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Cloud-based adaptive exon prediction for DNA analysis.

Srinivasareddy Putluri1, Md Zia Ur Rahman1, Shaik Yasmeen Fathima2

  • 1Department of ECE, K L University, Green Fields, Guntur DT 522 502, Andhra Pradesh, India.

Healthcare Technology Letters
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Summary

This study introduces a novel cloud-based genomic informatics system to efficiently store and process DNA sequence data. The system utilizes adaptive signal processing techniques for accurate exon identification, aiding disease research and drug design.

Keywords:
AEPDNADNA analysisDNA sequencingadaptive signal processingbase periodicitybioinformaticscloud computingcloud-based adaptive exon predictiondisease identificationgene informationgene sequencegenomic sequence databasehealthcaremolecular biophysics

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Area of Science:

  • Bioinformatics
  • Genomic Informatics
  • Computational Biology

Background:

  • Cloud computing offers economic and research benefits to healthcare organizations by providing secure storage for sensitive data.
  • Traditional gene information flow involves sending data to multiple sequence libraries, incurring significant DNA sequencing storage costs.
  • Accurate identification of exon regions in DNA sequences is crucial for disease identification and drug design.

Purpose of the Study:

  • To propose a novel genomic informatics system utilizing Amazon Cloud Services for storing and processing genomic sequence information.
  • To develop and evaluate adaptive exon predictors (AEPs) for efficient and accurate exon identification.
  • To leverage the three-base periodicity property of exons for improved bioinformatics analysis.

Main Methods:

  • Implementation of a novel genomic informatics system on Amazon Cloud Services.
  • Development of adaptive exon predictors (AEPs) using variable normalized least mean square and its maximum normalized variants.
  • Utilizing adaptive signal processing techniques for exon identification based on three-base periodicity.

Main Results:

  • The proposed system effectively stores and accesses genomic sequence information using cloud services.
  • Developed AEPs demonstrated promising performance in identifying exon regions.
  • Performance evaluation using standard genomic datasets showed the effectiveness of the AEPs based on sensitivity, specificity, and precision.

Conclusions:

  • Cloud-based genomic informatics systems can minimize DNA sequencing storage costs.
  • Adaptive signal processing techniques offer a promising approach for exon identification.
  • The developed AEPs provide an efficient method for analyzing genomic sequences, supporting disease research and drug discovery.