Biology subjects

Ellis, S. E.

Publications and source records attributed to Ellis, S. E..

4 recordsLinked to original sources

bioRxiv2025

Clinical performance evaluation of a tiling amplicon panel for whole genome sequencing of respiratory syncytial virus

Accurate genomic characterization of respiratory syncytial virus (RSV) is crucial for studies of epidemiology and viral evolution, and monitoring potential escape from newly authorized vaccines and antivirals. We adapted a viral whole genome tiling amplicon panel (UW-ARTIC) and developed a custom bioinformatic pipeline for high-throughput, cost-effective sequencing of RSV-A and RSV-B. We established genome acceptability criteria and determined the performance characteristics of the panel including assay sensitivity, specificity, breadth of genome recovery, accuracy, and precision using contrived and remnant clinical specimens. High-quality genomes (>95% genome completeness; >500X and >1000X average depth for whole genome and fusion gene respectively) were recovered from samples with Ct [≤] 30 ([~]594 and 2,004 copies per reaction for RSV-A and RSV-B respectively). Minor variants were accurately identified in sample mixtures of 5:95 and higher. The assay showed high accuracy when compared against Sanger, shotgun metagenomic, and hybridization capture-based sequencing; and high repeatability and reproducibility. The UW-ARTIC RSV panel has utility in genomic surveillance, clinical and research applications. It has been used to generate FDA-reportable data for clinical trials of RSV antiviral products, with robust performance characteristics in samples from around the globe from as recently as the 2023/24 season. Continued genomic surveillance and future updates to primer sets will be essential for continued recovery of genomes as RSV continues to evolve.

genomics↗

bioRxiv2019

recount-brain: a curated repository of human brain RNA-seq datasets metadata

The usability of publicly-available gene expression data is often limited by the availability of high-quality, standardized biological phenotype and experimental condition information (\"metadata\"). We released the recount2 project, which involved re-processing [~]70,000 samples in the Sequencing Read Archive (SRA), Genotype-Tissue Expression (GTEx), and The Cancer Genome Atlas (TCGA) projects. While samples from the latter two projects are well-characterized with extensive metadata, the [~]50,000 RNA-seq samples from SRA in recount2 are inconsistently annotated with metadata. Tissue type, sex, and library type can be estimated from the RNA sequencing (RNA-seq) data itself. However, more detailed and harder to predict metadata, like age and diagnosis, must ideally be provided by labs that deposit the data.\n\nTo facilitate more analyses within human brain tissue data, we have complemented phenotype predictions by manually constructing a uniformly-curated database of public RNA-seq samples present in SRA and recount2. We describe the reproducible curation process for constructing recount-brain that involves systematic review of the primary manuscript, which can serve as a guide to annotate other studies and tissues. We further expanded recount-brain by merging it with GTEx and TCGA brain samples as well as linking to controlled vocabulary terms for tissue, Brodmann area and disease. Furthermore, we illustrate how to integrate the sample metadata in recount-brain with the gene expression data in recount2 to perform differential expression analysis. We then provide three analysis examples involving modeling postmortem interval, glioblastoma, and meta-analyses across GTEx and TCGA. Overall, recount-brain facilitates expression analyses and improves their reproducibility as individual researchers do not have to manually curate the sample metadata. recount-brain is available via the add_metadata() function from the recount Bioconductor package at bioconductor.org/packages/recount.

genomics↗

bioRxiv2017

Improving the value of public RNA-seq expression data by phenotype prediction

BackgroundPublicly available genomic data are a valuable resource for studying normal human variation and disease, but these data are often not well labeled or annotated. The lack of phenotype information for public genomic data severely limits their utility for addressing targeted biological questions.\n\nResultsWe develop an in silico phenotyping approach for predicting critical missing annotation directly from genomic measurements using, well-annotated genomic and phenotypic data produced by consortia like TCGA and GTEx as training data. We apply in silico phenotyping to a set of 70,000 RNA-seq samples we recently processed on a common pipeline as part of the recount2 project (https://jhubiostatistics.shinyapps.io/recount/). We use gene expression data to build and evaluate predictors for both biological phenotypes (sex, tissue, sample source) and experimental conditions (sequencing strategy). We demonstrate how these predictions can be used to study cross-sample properties of public genomic data, select genomic projects with specific characteristics, and perform downstream analyses using predicted phenotypes. The methods to perform phenotype prediction are available in the phenopredict R package (https://github.com/leekgroup/phenopredict) and the predictions for recount2 are available from the recount R package (https://bioconductor.org/packages/release/bioc/html/recount.html)\n\nConclusionHaving leveraging massive public data sets to generate a well-phenotyped set of expression data for more than 70,000 human samples, expression data is available for use on a scale that was not previously feasible.

bioinformatics↗

bioRxiv2016

Cross-tissue integration of genetic and epigenetic data offers insight into autism spectrum disorder

Epigenetics is an emerging area of investigation for Autism Spectrum Disorder (ASD). Integration of epigenetic information with ASD genetic results may elucidate functional insights not possible via either source of information in isolation. We used concurrent genotype and DNA methylation (DNAm) data from cord blood and peripheral blood from preschool-aged children to identify SNPs associated with DNA methylation, or methylation quantitative trait loci (meQTLs), and combined this with publicly available fetal brain and lung meQTL lists to assess enrichment of ASD GWAS results for tissue-specific meQTLs. ASD-associated SNPs were enriched for fetal brain (OR = 3.55; p < 0.001) and peripheral blood meQTLs (OR = 1.58; p < 0.001). The CpG site targets of ASD meQTLs across cord, blood, and brain tissues were enriched for immune-related pathways, consistent with other expression and DNAm results in ASD, and revealing pathways not implicated by genes identified from ASD rare variant work. Further, DNaseI hypersensitive sites and the STAT1 and TAF1 transcription factor binding sites were enriched for meQTL target CpGs of SNPs associated with psychiatric conditions. This joint analysis of genotype and DNAm demonstrates the potential utility of both brain and blood-based DNAm for insights into ASD and psychiatric phenotypes more broadly.

genomics↗