Dr. Glusman investigates genome structure and evolution; multi-gene families; prediction and discovery of genes and transcripts; genes not coding for proteins; visualization of complex data; and image analysis.
Representing the diversity of human proteins: from sequence to structure annotation.
Annotating the effects of human protein variations in the context of cancers and genetic diseases, and analysing results of high-throughput experiments to shed light on the function of selected sets of uncharacterized human proteins
Mining the 1000 Bull genomes, to link gene variants to protein function.
We are interested in how the same, within an individual mostly invariant genome, can give rise to functionally extremely diverse cell types – such as the ones that are the building blocks of the human brain.
Lunch & Poster Session
Session 3: Mapping Variations to Structures - Part 1
Dr. Omenn's research focuses on cancer proteomics and informatics. He is especially interested in the role of differential expression of alternative splice isoforms of proteins and transcripts in specific cancer-related pathways.
3D variant interpretation using whole genome sequencing data
Dr. Hannah Carter’s research focuses on computationally modeling how DNA mutations in tumor genomes impact intracellular biological processes and cellular behaviors, and how these cellular level changes cause cancer.
Comparing mutations in tumour supressors and oncogenes
Frances Pearl Bioinformatics Academic Research Manager (Biochemistry), University of Sussex
Geoff Barton is Professor of Bioinformatics and Head of the Division of Computational Biology at the University of Dundee School of Life Sciences. Before moving to Dundee in 2001 he was Head of the Protein Data Bank in Europe and Research and Development Team leader at the EMBL European Bioinformatics Institute, Hinxton, Cambridge. Prior to EMBLEBI he was Head of Genome Informatics at the Wellcome Trust Centre for Human Genetics, University of Oxford, a position he held concurrently with a Royal Society University Research Fellowship in the Department of Biochemistry.
Geoff’s longest running research interest is the study of the relationship between a protein’s sequence, its structure and function by computational methods. His work exploits large publicly available datasets to identify novel properties and to develop effective prediction methods. His group have contributed many tools and techniques in the area of protein sequence and structure analysis and structure prediction. Two of the best known are the Jalview (www.jalview.org) multiple alignment visualisation and analysis workbench which is in use by over 70,000 groups for research and teaching, and the JPred (www.compbio.dundee.ac.uk/jpred) multi-Neural Net protein secondary structure prediction algorithm that performs predictions on up to 250,000 proteins/month for users worldwide. In addition to his work on protein sequence and structure, Geoff has collaborated on many projects that probe biological processes by proteomics and highthroughput sequencing. In particular, he has a long-running collaboration with Prof Gordon Simpson that seeks to understand the control of mRNA termination and methylation. Geoff’s group has deep expertise in RNA-seq methods and has recently published a two-condition 48 replicate RNA-seq study that is now a key reference work for users of this technology.
Using high throughput assays of mutation effects to drive protein design.
Mapping variant-specific cystic fibrosis-related endophenotypes to CFTR structure
We are mapping continuous-valued functional and clinical data from cystic fibrosis-associated CFTR variants onto CFTR homology models. Two functional assays report either the degree of CFTR misfolding or the attenuation in chloride conductance resulting from specific CFTR variants. Additionally, we consider a clinical measurement of variant-specific cystic fibrosis disease severity (sweat chloride level). The clustering of these endophenotypes on the 3D CFTR structure could help identify specific CFTR structural regions that are enriched for disease-causing variants, and to infer mechanism.
David Masica Assistant Research Professor, Johns Hopkins University
Sheila is interested in applying a wide range of machine-learning techniques to large heterogeneous datasets to further our understanding of the complex interplay of genomic rearrangements and epigenetic alterations in cancer and to help translate these findings into new and improved treatment protocols.
Lunch & Poster Session
Session 7: Breakout Sessions
Chair: Eric Deutsch
Breakout organizers: Eric Deutsch, Andreas Prlić, Gustavo Glusman, Gil Omenn
Session 8: Future
Chair: Andreas Prlić
Reports back from breakout groups
Open Discussion Day 2
Eric Deutsch Senior Research Scientist, Institute for Systems Biology