BTEP Distinguished Speakers Seminar Series

Katie Pollard and and her team develop models that enable them to decode how genomes work, evolve, and break in disease. Their analyses of massive sets of genomic and epigenomic data include investigating human genetic variation, understanding what makes humans unique compared to other species, and characterizing the genomic diversity of the human microbiome, the group of bacteria that populate our digestive system and other body sites. An evolutionary focus, coupled with rigorous statistical methods and bioinformatics tool development, gives the lab a unique perspective on human biology and disease.

Dr. Dudoit's research and teaching activities concern the development and application of statistical learning methods and software for the analysis of high-throughput -omic data in both basic biology and precision health and medicine.

In this talk, Dr. Stergachis will present data on using Fiber-seq and deaminase-assisted Fiber-seq (DAF-seq) to resolve the functional impact of both germline and somatic genetic variants, as well as to identify somatic epimutations that arise during normal human development and oncogenesis.

The cost of handling rare and/or late-stage diseases far exceeds preventative strategies. Cell and gene therapies can be quite cost-effective when applied at large population scales (e.g. pandemic vaccines and transfusions).   AI ML is revolutionizing protein and RNA design and delivery.

Please note: Registration is required to get the Meeting Link for this event. Please pre-register.

Aviv Regev is head of Genentech Research and Early Development. Formerly, Regev was Chair of the Faculty and Core Member at the Broad Institute of MIT and Harvard, Professor of Biology at MIT, and a Howard Hughes Medical Institute Investigator. She is founding co-chair of the Human Cell Atlas and a leader in deciphering molecular circuits that govern cells, tissues, and organs in health and their malfunction in disease. She has pioneered foundational experimental and computational methods in single-cell genomics, enabling greater understanding of cell and tissue functions. Regev is a member of the National Academy of Sciences, National Academy of Medicine, American Academy of Arts and Sciences, and a Fellow of the International Society of Computational Biology. Her many honors include the ISCB Overton and Innovator Prizes, Paul Marks Prize, Lurie Prize in Biomedical Sciences, Keio Medical Science Prize, HFSP Nakasone Award, and L'Oréal-UNESCO for Women in Science Award.

This event will not be recorded at the speakers' request.

Dr. Blackshaw's work investigates the molecular mechanisms controlling neurogenesis and cell fate specification in the vertebrate forebrain, with a particular focus on the retina. He currently focuses on the use of comparative Single-Cell Multiomic Analysis to identify gene regulatory networks that control retinal development and injury-induced regeneration. He will describe recent work that has used insights from both studying both development and injury-induced neurogenesis in zebrafish to induce glia in mammalian retina to generate neurons.

Telomere to telomere (T2T) genome assemblies represent a paradigm shift in comparative genomics, offering insights into chromosome structure, evolution, and function at the highest resolution. Dr. O'Neill's lab has made recent efforts employing long-read based genome assembly, coupled with epigenetic, functional and repeat analyses, which have afforded the opportunity to delineate key elements participant in centromere function and chromosome rearrangement. Using a comparative approach and long-read, gapless genome assemblies, their studies provide insight into the diversity, distribution, and evolution of repetitive regions that shape chromosome structure and evolution in human and in species groups experiencing rapid karyotypic change. 

Dr. Mardis is an internationally recognized expert in cancer genomics, with ongoing interests in the integrated characterization of cancer genomes, defining DNA-based somatic and germline interactions and RNA-based pathways, and immune microenvironments that lead to cancer onset and progression, specifically involving pediatric cancers. Most recently, her research has been oriented toward translational aspects of cancer genomics, specifically identifying how the cancer genome changes with treatment, including acquired resistance, the use of genomics in understanding immune therapy response, and the clinical benefit of cancer molecular profiling in the pediatric setting.

The Elemento lab combines Big Data analytics with experimentation to develop entirely new ways to help prevent, diagnose, understand, treat and ultimately cure disease. Our research involves routine use of ultrafast DNA sequencing, proteomics, high-performance computing, mathematical modeling, and artificial intelligence/machine learning. We’re revolutionizing healthcare by developing innovative approaches to better predict, diagnose, treat, and prevent disease to improve clinical care for every patient.  

The Brooks Lab developed a computational tool called FLAIR (Full-Length Alternative Isoform Analysis of RNA) to produce confident transcript isoforms from long-read RNA-seq data with the aim of alternative isoform detection and quantification. With an increase in the usage of long-read RNA-seq, there is a growing need for a systematic evaluation of this approach. We are part of an international community effort called the Long-read RNA-seq Genome Annotation Assessment Project (LRGASP) to perform such an evaluation. The Brooks Lab is extending FLAIR to incorporate sequence variation, RNA editing, and RNA modification in isoform detection as well as detection of complex gene fusions from long-read sequencing data.

An exciting opportunity at the intersection of the biomedical sciences and machine learning stems from the growing availability of large-scale multi-modal data (imaging-based and sequencing-based, observational and perturbational, at the single-cell level, tissue-level, and organism-level). Traditional representation learning methods, although often highly successful in predictive tasks, do not generally elucidate underlying causal mechanisms. Dr. Uhler will present initial ideas towards building a statistical and computational framework for causal representation learning and its applications towards identifying novel disease biomarkers as well as inferring gene regulation in health and disease.

In this seminar, Dr. Furlan will share data using single cell genomic technologies after hematopoietic cell transplantation including the molecular approaches and computational tools they have used and developed as they relate to this field.

