Joshua Levin
Co-PI (Core Leadership)
Broad Institute
Dr. Joshua Levin is a senior group leader and research scientist in the Broad Institute of MIT and Harvard’s Stanley Center for Psychiatric Research, as well as in the Klarman Cell Observatory. Before joining the Broad Institute in 2007, Levin worked for ten years in the biotechnology industry, first at Syngenta (formerly Ciba-Geigy and Novartis) and later at Novartis Pharmaceuticals using model organisms in functional genomics studies.
Recent ASAP Preprints & Published Papers
Human Postmortem-Derived Brain Sequencing Collection (Harmonized Collection)
The Human Postmortem-derived Brain Sequencing Collection is a harmonized repository comprised of sequencing data (Single-Nucleus RNA-seq and PolyA RNA-seq) contributed by ASAP CRN teams. The samples have been harmonized across cell ranger data. The current collection will be expanded and improved as additional Human PMDBS data is uploaded into the ASAP CRN Cloud. When complete, the collection will provide sequencing data produced by the following techniques: single-nucleus RNAseq, single-cell RNAseq, bulk RNA-seq, ATAC-seq, long read WGS, and single-nucleus multiome sequencing (paired snRNAseq, snATACseq).
Experimental and Computational Methods for Allelic Imbalance Analysis from Single-Nucleus RNA-seq Data
Single-cell RNA-seq (scRNA-seq) is emerging as a powerful tool for understanding gene function across diverse cells. Recently, this has included the use of allele-specific expression (ASE) analysis to better understand how variation in the human genome affects RNA expression at the single-cell level. We reasoned that because intronic reads are more prevalent in single-nucleus RNA-Seq (snRNA-Seq), and introns are under lower purifying selection and thus enriched for genetic variants, that snRNA-seq should facilitate single-cell analysis of ASE. Here we demonstrate how experimental and computational choices can improve the results of allelic imbalance analysis. We explore how experimental choices, such as RNA source, read length, sequencing depth, genotyping, etc., impact the power of ASE-based methods. We developed a new suite of computational tools to process and analyze scRNA-seq and snRNA-seq for ASE. As hypothesized, we extracted more ASE information from reads in intronic regions than those in exonic regions and show how read length can be set to increase power. Additionally, hybrid selection improved our power to detect allelic imbalance in genes of interest. We also explored methods to recover allele-specific isoform expression levels from both long- and short-read snRNA-seq. To further investigate ASE in the context of human disease, we applied our methods to a Parkinson's disease cohort of 94 individuals and show that ASE analysis had more power than eQTL analysis to identify significant SNP/gene pairs in our direct comparison of the two methods. Overall, we provide an end-to-end experimental and computational approach for future studies.
