SigMine and OPathDb: a literature-mining pipeline and database of potential opportunistic pathogens

Clinical Snapshot

Key Findings

• P Value: Not reported in abstract; statistical significance thresholds and methods are not specified

• Effect Size: Not reported — co-occurrence counts and significance are described qualitatively; no effect size metrics (e.g., odds ratio, pointwise mutual information scores) are provided in the abstract

• Primary Outcome: Construction of OPathDb containing 5,626 potential opportunistic pathogens significantly co-occurring with 1,440 diseases and 7,121 genes, mined from 25,000 PMC articles

• Nnt Or Sensitivity: Not applicable for a database construction study; NER performance metrics (sensitivity/specificity/F1 for entity recognition) are not reported, representing a critical methodological gap

• Confidence Interval: Not reported

Clinical Application

OPathDb is freely accessible via web interface with query functionality by organism, disease, tissue, gene, protein, and metabolite. Network visualisation is available. The SigMine pipeline requires Python v3.13 competency for local deployment but the GUI reduces technical barriers. Feasibility for clinical use is limited to hypothesis generation — no direct clinical decision support application is appropriate without experimental validation of associations. Australia has a distinct opportunistic pathogen landscape including Burkholderia pseudomallei (melioidosis, endemic in northern Australia), Cryptococcus gattii (endemic in parts of Australia), and a high burden of opportunistic infections in Aboriginal and Torres Strait Islander communities with elevated rates of immunocompromising conditions. OPathDb's global literature base may underrepresent these regionally specific pathogens. The database is not PBS- or TGA-relevant at this stage. RACGP and ASID (Australasian Society for Infectious Diseases) guidelines for immunocompromised patient management could potentially benefit from hypothesis-generating tools like OPathDb, but clinical translation requires Australian-specific validation. Researchers at Australian institutions (e.g., Menzies School of Health Research, Peter Doherty Institute) may find OPathDb useful for generating research hypotheses in tropical and Indigenous health infectious disease contexts. Researchers and clinicians in infectious disease, clinical microbiology, immunology, and microbiome science seeking hypothesis-generating data on opportunistic pathogen–disease associations in immunocompromised or at-risk patient populations

Abstract

Conversion of unstructured biomedical literature into structured knowledge for identifying cross-domain associations between biological entities remains a challenging task. SigMine is an automated pipeline constructed to mine biomedical literature to identify significantly associated biological entities. SigMine performs biomedical entity recognition from PMC articles using the EuropePMC Annotation API. Advanced entity recognition was performed using Python scripting, NCBI E-Utilities, and an n-gram algorithm followed by extensive data cleaning and mapping against standard databases. Statistical evaluation identified significantly co-occurring entities. The entire workflow was automated through a modular framework developed in Python v3.13 with a Tkinter-based Graphical User Interface. SigMine enhances usability while retaining the flexibility to use new dictionaries for annotation. SigMine was used to construct a literature-derived potential human Opportunistic Pathogens Database (OPathDb), housing 5,626 potential opportunistic pathogens significantly co-occurring with 1440 diseases and 7121 genes mined from 25,000 PMC articles. Additional annotation of 598 significantly co-occurring metabolites and 30 affected tissues is available for 3204 and 227 pathogens, respectively. OpathDb has a user-friendly query interface searchable by organism, disease, tissue, gene, protein and metabolite available at https://www.opathdb.cbsblab-nsut.in . Organism-entity associations can be visualized as weighted networks, with color-coded nodes and significance-scaled edges. Significant associations of opportunistic pathogens like Akkermansia mucinifila with colorectal cancer and Segatella copri with glucose intolerance can be identified through OpathDb. Through this database, the SigMine framework demonstrates conversion of unstructured text in vast and heterogenous corpora into standardized and well-organized information. Statistically inferred associations in OPathDb are potential candidates for clinical and experimental validation.

References

1. 1.Rani, U., Nair, A., & Bhatnagar, S. (2026). SigMine and OPathDb: a literature-mining pipeline and database of potential opportunistic pathogens. Archives of Microbiology. PubMed ID: 42435073.