Identifying genes involved in biocorrosion from the literature using text-mining

Abstract

Background: The central repository of scientific models from the literature; however, the manual knowledge is research publications which also plays a selection of pertinent information from databases like crucial role in communication within the scientific PubMed, PMC, Dimension, Google scholar, and community. The public repositories like PubMed, Semantic scholar can be tedious; therefore, a robust PMC, Dimension, Google scholar, and Semantic approach like text mining can be used for this process. scholar act as a storehouse of biological systems data. Text mining can be defined as a practical approach to A substantial amount of information can be recovered extracting biologically relevant information from the in a semi-structured form in the literature. The main growing amount of published literature. It comprises obstacle to large-scale analysis of this kind of data is three main tasks: information retrieval from relevant their highly unstructured and heterogenous format, documents, extraction of information of interest, and making it even harder to extract information contained data mining, which allows identifying new within the literature. Nonetheless, this information is associations among the extracted set of information. inherently helpful in a variety of genomics and Here, we show that a text mining approach can exploit systems biology contexts. For example, it is a standard large literature databases like PubMed and PMC to practice in the genomics community to manually extract genes/proteins related to biocorrosion by curate and extract literature-derived protein-protein Sulfate-reducing bacteria(SRB). The corrosion of metal due to microbial activity is known as biocorrosion or MIC(Microbial Induced corrosion). The primary class of bacteria associated with corrosion of metals in aquatic and terrestrial habitats is Sulfur Reducing Bacteria(SRB). Biocorrosion results from collaborative interactions between the metal surface, corrosion products, and bacterial cells and their metabolites. SRB are nonpathogenic and anaerobic bacteria, but SRB can act as a catalyst in the reduction reaction of sulfate to sulfide. It means they can make severe corrosion of metals in a water system by producing enzymes, which can accelerate the reduction of sulphate compounds to hydrogen sulfide. MIC is also known as metabolite corrosion or chemical microbially influenced corrosion (CMIC) owing to the generation of corrosive metabolite (hydrogen sulfide).In contrast, corrosion through direct withdrawal of electrons is called electrical microbial influenced corrosion. The presence of biofilm affects microbial corrosion; It is recognized that under the biofilm at the metal/biofilm interface, the concentrations of acidic metabolites are much greater, and their impact is amplified, leading to higher metal corrosion. It is also becoming apparent that one predominant mechanism of biocorrosion does not exist, and experimentally validating each of these theories can be laborious. Therefore, there is a need for an advanced technique for identifying genes and proteins of SRB involved in biocorrosion; this can help construct other biological processes, related pathways, and other processes associated with these genes.