Studying the process of de novo mutation from deep-sequencing of related samples is a difficult task. Because de novos are rare, artifacts generated by experimental and biological error tend to be more common than true positives. While de novos can be identified through validation, this is a slow process. In order to estimate mutation rates on large datasets in an automated way, we need to develop new probabilistic models that can handle sources of false positives.
In this talk I will be discussing new computational methods to detect de novo mutations and their application to three different systems: human trios, ciliate mutation accumulation experiments, and yellow box eucalyptus.
TIC Techniques In Computational Genomics
- A community of researchers engaged in, or dependent on, computational analysis of genomic data
- Weekly seminars by volunteers
- Drop-in sessions/round-table discussions convened by the Genome Discovery Unit (GDU)
- Venue alternates between JCSMR and RSB, ANU.
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