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Updated: Jan 26, 2026
05:39
Determination of the Mating Efficiency of Haploids in Saccharomyces cerevisiae
Published on: December 2, 2022
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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
Summary
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.
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.
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