Levi Waldron

This study identifies dysbiotic gut microbiome features in Parkinson's disease, links them to genetic variants, and proposes microbiome-based biomarkers for targeted treatment.

Oral Faecalibacterium prausnitzii, the taxon most consistently depleted in PD stool, improved motor and GI deficits and lowered α-synuclein aggregates in male Thy1-ASO mice.

The paper curates and harmonizes metadata from metagenomic and cancer genomic repositories to improve data reusability and cross-study integration.

The paper models prokaryotic antivirus defense strategies and finds that immunity is preferred at high viral pressure and PCD at low host population size, with potential antagonism from overinvestment in immunity.

The paper provides an open-source online book for analyzing spatial transcriptomics data using Bioconductor in R, with reproducible code and datasets.

The paper introduces an open-source ecosystem for microbiome data science using Bioconductor, supporting modular workflows for analyzing complex, multi-modal datasets.

This paper benchmarks five scRNA-seq analysis frameworks on scalability, efficiency, and accuracy using large and small datasets, finding GPU acceleration and optimized algorithms improve performance.

The paper benchmarks differential abundance methods using microbiome datasets with known biological ground truth, finding that compositional methods are less effective than commonly used non-compositional approaches.

The paper provides a Bioconductor package with curated landmark datasets for single-cell multimodal data, enabling easy access and analysis of seven different technologies.

This paper introduces HAllA, a method for discovering associations in multi-omic datasets, showing it outperforms existing approaches and identifies new biological relationships.

The paper describes AnVIL, a cloud-based platform that enables genomic data analysis without data transfer, improving security, scalability, and collaboration.

GenomicSuperSignature compares new RNA-seq datasets to public archives for rapid interpretation using precomputed axes of variation.

SIMON is an open-source platform that enables biomedical researchers to apply machine learning algorithms to high-dimensional data without requiring advanced programming skills.

HGNChelper identifies and corrects invalid gene symbols in human and mouse data by leveraging authoritative databases and addressing spreadsheet-related errors.

The paper benchmarks statistical methods for microbiome data against RNA-seq methods, finding that univariate RNA-seq methods perform as well as microbiome-specific methods.

The paper introduces STORMS, a reporting guideline for human microbiome studies, providing a 17-item checklist to standardize and improve the organization and reporting of microbiome research.

The paper presents a workflow for allele-specific CNA analysis and SNV classification from tumor-only WES data, showing high concordance with gold standard methods.

This paper shows that molecular subtypes and gene signatures in ovarian cancer are influenced by stromal cell content, suggesting that bulk tissue analysis may be unreliable without accounting for cell admixture.

This paper provides an overview of single-cell RNA sequencing analysis using Bioconductor, highlighting its tools and methods for handling high-dimensional data.

The paper benchmarks gene set enrichment methods using a curated compendium of disease datasets, identifying performance differences and providing recommendations for improved analysis.

MetaGxData provides standardized cancer transcriptomic and clinical data for cross-tumour analyses, identifying common prognostic genes and creating a multi-cancer gene signature.

HMP16SData provides integrated 16S rRNA sequencing data from the Human Microbiome Project, enabling easier analysis for researchers with basic R skills.

This study investigates how different sources of heterogeneity affect the accuracy of genomic prediction models across studies, finding that some sources have limited impact while others, like identical generative models, significantly improve consistency.

The paper introduces curatedMetagenomicData, a resource providing curated metagenomic profiles for analysis in R with minimal bioinformatic expertise.

The paper introduces MultiAssayExperiment, a Bioconductor package for integrating multi-omics data, demonstrating its use with TCGA datasets to simplify analysis and visualization.