KEYNOTES SPEAKERS of RECOMB
Russ Biagio Altman
Kenneth Fong Professor of Bioengineering, Genetics, Medicine and Biomedical Data Science at Stanford University, USA
Associate Director, Stanford Institute for Human-Centered AI (HAI)
Talk title: Machine learning methods to triage rare pharmacogenetic variation
Pharmacogenetics is the study of how genetics influences drug response phenotypes. It has moved from a research activity into clinical implementation. Most pharmacogenetics discovery has occurred on common genetic variations—in both metabolizing genes as well as genes involved in drug action. However, rare variations in these very important pharmacogenes undoubtedly plays an important role in modulating drug response for a large segment of the population. Thus, in order to bring the full benefits of pharmacogenetics to all, we need methods for discovering and interpreting the effects of rare variations in important genes. This talk will review a recent analysis of the UK Biobank in which we did a population scale inventory of variation, and documented the need to interpret new genetic variation. Next, we will present our efforts to use transfer learning to predict the impact of variation in an important pharmacogene, CYP2D6, as an example of how we can begin to develop clinically-useful interpretations of rare variation.
Russ Biagio Altman is the Kenneth Fong Professor of Bioengineering, Genetics, Medicine, Biomedical Data Science and (by courtesy) Computer Science) and past chairman of the Bioengineering Department at Stanford University. His primary research interests are in the application of computing (AI, data science and informatics) to problems relevant to medicine. He is particularly interested in methods for understanding drug action at molecular, cellular, organism and population levels. His lab studies how human genetic variation impacts drug response (e.g., http://www.pharmgkb.org/). Other work focuses on the analysis of biological molecules to understand the actions, interactions and adverse events of drugs (e.g., http://feature.stanford.edu/). He helps lead an FDA-supported Center of Excellence in Regulatory Science & Innovation.
Dr. Altman holds an AB from Harvard College, and an MD from Stanford Medical School, and a PhD in Medical Information Sciences from Stanford. He received the U.S. Presidential Early Career Award for Scientists and Engineers and a National Science Foundation CAREER Award. He is a fellow of the American College of Physicians (ACP), the American College of Medical Informatics (ACMI), the American Institute of Medical and Biological Engineering (AIMBE), and the American Association for the Advancement of Science (AAAS). He is a member of the National Academy of Medicine (formerly the Institute of Medicine, IOM). He is a past-president, founding board member, and a fellow of the International Society for Computational Biology (ISCB), and a past-president of the American Society for Clinical Pharmacology & Therapeutics (ASCPT). He has chaired the Science Board advising the FDA commissioner, and has served on the NIH Director’s Advisory Committee, and as cochair of the IOM Drug Forum. He is an organizer of the annual Pacific Symposium on Biocomputing, and a founder of Personalis (NASDAQ: PSNL). Dr. Altman is board certified in Internal Medicine and in Clinical Informatics. He received the Stanford Medical School graduate teaching award in 2000 and mentorship award in 2014. He is the founding editor of the Annual Reviews of Biomedical Data Science, and hosts a SiriusXM radioshow (and podcast) entitled “The Future of Everything.”
Manuela Helmer Citterich
Professor of Molecular Biology and Bioinformatics, University of Rome Tor Vergata, Italy
Talk title: RNA secondary structure: chasing functions for lncRNAs
Protein sequence and structure analysis has been at the center of computational biology for years and the algorithms developed reached a great complexity and excellent performances. In the last years, the discovery that eukaryotic genomes share protein coding and also non-coding genes proposed the challenging scope of analyzing the role of non-coding genes in cells. Not all scientists agree on the functional relevance of these genes. Non-coding genes are actively transcribed and processed in cells, but most of them are not yet associated to a known function. New tools for RNA analysis are badly needed, since sequence comparison is a fundamental tool for functional annotation, but often fails when comparing RNA sequence sharing less than 60% sequence identity. It is possible that important functional information is encoded into RNA structure, but experimental data on RNA 3D structure is very sparse and poor. Paying attention to the differences and peculiarities of the different subjects, we repurposed some of the ideas that were successfully exploited in protein bioinformatics, with the final aim of exploring the functional annotation of lncRNAs. All sequences are in hands, but very few 3D structures. We then decided to take advantage of secondary structure analysis, with its pros and cons. RNA secondary structure can be predicted with computational methods, and therefore can be calculated for all RNA molecules of interest, but the reliability of the prediction is not yet entirely satisfactory. We defined an alphabet for the description of RNA secondary structure, and therefore we translate a secondary structure into a sequence of characters. Thanks to this alphabet, we could compute a substitution matrix of secondary structure elements, with the rates of variation of structural elements in functionally related RNAs. The alphabet and the matrix were used for building tools for the global and local comparison of RNAs with very low computational costs, also including evolutionary information. The algorithms for global comparison can be applied to the thousands of transcripts available for the identification of evolutionary relationships. Local comparison is of help in the identification of functional motifs, that can be involved in protein binding or other regulatory roles.
