Dragon polya spotter: Predictor of poly(A) motifs within human genomic DNA sequences

Handle URI:
http://hdl.handle.net/10754/325432
Title:
Dragon polya spotter: Predictor of poly(A) motifs within human genomic DNA sequences
Authors:
Kalkatawi, Manal M. ( 0000-0002-9820-4129 ) ; Rangkuti, Farania; Schramm, Michael C.; Jankovic, Boris R.; Kamau, Allan; Chowdhary, Rajesh; Archer, John A.C. ( 0000-0002-3302-3933 ) ; Bajic, Vladimir B. ( 0000-0001-5435-4750 )
Abstract:
Motivation: Recognition of poly(A) signals in mRNA is relatively straightforward due to the presence of easily recognizable polyadenylic acid tail. However, the task of identifying poly(A) motifs in the primary genomic DNA sequence that correspond to poly(A) signals in mRNA is a far more challenging problem. Recognition of poly(A) signals is important for better gene annotation and understanding of the gene regulation mechanisms. In this work, we present one such poly(A) motif prediction method based on properties of human genomic DNA sequence surrounding a poly(A) motif. These properties include thermodynamic, physico-chemical and statistical characteristics. For predictions, we developed Artificial Neural Network and Random Forest models. These models are trained to recognize 12 most common poly(A) motifs in human DNA. Our predictors are available as a free web-based tool accessible at http://cbrc.kaust.edu.sa/dps. Compared with other reported predictors, our models achieve higher sensitivity and specificity and furthermore provide a consistent level of accuracy for 12 poly(A) motif variants. The Author(s) 2011. Published by Oxford University Press. All rights reserved.
KAUST Department:
Computational Bioscience Research Center (CBRC)
Citation:
Kalkatawi M, Rangkuti F, Schramm M, Jankovic BR, Kamau A, et al. (2011) Dragon PolyA Spotter: predictor of poly(A) motifs within human genomic DNA sequences. Bioinformatics 28: 127-129. doi:10.1093/bioinformatics/btr602.
Publisher:
Oxford University Press (OUP)
Journal:
Bioinformatics
Issue Date:
15-Nov-2011
DOI:
10.1093/bioinformatics/btr602
PubMed ID:
22088842
PubMed Central ID:
PMC3244764
Type:
Article
ISSN:
13674803
Appears in Collections:
Articles; Computational Bioscience Research Center (CBRC)

Full metadata record

DC FieldValue Language
dc.contributor.authorKalkatawi, Manal M.en
dc.contributor.authorRangkuti, Faraniaen
dc.contributor.authorSchramm, Michael C.en
dc.contributor.authorJankovic, Boris R.en
dc.contributor.authorKamau, Allanen
dc.contributor.authorChowdhary, Rajeshen
dc.contributor.authorArcher, John A.C.en
dc.contributor.authorBajic, Vladimir B.en
dc.date.accessioned2014-08-27T09:51:06Z-
dc.date.available2014-08-27T09:51:06Z-
dc.date.issued2011-11-15en
dc.identifier.citationKalkatawi M, Rangkuti F, Schramm M, Jankovic BR, Kamau A, et al. (2011) Dragon PolyA Spotter: predictor of poly(A) motifs within human genomic DNA sequences. Bioinformatics 28: 127-129. doi:10.1093/bioinformatics/btr602.en
dc.identifier.issn13674803en
dc.identifier.pmid22088842en
dc.identifier.doi10.1093/bioinformatics/btr602en
dc.identifier.urihttp://hdl.handle.net/10754/325432en
dc.description.abstractMotivation: Recognition of poly(A) signals in mRNA is relatively straightforward due to the presence of easily recognizable polyadenylic acid tail. However, the task of identifying poly(A) motifs in the primary genomic DNA sequence that correspond to poly(A) signals in mRNA is a far more challenging problem. Recognition of poly(A) signals is important for better gene annotation and understanding of the gene regulation mechanisms. In this work, we present one such poly(A) motif prediction method based on properties of human genomic DNA sequence surrounding a poly(A) motif. These properties include thermodynamic, physico-chemical and statistical characteristics. For predictions, we developed Artificial Neural Network and Random Forest models. These models are trained to recognize 12 most common poly(A) motifs in human DNA. Our predictors are available as a free web-based tool accessible at http://cbrc.kaust.edu.sa/dps. Compared with other reported predictors, our models achieve higher sensitivity and specificity and furthermore provide a consistent level of accuracy for 12 poly(A) motif variants. The Author(s) 2011. Published by Oxford University Press. All rights reserved.en
dc.language.isoenen
dc.publisherOxford University Press (OUP)en
dc.rightsThis is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.en
dc.rights.urihttp://creativecommons.org/licenses/by-nc/3.0en
dc.subjectpolyadenylic aciden
dc.subjectalgorithmen
dc.subjectartificial neural networken
dc.subjectcomputer programen
dc.subjectgeneticsen
dc.subjecthuman genomeen
dc.subjectInterneten
dc.subjectsensitivity and specificityen
dc.subjectAlgorithmsen
dc.subjectGenome, Humanen
dc.subjectInterneten
dc.subjectNeural Networks (Computer)en
dc.subjectPoly Aen
dc.subjectSensitivity and Specificityen
dc.subjectSoftwareen
dc.titleDragon polya spotter: Predictor of poly(A) motifs within human genomic DNA sequencesen
dc.typeArticleen
dc.contributor.departmentComputational Bioscience Research Center (CBRC)en
dc.identifier.journalBioinformaticsen
dc.identifier.pmcidPMC3244764en
dc.eprint.versionPublisher's Version/PDFen
dc.contributor.institutionBiomedical Informatics Research Center, MCRF, Marshfield Clinic, 1000 North Oak Avenue, Marshfield, WI 54449, United Statesen
dc.contributor.affiliationKing Abdullah University of Science and Technology (KAUST)en
kaust.authorKalkatawi, Manal M.en
kaust.authorRangkuti, Faraniaen
kaust.authorArcher, John A.C.en
kaust.authorBajic, Vladimir B.en
kaust.authorSchramm, Michael C.en
kaust.authorJankovic, Boris R.en
kaust.authorKamau, Allanen

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