Abstracts (first author)

Invited Speaker 

Anchored Phylogenomics: Accelerating the Resolution of Life

Author(s): Lemmon EM

Summary:

The field of phylogenomics is undergoing a revolution, enabled by new methods of data collection that leverage both genomic resources and recent advances in DNA sequencing. We developed a cost-efficient and rapid approach to obtaining data from 100s to 1000s of loci and individuals for deep and shallow phylogenetic studies. Specifically, we designed probes for target hybrid enrichment of 100s of loci in conserved anchor regions of genomes (flanked by less conserved regions). We enriched genomic DNA libraries for these anchor regions, and sequenced these targets using high-throughput sequencing. The resulting data sets contain 100s of loci with low levels of missing data and high levels of phylogenetic information across taxonomic scales and produce phylogenies with high levels of resolution. This approach is expediting resolution of deep-scale portions of the Tree of Life and greatly accelerating resolution of the large number of shallow clades that remain unresolved. The combination of low cost (∼1% of the cost of traditional Sanger sequencing and ∼3.5% of the cost of high-throughput amplicon sequencing for projects on the scale of 500 loci × 100 individuals) and rapid data collection (∼2 weeks of laboratory time) make this approach tractable even for researchers working on systems with limited genomic resources. Here, I present new work from vertebrates and unpublished data from various non-vertebrate (e.g., Coleoptera, Hymenoptera, etc.) and plant (e.g., Angiosperm) clades; I also discuss future directions and new applications.



Contacts

Chairman: Octávio S. Paulo
Tel: 00 351 217500614 direct
Tel: 00 351 217500000 ext22359
Fax: 00 351 217500028
email: mail@eseb2013.com

Address

XIV Congress of the European Society for Evolutionary Biology

Organization Team
Department of Animal Biology (DBA)
Faculty of Sciences of the University of Lisbon
P-1749-016 Lisbon
Portugal

Website

Computational Biology & Population Genomics Group 
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