MsDetector: toward a standard computational tool for DNA microsatellites detection

MsDetector: toward a standard computational tool for DNA microsatellites detection

Microsatellites (MSs) are DNA regions consisting of repeated short motif(s). MSs are linked to several diseases and have important biomedical applications. Thus, researchers have developed several computational tools to detect MSs. However, the currently available tools require adjusting many parame...

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Main Authors: Girgis, Hani Z., Sheetlin, Sergey L.
Format: Online
Language:English
Published: Oxford University Press 2013
Online Access:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3592430/
id pubmed-3592430
recordtype oai_dc
spelling pubmed-35924302013-03-08 MsDetector: toward a standard computational tool for DNA microsatellites detection Girgis, Hani Z. Sheetlin, Sergey L. Methods Online Microsatellites (MSs) are DNA regions consisting of repeated short motif(s). MSs are linked to several diseases and have important biomedical applications. Thus, researchers have developed several computational tools to detect MSs. However, the currently available tools require adjusting many parameters, or depend on a list of motifs or on a library of known MSs. Therefore, two laboratories analyzing the same sequence with the same computational tool may obtain different results due to the user-adjustable parameters. Recent studies have indicated the need for a standard computational tool for detecting MSs. To this end, we applied machine-learning algorithms to develop a tool called MsDetector. The system is based on a hidden Markov model and a general linear model. The user is not obligated to optimize the parameters of MsDetector. Neither a list of motifs nor a library of known MSs is required. MsDetector is memory- and time-efficient. We applied MsDetector to several species. MsDetector located the majority of MSs found by other widely used tools. In addition, MsDetector identified novel MSs. Furthermore, the system has a very low false-positive rate resulting in a precision of up to 99%. MsDetector is expected to produce consistent results across studies analyzing the same sequence. Oxford University Press 2013-01 2012-10-02 /pmc/articles/PMC3592430/ /pubmed/23034809 http://dx.doi.org/10.1093/nar/gks881 Text en Published by Oxford University Press 2012. http://creativecommons.org/licenses/by/3.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/), which permits unrestricted, distribution, and reproduction in any medium, provided the original work is properly cited.
repository_type Open Access Journal
institution_category Foreign Institution
institution US National Center for Biotechnology Information
building NCBI PubMed
collection Online Access
language English
format Online
author Girgis, Hani Z.
Sheetlin, Sergey L.
spellingShingle Girgis, Hani Z.
Sheetlin, Sergey L.
MsDetector: toward a standard computational tool for DNA microsatellites detection
author_facet Girgis, Hani Z.
Sheetlin, Sergey L.
author_sort Girgis, Hani Z.
title MsDetector: toward a standard computational tool for DNA microsatellites detection
title_short MsDetector: toward a standard computational tool for DNA microsatellites detection
title_full MsDetector: toward a standard computational tool for DNA microsatellites detection
title_fullStr MsDetector: toward a standard computational tool for DNA microsatellites detection
title_full_unstemmed MsDetector: toward a standard computational tool for DNA microsatellites detection
title_sort msdetector: toward a standard computational tool for dna microsatellites detection
description Microsatellites (MSs) are DNA regions consisting of repeated short motif(s). MSs are linked to several diseases and have important biomedical applications. Thus, researchers have developed several computational tools to detect MSs. However, the currently available tools require adjusting many parameters, or depend on a list of motifs or on a library of known MSs. Therefore, two laboratories analyzing the same sequence with the same computational tool may obtain different results due to the user-adjustable parameters. Recent studies have indicated the need for a standard computational tool for detecting MSs. To this end, we applied machine-learning algorithms to develop a tool called MsDetector. The system is based on a hidden Markov model and a general linear model. The user is not obligated to optimize the parameters of MsDetector. Neither a list of motifs nor a library of known MSs is required. MsDetector is memory- and time-efficient. We applied MsDetector to several species. MsDetector located the majority of MSs found by other widely used tools. In addition, MsDetector identified novel MSs. Furthermore, the system has a very low false-positive rate resulting in a precision of up to 99%. MsDetector is expected to produce consistent results across studies analyzing the same sequence.
publisher Oxford University Press
publishDate 2013
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3592430/
_version_ 1611960838525026304

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