Geneticist; Crop Improvement and Genetics Research Unit, Agricultural Research Service, Western Regional Research Center, Albany, CA USA.
Tim Levine trained first as a medic then moved into membrane cell biology, and then into intracellular lipid traffic. He showed that inter-organellar contacts are important sites for non-vesicular traffic inside cells. This was part of a revolution in our understanding of intracellular organelles. For over 40 years previously membrane contact sites had been largely ignored or dismissed as artefacts. Tim initially found a lipid transfer protein that localised to a contact site, and showed that it bound to the endoplasmic reticulum (ER) protein VAP via a motif he named the FFAT motif. FFAT motifs are present in several other lipid transfer proteins leading Tim to propose that FFAT-motif proteins would act at contact sites by binding simultaneously to both the ER and another membrane. By improving the definition of FFAT-like motifs, Tim showed they are present in numerous other proteins, facilitating molecular research of many contact site components. Tim organised the first two conferences on contact sites in 2005 and 2011, linking advances in lipid traffic to those in calcium traffic to bring together these overlapping sub-disciplines.
Tim has also used remote homology tools to identify a new family of lipid transfer proteins anchored at contact sites, and highlighted the power of these tools through specific examples and a ‘How-To’ guide.
Staff Scientist at Lawrence Berkeley National Laboratory. Fellow of the American Association for the Advancement of Science. Joint winner, American Association for the Advancement of Science Newcomb Cleveland Prize for best paper of the year: "The genome sequence of D. melanogaster."
I am an Assistant Professor in the Department of Statistics and Department of Human Genetics at University of California, Los Angeles. I am also a faculty member in the Interdepartmental Ph.D. Program in Bioinformatics and a member in the Jonsson Comprehensive Cancer Center (JCCC) Gene Regulation Research Program Area. Prior to joining UCLA, I obtained my Ph.D. degree from the Interdepartmental Group in Biostatistics at University of California, Berkeley, where I worked with Profs Peter J. Bickel and Haiyan Huang. I received my B.S. (summa cum laude) from Department of Biological Sciences and Technology at Tsinghua University, China in 2007.
Dr. Xing Li is an Assistant Professor and Associate Consultant in Division of Biomedical Statistics and Informatics, Department of Health Science Research at Mayo Clinic, the best hospital by U.S. News & World Report. Dr. Li completed his PhD in Bioinformatics from The University of Michigan at Ann Arbor, Michigan, USA. Dr. Li also hold a Master Degree in Biochemistry and Molecular Biology and Bachelor Degree in Microbiology. Dr. Li’s research interests focus on machine learning, bioinformatics, and statistical data mining in large scale data in biomedical research, such as next generation sequencing data (whole genome sequencing, RNA-seq, microarray data), in the file. He has published more than 20 peer-reviewed papers in reputed journals and book chapters in the fields of Bioinformatics and Biostatistics, cancer research, cardiovascular disease, embryonic stem cell (ESC) and induced pluripotent stem cell (iPSC) research, and human genomics, genetics and development, and Microbiology. Dr. Li’s publications have been highlighted as Journal Cover Stories, Journal Featured Articles, Highlights Section Papers, Must Read by Faculty 1000, and ESC & iPSC News, etc. Dr. Li has been developing data analysis tools, such as RCircle and PCA3d, etc. Dr. Li is also a member of American Association for Cancer Research (AACR), International Society for Computational Biology (ISCB), American Statistics Association (ASA) and American Heart Association (AHA).
Professor in Bioinformatics, Biology Department, Miami University, Ohio, USA
Research Assistant Professor of Biomedical Informatics, Vanderbilt University
I am an Assistant professor at Department of Biochemistry, State University of New York at Buffalo. I have expertise and extensive experience with developing and applying computational approaches for transcriptional and epigenetics regulation studies. As a postdoctoral fellow at Dana-Farber Cancer Institute, I developed widely used open-source algorithms, including MACS (cited over 3,200 times according to Google Scholar) to analyze ChIP-seq data, and an integrative platform for comprehensive analyses on cis-regulatory elements (http://cistrome.org/ap), which has over 3,000 users. I was a member of the Data Analysis Center and Analysis Working Group of the ENCODE and modENCODE consortium and was involved in deciphering functional elements through analyzing high-throughput profiles of chromatin factors and in comparing chromatin features between fly, worm and human genome. I have actively participated in the development of ChIP-seq guidelines for the broad scientific communities. My laboratory at University at Buffalo is focused on studying transcriptional and epigenetic regulatory mechanisms, and the influence of the genetic variations at regulatory elements.
Nick works as an Independent Research Fellow in the Institute for Microbiology and Infection at the University of Birmingham, sponsored by an MRC Fellowship in Biomedical Informatics. His research explores the use of cutting-edge genomics and metagenomics approaches to the diagnosis, treatment and surveillance of infectious disease. Nick has so far used high-throughput sequencing to investigate outbreaks of important pathogens such as Pseudomonas aeruginosa,Acinetobacter baumannii and Shiga-toxin producing Escherichia coli. His current work focuses on the application of novel sequencing technologies such as the Oxford Nanopore for genome diagnosis and epidemiology of important pathogens, including most recently real-time surveillance of the Ebola outbreak in West Africa. A more general aim is to develop bioinformatics tools to aid the interpretation of genome and metagenome-scale data in routine clinical practice in collaboration.
Tao Lu Professor, Department of Medicine, State University of New York.
Research interests include: Clinical research of infectious diseases; immunology; Diabetes; Signaling pathway; Endocrinology; Psychiatry; Respiratory Medicine; Sports medicine
Professor, Graduate School of Library & Information Science (GSLIS), National Center for Supercomputing Applications (NCSA), and Dept. of Computer Science at UIUC; Director, Center for Informatics Research in Science & Scholarship (CIRSS).
Previously Professor at the Dept. of Computer Science & Genome Center, UC Davis.
MS in Computer Science from U Karlsruhe (now KIT), PhD in Computer Science from U Freiburg (Germany). Research scientists at SDSC/UCSD until 2004.
My group is interested in investigating the processes of evolution and biology using computational methods. We apply machine learning methods (HMMs, Bayesian statistics, particle filters, deep learning) to large data sets to study for example human demographic history or non-coding functional elements in the genome.