Biology subjects

Edwards, N. J.

Publications and source records attributed to Edwards, N. J..

6 recordsLinked to original sources

bioRxiv2024

scSNViz: a user-friendly toolset for visualization and analysis of Cell-Specific Expressed SNVs

SummaryAccurately characterizing expressed genetic variation at the single-cell level is essential for understanding transcriptional heterogeneity, allelic regulation, and mutational dynamics within complex tissues. However, few tools enable comprehensive visualization and quantitative analysis of expressed variants across individual cells. scSNViz is an R package for the exploration, quantification, and visualization of expressed single-nucleotide variants (SNVs) from cell-barcoded single-cell RNA sequencing (scRNA-seq) data. The software supports estimation of variant allele fractions, clustering of SNV expression profiles, and 2D and 3Dl visualization of individual SNVs or user-defined SNV groups. Beyond visualization, scSNViz facilitates investigation of cell-, cluster-, or lineage-specific variant expression patterns, as well as allelic dynamics including imprinting, random allele inactivation, and transcriptional bursting. It interoperates seamlessly with established single-cell frameworks - Seurat for clustering, Slingshot for trajectory inference, scType for cell-type annotation, and CopyKat for copy-number profiling - enabling integrative multi-omic analyses of expressed variation. Availability and ImplementationscSNViz is implemented in R and freely available at https://github.com/HorvathLab/scSNViz (DOI: 10.5281/zenodo.17307516). The package includes comprehensive documentation and example workflows designed for users with limited bioinformatics experience.

genomics↗

bioRxiv2022

Extracellular heparan 6-O-endosulfatases SULF1 and SULF2 in HNSC and other malignancies

SULF1 and SULF2 are oncogenic in a number of human malignancies, including head and neck squamous cell carcinoma (HNSC). The function of these two heparan sulfate editing enzymes was previously considered largely redundant but the biology of cancer suggests differences that we explore in our RNAseq and RNAScope studies of HNSC and in a pan cancer analysis using the TCGA and CPTAC (proteomics) data. Our studies document a consistent upregulation of SULF1 and SULF2 in HNSC which is associated with poor survival outcomes. SULF2 expression increases in multiple malignancies but less consistently than SULF1, which uniformly increases in the tumor tissues and negatively impacts survival in several types of cancer. Meanwhile, SULF1 showed low expression in cancer cell lines and a scRNAseq study of HNSC shows that SULF1 is not supplied by epithelial tumor cells, like SULF2, but is secreted by cancer associated fibroblasts. Our RNAScope and PDX analysis of the HNSC tissues fully confirm the stromal source of SULF1 and explain the uniform impact of this enzyme on the biology of multiple malignancies. In summary, the SULF1 enzyme, supplied by a subset of cancer associated fibroblasts, is upregulated and negatively impacts HNSC survival at an early stage of the disease progression while the SULF2 enzyme, supplied by tumor cells, impacts survival at later stages of HNSC. This paradigm is common to multiple malignancies and suggests a potential for diagnostic and therapeutic targeting of the heparan sulfatases in cancer diseases.

cancer biology↗

bioRxiv2022

A wealth of novel cell-specific expressed SNVs from tumor and normal scRNA-seq datasets

We demonstrate a novel variant calling strategy using barcode-stratified alignments on 25 tumor and normal 10XGenomics scRNA-seq datasets (>200,000 cells). Our approach identified 24,528 exonic non-dbSNP single cell expressed (sce)SNVs, a third of which are shared across multiple samples. The novel sceSNVs include unreported somatic and germline variants, as well as RNA-originating variants; some are expressed in up to 17% of the cells, and many are found in known cancer genes. Our findings suggest that there is an unacknowledged repertoire of expressed genetic variants, possibly recurrent and common across samples, in the normal and cancer transcriptome.

