Abstract
Comparative genomics has proven a fruitful approach to acquire many functional and evolutionary insights into core cellular processes. Here it is argued that in order to perform accurate and interesting comparative genomics, one first and foremost has to be able to recognize, postulate, and revise different evolutionary scenarios. After all, these studies lack a simple protocol, due to different proteins having different evolutionary dynamics and demanding different approaches. The authors here discuss this challenge from a practical (what are the observations?) and conceptual (how do these indicate a specific evolutionary scenario?) viewpoint, with the aim to guide investigators who want to analyze the evolution of their protein(s) of interest. By sharing how the authors draft, test, and update such a scenario and how it directs their investigations, the authors hope to illuminate how to execute molecular evolution studies and how to interpret them. Also see the video abstract here https://youtu.be/VCt3l2pbdbQ.
Highlights
Comparative genomics has proven a fruitful approach to acquire many functional and evolutionary insights into core cellular processes
We argue that the most crucial yet overlooked skill is to be able to recognize, postulate, and revise different evolutionary scenarios, a process that is typically difficult to automate
We assume we search for orthologs in other eukaryotes and aim to infer the evolutionary events that happened since last eukaryotic common ancestor (LECA)
Summary
Comparative genomics has proven a fruitful approach to acquire many functional and evolutionary insights into core cellular processes. In collaboration with molecular biologists, we studied diverse eukaryotic proteins, varying from chromatin remodelers and bZIP transcription factors to flagellum and kinetochore components.[2,33,34,35] We experienced that for non‐ bioinformaticians various challenges arise regarding the use of tools, the knowledge of genome evolution and of the species tree, and, importantly, how complicated evolutionary histories are reflected in a myriad of computational tools and sequence databases Such challenges may cause flawed evolutionary inferences, which in turn may cause incorrect, or incomplete, function prediction.[30] We here detail principles that explain “best practices” from the field, while we draw attention to what we consider neglected. We argue that the most crucial yet overlooked skill is to be able to recognize, postulate, and revise different evolutionary scenarios, a process that is typically difficult to automate
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