Biology subjects

Spurr, L.

Publications and source records attributed to Spurr, L..

6 recordsLinked to original sources

bioRxiv2020

scReQTL: an approach to correlate SNVs to gene expression from individual scRNA-seq datasets

Recently, pioneering eQTLs studies on single cell RNA-seq (scRNA-seq) data have revealed new and cell-specific regulatory SNVs. Because eQTLs correlate genotypes and gene expression across multiple individuals, they are confined to SNVs with sufficient population frequency. Here, we present an alternative sc-eQTL approach - scReQTL - wherein we substitute the genotypes with expressed Variant Allele Fraction (VAFRNA) at heterozygous SNV sites. Our approach employs the advantage that, when estimated from multiple cells, VAFRNA can be used to assess effects of rare SNVs in a single individual. ScReQTLs are enriched in known genetic interactions, therefore can be used to identify novel regulatory SNVs.

genomics↗

bioRxiv2019

Estimating allele-specific expression of SNVs from 10x Genomics Single-Cell RNA-Sequencing Data

With the recent advances in single-cell RNA-sequencing (scRNA-seq) technologies, estimation of allele expression from single cells is becoming increasingly reliable. Allele expression is both quantitative and dynamic and is an essential component of the genomic interactome. Here, we systematically estimate allele expression from heterozygous single nucleotide variant (SNV) loci using scRNA-seq data generated on the 10x Genomics platform. We include in the analysis 26,640 human adipose-derived mesenchymal stem cells (from three healthy donors), with an average sequencing reads over 120K/cell (more than 4 billion scRNA-seq reads total). High quality SNV calls assessed in our study contained approximately 15% exonic and >50% intronic loci. To analyze the allele expression, we estimate the expressed Variant Allele Fraction (VAFRNA) from SNV-aware alignments and analyze its variance and distribution (mono- and bi-allelic) at different cutoffs for required minimal number of sequencing reads. Our analysis shows that when assessing SNV loci covered by a minimum of 3 unique sequencing reads, over 50% of the heterozygous SNVs show bi-allelic expression, while at minimum of 10 reads, nearly 90% of the SNVs are bi-allelic. Consistent with single cell studies on RNA velocity and models of transcriptional burst kinetics, we observe a substantially higher rate of monoallelic expression among intronic SNVs, signifying the usefulness of scVAFRNA to assess dynamic cellular processes. Our analysis demonstrates the feasibility of scVAFRNA estimation from current scRNA-seq datasets and shows that the 3-based library generation protocol of 10x Genomics scRNA-seq data can be highly informative in SNV-based analyses.

genomics↗

bioRxiv2019

RsQTL: correlation of expressed SNVs with splicing using RNA-sequencing data

RsQTL is a tool for identification of splicing quantitative trait loci (sQTLs) from RNA-sequencing (RNA-seq) data by correlating the variant allele fraction at expressed SNV loci in the transcriptome (VAFRNA) with the proportion of molecules spanning local exon-exon junctions at loci with differential intron excision (percent spliced in, PSI). We exemplify the method on sets of RNA-seq data from human tissues obtained though the Genotype-Tissue Expression Project (GTEx). RsQTL does not require matched DNA and can identify a subset of expressed sQTL loci. Due to the dynamic nature of VAFRNA, RsQTL is applicable for the assessment of conditional and dynamic variation-splicing relationships. Availability and implementationhttps://github.com/HorvathLab/RsQTL. Contacthorvatha@gwu.edu or jsein@gwmail.gwu.edu Supplementary InformationRsQTL_Supplementary_Data.zip

bioinformatics↗

bioRxiv2018

GeTallele: a mathematical model and a toolbox for integrative analysis and visualization of DNA and RNA allele frequencies

