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Brief Bioinform. 2018 Sep 3. doi: 10.1093/bib/bby074. [Epub ahead of print]

Computational determination of gene age and characterization of evolutionary dynamics in human.

Yin H1, Li M2,3, Xia L2,3, He C1, Zhang Z2,3,4.

Author information

1
Hainan Key Laboratory for Sustainable Utilization of Tropical Bioresources, Institute of Tropical Agriculture and Forestry, Hainan University, China. Her research interests lie in computational evolutionary biology and mechanisms of plant disease resistance.
2
CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences. His research focuses on cancer bioinformatics and DNA methylation.
3
Hainan Key Laboratory for Sustainable Utilization of Tropical Bioresources, Institute of Tropical Agriculture and Forestry, Hainan University, China. His research interests include molecular genetics in plant development and disease resistance.
4
BIG Data Center & CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences. His research interests are big data integration and computational evolutionary & health genomics.

Abstract

Genes originate at different evolutionary time scales and possess different ages, accordingly presenting diverse functional characteristics and reflecting distinct adaptive evolutionary innovations. In the past decades, progresses have been made in gene age identification by a variety of methods that are principally based on comparative genomics. Here we summarize methods for computational determination of gene age and evaluate the effectiveness of different computational methods for age identification. Our results show that improved age determination can be achieved by combining homolog clustering with phylogeny inference, which enables more accurate age identification in human genes. Accordingly, we characterize evolutionary dynamics of human genes based on an extremely long evolutionary time scale spanning ~4,000 million years from archaea/bacteria to human, revealing that young genes are clustered on certain chromosomes and that Mendelian disease genes (including monogenic disease and polygenic disease genes) and cancer genes exhibit divergent evolutionary origins. Taken together, deciphering genes' ages as well as their evolutionary dynamics is of fundamental significance in unveiling the underlying mechanisms during evolution and better understanding how young or new genes become indispensable integrants coupled with novel phenotypes and biological diversity.

PMID:
30184145
DOI:
10.1093/bib/bby074

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