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VIRsiRNApred: a web server for predicting inhibition efficacy of siRNAs targeting human viruses
BACKGROUND: Selection of effective viral siRNA is an indispensable step in the development of siRNA based antiviral therapeutics. Despite immense potential, a viral siRNA efficacy prediction algorithm is still not available. Moreover, performances of the existing general mammalian siRNA efficacy pre...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2013
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3878835/ https://www.ncbi.nlm.nih.gov/pubmed/24330765 http://dx.doi.org/10.1186/1479-5876-11-305 |
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author | Qureshi, Abid Thakur, Nishant Kumar, Manoj |
author_facet | Qureshi, Abid Thakur, Nishant Kumar, Manoj |
author_sort | Qureshi, Abid |
collection | PubMed |
description | BACKGROUND: Selection of effective viral siRNA is an indispensable step in the development of siRNA based antiviral therapeutics. Despite immense potential, a viral siRNA efficacy prediction algorithm is still not available. Moreover, performances of the existing general mammalian siRNA efficacy predictors are not satisfactory for viral siRNAs. Therefore, we have developed “VIRsiRNApred” a support vector machine (SVM) based method for predicting the efficacy of viral siRNA. METHODS: In the present study, we have employed a new dataset of 1725 viral siRNAs with experimentally verified quantitative efficacies tested under heterogeneous experimental conditions and targeting as many as 37 important human viruses including HIV, Influenza, HCV, HBV, SARS etc. These siRNAs were divided into training (T(1380)) and validation (V(345)) datasets. Important siRNA sequence features including mono to penta nucleotide frequencies, binary pattern, thermodynamic properties and secondary structure were employed for model development. RESULTS: During 10-fold cross validation on T(1380) using hybrid approach, we achieved a maximum Pearson Correlation Coefficient (PCC) of 0.55 between predicted and actual efficacy of viral siRNAs. On V(345) independent dataset, our best model achieved a maximum correlation of 0.50 while existing general siRNA prediction methods showed PCC from 0.05 to 0.18. However, using leave one out cross validation PCC was improved to 0.58 and 0.55 on training and validation datasets respectively. SVM performed better than other machine learning techniques used like ANN, KNN and REP Tree. CONCLUSION: VIRsiRNApred is the first algorithm for predicting inhibition efficacy of viral siRNAs which is developed using experimentally verified viral siRNAs. We hope this algorithm would be useful in predicting highly potent viral siRNA to aid siRNA based antiviral therapeutics development. The web server is freely available at http://crdd.osdd.net/servers/virsirnapred/. |
format | Online Article Text |
id | pubmed-3878835 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2013 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-38788352014-01-07 VIRsiRNApred: a web server for predicting inhibition efficacy of siRNAs targeting human viruses Qureshi, Abid Thakur, Nishant Kumar, Manoj J Transl Med Research BACKGROUND: Selection of effective viral siRNA is an indispensable step in the development of siRNA based antiviral therapeutics. Despite immense potential, a viral siRNA efficacy prediction algorithm is still not available. Moreover, performances of the existing general mammalian siRNA efficacy predictors are not satisfactory for viral siRNAs. Therefore, we have developed “VIRsiRNApred” a support vector machine (SVM) based method for predicting the efficacy of viral siRNA. METHODS: In the present study, we have employed a new dataset of 1725 viral siRNAs with experimentally verified quantitative efficacies tested under heterogeneous experimental conditions and targeting as many as 37 important human viruses including HIV, Influenza, HCV, HBV, SARS etc. These siRNAs were divided into training (T(1380)) and validation (V(345)) datasets. Important siRNA sequence features including mono to penta nucleotide frequencies, binary pattern, thermodynamic properties and secondary structure were employed for model development. RESULTS: During 10-fold cross validation on T(1380) using hybrid approach, we achieved a maximum Pearson Correlation Coefficient (PCC) of 0.55 between predicted and actual efficacy of viral siRNAs. On V(345) independent dataset, our best model achieved a maximum correlation of 0.50 while existing general siRNA prediction methods showed PCC from 0.05 to 0.18. However, using leave one out cross validation PCC was improved to 0.58 and 0.55 on training and validation datasets respectively. SVM performed better than other machine learning techniques used like ANN, KNN and REP Tree. CONCLUSION: VIRsiRNApred is the first algorithm for predicting inhibition efficacy of viral siRNAs which is developed using experimentally verified viral siRNAs. We hope this algorithm would be useful in predicting highly potent viral siRNA to aid siRNA based antiviral therapeutics development. The web server is freely available at http://crdd.osdd.net/servers/virsirnapred/. BioMed Central 2013-12-11 /pmc/articles/PMC3878835/ /pubmed/24330765 http://dx.doi.org/10.1186/1479-5876-11-305 Text en Copyright © 2013 Qureshi et al.; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Qureshi, Abid Thakur, Nishant Kumar, Manoj VIRsiRNApred: a web server for predicting inhibition efficacy of siRNAs targeting human viruses |
title | VIRsiRNApred: a web server for predicting inhibition efficacy of siRNAs targeting human viruses |
title_full | VIRsiRNApred: a web server for predicting inhibition efficacy of siRNAs targeting human viruses |
title_fullStr | VIRsiRNApred: a web server for predicting inhibition efficacy of siRNAs targeting human viruses |
title_full_unstemmed | VIRsiRNApred: a web server for predicting inhibition efficacy of siRNAs targeting human viruses |
title_short | VIRsiRNApred: a web server for predicting inhibition efficacy of siRNAs targeting human viruses |
title_sort | virsirnapred: a web server for predicting inhibition efficacy of sirnas targeting human viruses |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3878835/ https://www.ncbi.nlm.nih.gov/pubmed/24330765 http://dx.doi.org/10.1186/1479-5876-11-305 |
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