There is an urgent need to take what we have learned in our new data-driven era of medicine, and use it to create a new system of precision medicine, delivering the best, safest, cost-effective preventative or therapeutic intervention at the right time, for the right patients.  Dr. Butte's teams at the University of California build and apply tools that convert trillions of points of molecular, clinical, and epidemiological data -- measured by researchers and clinicians over the past decade and now commonly termed “big data” -- into diagnostics, therapeutics, and new insights into disease.  Dr. Butte, a computer scientist and pediatrician, will highlight his center’s recent work on integrating electronic health records data from over 8 million patients across the entire University of California, and how analytics on this “real world data” can lead to new evidence for drug efficacy, new savings from better medication choices, and new methods to teach intelligence – real and artificial – to more precisely practice medicine.

While there is a positive correlation between cancer and aging, the mechanisms underlying this relationship remain unclear. Clonal hematopoiesis, a benign condition that is both associated with aging and predisposes to increased risk of development of blood cancers, presents an opportunity to understand the connection between cancer and aging. This seminar will discuss emerging discoveries of mechanisms acting within the hematopoietic stem cells as well as alterations in the bone marrow microenvironment that promote clonal hematopoiesis and transformation to blood cancers.

AI Models of Cancer and Precision Medicine: Building a Mind for Cancer

The long-term objective of the Ideker Lab is to create artificially intelligent, mechanistic models of cancer and neurodegenerative diseases for translation of patient data to precision diagnosis and treatment. We seek to advance this goal by addressing fundamental questions in the field: What are the genetic and molecular networks that promote disease, and how do we best chart these? How do we use knowledge of these networks in intelligent systems for predicting the effects of genotype on phenotype? – Ideker Lab, https://idekerlab.ucsd.edu/research/cancer/

This webinar will be recorded and made available on the BTEP web site: https://bioinformatics.ccr.cancer.gov/btep/btep-video-archive-of-past-classes/ within 48 hours after the event ends. 

The avalanche of easy-to-create genomics data has impacted almost all areas of medicine and science, from cancer patients and microbial diagnostics to molecular monitoring for astronauts in space. In this lecture, new discoveries from RNA- and DNA-sequencing with the FDA’s SEQC study show the ability of single-molecule methods to reveal rare alleles and provide more comprehensive epigenomics maps of patients and cancers. Also, recent technologies and algorithms from our laboratory and others demonstrate that an integrative, cross-kingdom view of patients (precision metagenomics) holds unprecedented biomedical potential to discern risk, improve diagnostic accuracy, and to map both genetic and epigenetic states, as well as clonal changes in mutations with clonal hematopoiesis. Finally, these methods and molecular tools work together to guide comprehensive, longitudinal, multi-omic views of human astronaut physiology and biology in the NASA Twins Study and several other missions with SpaceX and Axiom, which lay the foundation for future, long-duration spaceflight, including sequencing, quantifying, and engineering genomes to survive on other planets over the next 500 years (https://mitpress.mit.edu/books/next-500-years).

The efforts of our laboratory are split evenly between experimental and computational biology.  We develop new experimental methods to sequence single cells and isolate rare subpopulations and develop new analytical approaches to detect variants and apply statistical methods to these data sets.  We focus mainly on breast cancer to understand the role of clonal diversity in the evolution of invasion, metastasis and response to chemotherapy.  We are also using these tools to study rare tumor cell subpopulations including circulating tumor cells and cancer stem cells.  Our goal is to understand the role of clonal diversity in tumor evolution so that we can exploit this diversity for therapeutic vulnerabilities and improve diagnostic tools and the early detection of cancer.  We fully expect that applying these tools to human patients will lead to reduced morbidity in breast cancer.

Sarah Teichmann is co-founder and principal leader of the Human Cell Atlas (HCA) international consortium. The International Human Cell Atlas initiative aims to create comprehensive reference maps of all human cells to further understand health and disease.

The 37 trillion cells of the human body have a remarkable array of specialized functions, and must cooperate and collaborate in time and space to construct a functioning human. In this talk I will describe my lab’s efforts to understand this cellular diversity through a programme of cell atlasing. Harnessing cutting edge single cell genomics, imaging and computational technologies, we investigate development, homeostasis and disease states, at scale and in 3D, with a particular focus on immunity. I will illustrate the relevance of cell atlas-ing for engineering organoids and regenerative medicine, and will share new results providing insights into pacemaker cells from the sinoatrial node of this heart. Overall I hope to illustrate the power of single cell approaches in unlocking fundamental knowledge about the human body.

Making data reusable for discovery and shared analytics across domains is a laborious, specific-skill requiring task that most data providers do not have the resources, expertise, or perspective to perform. Equally challenged are the data re-users, who function in a landscape of bespoke schemas, formats, and coding – when they can get past understanding the licensing and access control issues. Making the most of our collective data requires partnerships between basic researchers, clinicians, patients, and informaticians, as well as sophisticated strategies to address a myriad of interoperability issues. This talk will review different communities endeavors towards these ends from across the translational spectrum.

Our goal is to understand how cellular heterogeneity encodes the molecular structure, function, and regulation of complex biological systems. Primarily using single cell genomics, we analyze systems by profiling their most fundamental units individually – a ‘bottom-up’ approach that allows us to study how diverse groups of cells work together to drive biological processes and behaviors. – Satija Lab

Since Dr. Satija will be presenting unpublished work in this webinar, it will not be recorded or distributed.