Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.12188/24350
Title: TMO: time and memory optimized algorithm applicable for more accurate alignment of trinucleotide repeat disorders associated genes
Authors: Stojanov, Done
Madevska Bogdanova, Ana
Marcin Orzechowski, Tomasz
Keywords: Huntington’s disease; genes; computational; improved; algorithm
Issue Date: 3-Mar-2016
Publisher: Taylor & Francis
Journal: Biotechnology & Biotechnological Equipment
Abstract: In this study, time and memory optimized (TMO) algorithm is presented. Compared with Smith Waterman’s algorithm, TMO is applicable for a more accurate detection of continuous insertion/deletions (indels) in genes’ fragments, associated with disorders caused by overrepetition of a certain codon. The improvement comes from the tendency to pinpoint indels in the least preserved nucleotide pairs. All nucleotide pairs that occur less frequently are classified as less preserved and they are considered as mutated codons whose mid-nucleotides were deleted. Other benefit of the proposed algorithm is its general tendency to maximize the number of matching nucleotides included per alignment, regardless of any specific alignment metrics. Since the structure of the solution, when applying Smith Waterman, depends on the adjustment of the alignment parameters and, therefore, an incomplete (shortened) solution may be derived, our algorithm does not reject any of the consistent matching nucleotides that can be included in the final solution. In terms of computational aspects, our algorithm runs faster than Smith Waterman for very similar DNA and requires less memory than the most memory efficient dynamic programming algorithms. The speed up comes from the reduced number of nucleotide comparisons that have to be performed, without having to imperil the completeness of the solution. Due to the fact that four integers (16 Bytes) are required for tracking matching fragment, regardless its length, our algorithm requires less memory than Huang’s algorithm.
URI: http://hdl.handle.net/20.500.12188/24350
Appears in Collections:Faculty of Computer Science and Engineering: Journal Articles

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