In addition to three keynote speakers, Friday morning, there will be a parallel -omics track featuring three speakers, Dr. Nesvizhskii, an thought leader in proteogenomics, Dr. Girke whose focus is chemical informatics and analysis of RNA-seq data, and Dr. Emmert-Streib, an expert in medical genomics.
Prof. Alexey I. Nesvizhskii, Ph.D.
Dr. Alexey Nesvizhskii is a tenured Associate Professor in the Departments of Computational Medicine & Bioinformatics and Pathology at the University of Michigan, Ann Arbor. He received his M.S. degree (with honors) from St. Petersburg State Technical University, Department of Physics and Technology, St. Petersburg, Russia in 1995 and Ph.D. degree in Physics from the University of Washington, Seattle, USA, in 2001. He then completed post doctoral training in the area of bioinformatics and proteomics in Ruedi Aebersold Lab at the Institute for Systems Biology in Seattle, Washington from 2001-2003, and joined the staff as a Research Scientist upon completion of training. He joined the faculty at the University of Michigan in November of 2005.
Dr. Nesvizhskii's research laboratory (www.nesvilab.org) is working in the area of bioinformatics, proteomics, and systems biology. The computational tools previously developed by Dr. Nesvizhskii and his colleagues, such as Trans-Proteomic Pipeline (including PeptideProphet and ProteinProphet), PeptideAtlas, SAINT, and CRAPome, are used by hundreds of laboratories worldwide. His lab actively collaborates with technology developers, biologists, and clinical scientists on a variety of projects, including analysis protein interaction networks, integrative modeling of multi-omics data to reconstruct targetable pathways in cancer, and biomarker discovery. Dr. Nesvizhskii’s research is currently funded by the US National Institutes of Health and the National Institute of Standards and Technology.
Dr. Nesvizhskii has published more than 100 manuscripts in international scientific journals, including first or senior author publications in such leadingjournals as Science, Nature Methods, Molecular Systems Biology, and Nature Communications. His works are cited more than 12,000 times, H-index of 42 (Google Scholar; August 2014). In 2007, he was named a "Rising Young Investigator" by Genome Technology magazine (USA). Dr. Nesvizhskii serves as Senior Editor in the area of bioinformatics and biostatistics for international journals Proteomics and Proteomics-Clinical Applications, as Section Editor in the area of proteomics for BMC Bioinformatics, and on the Editorial Boards of Molecular and Cellular Proteomics. Dr. Nesvizhskii also serves on the Scientific Advisory Board for Swiss Institute of Bioinformatics and on the Board of Directors for the US Human Proteome Organization. He is frequently invited to present his research at seminars and conferences in the United States and internationally, and to serve on grant review panels for the National Institutes of Health (NIH), National Science Foundation, Genome Canada, Dutch Cancer Society, and other agencies.
As an enthusiastic educator, Dr. Nesvizhskii directs the NIH funded T32 Proteome Informatics of Cancer Training Program at the University of Michigan, and teaches graduate-level courses in the area of bioinformatics, proteomics, and systems biology. In addition, he has developed and taught several internationally recognized hands-on workshops and tutorials, including a five day Proteome Informatics course at the Institute for Systems Biology in Seattle. His educational and research efforts have been recognized by several awards, including induction in the League of Educational Excellence at the University of Michigan Medical School.
Dr. Thomas Girke
Dr. Girke has over 12 years of experience in developing and managing computational biology research projects in industry and academia. Dr. Girke’s current research focuses on the development of computational data analysis methods for genome biology and small molecule discovery. This includes discovery-oriented data mining projects, as well as algorithm and software development projects for data types from a variety of high-throughput technologies, such as next generation sequencing (NGS) and chemical genomics.
Another important activity is the development of data analysis environments for the open source software projects R and Bioconductor.
Currently, Dr. Girke’s group maintains a total of 7 cheminformatics and NGS analysis packages at Bioconductor: ChemmineR, ChemmineOB, fmcsR, eiR, bioassayR, ChemmineDrugs and systemPipeR. As part of my teaching curriculum, I am instructing two advanced bioinformatics classes for undergraduate and graduate students. Furthermore, Dr. Girke is the main instructor of an annual 5-day next generation data analysis workshop that is attended every year by over 75 internal and external participants from academia and industry. Dr. Girke is also instructing similar events at external trainings events. In role as Director of the Bioinformatics Facility in the Institute of Integrative Genome Biology (IIGB) at UCR, Dr. Girke have been managing a state-of-the-art research compute facility that includes large compute clusters, data storage systems and hundreds of software applications for analyzing next generation sequencing and many other high-throughput data. Well over 150 scientists from 50 research groups are using this infrastructure extensively.
Introduction to R
- Basics on Analyzing Next Generation Sequencing Data with R/Bioconductor
- Analysis of RNA-Seq Data with R/Bioconductor
Dr. Frank Emmert-Streib
Frank Emmert-Streib studied physics at the University of Siegen, Germany, and earned his Ph.D in theoretical physics from the University of Bremen. After postdoc positions in the United States, he joined the Center for Cancer Research and Cell Biology at the Queen’s University Belfast (United Kingdom), where he is currently an associate professor (senior lecturer) leading the Computational Biology and Machine Learning Laboratory. His research interests are in the fields of computational biology, biostatistics, and network medicine and are focused on the development and application of methods from statistics and machine learning for the analysis of high-dimensional data from genomics experiments.