-generated.

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We developed a faster, exact forward algorithm for haplotype inheritance models like Li and Stephens. This computational advance handles large genetic datasets efficiently, improving sequence analysis scalability.

Area of Science:

  • Genetics
  • Computational Biology
  • Bioinformatics

Background:

  • Hidden Markov models (HMMs) like the Li and Stephens model are essential for calculating haplotype inheritance probabilities.
  • Current HMM implementations face computational limitations with large reference panels, hindering analysis of massive sequencing datasets.

Purpose of the Study:

  • To develop a computationally tractable forward algorithm for the haploid Li and Stephens model applicable to large-scale genetic datasets.
  • To overcome the linear runtime complexity of existing models with respect to reference panel size.

Main Methods:

  • Developed a numerically exact forward algorithm.
  • Utilized sparse dynamic programming matrices.
  • Implemented lazy evaluation techniques.
Keywords:
ComplexityForward algorithmHaplotypeSublinear algorithms

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Related Experiment Videos

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Main Results:

  • Achieved sublinear average-case runtime with respect to reference panel size (k).
  • Demonstrated computational tractability for large datasets, validated on the 1000 Genomes dataset.
  • Eliminated the tradeoff between runtime and model complexity.

Conclusions:

  • The new forward algorithm enhances the scalability of HMMs for haplotype inheritance analysis.
  • The employed strategies (sparse matrices, lazy evaluation) offer potential for optimizing other sequence analysis algorithms.
  • Enables efficient analysis of population-scale genomic data.