Eleanor C Williams

SMEW: An interactive multi-scale toolkit for cross-condition and network-based analysis of spatial metabolomics data

SMEW provides a code-free, interactive platform for analyzing spatial metabolomics data across multiple scales and conditions to identify metabolic patterns and biological pathways.

Spatially resolved integrative analysis of transcriptomic and metabolomic changes in tissue injury studies

The paper introduces MAGPIE, a framework for integrating spatial transcriptomics, metabolomics, and tissue morphology data, demonstrating its application in studying drug-induced pulmonary fibrosis.

Spatio-Temporal Multi-Omics Profiling of Mechanisms and Biomarkers in Inhaled Drug-Induced Lung Toxicity

This study develops a multi-omics framework to identify biomarkers of inhaled drug-induced lung toxicity using spatial transcriptomics, proteomics, and histopathological data.

Increased Cholesterol Synthesis Drives Neurotoxicity in Patient Stem Cell-Derived Model of Multiple Sclerosis

This study identifies increased cholesterol synthesis in PMS patient-derived neural progenitor cells as a driver of neurotoxicity, which can be mitigated by simvastatin.

Discovery of potent SARS-CoV-2 nsp3 macrodomain inhibitors uncovers lack of translation to cellular antiviral response

The paper identifies potent nsp3-mac1 inhibitors but finds they do not translate to antiviral activity in cell culture models.

bulkAnalyseR: An accessible, interactive pipeline for analysing and sharing bulk multi-modal sequencing data

bulkAnalyseR provides an interactive pipeline for analyzing and sharing bulk multi-modal sequencing data, offering quality checks, differential expression, and regulatory network analysis.

The sum of two halves may be different from the whole. Effects of splitting sequencing samples across lanes.

This paper investigates how splitting sequencing samples across lanes affects results in bulk and single-cell sequencing, showing reduced detail and potential bias in analyses.

noisyR: Enhancing biological signal in sequencing datasets by characterising random technical noise

noisyR reduces technical noise in sequencing data to enhance biological signal detection and improve downstream analysis consistency.

feamiR: Feature selection based on Genetic Algorithms for predicting miRNA-mRNA interactions

feamiR uses genetic algorithms to select features for predicting miRNA-mRNA interactions, improving accuracy by analyzing seed, compensatory, and flanking regions.