Prediction of miRNA in HIV-1 genome and its targets through artificial neural network: a bioinformatics approach

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• 作者：Manish Kumar Gupta (1)
  Kavita Agarwal (2)
  Nutan Prakash (3)
  Dev Bukhsh Singh (4)
  Krishna Misra (5)
  
• 关键词：MicroRNA ; Pre ; miRNA ; ANN ; HIV ; RNAFold ; MiRBASE ; Non ; coding RNA ; Gene target
• 刊名：Network Modeling Analysis in Health Informatics and Bioinformatics
• 出版年：2012
• 出版时间：December 2012
• 年：2012
• 卷：1
• 期：4
• 页码：141-151
• 全文大小：2254KB
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• 作者单位：Manish Kumar Gupta (1)
  Kavita Agarwal (2)
  Nutan Prakash (3)
  Dev Bukhsh Singh (4)
  Krishna Misra (5)
  
  1. Department of Bioinformatics, University Institute of Engineering and Technology, Chhatrapati Shahu Ji Maharaj University, Kanpur, UP, 208024, India
  2. National Institute of Immunology, New Delhi, 110067, India
  3. Department of Biotechnology, Shree M. & N. Virani Science College, Rajkot, Gujarat, 360005, India
  4. Department of Biotechnology, Institute of Biosciences and Biotechnology, Chhatrapati Shahu Ji Maharaj University, Kanpur, UP, 208024, India
  5. Centre of Biomedical Magnetic Resonance, Sanjay Gandhi Post-Graduate Institute of Medical Sciences, Campus Raebareli Road, Lucknow, UP, 226014, India
  

文摘

MicroRNAs (miRNA) are a class of non-coding RNA which inhibits the expression of a particular gene by the process of nucleotide-sequence-specific post-transcriptional gene silencing method. miRNAs are ~21?nt long non-coding RNAs that are derived from larger hairpin RNA precursors. The short length of the miRNA sequences and relatively low conservation of pre-miRNA sequences restrict the conventional sequence-alignment-based methods of finding only relatively close homologs. On the other hand, it has been reported that miRNA genes are more conserved in the secondary structure of their precursor rather than in primary sequences. Therefore, secondary structural features should be fully exploited in the homologue search for new miRNA genes. In this study, an approach for identification and prediction of miRNA in viruses through artificial neural networks (ANN) has been proposed. This idea uses both sequential and structural features of pre-miRNA to train the ANN for identification of miRNA in new viral genomes. The designed ANN was found with an accuracy of 93.68?% for the training dataset and 55.55?% for the validation dataset. In case of HIV, this trained ANN identifies pre-miRNA which does not show sufficient homology to known pre-miRNA sequences, but are highly conserved in their structure. Finally, single miRNA of length 19 mer has been predicted targeting four genes namely NDUFS7, WNT3A, SUFU, and FOXK1 a strict threshold at score 19. The results indicate that this method can be used for identifying novel miRNAs in other viral genomes with considerable success.