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Text-Mining to Identify Gene Sets Involved in Biocorrosion by Sulfate-Reducing Bacteria: A Semi-Automated Workflow.

Payal Thakur1,2, Mathew O Alaba3, Shailabh Rauniyar1,4

  • 1Department of Chemical and Biological Engineering, South Dakota School of Mines and Technology, Rapid City, SD 57701, USA.

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|January 21, 2023
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Summary

This study introduces a text mining workflow to identify biocorrosion genes in sulfate-reducing bacteria (SRB). The method efficiently extracts key genes from scientific literature, aiding biocorrosion research.

Keywords:
biocorrosionmetal ionsulfate-reducing bacteriasulfur metabolismtext mining

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Area of Science:

  • Microbiology
  • Bioinformatics
  • Materials Science

Background:

  • Biocorrosion research generates vast literature, making manual gene and protein extraction challenging.
  • Limited curated gene collections exist for biocorrosion processes, particularly in sulfate-reducing bacteria (SRB).

Purpose of the Study:

  • To develop and validate a text mining workflow for extracting biocorrosion-associated genes and proteins from scientific literature.
  • To identify key genes involved in biocorrosion within sulfate-reducing bacteria (SRB).

Main Methods:

  • A semi-automatic text mining workflow utilizing Named Entity Recognition (NER) and a Convolutional Neural Network (CNN) model.
  • Extraction of genes from PubMed and PMC databases, followed by functional validation using Gene Ontology (GO) enrichment and biological network analysis (STRING-DB).
  • Mapping of identified genes to SRB pangenomes for distribution analysis.

Main Results:

  • The workflow identified 227 genes associated with biocorrosion in Desulfovibrio species.
  • GO analysis highlighted molecular functions including metal ion binding, sulfur binding, and electron transport.
  • Network analysis revealed gene clusters involved in metal ion binding, cellular respiration, and electron transfer, with manual curation validating key genes like hysB, hydA, sat, and dsrB.

Conclusions:

  • The text mining framework effectively extracts biocorrosion-related genes from literature, significantly reducing manual curation time.
  • Identified genes and their functions support the proposed mechanisms of SRB-mediated biocorrosion, involving electron transfer and sulfur metabolism.
  • The developed workflow can be a valuable tool for accelerating biocorrosion research by automating gene and protein discovery.