BackgroundAsymmetric allele expression typically indicates functional and/or structural features associated with the underlying genetic variants. When integrated, RNA and DNA allele frequencies can reveal patterns characteristic of a wide-range of biological traits, including ploidy changes, genome admixture, allele-specific expression and gene-dosage transcriptional response. ResultsTo assess RNA and DNA allele frequencies from matched sequencing datasets, we introduce a method for generating model distributions of variant allele frequencies (VAF) with a given variant read probability. In contrast to other methods, based on whole sequences or single SNV, proposed methodology uses continuous multi-SNV genomic regions. The methodology is implemented in a GeTallele toolbox that provides a suite of functions for integrative analysis, statistical assessment and visualization of Genome and Transcriptome allele frequencies. Using model VAF probabilities, GeTallele allows estimation and comparison of variant read probabilities (VAF distributions) in a sequencing dataset. We demonstrate this functionality across cancer DNA and RNA sequencing datasets. ConclusionBased on our evaluation, variant read probabilities can serve as a dependable indicator to assess gene and chromosomal allele asymmetries and to aid calls of genomic events in matched sequencing RNA and DNA datasets. ContactP.M.Slowinski@exeter.ac.uk

bioinformatics↗

bioRxiv2018

ReQTL - an allele-level measure of variation-expression genomic relationships

MotivationBy testing for association of DNA genotypes with gene expression levels, expression quantitative trait locus (eQTL) analyses have been instrumental in understanding how thousands of single nucleotide variants (SNVs) may affect gene expression. As compared to DNA genotypes, RNA genetic variation represents a phenotypic trait that reflects the actual allele content of the studied system. RNA genetic variation can be measured at expressed genome regions, and differs from the DNA genotype in sites subjected to regulatory forces. Therefore, assessment of correlation between RNA genetic variation and gene expression can reveal regulatory genomic relationships in addition to eQTLs.\n\nResultsWe introduce ReQTL, an eQTL modification which substitutes the DNA allele count for the variant allele frequency (VAF) at expressed SNV loci in the transcriptome. We exemplify the method on sets of RNA-sequencing data from human tissues obtained though the Genotype-Tissue Expression Project (GTEx) and demonstrate that ReQTL analyses show consistently high performance and sufficient power to identify both previously known and novel molecular associations. The majority of the SNVs implicated in significant cis-ReQTLs identified by our analysis were previously reported as significant cis-eQTL loci. Notably, trans ReQTL loci in our data were substantially enriched in RNA-editing sites. In summary, ReQTL analyses are computationally feasible and do not require matched DNA data, hence they have a high potential to facilitate the discovery of novel molecular interactions through exploration of the increasingly accessible RNA-sequencing datasets.\n\nAvailability and implementationSample scripts used in our ReQTL analyses are available with the Supplementary Material (ReQTL_sample_code).\n\nContacthorvatha@gwu.edu or lfspurr@gwmail.gwu.edu\n\nSupplementary InformationRe_QTL_Supplementary_Data.zip

genomics↗

bioRxiv2017

CBF-1 promotes the establishment and maintenance of HIV latency by recruiting Polycomb repressive complexes, PRC1 and PRC2, at HIV LTR

The C-promoter binding factor-1 (CBF-1) is a potent and specific inhibitor of the HIV-1 LTR promoter. Here we demonstrate that the knockdown of endogenous CBF-1 in latently infected primary CD4+ T cells, using specific small hairpin RNAs (shRNA), resulted in the reactivation of latent HIV proviruses. Chromatin immunoprecipitation (ChIP) assays using latently infected primary T cells and Jurkat T-cell lines demonstrated that CBF-1 induces the establishment and maintenance of HIV latency by recruiting Polycomb Group (PcG/PRC) corepressor complexes or Polycomb repressive complexes 1 and 2 (PRC1 and PRC2). Knockdown of CBF-1 resulted in the dissociation of PRCs corepressor complexes enhancing the recruitment of RNA polymerase II (RNAP II) at HIV LTR. Knockdown of certain components of PRC1 and PRC2 also led to the reactivation of latent proviruses. Similarly, treatment of latently infected primary CD4+ T cells with the EZH2 inhibitor, 3-deazaneplanocin A (DZNep), led to their reactivation.

molecular biology↗