Valerio Arnaboldi - A Gene Summary Generation System for Animal Genomes
Channel: Biocuration Conference 2023
Date: 21/06/2023

*Session:* April 25 - Ai And Text Mining 1 - 01

*Abstract:* Knowledgebases that curate and organize gene data also provide manual text paragraphs that describe gene function. These short paragraphs, referred to as gene summaries, are valued by users for the ease with which they convey key aspects of gene function. However, the manual writing of gene summaries is time- and labor-intensive and does not scale to match the fast-growing scientific literature. Our solution to these challenges is a system that automatically generates reader-friendly concise gene summaries that simulate natural language and are based on curated, structured gene data referred to as annotations. This system is currently implemented at WormBase and the Alliance of Genome Resources and leverages the different types of professional, specialized gene-related data curation such as Gene Ontology (GO) curation, orthology predictions, gene expression curation, etc. and does not require the user to be familiar with the database, data structures, or biological and biomedical vocabularies. One of the main features of our method is the use orthology data to generate summaries for: (i) less studied genes, thus providing more coverage of genomes; (ii) genes that are relevant to the study of human disease pathogenesis, facilitating the discovery of established and potential models of disease; (iii) genes in species that can be related to a “reference species”, thus providing summaries for genes where no formal curation projects exist. In addition, the modularity and flexibility of the system allows the gene summaries to be updated frequently. Recently, we improved gene summaries at the Alliance by including gene product-to-term relationships from the GO to more accurately reflect gene function, for example, to describe whether a gene is directly “involved in” a biological process or “contributes to” a molecular activity. Further, we have added gene summaries for two frog species, X. laevis and X. tropicalis demonstrating that the system is readily generalizable. We plan to expand the system to many more species and more data types, and also include AI language models (such as BERT and GPT3) to generate summaries on unstructured data from the literature. The methods, workflow and examples will be described in the poster.