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

Manal M. Kalkatawi, Farania Rangkuti, Michael C. Schramm, Boris R. Jankovic, Allan Kamau, Rajesh Chowdhary, John A.C. Archer, Vladimir B. Bajic

Research output: Contribution to journalArticlepeer-review

33 Scopus citations

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.
Original languageEnglish (US)
Pages (from-to)127-129
Number of pages3
JournalBioinformatics
Volume28
Issue number1
DOIs
StatePublished - Nov 15 2011

ASJC Scopus subject areas

  • Biochemistry
  • Computational Theory and Mathematics
  • Computational Mathematics
  • Molecular Biology
  • Statistics and Probability
  • Computer Science Applications

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