Population genomics for non-model species

Description

Genome sequencing of model species has yielded important insights into population history, adaptation and speciation. Applying population genomics methods across species with diverse ecologies and evolutionary histories offers great potential, but also can be challenging when applying methods optimised for model species such as humans. 

In this event, we will bring together experienced practitioners in population genomics to highlight both the promise and pitfalls (or lessons learnt). 

The day will begin with a morning of short research presentations from invited speakers to highlight the variety of applications of population genomics in non-model species. Aside from the research outcomes, each presenter has been asked to include one slide to highlight 'roadblock(s)' experienced and how this was solved (or worked around).

After lunch, we will revisit these roadblocks - whether in sequence generation, bioinformatics, or inference - and share possible solutions. One possible outcome is to identify opportunity to develop or extend bioinformatics/inference pipelines for handling population-level sequence data.

You are welcome to join us for the morning presentations, the afternoon discussion, or both.

We will provide light coffee breaks (BYO cup) and refreshments afterwards (5pm). Lunch will not provided, however the new Kambri precinct is nearby, or if you want to bring your lunch, there is a small kitchen with fridge, etc. next to the workshop room.

Please register by 5pm Mon 8 April for catering purposes.

Schedule and more information here.

Date & time

11 April 2019

Location

Sciences Teaching Building, Bldg 136, Linnaeus Way, Rm S2 (top floor), ANU

Speakers

Leonie Moyle, Matt Hahn, Dan Vanderpool (Indiana University) Jeff Good (University of Montana) Jason Bragg (Royal Botanic Gardens Sydney) Justin Borevitz, Angela McGaughran, Sacha

Contacts

 Claire Stephens

Updated:  24 August 2019/Responsible Officer:  Director RSB/Page Contact:  Webmaster RSB