CFP last date
20 January 2025
Reseach Article

A Hybrid Approach for Sequence Alignment over Genome Data using Compressive Sensing and HBLAST

by Prabhat Gupta, Rajeev Pandey, Anjna Deen, S. P. Pandey
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 179 - Number 15
Year of Publication: 2018
Authors: Prabhat Gupta, Rajeev Pandey, Anjna Deen, S. P. Pandey
10.5120/ijca2018915709

Prabhat Gupta, Rajeev Pandey, Anjna Deen, S. P. Pandey . A Hybrid Approach for Sequence Alignment over Genome Data using Compressive Sensing and HBLAST. International Journal of Computer Applications. 179, 15 ( Jan 2018), 1-7. DOI=10.5120/ijca2018915709

@article{ 10.5120/ijca2018915709,
author = { Prabhat Gupta, Rajeev Pandey, Anjna Deen, S. P. Pandey },
title = { A Hybrid Approach for Sequence Alignment over Genome Data using Compressive Sensing and HBLAST },
journal = { International Journal of Computer Applications },
issue_date = { Jan 2018 },
volume = { 179 },
number = { 15 },
month = { Jan },
year = { 2018 },
issn = { 0975-8887 },
pages = { 1-7 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume179/number15/28873-2018915709/ },
doi = { 10.5120/ijca2018915709 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:55:24.303774+05:30
%A Prabhat Gupta
%A Rajeev Pandey
%A Anjna Deen
%A S. P. Pandey
%T A Hybrid Approach for Sequence Alignment over Genome Data using Compressive Sensing and HBLAST
%J International Journal of Computer Applications
%@ 0975-8887
%V 179
%N 15
%P 1-7
%D 2018
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Medical data is an exponential growth in all the hospitality service area. Genome is an special type of data which deals with the small unit of medical cells. Various matching operation over the genome data is required because of some medical issues arise in various cases. DNA matching, sequence matching, pattern analysis and matching is so called requirement in this area. There are some techniques such as BLAST, HBLAST, RMAP is involved and performed by past researcher. The technique use pre-processing and other filteration , sequence finding is performed. Past approach finds limitation where the large data processing, sequence detection and combine score generation for overall data processing is not performed. In this paper proposed approach is given which work towards the enhancement of previous approach extended with compressive sensing usage for pre-fetching of data and its filteration. It make use of compressive sensing with which a noise removal, filtering process is executed and thus a refined data is observed for Hadoop processing Mapping approach. Our proposed technique executed with different data set of sequence, count of data present in millions and it gives an effective results while comparing with existing scenario. A further implementation on security usage can performed by us.

References
  1. Miss. Anju Ramesh Ekre1 , Prof. Ravi. V. Mante, “Hadoop Based Clustering System For Genome Sequencing”, 2016 Second International Conference on Science Technology Engineering and Management (ICONSTEM).
  2. Aisling O’Driscoll, Vladislav Belogrudoy, John Carroll, Kai Kropp, Paul Walsh, Peter Ghazal, Roy D. Sleator, “HBLAST: Parallelised sequence similarity – A Hadoop MapReducable basic local alignment search tool” ScienceDirect, Elsevier, Journal of Biomedical Informatics 54 (2015) 58–64, https://doi.org/10.1016/j.jbi.2015.01.008.
  3. Fritz J Sedlazeck,Philipp Reschender, Arndt von Haeseler, “NextGenMap: Fast and Accurate read mapping in highly polymorphic genomes,” Bioinformatics advanced access, August 2013.
  4. Kun Sun, Yuet-Ping Yuen, Hauting Wang, Hao Sun, “The Online diagnosis system for Sanger Sequencing based genetic testing,” BigComp, 2014.
  5. Quan Zou, Xu-Bin Li, Wen-Rui Jiang, Zi-Yu Lin, Gui-Lin Li, Ke Chen, “Survey of MapReduce frame operation in Bioinfromatics,” Briefings of Bioinformatics, Feb 2013.
  6. Liu, F., Guo, W., Yu, D., Gao, Q., Gao, K., Xue, Z., ... Chen, H., 2012. Classification of different therapeutic responses of major depressive disorder with multivariate pattern analysis method based on structural MR scans. PLoS One 7 (7), e40968. http://dx.doi.org/ 10.1371/journal.pone.0040968.
  7. Dimitrios Milioris, “Classification in Twitter via Compressive Sensing”, IEEE International Conference on Computer Communications (INFOCOM), Apr 2015, Hong Kong, Hong Kong SAR China.
  8. Vink, M., Raemaekers, M., van der Schaaf, A., Mandl, R., Ramsey, N., 2007. Pre-processing and Analysis.
  9. Hsu, C., Chang, C., Lin, C., 2010. A practical guide to support vector classification. Tech. rep. Department of Computer Science, National Taiwan University.
  10. Pelle Jakovits, Satish Narayana Srirama, “Evaluating MapReduce Frameworks for Iterative Scientific Computing Applications,” 2014, pp. 226-233.
  11. Zefeng Zhang, Hao Lin, Bin Ma, “ZOOM Lite: nextgeneration sequencing data mapping and visualization software,” Nucleic Acid Research, vol. 38, pp. w743-w748, June 2010.
  12. D. Donoho, “Compressive sensing”, in IEEE Trans. on Information Theory, Vol. 52, No. 4, pp. 1289–1306, April 2006.
  13. L. M. Aiello et al., “Sensing Trending Topics in Twitter”, in IEEE Transactions on Multimedia, Vol. 15, Iss. 6, pp. 1268–1282, Oct 2013.
  14. G. Tzagkarakis, D. Milioris and P. Tsakalides, “Multiple-Measurement Bayesian Compressive Sensing using GSM Priors for DOA Estimation”, in 35th Int. Conf. on Acoustics, Speech, and Sig. Proc. (ICASSP), Dallas, TX, Mar. 2010.
  15. https://blast.ncbi.nlm.nih.gov/Blast.cgi
Index Terms

Computer Science
Information Sciences

Keywords

BLAST Compressive sensing big data protein DB sequence alignment