Transcription Direction Patterns of Adjacent Genes in Mycobacterium Tuberculosis Using GENAVIS

Angela Uche Makolo

doi:10.11648/j.cbb.20190701.11

Abstract

The understanding of the relationship of a gene with other genes in its neighbourhood and the implication of this relationship on the biochemical activities of the entire genome need an efficient computational tool to unfold the relationship. GENAVIS: (GENe Adjacency VIsualization Software) is an open source, platform independent web-based software for modeling neighborhood genes as binary codes. We also incorporated the feature of having an interactive visual representation of patterns of the binary code for a specific gene family in multiple microbial genomes. The concept of using binary code for representation is derived from computational thinking techniques which models problems using computer logic of applying abstraction and pattern matching to extract hidden patterns aimed at knowledge discovery. The result provides an insight into the analysis of transcriptional unit with more than one gene and genes encoding for universal stress protein, which also allows for a comparative analysis of multiple genomes as the basis for biosynthetic pathways and multi-gene function prediction.

Highlights

The availability of computational tools for Knowledgebuilding, sense-making and decision making on gene neighborhood in post genomics era remains a challenge to Biomedical scientist
The genomic location has some impact on gene expression which generally has influence on the gene function within a framework of expression defined by that neighbourhood
The understanding of gene neighbourhood has been applied in several studies; it was used as entropy measure under the frame of neighborhood rough sets to develop a novel gene selection method for tackling the uncertainty and noisy of gene expression data [3]

Summary

Introduction

The availability of computational tools for Knowledgebuilding, sense-making and decision making on gene neighborhood in post genomics era remains a challenge to Biomedical scientist. Several studies have been conducted to understand the effect of location of genes in the entire genome on their biochemical activities. The understanding of gene neighbourhood has been applied in several studies; it was used as entropy measure under the frame of neighborhood rough sets to develop a novel gene selection method for tackling the uncertainty and noisy of gene expression data [3]. Their gene selection model was applied on tumor classification for the discovery of compact gene subsets with improved accuracy. A novel gene-ranking method based on neighborhood rough set reduction for molecular cancer classification based on gene expression profile was developed [4]

Methods

Results

Discussion

Conclusion