Enhancing Pan-Genome Analysis in Plants

Welcome to the video hub for the E-PAN Online Workshop, held on May 18th. This workshop highlighted the key outcomes of the E-PAN initiative—a project funded by ELIXIR Commissioned Services under the "Science Tier: Biodiversity, Food Security, and Pathogens" track.

As plant pan-genome datasets grow exponentially, researchers face critical challenges in quality control (QC), structural variant detection, gene presence/absence variation, data integration, and visualization. The E-PAN project addresses these hurdles by establishing clear QC standards, harmonized reporting approaches, and user-friendly visualization tools.

Below, you can watch the recorded sessions, explore practical tutorials, and learn how these tools can support reliable and interoperable pan-genome research across plant species.

🎬 Watch the Workshop Sessions
Part 1: Keynote & Quality Control

🔹 Welcome & Introduction
Speaker: Klaas Vandepoele (VIB-UGent)
Summary: An introduction to the E-PAN initiative, its goals, and the workshop agenda.

🔹 E-PAN White Paper Overview: Achievements, Gaps, and Future Directions
Speaker: Marc C. Heuermann (IPK Gatersleben)
Summary: A comprehensive breakdown of the E-PAN White Paper, outlining major achievements in plant pan-genomics, existing technical limitations, and future research perspectives.

🔹 Pan-Genome Quality Control Tutorial
Speaker: Pedro Barros (ITQB NOVA)
Summary: Practical guidance, recommendations, and established standards for performing robust quality control (QC) on plant pan-genome datasets.

Part 2: Pan-Genome Visualization Tools Showcase
Learn how to explore, analyze, and visualize complex pan-genomic data using three powerful platforms:

🟢 PanBarlex
Speaker: Patrick König (IPK Gatersleben)
Focus: Interactive pan-genome exploration for barley and related crops.

🟢 PLAZA
Speaker: Michiel Van Bel (VIB-UGent)
Focus: Comparative genomics and pan-genomic data integration across diverse plant species.

🟢 KnetMiner
Speaker: Keywan Hassani-Pak (Rothamsted Research)
Focus: Knowledge graph-based discovery connecting pan-genome data to gene networks, traits, and phenotypes.