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Computational Approaches for Variant Identification

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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2017
Authors:
Diksha Garg, Ankita Jiwan, Shailendra Singh
10.5120/ijca2017913970

Diksha Garg, Ankita Jiwan and Shailendra Singh. Computational Approaches for Variant Identification. International Journal of Computer Applications 165(8):18-24, May 2017. BibTeX

@article{10.5120/ijca2017913970,
	author = {Diksha Garg and Ankita Jiwan and Shailendra Singh},
	title = {Computational Approaches for Variant Identification},
	journal = {International Journal of Computer Applications},
	issue_date = {May 2017},
	volume = {165},
	number = {8},
	month = {May},
	year = {2017},
	issn = {0975-8887},
	pages = {18-24},
	numpages = {7},
	url = {http://www.ijcaonline.org/archives/volume165/number8/27594-2017913970},
	doi = {10.5120/ijca2017913970},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

Variant identification is a fundamental part in the analysis of genetic diseases. Variants are the alterations which occur in the arrangement of nucleotide in the DNA sequence. Genetic diseases are caused by variations occurring in genes which may cause change in protein, affecting the survival and adaptation of an individual. A number of computational techniques are applied to identify these variant. Precise diagnosis of genetic diseases is important for proper treatment of patients and to determine explicit prevention strategies. Introduction of next generation sequencing (NGS) techniques in the past have made large number of DNA sequences easily available. This has made variant identification using NGS data a area of interest. This paper briefly discussed the analysis steps followed for NGS data analysis. This paper later explains in detail a few approaches that are used for identifying variants such as Support vector machine based approach, Machine learning based approach, MOSAIK: hash-base approach, Bayesian statistical based approach, JointSLM based approach.

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Keywords

Variants, Variations, Mutations, Genetic Disease, Variant Identification.

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