genomics↗

bioRxiv2022

SCExecute: cell barcode-stratified analyses of scRNA-seq data

MotivationIn single-cell RNA-sequencing (scRNA-seq) data, stratification of sequencing reads by cellular barcode is necessary to study cell-specific features. However, apart from gene expression, the analyses of cell-specific features are not supported by available tools that are designed for bulk RNA-Seq data. ResultsWe introduce a tool - SCExecute - which executes a user-provided command on barcode-stratified, extracted on-the-fly, single cell binary alignment map (scBAM) files. SCExecute extracts the cell barcode from aligned, pooled single-cell sequencing data. The user-specified command option executes all the commands defined in the session from monolithic programs and multi-command shell-scripts to complex shell-based pipelines. The execution can be further restricted to barcodes or/and genomic regions of interest. We demonstrate SCExecute with two popular variant callers - GATK and Strelka2 - combined with modules for bam file manipulation and variant filtering, to detect single cell-specific expressed Single Nucleotide Variants (sceSNVs) from droplet scRNA-seq data (10X Genomics Chromium System). ConclusionSCExecute facilitates custom cell-level analyses on barcoded scRNA-seq data using currently available tools and provides an effective solution for studying low (cellular) frequency transcriptome features. AvailabilitySCExecute is implemented in Python3 using the PySAM package and distributed for Linux and Python environments from https://github.com/HorvathLab/NGS/tree/master/SCExecute.

genomics↗

bioRxiv2020

SCReadCounts: Estimation of cell-level SNVs from scRNA-seq data

Recent studies have demonstrated the utility of scRNA-seq SNVs to distinguish tumor from normal cells, characterize intra-tumoral heterogeneity, and define mutation-associated expression signatures. In addition to cancer studies, SNVs from single cells have been useful in studies of transcriptional burst kinetics, allelic expression, chromosome X inactivation, ploidy estimations, and haplotype inference. To aid these types of studies, we have developed a tool, SCReadCounts, for cell-level tabulation of the sequencing read counts bearing SNV reference and variant alleles from barcoded scRNA-seq alignments. Provided genomic loci and expected alleles, SCReadCounts generates cell-SNV matrices with the absolute variant- and reference-harboring read counts, as well as cell-SNV matrices of expressed Variant Allele Fraction (VAFRNA) suitable for a variety of downstream applications. We demonstrate three different SCReadCounts applications on 59,884 cells from seven neuroblastoma samples: (1) estimation of cell-level expression of known somatic mutations and RNA-editing sites, (2) estimation of celllevel allele expression of germline heterozygous SNVs, and (3) a discovery mode assessment of the reference and each of the three alternative nucleotides at genomic positions of interest that does not require prior SNV information. For the later, we applied SCReadCounts on the coding regions of KRAS, where it identified known and novel recurrent somatic mutations in a low-to-moderate proportion of cells. The SCReadCounts read counts module is benchmarked against the analogous modules of GATK and Samtools. SCReadCounts is freely available (https://github.com/HorvathLab/NGS) as 64-bit self-contained binary distributions for Linux and MacOS, in addition to Python source.

bioinformatics↗

bioRxiv2020

DreamAI: algorithm for the imputation of proteomics data

Deep proteomics profiling using labeled LC-MS/MS experiments has been proven to be powerful to study complex diseases. However, due to the dynamic nature of the discovery mass spectrometry, the generated data contain a substantial fraction of missing values. This poses great challenges for data analyses, as many tools, especially those for high dimensional data, cannot deal with missing values directly. To address this problem, the NCI-CPTAC Proteogenomics DREAM Challenge was carried out to develop effective imputation algorithms for labeled LC-MS/MS proteomics data through crowd learning. The final resulting algorithm, DreamAI, is based on an ensemble of six different imputation methods. The imputation accuracy of DreamAI, as measured by Pearson correlation, is about 15%-50% greater than existing tools among less abundant proteins, which are more vulnerable to be missed in proteomics data sets. This new tool notably enhances data analysis capabilities in proteomics research.

bioinformatics↗