Title

RetroPred: A tool for prediction, classification and extraction of non-LTR retrotransposons (LINEs & SINEs) from the genome by integrating PALS, PILER, MEME and ANN

Authors

Pradeep Kumar Naik^{1, *}, Vinay Kumar Mittal¹ and Sumit Gupta¹

Affiliation

¹Department of Biotechnology and Bioinformatics, Jaypee University of Information Technology, Waknaghat, Distt.-Solan, 173 215, Himachal Pradesh, India

Email

pknaik73@rediffmail.com; * Corresponding author

Article Type

Prediction Model

Date

received January 04, 2008; revised January 14, 2008; accepted January 19, 2008; published January 27, 2008

Abstract

The problem of predicting non-long terminal repeats (LTR) like long interspersed nuclear elements (LINEs) and short interspersed nuclear elements (SINEs) from the DNA sequence is still an open problem in bioinformatics. To elevate the quality of annotations of LINES and SINEs an automated tool “RetroPred” was developed.  The pipeline allowed rapid and thorough annotation of non-LTR retrotransposons. The non-LTR retrotransposable elements were initially predicted by Pairwise Aligner for Long Sequences (PALS) and Parsimonious Inference of a Library of Elementary Repeats (PILER). Predicted non-LTR elements were automatically classified into LINEs and SINEs using ANN based on the position specific probability matrix (PSPM) generated by Multiple EM for Motif Elicitation (MEME). The ANN model revealed a superior model (accuracy = 78.79 ± 6.86 %, Q_pred = 74.734 ± 17.08 %, sensitivity = 84.48 ± 6.73 %, specificity = 77.13 ± 13.39 %) using four-fold cross validation. As proof of principle, we have thoroughly annotated the location of LINEs and SINEs in rice and Arabidopsis genome using the tool and is proved to be very useful with good accuracy. Our tool is accessible at http://www.juit.ac.in/RepeatPred/home.html.

Keywords

prediction; non-LTR retrotransposons; classification; LINEs; SINEs; artificial neural network

Citation

 Naik, et al., Bioinformation 2(6): 263-270 (2008)

Edited by

P. Kangueane

ISSN

0973-2063

Publisher

Biomedical Informatics

License

This is an Open Access article which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. This is distributed under the terms of the Creative Commons Attribution License.