Dragon polya spotter: Predictor of poly(A) motifs within human genomic DNA sequences
AuthorsKalkatawi, Manal M.
Schramm, Michael C.
Jankovic, Boris R.
Archer, John A.C.
Bajic, Vladimir B.
KAUST DepartmentComputational Bioscience Research Center (CBRC)
Computer Science Program
Applied Mathematics and Computational Science Program
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Online Publication Date2011-11-15
Print Publication Date2012-01-01
Permanent link to this recordhttp://hdl.handle.net/10754/325432
MetadataShow full item record
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.
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.
PublisherOxford University Press (OUP)
PubMed Central IDPMC3244764
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Except where otherwise noted, this item's license is described as This 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.
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