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October 22

Creating the next generation of genome analysis tools with deep learning

Kishwar Shafin
Google

Part Of

11:30A
- 1:00P

Location

32-G575
Add to Calendar 2025-10-22 11:30:00 2025-10-22 13:00:00 America/New_York Creating the next generation of genome analysis tools with deep learning Deep learning is fueling a revolution in genomics, enabling the development of a new generation of analysis tools that offer unprecedented accuracy. This talk presents a suite of deep learning models designed to address fundamental challenges in variant calling and generating high-quality genome assemblies. We begin with DeepVariant, a convolutional neural network that redefined the standard for germline variant calling, and its extension, DeepSomatic, which adapts this technology to the critical task of identifying low-frequency somatic mutations in cancer genomes. Moving from variant analysis to genome construction, we introduce DeepPolisher. This tool leverages a powerful Transformer-based architecture to significantly reduce errors in genome assemblies, providing a more accurate and reliable foundation for downstream research. Finally, we explore the future of variant calling by integrating these methods with emerging pangenome references. We demonstrate how a pangenome-aware approach allows for a more comprehensive survey of human genetic diversity, resolving variation in previously intractable regions of the genome. Together, these tools represent a cohesive framework that is building the next generation of genomic analysis, transforming our ability to accurately read and interpret the code of life. TBD

October 15

From Data to Knowledge: Integrating Clinical and Molecular Data for Predictive Medicine

Marina Sirota
UC San Francisco

Part Of

11:30A
- 1:00P

Location

32-G575
Add to Calendar 2025-10-15 11:30:00 2025-10-15 13:00:00 America/New_York From Data to Knowledge: Integrating Clinical and Molecular Data for Predictive Medicine AbstractAlzheimer’s disease (AD) remains one of the most pressing medical challenges, with limited therapeutic options and heterogeneous disease trajectories complicating diagnosis and treatment. Recent advances in computational biology and artificial intelligence (AI) together with availability of rich molecular and clinical data, offer new opportunities to address these challenges by integrating molecular, clinical, and systems-level insights. In our recent studies, we developed a cell-type-directed, network-correcting approach to identify and prioritize rational drug combinations for AD, enabling targeted modulation of disease-relevant pathways across distinct cellular contexts (Li et al., Cell 2025). Complementarily, by leveraging large-scale electronic medical records (EMRs) integrated with biological knowledge networks, we demonstrated the ability to predict disease onset and progression while uncovering mechanistic insights into AD heterogeneity (Tang et al., Nature Aging 2024). Together, these complementary approaches illustrate the power of combining real-world clinical data, knowledge networks, and systems pharmacology to advance precision medicine for AD. This work highlights a paradigm shift toward AI-enabled, data-driven strategies that bridge molecular discovery and clinical application, ultimately informing novel therapeutic interventions and improving patient care.Speaker BioMarina is currently a Professor and the Interim Director at the Bakar Computational Health Sciences Institute at UCSF. Prior to that she has worked as a Senior Research Scientist at Pfizer where she focused on developing Precision Medicine strategies in drug discovery. She completed her PhD in Biomedical Informatics at Stanford University. Dr. Sirota’s research experience in translational bioinformatics spans nearly 20 years during which she has co-authored over 170 scientific publications. Her research interests lie in developing computational integrative methods and applying these approaches in the context of disease diagnostics and therapeutics with a special focus on women’s health. The Sirota laboratory is funded by NIA, NLM, NIAMS, Pfizer, March of Dimes, and the Burroughs Wellcome Fund. As a young leader in the field, she has been awarded the AMIA Young Investigator Award in 2017.  She leads the UCSF March of Dimes Prematurity Research Center at UCSF as well as co-directs ENACT, a center to study precision medicine for endometriosis. Dr. Sirota also is the founding director of the AI4ALL program at UCSF, with the goal of introducing high school girls to applications of AI and machine learning in biomedicine. TBD

October 08

AI integrating imaging and genetics to understand human evolution, development, aging, and disease

Vagheesh Narasimhan
UT Austin

Part Of

11:30A
- 1:00P

Location

32-G575
Projected in 32-G575
Add to Calendar 2025-10-08 11:30:00 2025-10-08 13:00:00 America/New_York AI integrating imaging and genetics to understand human evolution, development, aging, and disease AbstractImaging has been the primary means of diagnosing as well as tracking the progression of many diseases for decades but has largely been collected in isolation. Recently through the advent of large scale biobanks, this rich type of data has become linked with genetic and electronic health care record data at the level of tens of thousands of individuals, providing an unprecedented ability to study the relationship between genotype and phenotype directly in humans. I will discuss our group's work leveraging >1.2M medical images (DXA, MRI, and ultrasound) from ~60,000 individuals across multiple views of the heart, brain, skeleton, liver, and pancreas to provide new insights in 4 different domains of biological science: (a) to understand the evolution of the human skeletal form which underlies our ability to be bipedal; (b) examining the classical question in developmental biology of the genetic basis of left-right symmetry; (c) building biological aging clocks to study mechanisms of age acceleration/deceleration and to identify gene targets to combat aging; (d) multi-modal AI combining imaging, genetics, and metabolics to predict 10-year disease incidence for common complex disease.Speaker BioAfter initially training in Electrical Engineering focusing on computer vision and information theory, Vagheesh did a Masters in Biostatistics under Curtis Huttenhower, and then moved to the University of Cambridge to do a PhD in Genetics with Chris Tyler Smith and Richard Durbin. He returned to Harvard as a postdoc with David Reich and Nick Patterson, and since 2020 he has been an Assistant Professor in the Departments of Integrative Biology as well as Statistics and Data Science at the University of Texas at Austin. TBD

