Shiqi Xie

Principal Scientist, Technology @ Genentech

About

The Vision - Closing the AI-Validation Gap In the era of AI-driven drug discovery, the speed of hypothesis generation will soon outpace our capacity for physical validation. As computational models become more predictive, the industry's primary bottleneck will shift from knowing what to test to how quickly we can test it at scale. My mission is to architect high-throughput, automated genomic validation systems that bridge this gap, transforming AI-generated candidates into validated therapeutic leads with unprecedented speed and precision. Strategic Collaboration Leadership - I serve as the co-lead of the Joint Technology Team (JTT) for high-stakes external collaborations. In this role, I oversee the technical roadmap, align multi-company stakeholders, and ensure the seamless integration of partner technologies and insights into internal drug discovery pipelines. High-Content Screening Strategy - I lead a team of scientists focused on utilizing next-generation sequencing (NGS) and single-cell "multi-omic" platforms to identify and validate novel therapeutic targets and small molecule/biologic candidates. Cross-Functional Influence - I partner with therapeutic area heads, chemists, proteomics and computational/AI scientists to translate complex genomic assays into scalable, production-grade discovery engines. Technology Innovation - I drive the internal development of proprietary screening platforms, steering projects from initial concept and wet-lab validation through to production-scale computational analysis.

Country

United States

City

South San Francisco

Industry

Biotechnology

Skill

Molecular Biology, Cell Biology, Biochemistry, Molecular Cloning, Cell Culture, qPCR, Confocal Microscopy, Cell, Bioinformatics, Functional Genomics

Experience

Principal Scientist, Technology

Genentech

2020-4 - Present · 6 yrs 6 mos

South San Francisco, California, United States

Postdoctoral Fellow

UT Southwestern Medical Center

2015-1 - 2020-4 · 5 yrs 4 mos

Dallas, TX

My work in Hon Lab is primarily focused on dissecting the function of the non-coding regulatory elements of the genome, including enhancers, promoters. By combining single-cell RNA-seq and CRISPR technologies, I have established functional accessing assays to evaluate the contribution of the regulatory elements to the gene expression in their endogenous context. - Developed and optimized Mosaic-seq, a single-cell perturbation assay which interrogates function of endogenous enhancers in a highly multiplexed manner. - Developed and optimized Virtual-FACS, a statistic framework for Mosaic-seq analysis, which helps to identify direct and indirect target genes of enhancers that are perturbed in the assay. - Analyzed in house generated Mosaic-seq data from over 500 enhancers in K562 cells. - Implemented computational pipelines for enhancer feature identification by integration of different epigenomics datasets, such as ATAC-seq, ChIP-seq and Hi-ChIP. - Established most of the molecular and cell biology platforms / workﬂows in Hon lab, such as CRISPR-based cell editing, epigenetic modiﬁcation of cells, construction of large-scale CRISPR libraries etc., which are now widely used by other lab members. Selected Publications: 1. Xie, S., Duan, J., Li, B., Zhou, P. & Hon, G. C. Multiplexed Engineering and Analysis of Combinatorial Enhancer Activity in Single Cells. Mol. Cell 66, 285–299.e5 (2017). 2. Xie, S.*, Cooley, A.*, Armendariz, D., Zhou, P. & Hon, G. C. Frequent sgRNA-barcode recombination in single-cell perturbation assays. PLoS One 13, e0198635 (2018). 3. Xie, S. & Hon, G. C. Experimental and Computational Approaches for Single-Cell Enhancer Perturbation Assay. Methods Mol. Biol. 1935, 203–221 (2019). 3. Xie, S., Armendariz, D., Zhou, P., Duan, J. & Hon, G. C. Global Analysis of Enhancer Targets Reveals Convergent Enhancer-Driven Regulatory Modules. Cell Rep. 29, 2570–2578.e5 (2019)