Public page for the WEHI Research Computing Platform (RCP)
This is the list of projects for this intake. Here you will see:
These reports are written by the group to help share information to future interns and interested parties.
For more examples, see the Intake 11 - Summary Report Page
Please update and do a pull request for this page via Github here.
Our objective was …
What we did as a group was …
How far we got was …
Our objective was …
What we did as a group was …
How far we got was …
Our objective was to embed duplex-specific QC metric generation and reporting into the core pipeline in a reproducible and version-controlled way, improving upon earlier approaches that operated outside the main workflow. Duplex-specific metrics are currently calculated outside the main processing pipeline using custom R markdown, which are neither containerized nor version-controlled, making them difficult to reproduce and maintain. Additionally, some of these scripts rely on GPL-licensed components, which conflict with the MIT license of the in-house duplex pipeline, creating licensing incompatibilities. The Duplex QC results were also only available in standalone R-generated reports, while standard read-level metrics were presented in MultiQC. This fragmentation can make it harder for users to assess experimental quality efficiently.
To resolve these issues, we modularized the R Markdown notebook into a standalone R script to improve computational efficiency, enhance version control, facilitate automation within pipelines, and simplify testing. This was packaged within a Docker container to ensure reproducibility and compatibility by removing GPL-licensed dependencies. A custom MultiQC plugin was developed to render duplex-specific metrics and plots directly within the Duplex MultiQC report generated after consensus sequence creation, while leaving the standard read-level QC report unchanged. This integration ensures that visuals are clear, accessible, and tailored specifically for duplex quality assessment.
How far we got was completing all the planned work, including refactoring the R scripts, containerizing the workflow, developing the custom MultiQC plugin, and integrating everything into the main pipeline. The system has been tested, with team members reproducing each other’s results to ensure consistency and reliability. However, there remains considerable potential for extension on all fronts — from expanding the testing framework, to customizing the MultiQC plugin with additional metrics and visuals, as well as further optimizing the pipeline integration.
Our objective was …
What we did as a group was …
How far we got was …
The objective of my internship was to revive and modernize REDMANE, a modular Research Data Management (RDM) platform designed to help researchers securely manage, organize, and share biomedical datasets. The aim was to restore its functionality, improve stability, and prepare the system for future scalability and integration.
What I did was focus on rebuilding the system infrastructure by setting up and configuring the cloud environment, containers, and network layers to ensure stable communication across services. I worked on migrating the database system and successfully connected the database to the backend, improving backend functionality and deployment reliability. I also streamlined the setup process and created comprehensive technical documentation to support future maintenance and development.
How far I got was a nearly complete system, with the backend and database fully connected and running smoothly, and the final step being to establish the connection between the backend and frontend. This progress laid the groundwork for completing the full data flow and advancing REDMANE toward a production-ready stage.
Our objective was …
What we did as a group was …
How far we got was …
Our objective was …
What we did as a group was …
How far we got was …