Missing Links Between Gene Function and Physiology in Genomics

Abstract

Knowledge of biological organisms at the molecular level that has been gathered is now organized into databases, often within ontological frameworks. To enable computational comparisons of annotations across different genomes and organisms, controlled vocabularies have been essential, as is the case in the functional annotation classifications used for bacteria, such as MultiFun and the more widely used Gene Ontology. The function of individual gene products as well as the processes in which collections of them participate constitute a wealth of classes that describe the biological role of gene products in a large number of organisms in the three kingdoms of life. In this contribution, we highlight from a qualitative perspective some limitations of these frameworks and discuss challenges that need to be addressed to bridge the gap between annotation as currently captured by ontologies and databases and our understanding of the basic principles in the organization and functioning of organisms; we illustrate these challenges with some examples in bacteria. We hope that raising awareness of these issues will encourage users of Gene Ontology and similar ontologies to be careful about data interpretation and lead to improved data representation.

A roadmap for the functional annotation of protein families: a community perspective

“…With the pipelines in place and many years of experience applying these tools across primarily eukaryotic species, the time seems right to expand the PAINT pipeline to include more prokaryotic species. This would require a concerted community effort to capture more experimental GO annotations for prokaryotic proteins ( 62 ) and add many more bacterial/archaeal species into PANTHER trees, as only 43 are included in the current set.…”

Multi-Attribute Subset Selection enables prediction of representative phenotypes across microbial populations

“…As the price of DNA sequencing keeps decreasing, reading genomes is no longer the limiting factor in understanding an organism: interpreting genomes is. In principle, genomes carry information on how an organism will behave in different environments, but in practice, the interpretability of genomic information is severely limited by our lack of knowledge about the function of many individual genes 1 , 2 and the interactions between genes and environments 3 , 4 , as well as by a limited understanding of how genes work together to produce higher-level functions 5 , 6 . The challenge of predicting organismal functions from genomes is particularly relevant in microbes, where systems biology approaches provide a potential avenue for building mechanistic models of metabolism at the genome-scale 7 , 8 .…”

Prediction of representative phenotypes using Multi-Attribute Subset Selection

“…As the price of DNA sequencing has decreased, reading genomes is no longer the limiting factor in understanding microbes: interpreting genomes is. In principle, genomes carry information on how microbes will behave in different environments, but in practice the interpretability of genomic information is severely limited by our lack of knowledge about the function of many individual genes (Furnham et al, 2012; Roberts, 2004), as well as by a limited understanding of how genes work together to produce higher-level functions (Collado-Vides et al, 2022; Yu et al, 2016). In many biological problems where the goal is to understand the ultimate behavior of an organism or system, the new frontier of “phenomics” has become increasingly valuable (Acin-Albiac et al, 2020; Bochner, 2009; Jewett et al, 2006; Sauer, 2004; Schilling et al, 1999).…”

“…As the price of DNA sequencing keeps decreasing, reading genomes is no longer the limiting factor in understanding an organism: interpreting genomes is. In principle, genomes carry information on how an organism will behave in different environments, but in practice the interpretability of genomic information is severely limited by our lack of knowledge about the function of many individual genes (Roberts 2004; Furnham, de Beer, and Thornton 2012) and the interactions between genes and environments (Kishony and Leibler 2003; de Vos et al 2013), as well as by a limited understanding of how genes work together to produce higher-level functions (Collado-Vides, Gaudet, and de Lorenzo 2022; Yu et al 2016). The challenge of predicting organismal functions from genomes is particularly relevant in microbes, where systems biology approaches provide a potential avenue for building mechanistic models of metabolism at the genome-scale (Seaver et al 2021; Passi et al 2021).…”