GIW XXXI/ISCB-Asia V is fortunate to have several outstanding keynote speakers.
Seoul National University
Mega-scale protein structure prediction and search
U Mass Medical School
Annotating human and mouse candidate cis-regulatory elements in the ENCODE project
Yokohama City University
Technological advances in NGS and bioinformatics for the analysis of human genetic diseases
Harvard Medical School
Hebrew University of Jerusalem
Title: Mega-scale protein structure prediction and search
Structure prediction with AlphaFold2 is set to have a huge impact on biology, medicine, and biotechnology. AlphaFold2 is not only accurate but, if optimized, also fast. Our ColabFold-AlphaFold2 pipeline accurately predicts the structures of a whole proteome within two days on a single GPU, approx. 100 times faster compared to the AlphaFold2 base system. The availability of these methods and Deepmind/EBIs large-scale effort to predict the structure of every UniRef90 protein sequence (>100Mio.) is rapidly increasing the number of available structures. Analysing these structural datasets became a major bottleneck. In particular, a simple search for homologous structures in a database of one million entries takes a week on a single core using currently available tools. To address this issue, we developed Foldseek for fast and sensitive similarity searching through large structural databases. Foldseek is about four orders or magnitude faster than current structural aligners allowing to search in seconds through millions of structures. During this talk I will explain how we designed Colabfold to predict highly accurate structures in seconds as well as how Foldseek efficiently queries large structural database. Both tools are open source and can be accessed at colabfold.com and foldseek.com, respectively.
Dr. Steinegger is an Assistant Professor at the Seoul National University, where he is affiliated to the Biology department, Institute of Molecular Biology and Genetics, Artificial Intelligence Institute and the Bioinformatics Graduate School. His research group focuses on the development of big data and machine learning algorithms to analyse genomic and proteomic sequence data. His group is best known for bioinformatics software to cluster (Linclust), assemble (Plass), search (MMseqs2) sequences and to predict protein structures (AlphaFold2/ColabFold) and search (Foldseek) them. These software packages are used by researchers around the world and were installed hundreds of thousands of times. He studied bioinformatics and computer science at the Technical University Munich and Ludwig Maximilian University of Munich. During this time, he worked as a research assistant of Professor Burkhard Rost, focusing on the development of methods for predicting protein mutation effects. He received his Ph.D. from the Technical University Munich in collaboration with Dr. Johannes Söding at the Max Planck Institute for Biophysical Chemistry for his work on computational methods to assemble, cluster and annotate metagenomic sequencing data. As a Postdoc in the group of Professor Steven L. Salzberg at the CCB at Johns Hopkins University, he developed methods for the identification of pathogenic agents in infectious diseases, the detection of assembly contamination in public datasets and the annotation of missing exons in the human proteome. Dr. Steinegger is an expert on large scale sequence data analysis and method development and an advocate for open science and open source.
Title: Annotating Human and Mouse Candidate cis-Regulatory Elements in the ENCODE Project
The Encyclopedia of DNA Elements (ENCODE) Project has generated tens of thousands of DNase-seq, ChIP-seq, RNA-seq datasets in human and mouse. As part of the ENCODE consortium, we developed a registry of 926,535 human and 339,815 mouse candidate cis-regulatory elements (cCREs), covering 7.9 and 3.4% of their respective genomes, by integrating selected datatypes associated with gene regulation (The ENCODE Consortium et al., Nature, 2020). We built a web-based server named SCREEN to provide flexible, user-defined access to the registry of cCREs and related genomic annotations generated by ENCODE. We created a similar resource Factorbook, focused on transcription factors and their binding sites and motifs derived from ENCODE ChIP-seq data. Recently, we leveraged the genomes of 240 mammals sequenced by the Zoonomia consortium to annotate further cCREs and transcription factor binding sites. I will describe how these resources aid our interpretation of human variants associated with traits and diseases.
Dr. Zhiping Weng is a Professor in Biochemistry and Molecular Pharmacology and Li Weibo Chair in Biomedical Research at the University of Massachusetts Medical School. In her institutional role as Director of Program in Bioinformatics and Integrative Biology, Dr. Weng leads all aspects of research, education, and professional development of the faculty, students, and staff in the Program. She received her B.S. in Electrical Engineering from the University of Science and Technology of China in 1992 and her Ph.D. in Biomedical Engineering from Boston University in 1997.
Dr. Weng’s scientific research and biomedical investigations focus on genomics, epigenomics, transcriptomics, and molecular recognition. Her laboratory develops and applies cutting-edge computational and statistical methods to study biological problems with large amounts of data, e.g., gene regulation by transcriptional and post-transcriptional mechanisms and protein-protein interaction. She has developed a systematic approach to define regulatory elements in the human and mouse genomes based on a select set of predictive epigenetic signals and annotate the activities of these elements across hundreds of cell and tissue types. Her lab has also developed a powerful engineering platform to allow biomedical and clinical scientists to interrogate individual elements and their myriads of annotations, including the variations that are associated with human diseases. Collaborating closely with experimental labs, her lab has also played a key role in dissecting the biogenesis and functions of small silencing RNAs.
