Integration of text mining and biological network analysis to access essential genes in Desulfovibrio alaskensis G20.

Essential genes are crucial for the survival and growth of any organism, and therefore alteration of such genes could result in unexpected behavioral change. Identification of essential genes and their role in functioning of organisms is a basic knowledge requirement for any research, which could be manipulated to understand the mechanisms of survival and growth [1]. Several decades have witnessed the virtues and iniquities of the gram-negative facultative anaerobes, sulfate reducing bacteria (SRB) in both ecological and commercial arena. Despite of relentless increase in the number of published articles that belong to diverse research areas-from industrial biotechnology (removal of heavy metals and waste valorization) to molecular biology (genetic architecture of the genes in biocorrosion and biofilm formation on metals), not much information about the essential genes of SRB community is known yet [2]. The Desulfovibrio alaskensis G20 (DA-G20) is a well-known SRB; its genes have been annotated but have large numbers that encode for hypothetical proteins. Till date no categorization is available for the genes of DA-G20 with reference to essentiality [3]. The in-vitro prediction of essential genes relies highly on the exhaustive multi-omics strategies. In the era of big-data and artificial intelligence research, demand of abstraction and interpretation of complex relationships of biological importance using text mining has increased. Therefore, to propose an alternative and economic method, text mining is a comparable method for the prediction of the essential genes. In this study, we reported the essential genes of DA-G20 using text mining and biological network analysis. Moreover, the present work provides a foundation for the expansion of genome wide investigation and identification of essential genes in prokaryotes using machine learning and data science approaches.