Rajiv McCoy, Johns Hopkins University

Rajiv McCoy is an Associate Professor in the Department of Biology, with a secondary appointment in the Department of Genetic Medicine, at Johns Hopkins University. He received his PhD in Biology from Stanford University and completed postdoctoral training at the University of Washington and Princeton University before joining Johns Hopkins in 2018. His lab applies computational and statistical approaches to large-scale human genetic datasets. Research accomplishments include contributing to analysis of the first complete human genome, development of functional genomic resources for globally diverse populations, and discovery and characterization of common genetic variants associated with aneuploidy—the leading cause of human pregnancy loss. The McCoy lab is supported by funding from the NIH, NSF, Lalor Foundation, and American Society of Hematology and is actively engaged in teaching and mentoring in computational genetics at both the graduate and undergraduate levels.

CCMB Faculty Host: Emilia Huerta-Sanchez

Events@Brown Page

"Human genome evolution within and across generations"

Genetic variation at the level of DNA sequence mediates much of the phenotypic diversity that exists in nature, both within and between species, including humans. My lab uses the tools of computational and statistical human genetics to answer questions about how germline and somatic evolution shape 1) genome function and 2) reproduction and development. Our work spans diverse systems but is unified around the goal of developing methods for elucidating the evolutionary forces that impact our genomes across scales of biological organization. Importantly, much of the phenotypic variation in nature traces not to differences in amino acid sequences, but to regulatory variation influencing transcription and splicing of RNA. I will describe my lab’s recent work generating and analyzing a large gene expression dataset from globally diverse human individuals, toward a more complete view of the mechanisms driving gene expression diversity and evolution within our species. In addition to the evolutionary processes operating across generations, my lab is also interested in the recurrent forces of natural selection that shape human development. For example, it is estimated that less than half of all human conceptions survive to birth, primarily due to chromosome mis-segregation during meiosis and mitosis. I will describe our latest work repurposing clinical genetic testing data from in vitro fertilized embryos to understand the genetic basis of variation in human chromosome abnormalities.

CCMB Graduate Student Talks

Speakers TBA

Pei Wang, Mt. Sinai

Dr. Wang is a Professor of Genetics and Genomic Sciences at Mount Sinai’s Department of Genetics and Genomics.  Dr. Wang’s research work has been focused on developing statistical and computational methods to address scientific questions based on data from high throughput biology/genetics experiments. Dr. Wang is a member of the Icahn Genomics Institute. She received her Ph.D. in Statistics from Stanford University in 2004. Between 2004-2013, she served as a faculty in Fred Hutchinson Cancer Research Center and University of Washington, Seattle, WA.

CCMB Faculty Host: Zhijin Wu

Events@Brown Page

Yang Lu, UW Madison

Yang Lu’s research focuses on developing machine learning and statistical methods for genomics and proteomics data analysis. He is particularly interested in developing interpretation methods to find scientifically interesting and statistically confident hypotheses from complex biological data. Before joining UW-Madison, Yang Lu was an assistant professor at the School of Computer Science, University of Waterloo. Before that, he was a postdoctoral researcher in Dr. William Noble’s group at the University of Washington. He obtained his Ph.D. in Computational Biology and Bioinformatics under the supervision of Dr. Fengzhu Sun from the University of Southern California.

CCMB Faculty Host: Ritambhara Singh

Events@Brown Page

April Wei, Cornell University

April Wei is interested in developing and applying population and evolutionary genetics theory to understand human evolution and health. Her recent research focuses on developing accurate and scalable methods for inferring complex demographic history and for understanding genetic and phenotypic evolution in light of population admixture.

CCMB Faculty Host: Emilia Huerta-Sanchez

Events@Brown Page

Sumaiya Iqbal, Broad Institute

Sumaiya Iqbal is a senior group leader in the Ladders to Cures (L2C) Accelerator at the Broad Institute of MIT and Harvard–an initiative to accelerate advances towards treatments and cures for patients with rare genetic diseases. She is also an associate member of the Cancer Data Sciences, Dana-Farber/Harvard Cancer Center (DF/HCC). She developed the Broad Institute Genomics 2 Proteins (G2P) portal, a human proteome-wide discovery platform for linking genetic screening outputs to protein sequences and structures.

Iqbal is a computer scientist by training but a life science researcher by determination. At the Broad, she leads the bioinformatics and machine learning group aimed at connecting genomics to proteins and mechanism using data sciences, statistics, and ML/AI. The focus of the Iqbal lab is: building bioinformatics resources to bridge the gap across complex multi-omics data types, developing methods to unveil the molecular effect of genetic/synthetic mutations on protein structure-function relationships, and building ML/AI-driven innovative tools for new target and small-molecule hit discovery. Iqbal lab works across all disease areas, with a special focus on rare genetic diseases and pediatric cancer.

Iqbal started her lab at the Broad Institute in 2023, after two years as a postdoc at the Analytic and Translational Genetics Unit of MGH, Harvard Medical School, and the Stanley Center for Psychiatric Research, and three years as a research scientist at the Center for Development of Therapeutics at the Broad. Iqbal has earned multiple awards including the Broad Institute SPARC award, the Merkin Institute award for Transformative Technologies in Healthcare, and the BroadIgnite award. She is also a recipient of Broad Institute Staff Scientists Distinction Award in Scientific Collaboration and serves as the chair for the Broad Institute Machine Learning for Drug Discovery symposium. 

Iqbal holds a Ph.D. in computer science from the LSU New Orleans, and a M.Sc. and B.Sc. in computer science from the Bangladesh University of Engineering and Technology.

CCMB Faculty Host: Erica Larschan

Events@Brown Page

"Connecting the Dots in Biology at Scale in the Age of AI"

We live in the era of big data and AI: the biomedical community now has access to millions of predicted and experimental protein structures, alongside clinical genomics data (genetic, disease-associated variants), functional genomics data (genome-edited synthetic mutations), and clinical/health data. Investigating clinical and functional data in the context of protein conformations, dynamics, and interactions helps generate hypotheses and design biophysical and therapeutic interventions—computationally and/or experimentally. However, this also demands seamless integration of data across genomics, transcriptomics, proteomics, and structural biology—which remains challenging. My lab develops methods and tools to bridge gaps across complex multi-omics data using data science, statistics, and ML/AI.

In this talk, I will dive deep into the background, rationale, and scalable applications of the Broad Institute Genomics 2 Proteins (G2P) portal (https://g2p.broadinstitute.org/), a human proteome-wide resource and discovery tool for linking genetic screening outputs to protein sequences and structures. I will then highlight computational methods we developed at the intersection of multiple omics for biologically actionable hypothesis generation using integrated data from the G2P portal and beyond, including: (1) interpretation of the molecular effects of genetic mutations, (2) structure–function analyses of base-editor tiling mutagenesis data, (3) prediction of ion channel variant function, and (4) discovery of novel drug targets in fusion-driven pediatric oncology.

Steve Reilly, Yale University

Steve Reilly is a genomicist specializing in human genetics, evolution, and gene-regulation. He is specifically interested in furthering our understanding of non-coding variation, the main cache of human genetic diversity. He develops novel computational + experimental approaches to identify and functionally characterize human variation at scale. These tools include DeepSweep: a machine learning method to identify variants under positive selection, HCR-FlowFISH: a method to directly characterize the functional targets of regulatory elements, and application of the Massively Parallel Reporter Assay (MPRA) to understand the regulatory impact of genomic variation.

CCMB Faculty Host: Katie Siddle

Events@Brown Page