Estimating Species Trees Workshop

Estimating Species Trees Workshop | Society of Systematic Biologists

Workshop at the University of Michigan, Jan 10-11, 2009
Estimating Species Trees: a Phylogenetic Paradigm for the 21st Century

Recent computational and modeling advances have produced methods for estimating species trees directly. Accurate estimates of phylogenetic relationships can be extracted from genetic data with these new approaches, sometimes with less data, by directly modeling the causes of discordance in topology and branch lengths among gene trees. Such inferences are commonly impossible under the traditional phylogenetic paradigm because of the potential for the idiosyncrasies of gene trees to obscure the actual history of species divergence.

We are offering this workshop to not only increase the visibility and use of these methods, but also address a number of significant challenges to estimating species trees, to assure that the advantages these methods offer reach a broad community of users. The goals of the workshop are to: (i) provide an understanding of the theoretical underpinnings of current methodology, (ii) present empirical examples demonstrating the utility of current methodology as well as its limitations, and (iii) offer instruction on the technical aspects involved in using current software. This will be accomplished through the combination of a series of lectures (day one) and hands-on computer training (day two).
For more information, click the "read more" link below, and see the flyer Estimating_Species_Trees.pdf.

Participation in the workshop requires registration (go to http://www.ummz.lsa.umich.edu/sptree.html) and is free for those attending the lectures (on Jan 10) and is $25 for those attending the computer training (on Jan 11; see website for programs that will be covered). To facilitate broad and diverse participation in this important workshop, funding is available to offset transportation and lodging costs (i.e., $500 for those from the US and $1000 for international participants – see website for details on how to apply).

Co-organizers: L. Lacey Knowles, University of Michigan, and Laura S. Kubatko, Ohio State University

Location of the workshop: University of Michigan, January 10-11, 2009.

Invited speakers for workshop:
Liang Liu, Harvard University
Laura Kubatko, Ohio State University
Dennis Pearl, Ohio State University
Célcile Ané, University of Wisconsin
James Degnan, University of Canterbury
L. Lacey Knowles, University of Michigan
Luay Nakhleh, Rice University
Karen Cranston, University of Arizona
Bret Larget, University of Wisconsin
Robb Brumsfield, Louisiana State Univ.
Lisle Gibbs, Ohio State University
Scott Edwards, Harvard University
Catherine Linnen, Harvard University
Natalia Belfiore, University of California, Berkeley

For more information please contact: Dr. L. Lacey Knowles, knowlesl@umich.edu

This workshop has been made possible by funds generously provided by the Museum of Zoology, University of Michigan.

TreeTapper

TreeTapper.org: "TreeTapper
Tools to better understand biology by tapping information in phylogenies"

This is a site for finding tools to better understand biology using trees, and to identify areas where tools are missing. It's not yet fully operational, but poke around the menus for more info; you should also go to the development blog to see how the site is being created and its current status. I would really appreciate any suggestions you have, too: email me at bcomeara@nescent.org

Phylogenomics: hace diez años!!

Vol. 8, Issue 3, 163-167, March 1998

INSIGHT/OUTLOOK
Phylogenomics: Improving Functional Predictions for Uncharacterized Genes by Evolutionary Analysis
Jonathan A. Eisen¹
Genome Research -- Eisen 8 (3): 163

The ability to accurately predict gene function based on gene sequence is an important tool in many areas of biological research. Such predictions have become particularly important in the genomics age in which numerous gene sequences are generated with little or no accompanying experimentally determined functional information. Almost all functional prediction methods rely on the identification, characterization, and quantification of sequence similarity between the gene of interest and genes for which functional information is available. Because sequence is the prime determining factor of function, sequence similarity is taken to imply similarity of function. There is no doubt that this assumption is valid in most cases. However, sequence similarity does not ensure identical functions, and it is common for groups of genes that are similar in sequence to have diverse (although usually related) functions. Therefore, the identification of sequence similarity is frequently not enough to assign a predicted function to an uncharacterized gene; one must have a method of choosing among similar genes with different functions. In such cases, most functional prediction methods assign likely functions by quantifying the levels of similarity among genes. I suggest that functional predictions can be greatly improved by focusing on how the genes became similar in sequence (i.e., evolution) rather than on the sequence similarity itself. It is well established that many aspects of comparative biology can benefit from evolutionary studies (Felsenstein 1985), and comparative molecular biology is no exception (e.g., Altschul et al. 1989; Goldman et al. 1996). In this commentary, I discuss the use of evolutionary information in the prediction of gene function. To appreciate the potential of a phylogenomic approach to the prediction of gene function, it is necessary to first discuss how gene sequence is commonly used to predict gene function and some general features about gene evolution.