Reconstructing the evolutionary history of complex human gene clusters

Authors:
Yu Zhang;Giltae Song;Tomáš Vinař;Eric D. Green;Adam Siepel;Webb Miller
Affiliations:
Center for Comparative Genomics and Bioinformatics, Penn State University, University Park, PA and Department of Statistics, Penn State University, University Park, PA;Center for Comparative Genomics and Bioinformatics, Penn State University, University Park, PA;Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, NY;Genome Technology Branch and NIH Intramural Sequencing Center, National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland;Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, NY;Center for Comparative Genomics and Bioinformatics, Penn State University, University Park, PA
Venue:
RECOMB'08 Proceedings of the 12th annual international conference on Research in computational molecular biology
Year:
2008

Citing 1
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Abstract

Clusters of genes that evolved from single progenitors via repeated segmental duplications present significant challenges to the generation of a truly complete human genome sequence. Such clusters can confound both accurate sequence assembly and downstream computational analysis, yet they represent a hotbed of functional innovation, making them of extreme interest. We have developed an algorithm for reconstructing the evolutionary history of gene clusters using only human genomic sequence data. This method allows the tempo of large-scale evolutionary events in human gene clusters to be estimated, which in turn will facilitate primate comparative sequencing studies that will aim to reconstruct their evolutionary history more fully.