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Beyond Bioinformatics | |
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Title |
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RetroPred: A tool for prediction, classification and extraction of non-LTR retrotransposons (LINEs & SINEs) from the genome by integrating PALS, PILER, MEME and ANN
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Authors |
Pradeep Kumar Naik1, *, Vinay Kumar Mittal1 and Sumit Gupta1
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Affiliation |
1Department of Biotechnology and Bioinformatics, Jaypee University of Information Technology, Waknaghat, Distt.-Solan, 173 215, Himachal Pradesh, India
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pknaik73@rediffmail.com; * Corresponding author
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Article Type |
Prediction Model
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Date |
received January 04, 2008; revised January 14, 2008; accepted January 19, 2008; published January 27, 2008
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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 %, Qpred = 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.
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Keywords |
prediction; non-LTR retrotransposons; classification; LINEs; SINEs; artificial neural network
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Citation |
Naik, et al., Bioinformation 2(6): 263-270 (2008)
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Edited by |
P. Kangueane
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ISSN |
0973-2063
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Publisher |
Biomedical Informatics Publishing Group
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Copyright |
Publisher
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Copyright Transfer Agreement |
The authors of published articles in Bioinformation automatically transfer the copyright to the publisher upon formal acceptance. However, the authors reserve right to use the information contained in the article for non commercial purposes.
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License |
This is an open-access article, which permits unrestricted use, distribution, and reproduction in any medium, for non-commercial purposes, provided the original author and source are credited.
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