Biology subjects

Worthey, E. A.

Publications and source records attributed to Worthey, E. A..

7 recordsLinked to original sources

bioRxiv2026

nf_xpatial: A Reproducible Framework for Standardized Preprocessing and Clustering of Xenium Data

Recent advances in spatial transcriptomics have enabled the profiling of increasingly larger numbers of genes while retaining single-cell and subcellular resolution in situ. However, standardized bioinformatics workflows for analyzing these datasets have lagged behind, with existing pipelines focusing primarily on image processing and cell segmentation. To address this gap, we present nf_xpatial, a best-practices Nextflow pipeline for the downstream analysis of 10x Genomics Xenium data. The pipeline performs quality control, filtering, log and cell area normalization, multi-sample integration, and both expression-driven and spatially informed clustering across systematic parameter sweeps, allowing users to evaluate and compare clustering resolutions and spatial modeling parameters within a single reproducible run. Overall, nf_xpatial streamlines the processing of Xenium data from platform outputs to integrated single-cell and spatial clustering datasets, providing a standardized starting point from which biologists can fine-tune parameters and proceed to hypothesis-driven spatial analyses.

bioinformatics↗

bioRxiv2025

scnanoseq: an nf-core pipeline for Oxford Nanopore single-cell RNA-sequencing

Recent advancements in long-read single-cell RNA sequencing (scRNA-seq) have facilitated the quantification of full-length transcripts and isoforms at the single-cell level. Historically, long-read data would need to be complemented with short-read single-cell data in order to overcome the higher sequencing errors to correctly identify cellular barcodes and unique molecular identifiers. Improvements in Oxford Nanopore sequencing, and development of novel computational methods have removed this requirement. Though these methods now exist, the limited availability of modular and portable workflows remains a challenge. Here we present, nf-core/scnanoseq, a secondary analysis pipeline for long-read single-cell and single-nuclei RNA that delivers gene and transcript-level quantification. The scnanoseq pipeline is implemented using Nextflow and is built upon the nf-core framework, enabling portability across computational environments, scalability and reproducibility of results across pipeline runs. The nf-core/scnanoseq workflow follows best practices for analyzing single-cell and single-nuclei data, performing barcode detection and correction, genome and transcriptome read alignment, unique molecular identifier deduplication, gene and transcript quantification, and extensive quality control reporting. AVAILABILITY OF DATA AND MATERIALSnf-core/scnanoseq is available at https://github.com/nf-core/scnanoseq under the MIT License and the documentation is available at https://nf-co.re/scnanoseq. The downstream analytical code for validation analysis is available at https://github.com/U-BDS/scnanoseq_analysis and all dataset sources have been disclosed under the methods section for each respective dataset.

bioinformatics↗

bioRxiv2023

QuaC: A Pipeline Implementing Quality Control Best Practices for Genome Sequencing and Exome Sequencing Data

Quality Control (QC) of human genome sequencing and exome sequencing data is necessary to ensure they are of sufficient quality for downstream analyses. While several QC tools are available to measure quality parameters at various levels post-sequencing, their output needs to be reviewed and interpreted in a very manual and time-consuming process. Such manual review is a major challenge towards standardization and consistency, as the process can be subjective depending on the reviewer. To address these difficulties, we have developed QuaC, which implements, integrates, and standardizes QC best practices at our Center. It performs three major steps: (1) runs several QC tools using data produced by the read alignment (BAM) and small variant calling (VCF) as input and optionally accepts QC output for raw sequencing reads (FASTQ); (2) executes QuaC-Watch to perform QC checkup based on the expected thresholds for quality metrics; and (3) aggregates QC metrics produced by all the QC tools as well as QuaC-Watch results into single, self-contained MultiQC report, both at the per-sample and across-project levels. This report provides aggregate summaries for all samples within a project/cohort for efficient comprehensive review while still allowing for granular review down to individual metrics for a single sample. Finally, we have developed a "Sample QC review system" schema to standardize QC reviewers logging of results and simplify downstream users interpretation of the reviewers finding.

bioinformatics↗

bioRxiv2019

Genome sequencing for early-onset dementia: high diagnostic yield and frequent observation of multiple contributory alleles

We assessed the utility of genome sequencing for early-onset dementia. Participants were selected from a memory disorders clinic. Genome sequencing was performed along with C9orf72 repeat expansion testing. All returned sequencing results were Sanger validated clinically. Prior clinical diagnoses included Alzheimers disease, frontotemporal dementia, and unspecified dementia. The mean age-of-onset was 54 (41-76). 50% of patients had a strong family history, 37.5% had some, and 12.5% had no known family history. Nine of 32 patients (28%) had a variant defined as pathogenic or likely pathogenic (P/LP) by American College of Medical Genetics standards, including variants in APP, C9orf72, CSF1R, and MAPT. Nine patients (including three with P/LP variants) harbored established risk alleles with moderate penetrance (odds ratios of about 2-5) in ABCA7, AKAP9, GBA, PLD3, SORL1, and TREM2. All six patients harboring these moderate penetrance variants but not P/LP variants also had one or two APOE {varepsilon}4 alleles. One patient had two APOE {varepsilon}4 alleles with no other established contributors. In total, 16 patients (50%) harbored one or more genetic variants likely to explain symptoms. We identified variants of uncertain significance (VUSs) in ABI3, ADAM10, ARSA, GRID2IP, MME, NOTCH3, PLCD1, PSEN1, TM2D3, TNK1, TTC3, and VPS13C, also often along with other variants. In summary, genome sequencing for early-onset dementia demonstrated high utility, with particular advantages where targeted testing may fail such as atypical variant-disease associations or presence of multiple moderate impact alleles. One or more established contributory alleles is often present in early-onset dementia, supporting an oligogenic model.

