  Shaped by natural selection and other evolutionary forces, an organism's
evolutionary history is reflected through its genome sequence, content of
functional elements and organization. Consequently, organisms connected through
phylogeny, metabolic or morphological traits, geographical proximity, or
habitat features are likely to exhibit similarities in their genomes. These
similarities give rise to expectations about the content of genomes within
these organism groups.
  Such expectations are often informally expressed in scientific literature,
focusing on the analysis of individual genomes or comparisons among related
groups of organisms. Our objective is to develop a system for formalized
expectations as rules, facilitating automated verification, and evaluation of
newly sequenced genomes.
  In this study, we present a database comprising rules manually extracted from
scientific literature. Furthermore, we explore the feasibility of automatizing
the extraction and analysis process using large language models, such as GPT3.5
and GPT4.
  We have developed a web application, EGCWebApp, which enables users to
visualize and edit the rules. Additionally, we provided a Python library and
command-line tools collection, egctools, to further extend the functionality
for processing and managing these rules.

Genomics