Biology subjects

Wong, A. K.

Publications and source records attributed to Wong, A. K..

5 recordsLinked to original sources

bioRxiv2023

CROTONdb: genome-wide variant effect prediction database for CRISPR/Cas9 editing outcomes

CRISPR/Cas9 is a genome editing tool widely used in biological research and clinical therapeutics. Natural human genetic variations, through altering the sequence context of CRISPR/Cas9 target regions, can significantly affect its DNA repair outcomes and ultimately lead to different editing efficiencies. However, these effects have not been systematically studied, even as CRISPR/Cas9 is broadly applied to primary cells and patient samples that harbor such genetic diversity. Here, we present comprehensive investigations of natural genetic variations on CRISPR/Cas9 outcomes across the human genome. The utility of our analysis is illustrated in two case studies, on both preclinical discoveries of CD33 knockout in Chimeric Antigen Receptor (CAR)-T cell therapy, and clinical applications of TTR inactivation for treating ATTR amyloidosis. We further expand our analysis to genome scale, population stratified common variants that may lead to gene editing disparity. Our analyses demonstrate pitfalls of failing to account for the widespread genetic variations in Cas9 target selection, and how they can be effectively examined and avoided using our method. To facilitate broad access to our analysis, a web platform CROTONdb is developed, which provides predictions for all possible CRISPR/Cas9 target sites in the coding region, spanning over 5.38 million gRNA targets and 90.82 million estimated variant effects. We anticipate CROTONdb having broad clinical utilities in gene and cellular therapies.

bioengineering↗

bioRxiv2021

A sequence-based global map of regulatory activity for deciphering human genetics

Sequence is at the basis of how the genome shapes chromatin organization, regulates gene expression, and impacts traits and diseases. Epigenomic profiling efforts have enabled large-scale identification of regulatory elements, yet we still lack a sequence-based map to systematically identify regulatory activities from any sequence, which is necessary for predicting the effects of any variant on these activities. We address this challenge with Sei, a new framework for integrating human genetics data with sequence information to discover the regulatory basis of traits and diseases. Our framework systematically learns a vocabulary for the regulatory activities of sequences, which we call sequence classes, using a new deep learning model that predicts a compendium of 21,907 chromatin profiles across >1,300 cell lines and tissues, the most comprehensive to-date. Sequence classes allow for a global view of sequence and variant effects by quantifying diverse regulatory activities, such as loss or gain of cell-type-specific enhancer function. We show that sequence class predictions are supported by experimental data, including tissue-specific gene expression, expression QTLs, and evolutionary constraints based on population allele frequencies. Finally, we applied our framework to human genetics data. Sequence classes uniquely provide a non-overlapping partitioning of GWAS heritability by tissue-specific regulatory activity categories, which we use to characterize the regulatory architecture of 47 traits and diseases from UK Biobank. Furthermore, the predicted loss or gain of sequence class activities suggest specific mechanistic hypotheses for individual regulatory pathogenic mutations. We provide this framework as a resource to further elucidate the sequence basis of human health and disease.

genomics↗

bioRxiv2020

Genome-wide landscape of RNA-binding protein dysregulation reveals a major impact on psychiatric disorder risk

Despite the strong genetic basis of psychiatric disorders, the molecular origins of these diseases are still largely unmapped. RNA-binding proteins (RBPs) are responsible for most post-transcriptional regulation, from splicing to translational to localization. RBPs thus act as key gatekeepers of cellular homeostasis, especially in the brain. Here, we leverage a deep learning approach to interrogate variant effects genome-wide, and discover that the dysregulation of RBP target sites is a principal contributor to psychiatric disorder risk. We show that specific modes of RBP regulation are genetically linked to the heritability of psychiatric disorders, and demonstrate that diverse RBP regulatory functions are reflected in distinct genome-wide negative selection signatures. Notably, RBP dysregulation has a stronger impact on psychiatric disorders than common coding region variants and explains heritability not currently captured by large-scale molecular QTL studies (expression QTLs and splicing QTLs). We share genome-wide profiles of RBP target site dysregulation, which we used to identify DDHD2 as a candidate schizophrenia risk gene, in a public web server. This resource provides a novel analytical framework to connect the full range of RNA regulation to complex disease.

genetics↗

bioRxiv2020

DeepArk: modeling cis-regulatory codes of model species with deep learning

To enable large-scale analyses of regulatory logic in model species, we developed DeepArk (https://DeepArk.princeton.edu), a set of deep learning models of the cis-regulatory codes of four widely-studied species: Caenorhabditis elegans, Danio rerio, Drosophila melanogaster, and Mus musculus. DeepArk accurately predicts the presence of thousands of different context-specific regulatory features, including chromatin states, histone marks, and transcription factors. In vivo studies show that DeepArk can predict the regulatory impact of any genomic variant (including rare or not previously observed), and enables the regulatory annotation of understudied model species.

bioinformatics↗

bioRxiv2018

Sequencing of Panax notoginseng genome reveals genes involved in disease resistance and ginsenoside biosynthesis

Panax notoginseng is a traditional Chinese herb with high medicinal and economic value. There has been considerable research on the pharmacological activities of ginsenosides contained in Panax spp.; however, very little is known about the ginsenoside biosynthetic pathway. We reported the first de novo genome of 2.36 Gb of sequences from P. notoginseng with 35,451 protein-encoding genes. Compared to other plants, we found notable gene family contraction of disease-resistance genes in P. notoginseng, but notable expansion for several ATP-binding cassette (ABC) transporter subfamilies, such as the Gpdr subfamily, indicating that ABCs might be an additional mechanism for the plant to cope with biotic stress. Combining eight transcriptomes of roots and aerial parts, we identified several key genes, their transcription factor binding sites and all their family members involved in the synthesis pathway of ginsenosides in P. notoginseng, including dammarenediol synthase, CYP716 and UGT71. The complete genome analysis of P. notoginseng, the first in genus Panax, will serve as an important reference sequence for improving breeding and cultivation of this important nutraceutical and medicinal but vulnerable plant species.

genomics↗