neuroscience↗

bioRxiv2019

Identification of Pathogenic Structural Variants in Rare Disease Patients through Genome Sequencing

PurposeClinical whole genome sequencing is becoming more common for determining the molecular diagnosis of rare disease. However, standard clinical practice often focuses on small variants such as single nucleotide variants and small insertions/deletions. This leaves a wide range of larger \"structural variants\" that are not commonly analyzed in patients.\n\nMethodsWe developed a pipeline for processing structural variants for patients who received whole genome sequencing through the Undiagnosed Diseases Network (UDN). This pipeline called structural variants, stored them in an internal database, and filtered the variants based on internal frequencies and external annotations. The remaining variants were manually inspected and then interesting findings were reported as research variants to clinical sites in the UDN.\n\nResultsOf 477 analyzed UDN cases, 286 cases ({approx} 60%) received at least one structural variant as a research finding. The variants in 16 cases ({approx} 4%) are considered \"Certain\" or \"Highly likely\" molecularly diagnosed and another 4 cases are currently in review. Of those 20 cases, at least 13 were identified originally through our pipeline with one finding leading to identification of a new disease. As part of this paper, we have also released the collection of variant calls identified in our cohort along with heterozygous and homozygous call counts. This data is available at https://github.com/HudsonAlpha/UDN_SV_export.\n\nConclusionStructural variants are key genetic features that should be analyzed during routine clinical genomic analysis. For our UDN patients, structural variants helped solve {approx} 4% of the total number of cases ({approx} 13% of all genome sequencing solves), a success rate we expect to improve with better tools and greater understanding of the human genome.

genomics↗

bioRxiv2019

VarSight: Prioritizing Clinically Reported Variants with Binary Classification Algorithms

MotivationIn genomic medicine for rare disease patients, the primary goal is to identify one or more variants that cause their disease. Typically, this is done through filtering and then prioritization of variants for manual curation. However, prioritization of variants in rare disease patients remains a challenging task due to the high degree of variability in phenotype presentation and molecular source of disease. Thus, methods that can identify and/or prioritize variants to be clinically reported in the presence of such variability are of critical importance. ResultsWe tested the application of classification algorithms that ingest variant predictions along with phenotype information for predicting whether a variant will ultimately be clinically reported and returned to a patient. To test the classifiers, we performed a retrospective study on variants that were clinically reported to 237 patients in the Undiagnosed Diseases Network. We treated the classifiers as variant prioritization systems and compared them to another variant prioritization algorithm and two single-measure controls. We showed that these classifiers outperformed the other methods with the best classifier ranking 73% of all reported variants and 97% of reported pathogenic variants in the top 20. AvailabilityThe scripts used to generate results presented in this paper are available at https://github.com/HudsonAlpha/VarSight. Contactjholt@hudsonalpha.org

bioinformatics↗

bioRxiv2018

Programmatic Detection of Diploid-Triploid Mixoploidy via Whole Genome Sequencing

PurposeMixoploidy is a type of mosaicism where an organism is a mixture of cells with different numbers of chromosomes. There are a broad range of phenotypes associated with mixoploidy that vary greatly depending on the fraction of cells that are non-diploid, their chromosome number, their distribution, and presumably the specific variation present in the patient. Clinical detection of mixoploidy is important for diagnosis.\n\nMethodsWe developed a method to detect mixoploidy from clinical whole genome sequencing (WGS) data through the identification of excess of variant calls centered on unusual B-allele frequencies. Our method isolates the signal from these variants using trio calls and then solves a basic linear equation to estimate levels of diploid-triploid mixoploidy within the sample.\n\nResultsWe show that our method reflects the results from a cytogenetic test. We provide examples detailing how our method has been used to identify diploid-triploid mixoploid individuals from within the NIH Undiagnosed Diseases Network. We present confirmatory findings obtained by clinical cytogenetic testing and show that our method can be used to identify the diploid-triploid ratio in these cases.\n\nConclusionWGS data from patients with rare diseases can be used to identify mixoploid individuals. Individuals with certain characteristics as discussed should be tested for mixoploidy as part of standard clinical pipeline procedures. Scripts that perform this calculation are publicly available at https://github.com/HudsonAlpha/mixoviz.

genomics↗