Biology subjects

Sexton, D.

Publications and source records attributed to Sexton, D..

5 recordsLinked to original sources

bioRxiv2021

CellDepot: A unified repository for scRNA-seq data and visual exploration

CellDepot serves as an integrated web application to assist users in exploring single-cell RNA-seq (scRNA-seq) datasets and comparing the datasets among various studies through a user-friendly interface with advanced visualization and analytical tools. To begin with, it provides an efficient data management system that users can upload single cell datasets and query the database by multiple attributes such as species and cell types. In addition, the advanced query function incorporated in MySQL database system and its conditional filtering, allows users to quickly query and compare the expression of gene(s) across the datasets of interest. Moreover, by embedding the cellxgene VIP tool, CellDepot enables fast exploration of individual dataset in the manner of interactivity and scalability to gain more refined insights such as cell composition, gene expression profiles, and differentially expressed genes among cell types. In summary, the web portal allows large scale single cell data sharing, analysis and visualization for supporting decision-making, and encouraging scientists to contribute to the single-cell community in a tractable and collaborative way. Finally, CellDepot is released as open-source software to motivate crowd contribution, broad adoption, and local deployment for private data.

bioinformatics↗

bioRxiv2021

OmicsView: omics data analysis through interactive visual analytics

With advances in NGS technologies, transcriptional profiling of human tissue across many diseases is becoming more routine, leading to the generation of petabytes of data deposited in public repositories. There is a need for bench scientists with little computational expertise to be able to access and mine this data to understand disease pathology, identify robust biomarkers of disease and the effect of interventions (in vivo or in vitro). To this end we release an open source analytics and visualization platform for expression data called OmicsView, http://omicsview.org. This platform comes preloaded with 1000s of samples across many disease areas and normal tissue, including the GTEx database, all processed with a harmonized pipeline. We demonstrate the power and ease-of-use of the platform by means of a Crohns disease data mining exercise where we can quickly uncover disease pathology and identify strong biomarkers of disease and response to treatment.

bioinformatics↗

bioRxiv2020

Pairwise genetic interactions modulate lipid plasma levels and cellular uptake

Genetic interactions (GIs), the joint impact of different genes or variants on a phenotype, are foundational to the genetic architecture of complex traits. However, identifying GIs through human genetics is challenging since it necessitates very large population sizes, while findings from model systems not always translate to humans. Here, we combined exome-sequencing and genotyping in the UK Biobank with combinatorial RNA-interference (coRNAi) screening to systematically test for pairwise GIs between 30 lipid GWAS genes. Gene-based protein-truncating variant (PTV) burden analyses from 240,970 exomes revealed additive GIs for APOB with PCSK9 and LPL, respectively. Both, genetics and coRNAi identified additive GIs for 12 additional gene pairs. Overlapping non-additive GIs were detected only for TOMM40 at the APOE locus with SORT1 and NCAN. Our study identifies distinct gene pairs that modulate both, plasma and cellular lipid levels via additive and non-additive effects and nominates drug target pairs for improved lipid-lowering combination therapies.

genetics↗

bioRxiv2020

cellxgene VIP unleashes full power of interactive visualization, plotting and analysis of scRNA-seq data in the scale of millions of cells

To meet the growing demands from scientists to effectively extract deep insights from single cell RNA sequencing, spatial transcriptomics, and emerging multiome datasets, we developed cellxgene VIP (Visualization In Plugin), a frontend interactive visualization plugin of cellxgene framework, which greatly expanded capabilities of the base tool in the following aspects. First, it generates a comprehensive set of over eighteen commonly used quality control and analytical plots in high resolution with highly customizable settings in real time. Second, it provides more advanced analytical functions to gain insights on cellular compositions and deep biology, such as marker gene identification, differential gene expression analysis, and gene set enrichment analysis. Third, it empowers advanced users to perform analysis in a Jupyter Notebook like environment, dubbed Command Line Interface (CLI) by programming in Python and/or R directly without limiting themselves to functional modules available via graphical user interface (GUI). Finally, it pioneers methods to visualize multi-modal data, such as spatial transcriptomics embedding aligned with histological image on one slice or multiple slices in a grid format, and the latest 10x Genomic Multiome dataset where both DNA accessibility and gene expression in the same cells are measured, under the same framework in an integrative way to fully leverage the functionalities mentioned above. Taken together, the open-source tool makes large scale single cell data visualization and analysis more accessible to biologists in a user-friendly manner and fosters computational reproducibility by simplifying data and code reuse through the CLI. Going forward, it has the potential to become a crowdsourcing ecosystem for the scientific community to contribute even more modules to the Swiss Army knife of single cell data exploration tools.

bioinformatics↗

bioRxiv2020

The burden of rare protein-truncating genetic variants on human lifespan

Genetic predisposition is believed to contribute substantially to the age at which we die. Genome-wide association studies (GWAS) have implicated more than 20 genetic loci to phenotypes related to human lifespan1. However, little is known about how lifespan is impacted by gene loss-of-function. Through whole-exome sequencing of 238,239 UK Biobank participants, we assessed the relevance of protein-truncating variant (PTV) gene burden on individual and parental survival. We identified exome-wide (P<2.5e-6) significant associations between BRCA2, BRCA1, TET2, PPM1D, LDLR, EML2 and DEDD2 PTV-burden with human lifespan. Gene and gene-set PTV-burden phenome-wide association studies (PheWAS) further highlighted the roles of these genes in cancer and cardiovascular disease as relevant for overall survival. The overlap between PTV-burden and prior GWAS results was modest, underscoring the value of sequencing in well-powered cohorts to complement GWAS for identifying loci associated with complex traits and disease.

genetics↗