October 01

Furthering our understanding of human genetic variation: the human pangenome reference project second release

Benedict Paten
UC Santa Cruz

Part Of

11:30A
- 1:00P

Location

32-G575
Add to Calendar 2025-10-01 11:30:00 2025-10-01 13:00:00 America/New_York Furthering our understanding of human genetic variation: the human pangenome reference project second release AbstractHuman genomics has relied on a single reference genome for the last twenty years. This reference genome is a cornerstone of much of what we do in genomics but it can not, by definition, represent the variation present in the human population, and as a reference introduces a pervasive bias into genomic analyses. I will survey our recent efforts, through the Human Pangenome Reference Consortium, to build and use a reference pangenome—a collection of extremely high-quality reference genomes related together by a consensus genome alignment that we intend as a replacement for the reference genome.Speaker BioDr. Benedict Paten is a professor in the department of Biomolecular Engineering at the University of California, Santa Cruz. He is also associate director of the UC Santa Cruz Genomics Institute. He received his Ph.D. in computational biology from the University of Cambridge and the European Molecular Biology Laboratory. Dr. Paten’s work is broadly focused on the growing field of computational genomics. He is involved in a number of large-scale efforts, currently he is a PI of the Human Cell Atlas Data Platform, the NHGRI AnVIL, HuBMAP, GENCODE, and the Human Pangenome Reference Consortium. Through these efforts he is helping to develop methods to further our ability to assay and understand genomes. TBD

September 24

Discovering New Biochemistry from Biological Conflicts

L. Aravind
NCBI

Part Of

11:30A
- 1:00P

Location

32-G575
Add to Calendar 2025-09-24 11:30:00 2025-09-24 13:00:00 America/New_York Discovering New Biochemistry from Biological Conflicts AbstractBiological replicators are locked in deeply intertwined genetic conflicts with each other. Using comparative genomics, protein sequence and structure analysis and evolutionary investigations, my lab has uncovered a staggering diversity of molecular armaments and mechanisms regulating their deployment, collectively termed biological conflict systems. These include toxins used in interorganismal interactions and a host of mechanisms involved in self/nonself discrimination, especially in the context of host-selfish element conflicts. Our studies have helped identify shared syntactical features in the organizational logic of biological conflict systems. These principles can be exploited to discover new conflict systems through computational analyses. Further, we find that across the range of biological organization, from intragenomic conflicts to interorganismal conflicts, a circumscribed set of effector protein domain families is deployed, targeting genetic information flow through the Central Dogma, certain membranes, and key molecules like NAD+ and NTPs. This has led to significant advances in discovering new biochemistry of these systems and furnished new biotechnological reagents for genome editing, sequencing and beyond. I’ll discuss this using specific examples of toxins in interorganismal conflict and effectors in antiviral immunity.Speaker BioI obtained my PhD (computational biology) in 1999 from Texas A & M University, though I did most of my dissertation research at the NIH. Resuming my research as a staff scientist at the NLM/NIH in 2000, I started my own lab at the same place at the beginning of 2002. My research encompasses the evolutionary classification of proteins, the prediction of novel biochemical activities and the inference of organismal biology from comparative sequence, structure and genome analysis. My research team and I have made several discoveries, predicting previously unknown enzymatic and ligand interactions of numerous protein domains, novel transcription factors and understanding the interplay between natural selection and structural/genomic constraints in shaping the diversity of protein domains. The fundamental contributions of my lab include the discovery of key proteins participating in RNA biochemistry, protein stability, DNA modification, toxin systems involved in biological conflicts, apoptosis and novel immune mechanisms (e.g., key components of the CRISPR systems) and providing the theoretical framework for their functioning. I have developed the synthetic hypothesis on the role of biological conflicts in shaping biochemical innovation and major evolutionary transitions. Trainees (postdocs and students)from my lab have gone on to become faculty in institutions around the world or serve in the industry. TBD

September 17

Discovering Safe, Effective Drugs via Machine Learning and Simulation of 3D Structure

Ron Dror
Stanford University

Part Of

11:30A
- 1:00P

Location

TBD
Projected in 32-G575
Add to Calendar 2025-09-17 11:30:00 2025-09-17 13:00:00 America/New_York Discovering Safe, Effective Drugs via Machine Learning and Simulation of 3D Structure AbstractRecent years have seen dramatic advances in both experimental determination and computational prediction of macromolecular structures. These structures hold great promise for the discovery of highly effective drugs with minimal side effects, but structure-based design of such drugs remains challenging. I will describe recent progress toward this goal, using both atomic-level molecular simulations and machine learning on three-dimensional structures.Speaker BioRon Dror is the Cheriton Family Professor of Computer Science in the Stanford Artificial Intelligence Lab and a professor, by courtesy, of Structural Biology and of Molecular and Cellular Physiology at the Stanford School of Medicine. He leads a research group that uses molecular simulation and machine learning to elucidate biomolecular structure, dynamics, and function, and to guide the development of more effective medicines. He collaborates extensively with experimentalists in both academia and industry. Before moving to Stanford, he served as second-in-command of D. E. Shaw Research, a hundred-person company, having joined as its first hire. Dr. Dror earned a PhD in Electrical Engineering and Computer Science at MIT and an MPhil in Biological Sciences as a Churchill Scholar at the University of Cambridge.This talk is part of the MIT Bioinformatics Seminar Series. TBD