AROHap: An effective algorithm for single individual haplotype reconstruction based on asexual reproduction optimization. (February 2018)

• Record Type: Journal Article
• Authors: Olyaee, Mohammad-H; Khanteymoori, Alireza
• Is Part Of: Computational biology and chemistry. Volume 72(2018)
• Page Start: 1
• Page End: 10
• Publication Date: 2018-02
• Subjects: Bioinformatics -- Haplotype reconstruction -- Minimum error correction -- Asexual reproduction optimization; Chemistry -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; Biochemistry -- Data processing; Biology -- Data processing; Molecular biology -- Data processing; Periodicals; Electronic journals; 542.85
• DOI: 10.1016/j.compbiolchem.2017.12.005
• Languages: English
• ISSNs: 1476-9271

Abstract

Highlights: An efficient two-step method is proposed for haplotype reconstruction. A modified metric is used for initial clustering of input fragments and building a weighted graph. Asexual Reproduction Optimization(ARO) as a high speed and bio-inspired algorithm is used to improve bi-partitioning of the input fragments. The convergence speed of ARO is increased based on the similarities and dissimilarities of the input fragments. Abstract: In this paper, a method for single individual haplotype (SIH) reconstruction using Asexual reproduction optimization (ARO) is proposed. Haplotypes, as a set of genetic variations in each chromosome, contain vital information such as the relationship between human genome and diseases. Finding haplotypes in diploid organisms is a challenging task. Experimental methods are expensive and require special equipment. In SIH problem, we encounter with several fragments and each fragment covers some parts of desired haplotype. The main goal is bi-partitioning of the fragments with minimum error correction (MEC). This problem is addressed as NP-hard and several attempts have been made in order to solve it using heuristic methods. The current method, AROHap, has two main phases. In the first phase, most of the fragments are clustered based on a practical metric distance. In the second phase, ARO algorithm as a fast convergence bio-inspired method is used to improve the initial bi-partitioning of the fragments in the previous step. AROHap is implemented with several benchmark datasets. The experimental results demonstrate that satisfactory results were obtained, proving that AROHap can be used for SIH reconstruction problem.