Manuela Helmer Citterich has a degree in Physics, but was attracted by biology very early in her career. She spent 12 years working as an experimental molecular biologist on N crassa mitochondria at Sapienza university in Rome, and on the mechanisms of ColE1 regulation in E coli at the EMBL, Heidelberg. She then switched to computational biology and worked for 2 years with Anna Tramontano, developing a protein docking program at IRBM, in Pomezia. She came back to the university of Tor Vergata in Rome working on the analysis of protein structure, developing methods for the identification of functional 3D motifs and for the decryption of the rules for protein interaction specificity. In the last years, her research group dedicated to the analysis of gene expression in cancer, to the development of a method for the analysis of RNA-seq data in single cell experiments and on the functional annotation of lncRNAs.
Professor of Biological Chemistry at the Hebrew University of Jerusalem, Israel
Director of The Sudarsky Center for Computational Biology
Talk Title: A Magical Mystery Tour in the Viral World
The COVID-19 disease has been announced as a pandemic in March 2020, and within few months plagued the globe resulting in over 8 million identified cases and 430,000 deaths. We will discuss the co-evolution of viruses and their hosts for replication efficiency, protein translation and assembly, and the optimal use of cellular resources. Several unique genomic features make SARS-CoV-2 so successful. The selection of codons information is one way of optimization manifested by viruses. Additionally, viruses find ways to effectively overcome the host defense lines. We will present some epidemiological observations that correlate with the variability in the outcomes of COVID-19 across countries. The possibility that the virus exhausts the cell resources will be presented given the host translation machinery. Lastly, identifying the Achilles Heel of the virus is fundamental for developing drugs and vaccines. Such a global effort combines genomics, proteomics, metabolomics, evolution, and medical data while applying modern computational methods to create a coherent view of the viral infections cycle.
Michal Linial is a Professor of Biochemistry and Bioinformatics of the Hebrew University of Jerusalem (HUJI) and the Director of the Israeli Institute for Advance studies (IIAS) in Jerusalem (from 2012-2018). She served as the Director of the Sudarsky Center for Computational Biology at the Hebrew University and the head of Node for ELIXIR-IL.
Prof. Linial obtained her Ph.D. in Molecular biology (1986) from HUJI studying mechanistic aspects of replication in parasites. She then completed her post-doctoral training at Stanford, CA-USA, on the field of cellular neurochemistry. On 1989 she joined the faculty of HUJI in the Biological Chemistry department. She is a founder (imitated in 1999) and chair of the honor educational program for Computational Biology in HUJI. She is the representative of Israel in the pan-European project of ELIXIR. ML serves in the Board of Directors and as a Vice-President of the International Society of Computational Biology and was selected as a fellow of the society. From 2005 she serves at the steering committee of ISMB, RECOM and ECCB the flagship international conferences of the Bioinformatics and Computational Biology communities. ML was a visiting professor in University of Washington in Seattle and Microsoft Research Center in Cambridge, MA-USA. ML has authored over 180 peer-reviewed papers and contributed to the development of bioinformatics databases and websites that are open to the large communities of the biomedical and Life science researchers. Her current research interests covers host-pathogen co-evolution, protein family evolution, microRNA mode of action in extreme cellular conditions and researching the molecular basis of aging and metabolic diseases. She applies large-scale technologies including next generation sequencing, genomics, GWAS, protein structure, mass spectrometry and evolution for revealing the different regulation levels cell in health and disease.
