John M. Chilton

This paper demonstrates agentic science on public infrastructure to ensure reproducibility by making all agent actions and workflows publicly accessible.

The paper demonstrates a framework using BRC-Analytics and agentic AI to standardize RNA-seq analysis of fungal pathogens, achieving reproducible results across studies with differing annotations.

Planemo is a command-line toolkit for developing, deploying, and executing Galaxy tools and workflows, promoting best practices in scientific data analysis.

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

GalaxyCloudRunner allows Galaxy servers to scale compute resources by dynamically accessing cloud providers, improving availability and flexibility.

The paper highlights the importance of open data and analytics in responding to public health emergencies, using COVID-19 genomic data to demonstrate the effectiveness of community-driven software tools and infrastructure.

The paper introduces GEDA, a framework enabling interoperability between Galaxy user data and external resources by accepting URLs pointing to user data.

This study develops quantitative metrics and simulated datasets to evaluate subclonal reconstruction methods in tumours.

The paper presents technologies that make computational reproducibility practical by integrating package management, containerization, and workflow systems.

The paper presents a community-driven framework for interactive data analytics training in life sciences, enabling researchers to learn through web-based computation without software installation.

The paper describes a hybrid platform integrating predefined workflows with ad hoc analytics to simplify and reproduce the raw data-to-publication process in biomedical research.

The paper integrates NCBI BLAST+ into Galaxy to enable sequence comparison workflows, making both simple and advanced BLAST tasks accessible.