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September 17, 2024  |  RNA sequencing

Experimental and computational methods for allelic imbalance analysis from single nucleus RNA-seq data

Authors: Sean K. Simmons1,2,3*, Xian Adiconis1,2,3, Nathan Haywood1,2,3, Jacob Parker1,4, Zechuan Lin1,4, Zhixiang Liao1,5, Idil Tuncali1,5, Aziz M. Al'Khafaji6, Asa Shin6, Karthik Jagadeesh2, Kirk Gosik2, Michael Gatzen7, Jonathan T. Smith7, Daniel N. El Kodsi1,5, Yuliya Kuras1,5, Clare Baecher-Allan8, Geidy E. Serrano1,9, Thomas G. Beach1,9, Kiran Garimella7, Orit RozenblattRosen2,10, Aviv Regev2,10, Xianjun Dong1,4, Clemens R. Scherzer1,4, Joshua Z. Levin1,2,3 1Aligning Science Across Parkinson’s (ASAP) Collaborative Research Network, Chevy Chase,MD 20815, USA 2 Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA 3 Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA 4 Stephen & Denise Adams Center for Parkinson’s Disease Research of Yale School of Medicine, New Haven, CT 06510, USA 5 Precision Neurology Program of Brigham & Women's Hospital, Harvard Medical School, Boston, MA 02115, USA 6 Broad Clinical Labs, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA 7 Data Sciences Platform, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA 8 Dept. of Neurology, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA 02115, USA 9 Banner Sun Health Research Institute, Sun City, AZ 85351, USA 10Present address: Genentech, South San Francisco, CA 94080, USA.

In a recent preprint, researchers from Aligning Science Across Parkinson’s MD, Broad, Yale, Harvard, Banner Sun AZ, Genentech conducted a detailed of analysis of protocols for “allele-specific expression (ASE) analysis to better understand how variation in the human genome affects RNA expression at the single-cell level”. The study employed single-nucleus RNA-Seq (snRNA-Seq) as introns in pre-mRNA that were enriched for heterozygous variants, thus facilitating allelic assignments for ASE, concluding that allele-specific expression (ASE) analysis is better with HiFi – more accurate, cost-effective, and enabling isoform resolution vs. ILMN short-reads.

Journal: bioRxiv
DOI:
Year: 2024

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