Cloud‐based adaptive exon prediction for DNA analysis. Issue 1 (22nd January 2018)

• Record Type: Journal Article
• Authors: Putluri, Srinivasareddy; Zia Ur Rahman, Md; Fathima, Shaik Yasmeen
• Is Part Of: Healthcare technology letters. Volume 5:Issue 1(2018)
• Page Start: 25
• Page End: 30
• Publication Date: 2018-01-22
• Subjects: DNA -- molecular biophysics -- cloud computing -- bioinformatics; DNA analysis -- cloud‐based adaptive exon prediction -- cloud computing -- healthcare -- gene information -- gene sequence -- DNA sequencing -- bioinformatics -- disease identification -- base periodicity -- adaptive signal processing -- AEP -- genomic sequence database; Biomedical engineering -- Periodicals; Medical technology -- Periodicals; 610.28
• DOI: 10.1049/htl.2017.0032
• Languages: English
• ISSNs: 2053-3713

Abstract

Abstract : Cloud computing offers significant research and economic benefits to healthcare organisations. Cloud services provide a safe place for storing and managing large amounts of such sensitive data. Under conventional flow of gene information, gene sequence laboratories send out raw and inferred information via Internet to several sequence libraries. DNA sequencing storage costs will be minimised by use of cloud service. In this study, the authors put forward a novel genomic informatics system using Amazon Cloud Services, where genomic sequence information is stored and accessed for processing. True identification of exon regions in a DNA sequence is a key task in bioinformatics, which helps in disease identification and design drugs. Three base periodicity property of exons forms the basis of all exon identification techniques. Adaptive signal processing techniques found to be promising in comparison with several other methods. Several adaptive exon predictors (AEPs) are developed using variable normalised least mean square and its maximum normalised variants to reduce computational complexity. Finally, performance evaluation of various AEPs is done based on measures such as sensitivity, specificity and precision using various standard genomic datasets taken from National Center for Biotechnology Information genomic sequence database.