Paper
15 October 2012 Heuristics for haplotype frequency estimation with a large number of analyzed loci
Michał Nowotka, Robert Nowak
Author Affiliations +
Proceedings Volume 8454, Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2012; 84541S (2012) https://doi.org/10.1117/12.2000202
Event: Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2012, 2012, Wilga, Poland
Abstract
Determining haplotypes with laboratory methods is an expensive and time-consuming activity therefore unsuit- able for the analysis of genetic data coming from a large number of tested individuals. Many existing algorithms for phasing genotypes operate on very impractical runtime and take into account only certain types of polymor- phisms, often without providing graphical user interface. The heuristic algorithm for estimating haplotype frequency developed in this work was examined in terms of time complexity, the speed of execution and the accuracy of results. Consequently, a Rich Internet Application that implements described algorithm has been created and its performance and accuracy to a known set of test data is analyzed. Eventually, a discussion on the architecture and the applications usability in bioinformatics applications is presented. Proposed algorithm can be used to improve the complexity of any algorithm that solves the problem of genotype phasing, which has a worse time complexity and is convergent. The algorithm is easy to scale and can achieve the desired ratio of calculations accuracy to execution time. Implemented application meets all requirements for the programs to solve problems in biology i.e. high performance, accessibility, scalability and usability.
© (2012) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Michał Nowotka and Robert Nowak "Heuristics for haplotype frequency estimation with a large number of analyzed loci", Proc. SPIE 8454, Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2012, 84541S (15 October 2012); https://doi.org/10.1117/12.2000202
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KEYWORDS
Expectation maximization algorithms

Organisms

Algorithm development

Bioinformatics

Genetics

Human-machine interfaces

Biology

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