ArticleBMC genomics2024
A putative scenario of how de novo protein-coding genes originate in the Saccharomyces cerevisiae lineage.
Article in BMC genomics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- Horizontal gene transfer in Saccharomyces cerevisiae and other Saccharomycotina yeasts: a review.World journal of microbiology & biotechnology · 2026Review
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Abstract
backgroundNovel protein-coding genes were considered to be born by re-organization of pre-existing genes, such as gene duplication and gene fusion. However, recent progress of genome research revealed that more protein-coding genes than expected were born de novo, that is, gene origination by accumulating mutations in non-genic DNA sequences. Nonetheless, the in-depth process (scenario) for de novo origination is not well understood.
resultsWe have conceived bioinformatic analysis for sketching a scenario for de novo origination of protein-coding genes. For each de novo protein-coding gene, we firstly identified an edge of a given phylogenetic tree where the gene was born based on parsimony. Then, from a multiple sequence alignment of the de novo gene and its orthologous regions, we constructed ancestral DNA sequences of the gene corresponding to both end nodes of the edge. We finally revealed statistical features observed in evolution between the two ancestral sequences. In the analysis of the Saccharomyces cerevisiae lineage, we have successfully sketched a putative scenario for de novo origination of protein-coding genes. (1) In the beginning was GC-rich genome regions. (2) Neutral mutations were accumulated in the regions. (3) ORFs were extended/combined, and then (4) translation signature (Kozak consensus sequence) was recruited. Interestingly, as the scenario progresses from (2) to (4), the specificity of mutations increases.
conclusionTo the best of our knowledge, this is the first report outlining a scenario of de novo origination of protein-coding genes. Our bioinformatic analysis can capture events that occur during a short evolutionary time by directly observing the evolution of the ancestral sequences from non-genic to genic. This property is suitable for the analysis of fast evolving de novo genes.
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