Special MIG Seminars – Rafael Irizarry & Marylyn Ritchie – 14th February, 2018

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Andrew Siebel


T: +61 3 8344 0707

Wednesday 14th February
FW Jones Theatre, Medical Building, The University of Melbourne

Rafael Irizarry

Department of Statistics, Harvard University & Dana–Farber Cancer Institute, US

Accounting for systematic bias in bulk and single cell RNA-Seq data

In this talk I will demonstrate the presence of bias, systematic error and unwanted variability in next generation sequencing. I will show the substantial effects these have on downstream results and how they can lead to misleading biological conclusions. I will do this using data from the public repositories as well as our own. We will then describe some preliminary solutions to these problems.



FW Jones Theatre, Medical Building, The University of Melbourne

Marylyn Ritchie

Department of Genetics, University of Pennsylvania, US
Director, Center for Translational Bioinformatics, Institute for Biomedical Informatics (IBI)

Exploring the relationship between the genome and the phenome

Modern technology has enabled massive data generation; however, tools and software to work with these data in effective ways are limited. Genome science, in particular, has advanced at a tremendous pace during recent years with dramatic innovations in molecular data generation technology, data collection, and a paradigm shift from single lab science to large, collaborative network/consortia science.  Still, the techniques to analyze these data to extract maximal information have not kept pace.  Comprehensive collections of phenotypic data can be used in more integrated ways to better subset or stratify patients based on the totality of his or her health information.  Similar, the availability of multi-omics data continues to increase.  With the complexity of the networks of biological systems, the likelihood that every patient with a given disease has exactly the same underlying genetic architecture is unlikely. Success in understanding the architecture of complex traits will require a multi-pronged approach.   Through applying machine learning to the rich phenotypic data of the EHR, these data can be mined to identify new and interesting patterns of disease expression and relationships.  Machine learning strategies can also be used for meta-dimensional analysis of multiple omics datasets.  We have been exploring machine learning technologies for evaluating both the phenomic and genomic landscape to improve our understanding of complex traits.  These techniques show great promise for the future of precision medicine.


Enquiries: Andrew Siebel (asiebel@unimelb.edu.au)