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Maximising Overlap Score in DNA Sequence Assembly Problem by Stochastic Diffusion Search

Majid al-Rifaie, Fatimah and Majid al-Rifaie, Mohammad. 2016. Maximising Overlap Score in DNA Sequence Assembly Problem by Stochastic Diffusion Search. In: al-Rifaie Fatimah Majid and Mohammad Majid al-Rifaie, eds. Intelligent Systems and Applications: Extended and Selected Results from the SAI Intelligent Systems Conference (IntelliSys) 2015. Cham: Springer International Publishing, pp. 301-321. ISBN 978-3-319-33386-1 [Book Section]

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Abstract or Description

This paper introduces a novel study on the performance of Stochastic Dif- fusion Search (SDS) – a swarm intelligence algorithm – to address DNA sequence assembly problem. This is an NP-hard problem and one of the primary problems in computational molecular biology that requires optimisation methodologies to recon- struct the original DNA sequence. In this work, SDS algorithm is adapted for this purpose and several experiments are run in order to evaluate the performance of the presented technique over several frequently used benchmarks. Given the promising results of the newly proposed algorithm and its success in assembling the input frag- ments, its behaviour is further analysed, thus shedding light on the process through which the algorithm conducts the task. Additionally, the algorithm is applied to over- lap score matrices which are generated from the raw input fragments; the algorithm optimises the overlap score matrices to find better results. In these experiments real- world data are used and the performance of SDS is compared with several other algorithms which are used by other researchers in the field, thus demonstrating its weaknesses and strengths in the experiments presented in the paper.

Item Type:

Book Section

Identification Number (DOI):

https://doi.org/10.1007/978-3-319-33386-1_15

Departments, Centres and Research Units:

Computing

Dates:

DateEvent
1 July 2016Published

Item ID:

18863

Date Deposited:

05 Sep 2016 09:53

Last Modified:

01 Aug 2018 04:32

URI:

http://research.gold.ac.uk/id/eprint/18863

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