Haplotype Inference with Boolean Constraint Solving: An Overview
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- TL;DR
- This paper provides an overview of how Boolean satisfiability (SAT) solvers can be applied to solve computational problems in bioinformatics, specifically haplotype inference.
- Problem
- While encoding combinatorial problems into Boolean logic is not always intuitive, the efficiency of modern SAT solvers makes it a powerful approach to consider.
- Method
- The paper reviews SAT-based approaches for solving the Haplotype Inference by Pure Parsimony (HIPP) problem, which aims to find the smallest set of haplotypes explaining a given set of genotypes, moving beyond earlier Integer Linear Programming formulations.
- Results
- Not specified in the abstract.
- Contributions
- Not specified in the abstract.
- Limitations
- Not specified in the abstract.
- Takeaways
- The paper outlines existing SAT-based methods for the HIPP problem and highlights current research directions in the field.
- Applications
- Haplotype inference in bioinformatics.
- Topics
- Haplotype Inference; Boolean Constraint Solving; Satisfiability (SAT); Bioinformatics
- For industry
- Not specified in the abstract.
- Why it matters
- Not specified in the abstract.
Abstract
Boolean satisfiability (SAT) finds a wide range of practical applications, including Artificial Intelligence and, more recently, Bioinformatics. Although encoding some combinatorial problems using Boolean logic may not be the most intuitive solution, the efficiency of state-of-the-art SAT solvers often makes it worthwhile to consider encoding a problem to SAT. One representative application of SAT in Bioinformatics is haplotype inference. The problem of haplotype inference under the assumption of pure parsimony consists in finding the smallest number of haplotypes that explains a given set of genotypes. The original formulations for solving the problem of Haplotype Inference by Pure Parsimony (HIPP) were based on Integer Linear Programming. More recently, solutions based on SAT have been shown to be remarkably more efficient. This paper provides an overview of SAT-based approaches for solving the HIPP problem and identifies current research directions.