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Computational Approaches for Variant Identification

by Diksha Garg, Ankita Jiwan, Shailendra Singh
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 165 - Number 8
Year of Publication: 2017
Authors: Diksha Garg, Ankita Jiwan, Shailendra Singh

Diksha Garg, Ankita Jiwan, Shailendra Singh . Computational Approaches for Variant Identification. International Journal of Computer Applications. 165, 8 ( May 2017), 18-24. DOI=10.5120/ijca2017913970

@article{ 10.5120/ijca2017913970,
author = { Diksha Garg, Ankita Jiwan, 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 = {9},
url = { },
doi = { 10.5120/ijca2017913970 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Journal Article
%1 2024-02-07T00:11:53.938977+05:30
%A Diksha Garg
%A Ankita Jiwan
%A Shailendra Singh
%T Computational Approaches for Variant Identification
%J International Journal of Computer Applications
%@ 0975-8887
%V 165
%N 8
%P 18-24
%D 2017
%I Foundation of Computer Science (FCS), NY, USA

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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Index Terms

Computer Science
Information Sciences


Variants Variations Mutations Genetic Disease Variant Identification.