Dr. Weng is a national and international leader in large-scale epigenomic sciences. She has led the Data Analysis Center of the ENCODE Consortium since 2012 and co-led the Data Analysis Center of the psychENCODE Consortium since 2015. The goals of these two consortia are to investigate the regulatory landscapes in the human genome, with ENCODE focusing on normal physiology and psychENCODE on psychiatric disorders. Dr. Weng exemplifies the next generation of computational genomicists who leverage the power of computing on big data to understand the mysteries of the human genome.
Dr. Weng has published more than 250 scientific papers, with a total citation of more than 64,500 and an H-index of 106 as of April 2022. She received a Professional Opportunities for Women award in 1998 and a CAREER Award in 2002 from the National Science Foundation. She was elected a Fellow of the American Institute for Medical and Biological Engineering in 2013 and a Fellow of the International Society of Computational Biology in 2020. In 2018, she was awarded the Li Weibo Chair in Biomedical Research by UMass Medical School. In 2020, she co-founded Rgenta, a biotech startup in Cambridge, MA which develops small molecule therapeutics and directs its Scientific Advisory Board. She won the 2022 Charles DeLisi Award in the College of Engineering at Boston University.
Title: Technological advances in NGS and bioinformatics for the analysis of human genetic diseases
Dr. Naomichi Matsumoto obtained his MD at Kyushu University and his PhD at Nagasaki University Graduate School of Medicine in Japan. He conducted his postdoctoral training at University of Chicago. Dr. Matsumoto is currently Full Professor of Human Genetics at Yokohama City University (Japan). He is a member of Japan Society of Human Genetics (a council member) and published more over 700 papers and served as the previous Editor-in-Chief for Journal of Human Genetics (Springer Nature Publishing) in 2014-2020. He has discovered 78 genes associated with various human genetic disorders including TGFBR2, NSD1, STXBP1, WDR45, NOTCH2NLC and many others. His research contributions have been recognized through several awards including the Japan Society of Human Genetics Award for Young Scientist (2003), The Japan Society of Human Genetics Award (2011) and Commendation for Science and Technology from the Japanese Minister of Education, Culture, Sports, Science and Technology.
Kyushu University School of Medicine, M.D., 1986
Graduate School of Medical Science, Nagasaki University School of Medicine, Ph.D., 1997
1986-1993: Obstetrics and Gynecology Practice at Kyushu University and related Hospitals
1997-2000: Postdoctoral Fellow (1997) and Research Associate (1998-2000) at University of Chicago
2000-2003: Associate Professor at Department of Human Genetics, Nagasaki University Graduate School of Biomedical Sciences
2003-present: Professor and Chair at Department of Human Genetics, Yokohama City University graduate School of Medicine
J Hum Genet (2007-) (Editor-in-Chief 2014-2020, Advisory editor 2020-),
Clin Genet (2005-),
Am J Med Genet Part A (2008-) Hum Genet (2014-)
Honors and awards
2003 Japan Society of Human Genetics Award for Young Scientist
2011 Japan Society of Human Genetics Award
2019 A Prize for Science and Technology, Research Category, The Commendation for Science and Technology by the Minister of Education, Culture, Sports, Science and Technology
Kun-Hsing "Kun" Yu, MD, PhD is an Assistant Professor in the Department of Biomedical Informatics at Harvard Medical School. He integrates cancer patients' multi-omics (genomics, epigenomics, transcriptomics, and proteomics) profiles with quantitative histopathology patterns to predict their clinical phenotypes. He developed the first fully automated algorithm to extract thousands of features from whole-slide histopathology images, discovered the molecular mechanisms underpinning the microscopic phenotypes of tumor cells, and successfully identified previously unknown cellular morphologies associated with patient prognosis. Dr. Yu's research interests include quantitative pathology, machine learning, and translational bioinformatics.
PhD, Biomedical Informatics, Stanford University (2012-2016)
PhD Minor, Computer Science, Stanford University (2012-2016)
MD, National Taiwan University, Taiwan (2004-2011)
National Institutes of Health (NIH) Maximizing Investigators' Research Award (2021)
Blavatnik Center for Computational Biomedicine Award (2020)
Schlager Family Award for Early Stage Digital Health Innovations (2018)
Harvard Data Science Fellowship (2017-2019)
Howard Hughes Medical Institute (HHMI) Fellowship (2015-2016)
Winston Chen Stanford Graduate Fellowship (2012-2016)
Best Intern Award, National Taiwan University Hospital, Taiwan (2011)
Presidential Award, National Taiwan University, Taiwan (2005, 2008-2011)