Jimmie Ye, PhD
The Ye lab is interested in how the interaction between genetics and environment affect human variation at the level of molecular phenotypes. To study these interactions, the lab couples high-throughput sequencing approaches that measure cellular response under environmental challenges with population genetics where such measurements are collected and analyzed across large patient cohorts. The lab develops novel experimental approaches that enable the large-scale collection of functional genomic data en masse and computational approaches that translate the data into novel biological insights. This approach is used to initially study primary human immune cells in both healthy and diseased patients to understand host pathogen interactions and its role in autoimmunity.
Postdoc, 2014 - Cell Circuits Program, Broad Institute
PhD, 2009 - Bioinformatics and Systems Biology, University of California San Diego
BS, 2002 - Bioengineering, University of California Berkeley
BS, 2002 - Electrical Engineering and Computer Sciences, University of California Berkeley
- Evaluation of SARS-CoV-2 serology assays reveals a range of test performance.
- lentiMPRA and MPRAflow for high-throughput functional characterization of gene regulatory elements.
- Intratumoral CD4+ T Cells Mediate Anti-tumor Cytotoxicity in Human Bladder Cancer.
- Test performance evaluation of SARS-CoV-2 serological assays.
- Pooled Knockin Targeting for Genome Engineering of Cellular Immunotherapies.
- Functional interpretation of single cell similarity maps.
- Ultrarare variants drive substantial cis heritability of human gene expression.
- How mutations express themselves in blood-cell production.
- Reverse gene-environment interaction approach to identify variants influencing body-mass index in humans.
- Unleashing Type-2 Dendritic Cells to Drive Protective Antitumor CD4+ T Cell Immunity.
- EmptyDrops: distinguishing cells from empty droplets in droplet-based single-cell RNA sequencing data.
- Genetic analysis of isoform usage in the human anti-viral response reveals influenza-specific regulation of ERAP2 transcripts under balancing selection.
- Lineage dynamics of murine pancreatic development at single-cell resolution.
- Genetic determinants of co-accessible chromatin regions in activated T cells across humans.
- Author Correction: Discovery of stimulation-responsive immune enhancers with CRISPR activation.
- Single-Cell RNA Sequencing of Lymph Node Stromal Cells Reveals Niche-Associated Heterogeneity.
- Principles of Systems Biology, No. 27.
- Multiplexed droplet single-cell RNA-sequencing using natural genetic variation.
- An ancestry-based approach for detecting interactions.
- Reconstructing the Molecular Function of Genetic Variation in Regulatory Networks.
- Covariate selection for association screening in multiphenotype genetic studies.
- Discovery of stimulation-responsive immune enhancers with CRISPR activation.
- CRISPR/Cas9-mediated PD-1 disruption enhances anti-tumor efficacy of human chimeric antigen receptor T cells.
- A tissue checkpoint regulates type 2 immunity.
- Transethnic Genetic-Correlation Estimates from Summary Statistics.
- Selection and explosive growth alter genetic architecture and hamper the detection of causal rare variants.
- Parsing the Interferon Transcriptional Network and Its Disease Associations.
- Generation of knock-in primary human T cells using Cas9 ribonucleoproteins.
- A Genome-wide CRISPR Screen in Primary Immune Cells to Dissect Regulatory Networks.
- Intersection of population variation and autoimmunity genetics in human T cell activation.
- Polarization of the effects of autoimmune and neurodegenerative risk alleles in leukocytes.
- Effectively identifying regulatory hotspots while capturing expression heterogeneity in gene expression studies.
- Common genetic variants modulate pathogen-sensing responses in human dendritic cells.
- Effectively identifying eQTLs from multiple tissues by combining mixed model and meta-analytic approaches.
- Mutations causing medullary cystic kidney disease type 1 lie in a large VNTR in MUC1 missed by massively parallel sequencing.
- Integrated computational and experimental analysis of the neuroendocrine transcriptome in genetic hypertension identifies novel control points for the cardiometabolic syndrome.
- Mixed-model coexpression: calculating gene coexpression while accounting for expression heterogeneity.
- Detecting the presence and absence of causal relationships between expression of yeast genes with very few samples.
- Using network component analysis to dissect regulatory networks mediated by transcription factors in yeast.
- Accurate discovery of expression quantitative trait loci under confounding from spurious and genuine regulatory hotspots.
- Discovering tightly regulated and differentially expressed gene sets in whole genome expression data.
- Orthologous repeats and mammalian phylogenetic inference.
- Assessing computational tools for the discovery of transcription factor binding sites.