November 30, 2018 –
Computational methods for single-cell genomics and genome editing
In this talk, I will first present STREAM, a computational method for single-cell analysis of transcriptomic and epigenomic data. This method can be used to disentangle complex cellular types and states in development, differentiation or in perturbation studies. It can accurately detect cellular hierarchies and recover complex developmental trajectories. In addition, it provides informative and intuitive visualization techniques to highlight important genes that can be used as markers to define sub-populations and rare cell types.
Then, I will present how to design and analyze recent CRISPR tiling screens using two methods we have recently developed called CRISPR-SURF and CRISPResso. Tiling perturbations allow a powerful and high-throughput functional interrogation of non-coding elements throughout the genome. Functional mapping can be achieved by densely tiling single guide RNAs (sgRNAs) across a non-coding region of interest, where each sgRNA enables linking a unique, genomic location to an observable phenotype. CRISPR-SURF and CRISPResso can be used to analyze tiling screens and provide the capability to discover functional non-coding regions and to dissect their critical elements thereby enabling a powerful characterization of genetic variants involved in traits or diseases.
Luca is an Assistant Professor at Massachusetts General Hospital and Harvard Medical School. He received his BA, MA, and Ph.D. in Computer Science and Mathematics from the University of Palermo in Italy. He had the good fortune to be part of the “omics” revolution and the opportunity to work on many computationally-challenging problems since he was an undergraduate student. During his postdoctoral research with Prof. Guo-Cheng Yuan at Dana-Farber Cancer Institute/Harvard School of Public health, he studied the role of chromatin structure in gene regulation and developed computational methods for single cell analysis. He has a background in computer science and extensive experience in machine learning, data mining, and web technologies. His research program uses computational approaches to systematically analyze the sources of variation that affect gene regulation: epigenetic variation, genetic variation and (single-cell) gene expression variability. In his free time, he likes to hike and roast green coffee.