An Optimized Graph-Based Metagenomic Gene Classification Approach: Metagenomic Gene Analysis

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Author(s): Md Sarwar Kamal (East West University, Bangladesh), Mohammad Ibrahim Khan (Chittagong University of Engineering and Technology, Bangladesh), Kaushik Dev (Chittagong University of Engineering and Technology, Bangladesh), Linkon Chowdhury (Chittagong University of Engineering and Technology, Bangladesh)and Nilanjan Dey (Department of Information Technology, Techno India College of Technology, Kolkata, India)
Copyright: 2020
Pages: 25
Source title: Data Analytics in Medicine: Concepts, Methodologies, Tools, and Applications
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-7998-1204-3.ch059

Keywords: Data Analysis & Statistics / Library & Information Science / Medical Information Science Reference / Medical Technologies

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Abstract

Biological interaction mainly depends on the interactions of various genes and genomes. To identify actual meaning of interactions we have to find out the facts and reasons for these interactions. Gene analysis allows to verify such environment. Gene annotation means to identify the exon regions in metagenomic samples. The de Bruijn graph plays significant role in gene prediction and next generation sequencing (NGS). Apart from that, Eular Path of de Bruijn graph introduced generalized gene annotation for translational and splicing signals, exon introns separation and coding regions. set of graph reduction rules have used to build a de Bruijn graph. Accurate solution for large scale sequencing, trims space complexity and generates optimal gene annotation have tested.

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An Optimized Graph-Based Metagenomic Gene Classification Approach: Metagenomic Gene Analysis

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