Professor, Human Genome Center, Institute of Medical Science, University of Tokyo, Japan
Talk Title: Cancer Big Data Challenges from Genomes to Networks
We present our computational methods and their analyses in Cancer Systems Biology that make full use of the supercomputers at Human Genome Center of University of Tokyo and K computer at RIKEN Center for Computational Science, then the post K computer named “Fugaku” whose performance is about 400PFLOPS to which we contributed as its co-design. The first is a pipeline Genomon (https://github.com/Genomon-Project/) for cancer genome analysis that enables us to perform very sensitive and accurate detection of most types of genomic variants and transcriptomic changes. It led us to important discoveries in cancer genomics and systems (NEJM 2015; 373(1): 35-47 (aplastic anemia), Nat Genet 2015; 47(11): 1304-15 (ATL), Nature 2016;534(7607):402-6, Nature 2019;565(7739):312-317 (age-related remodelling)). Genomon is now running on “Fugaku” that can perform more than 2000 WGS data analysis in a day. The second is a series of computational methods for unraveling gene networks and their diversity lying over genetic variations, mutations, environments and diseases from gene expression profiles of cancer cells. We developed methods for exhibiting how gene networks vary from samples to samples, according to a modulator representing characteristics of cells, e.g. survival, drug resistance. We performed a global search for lncRNAs affecting MYC activity using a systems biology-based approach with K supercomputer and the GIMLET algorithm based on local distance correlations. Consequently, a long noncoding RNA named MYMLR was identified and experimentally shown to maintain MYC transcriptional activity and cell cycle progression despite the low levels of expression (EMBO J 2019;38(17):e98441). The methods are also extended to analyze “clone networks” based on clonal diversity. Our preliminary analysis for clonal evolution of aplastic anemia is presented.
Satoru Miyano, PhD, is the Founding Director of M&D Data Science Center, Tokyo Medical and Dental University. After finishing the Director at Human Genome Center, Institute of Medical Science, University of Tokyo, he started this medical data science odyssey from April 2020. He received the B.S. (1977), M.S. (1979) and PhD (1984), all in Mathematics from Kyushu University, Japan. He has been working in the field of Bioinformatics and joined Human Genome Center in 1996. His research mission is to develop “Computational Medical Systems Biology towards Genomic Personalized Medicine”, in particular, cancer research and clinical sequence informatics. He has been involved as PI with MEXT Scientific Research on Innovative Areas “Systems Cancer Project”, “Systems Cancer in Neodimension”, the International Cancer Genome Consortium, MEXT Large-Scale Data Analysis with K computer, and Post-K Computer “Fugaku” Project. He is an ISCB 2013 Fellow. The 2016 Uehara Memorial Foundation Award was given for contributions to cancer genomics.
Ronald R. Taylor Chair and Distinguished Professor at University of California at San Diego, USA
Howard Hughes Medical Institute Professor, USA
Director, NIH Center for Computational Mass Spectrometry, USA
Talk Title: Genome Assembly: From Short To Long Reads
Long-read assemblies improved over the short-read assemblies because of their greater ability to disambiguate genomic repeats. However, most algorithms for assembling long reads construct contiguous genomic segments (contigs) but do not provide accurate repeat characterization (repeat graph) necessary for producing optimal assemblies. We present the Flye assembly algorithm that does not attempt to construct contigs at the initial assembly stage but instead generates arbitrary paths (disjointigs) in the unknown repeat graph and constructs a repeat graph from these error-riddled disjointigs. Counter-intuitively, this seemingly reckless approach results in an accurate repeat graph and improves on the state-of-the-art long-read assemblers. We further describe the development of the Flye assembly toolkit that includes metaFlye (metagenome assembly), centroFlye (centromere assembly), and mosaicFlye (assembly of segmental duplications).
This is a joint work with Mikhail Kolmogorov, Jeffrey Yuan, Yu Lin, Anton Bankevich, and Andrey Bzikadze.
