Accelerating Biological Insight for Understudied Genes. (10th May 2021)

• Record Type: Journal Article
• Authors: Reynolds, Kimberly A; Rosa-Molinar, Eduardo; Ward, Robert E; Zhang, Hongbin; Urbanowicz, Breeanna R; Settles, A Mark
• Is Part Of: Integrative and comparative biology. Volume 61:Number 6(2021)
• Page Start: 2233
• Page End: 2243
• Publication Date: 2021-05-10
• Subjects: Zoology -- Periodicals; Biology -- Periodicals; Physiology, Comparative -- Periodicals; 590.5
• DOI: 10.1093/icb/icab029
• Languages: English
• ISSNs: 1540-7063

Abstract

Synopsis: The rapid expansion of genome sequence data is increasing the discovery of protein-coding genes across all domains of life. Annotating these genes with reliable functional information is necessary to understand evolution, to define the full biochemical space accessed by nature, and to identify target genes for biotechnology improvements. The majority of proteins are annotated based on sequence conservation with no specific biological, biochemical, genetic, or cellular function identified. Recent technical advances throughout the biological sciences enable experimental research on these understudied protein-coding genes in a broader collection of species. However, scientists have incentives and biases to continue focusing on well documented genes within their preferred model organism. This perspective suggests a research model that seeks to break historic silos of research bias by enabling interdisciplinary teams to accelerate biological functional annotation. We propose an initiative to develop coordinated projects of collaborating evolutionary biologists, cell biologists, geneticists, and biochemists that will focus on subsets of target genes in multiple model organisms. Concurrent analysis in multiple organisms takes advantage of evolutionary divergence and selection, which causes individual species to be better suited as experimental models for specific genes. Most importantly, multisystem approaches would encourage transdisciplinary critical thinking and hypothesis testing that is inherently slow in current biological research.