A goal of multiomics experiments is to understand how mechanistic molecular biology is altered between conditions, typically a control group and experimental groups. Oftentimes, this involves studying changes in biomolecule relationships (e.g., interactions and metabolic relationships) of several types of biomolecules (e.g., lipids, metabolites, gene products like proteins). Though several databases contain relationships between biomolecules, understudied species may have little to no relationship information in databases and thus must be mined from the literature. There are several challenges to literature mining, including automated full-text extraction, duplicate biomolecule term collapsing, and implementation of complex machine learning tools. To make relationship extraction more accessible to the community, a Python package called DancePartner was developed to allow for the extraction of relationships from literature and databases, with functions to map biomolecule synonyms to standardized identifiers and visualize and characterize the resulting multiomics network. Here, two example data sets are provided to demonstrate the capabilities of DancePartner: one of 14,986 papers and abstracts for Caernohabditis elegans, and another of 33,606 papers and abstracts for Saccharomyces cerevisiae. These relationships are combined with relationships from KEGG, WikiPathways, UniProt, and LipidMaps, and they are visualized. Networks are then compared for their differences in build times.

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Journal Article

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Degnan DJ, Strauch CW, Obiri MY, VonKaenel ED, Adrian DW, Bramer LM

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