Pavel Pevzner is Ronald R. Taylor Professor of Computer Science and Engineering and Director of the NIH Center for Computational Mass Spectrometry at University of California, San Diego. He holds Ph.D. from Moscow Institute of Physics and Technology, Russia. He was named Howard Hughes Medical Institute Professor in 2006. He was elected the Association for Computing Machinery Fellow in 2010, the International Society for Computational Biology Fellow in 2012, the European Academy of Sciences member (Academia Europaea) in 2016, and the American Association for Advancement in Science (AAAI) Fellow in 2018. He was awarded a Honoris Causa (2011) from Simon Fraser University in Vancouver, the Senior Scientist Award (2017) by the International Society for Computational Biology, and the Kanellakis Theory and Practice Award from the Association for Computing Machinery (2019). Dr. Pevzner authored textbooks "Computational Molecular Biology: An Algorithmic Approach", "Introduction to Bioinformatics Algorithms" (with Neal Jones), “Bioinformatics Algorithms: an Active Learning Approach” (with Phillip Compeau), and “Learning Algorithms through Programming and Puzzle Solving” (with Alexander Kulikov). He co-developed the Bioinformatics and Data Structure and Algorithms online specializations on Coursera as well as the Algorithms Micro Master Program at edX.
Professor of Computer Science, Weizmann Institute, Israel
Talk Title: Personalizing treatments using microbiome and clinical data
Accumulating evidence supports a causal role for the human gut microbiome in obesity, diabetes, metabolic disorders, cardiovascular disease, and numerous other conditions. I will present our research on the role of the human microbiome in health and disease, ultimately aimed at developing personalized medicine approaches that combine human genetics, microbiome, and nutrition.
In one project, we tackled the subject of personalization of human nutrition, using a cohort of over 1,000 people in which we measured blood glucose response to >50,000 meals, lifestyle, medical and food frequency questionnaires, blood tests, genetics, and gut microbiome. We showed that blood glucose responses to meals greatly vary between people even when consuming identical foods; devised the first algorithm for accurately predicting personalized glucose responses to food based on clinical and microbiome data; and showed that personalized diets based on our algorithm successfully balanced blood glucose levels in prediabetic individuals.
Using the same cohort, we also studied the set of metabolites circulating in the human blood, termed the serum metabolome, which contain a plethora of biomarkers and causative agents. With the goal of identifying factors that determine levels of these metabolites, we devised machine learning algorithms that predict metabolite levels in held-out subjects. We show that a large number of these metabolites are significantly predicted by the microbiome and unravel specific bacteria that likely modulate particular metabolites. These findings pave the way towards microbiome-based therapeutics aimed at manipulating circulating metabolite levels for improved health.
Finally, I will present an algorithm that we devised for identifying variability in microbial sub-genomic regions. We find that such Sub-Genomic Variation (SGV) are prevalent in the microbiome across multiple microbial phyla, and that they are associated with bacterial fitness and their member genes are enriched for CRISPR-associated and antibiotic producing functions and depleted from housekeeping genes. We find over 100 novel associations between SGVs and host disease risk factors and uncover possible mechanistic links between the microbiome and its host, demonstrating that SGVs constitute a new layer of metagenomic information.
Eran Segal is a Professor at the Department of Computer Science and Applied Mathematics at the Weizmann Institute of Science, heading a lab with a multi-disciplinary team of computational biologists and experimental scientists in the area of Computational and Systems biology. His group has extensive experience in machine learning, computational biology, and analysis of heterogeneous high-throughput genomic data. His research focuses on Microbiome, Nutrition, Genetics, and their effect on health and disease. His aim is to develop personalized medicine based on big data from human cohorts.
Prof. Segal published over 150 publications, and received several awards and honors for his work, including the Overton prize, awarded annually by the International Society for Bioinformatics (ICSB) to one scientist for outstanding accomplishments in computational biology, and the Michael Bruno award. He was recently elected as an EMBO member and as a member of the young Israeli academy of science.
Before joining the Weizmann Institute, Prof. Segal held an independent research position at Rockefeller University, New York.
Prof. Segal was awarded a B.Sc. in Computer Science summa cum laude in 1998, from Tel-Aviv University, and a Ph.D. in Computer Science and Genetics in 2004, from Stanford University.
Lab website: http://genie.weizmann.ac.il
Due to rescheduling the following two keynotes will not be able to present at RECOMB 2020:
Chair and Professor of Neurology, University of Padua, Italy
Director of Research, Inria at University of Lyon 1, France
Marie-France Sagot will be a keynote speaker at RECOMB 2021 in Padova!