StrucBreak: A Computational Framework for Structural Break Detection in DNA Sequences.

Kamal, MS; Nimmy, SF

StrucBreak: A Computational Framework for Structural Break Detection in DNA Sequences.

Kamal, MS Nimmy, SF

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Publisher:: Springer
Publication Type:: Journal Article
Citation:: Interdisciplinary Sciences: Computational Life Sciences, 2017, 9, (4), pp. 512-527
Issue Date:: 2017-12

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Field	Value	Language
dc.contributor.author	Kamal, MS
dc.contributor.author	Nimmy, SF
dc.date.accessioned	2023-01-03T02:11:52Z
dc.date.available	2016-03-01
dc.date.available	2023-01-03T02:11:52Z
dc.date.issued	2017-12
dc.identifier.citation	Interdisciplinary Sciences: Computational Life Sciences, 2017, 9, (4), pp. 512-527
dc.identifier.issn	1867-1462
dc.identifier.issn	1867-1462
dc.identifier.uri	http://hdl.handle.net/10453/164635
dc.description.abstract	Damages or breaks in DNA may change the characteristics of genomes and causes various diseases. In this work we construct a system that incorporates the maximum likelihood-based probabilistic formula to assess the number of damages that have occurred in any DNA sequence. This approach has been progressively benchmarked by implementing simulated data sets so that the outcomes can be compared with a ground truth or reference value. At first the sequence data set order is checked through the statistical cumulative sum (STACUMSUM). The verified sequences are then estimated by prior and posterior probability to count the percentages of breaks and mutations. Maximum-likelihood estimation then finds out the exact numbers and positions of breaks and detections. In database manipulation, one factor that decides the orientation and order of the sequence is geometric distance between consecutive sequences. The geometric distance is measured for smooth representation of the genome or DNA sequences. Finally, we compared the performance of our system with DAMBE5: (A Comprehensive Software Package for Data Analysis in Molecular Biology and Evaluation), and in response to time and space complexity, StrucBreak is much faster and consumes much less space due to our algorithmic approaches.
dc.format	Print-Electronic
dc.language	eng
dc.publisher	Springer
dc.relation.ispartof	Interdisciplinary Sciences: Computational Life Sciences
dc.relation.isbasedon	10.1007/s12539-016-0158-7
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject.mesh	Algorithms
dc.subject.mesh	DNA
dc.subject.mesh	DNA Damage
dc.subject.mesh	Likelihood Functions
dc.subject.mesh	Algorithms
dc.subject.mesh	DNA
dc.subject.mesh	DNA Damage
dc.subject.mesh	Likelihood Functions
dc.subject.mesh	DNA Damage
dc.subject.mesh	DNA
dc.subject.mesh	Likelihood Functions
dc.subject.mesh	Algorithms
dc.title	StrucBreak: A Computational Framework for Structural Break Detection in DNA Sequences.
dc.type	Journal Article
utslib.citation.volume	9
utslib.location.activity	Germany
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2023-01-03T02:11:51Z
pubs.issue	4
pubs.publication-status	Published
pubs.volume	9
utslib.citation.issue	4

Abstract:

Damages or breaks in DNA may change the characteristics of genomes and causes various diseases. In this work we construct a system that incorporates the maximum likelihood-based probabilistic formula to assess the number of damages that have occurred in any DNA sequence. This approach has been progressively benchmarked by implementing simulated data sets so that the outcomes can be compared with a ground truth or reference value. At first the sequence data set order is checked through the statistical cumulative sum (STACUMSUM). The verified sequences are then estimated by prior and posterior probability to count the percentages of breaks and mutations. Maximum-likelihood estimation then finds out the exact numbers and positions of breaks and detections. In database manipulation, one factor that decides the orientation and order of the sequence is geometric distance between consecutive sequences. The geometric distance is measured for smooth representation of the genome or DNA sequences. Finally, we compared the performance of our system with DAMBE5: (A Comprehensive Software Package for Data Analysis in Molecular Biology and Evaluation), and in response to time and space complexity, StrucBreak is much faster and consumes much less space due to our algorithmic approaches.

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http://hdl.handle.net/10453/164635