Merge pull request #47 from AustralianBioCommons/user_meeting

update participants, news and events

Author: ziadbkh

_data/events.yml

@@ -3,6 +3,12 @@
 # List of events
 #
 
+- name: ABLeS user group meeting
+  date: 2025-12-02
+  location: Online
+  description: Join us for the upcoming ABLeS User Group Meeting, featuring updates from ABLeS team, NCI and Pawsey, and presentations from three ABLeS-supported researchers.
+  url: https://www.biocommons.org.au/events/ables-users
+
 - name: ABLeS user group meeting
   date: 2025-07-01
   location: Online

_data/news.yml

@@ -2,6 +2,20 @@
 
 # List of news
 
+- name: From cancer genomics to evolutionary biology - how ABLeS is powering Australian research
+  url: https://www.biocommons.org.au/news/ables-acknowledgements
+  date: 2025-09-30
+  participants:
+    - name: The Australian Amphibian and Reptile Genomics (AusARG) initiative
+      url: https://australianbiocommons.github.io/ables/participants/ausARG
+
+- name: Cancer researchers level up their high performance computing
+  url: https://www.biocommons.org.au/news/ables-cancer-research
+  date: 2025-08-29
+  participants:
+    - name: Anna Trigos lab, Peter MacCallum Cancer Centre
+      url: https://australianbiocommons.github.io/ables/participants/imaging
+
 - name: Australian palaeoenvironments and biodiversity to be reconstructed through metagenomic analysis of sedimentary ancient DNA by national collaboration with Indigenous partners
   url: https://www.biocommons.org.au/news/ciehf
   date: 2025-03-31

participants/aaa_aithm.md

@@ -0,0 +1,40 @@
+---
+title: Queensland Research Centre for Peripheral Vascular Disease, AITHM, James Cook University
+description: This project uses variants in different mouse genetic backgrounds to identify putative genetic modifiers of abdominal aortic aneurysm that could be used for diagnosis, risk prediction or therapeutic interventions. 
+toc: false
+type: ABLeS Participant
+---
+
+## Project title
+
+Exploiting mouse genetics to identify genomic biomarkers that drive abdominal aortic aneurysm risk, prediction and diagnosis
+
+## Contact(s)
+
+- Kristen Barratt, JCU, <[email protected]>,  <[email protected]>
+
+## Project description and aims
+
+- Background and context:
+Abdominal aortic aneurysm (AAA) occurs due to progressive weakening and expansion of the abdominal aorta. It affects 4-5% of the population 1,2, with males >60 years particularly at risk 3. Despite decades of pre-clinical research aimed at identifying diagnostic biomarkers and drug interventions that limit or prevent AAA growth, the most effective clinical strategies remain early detection and imaging surveillance of small, asymptomatic AAAs. Surgical repair is performed only for large or symptomatic AAA 3. Even with emergency surgical repair, the mortality rate of ruptured AAA remains 76-90% 4–6. These realities underscore the need for new approaches that can advance our mechanistic understanding of AAA and guide therapeutic development.
+In this project we will perform a comparative genetic modifier analysis to identify new AAA biomarkers that can be used for disease risk and development prediction. 
+
+- Aim: Identify genetic modifiers of AAA susceptibility using mouse strain–specific SNPs and assess their potential as human biomarkers.
+Twin studies estimate that genetic factors account for ~70% of AAA risk in human cohorts3, yet the strong environmental influence on this disease has made identifying modifier genes difficult. Though genome wide association studies have implicated 24 loci in AAA development, efforts have struggled to validate these as biomarkers. To overcome this limitation, we will exploit naturally occurring strain-specific genetic variation in inbred mouse lines to identify novel modifiers of AAA susceptibility with direct translational potential. 
+A landmark study in 20067 compared the susceptibility of eight inbred mouse strains to elastase-induced AAA and demonstrated that FVB and C57BL/6 backgrounds were highly permissive to aneurysm formation, whereas SvEv, SvJ, and CBA strains were resistant. Consequently, most subsequent AAA studies have relied on the C57BL/6 background to ensure consistent disease induction. Despite this long-standing recognition of strain-dependent susceptibility, the underlying genetic determinants distinguishing permissive from resistant phenotypes remain uncharacterized. This project will integrate strain-specific single nucleotide polymorphisms (SNP) data to pinpoint candidate modifier genes, which will then be cross-referenced with human genetic and expression data to identify conserved biomarkers of AAA risk.
+
+- Aim: High-quality SNP and variant calls for the resistant and permissive inbred strains will be obtained from the Mouse Genomes Project or JAX, or generated by whole-genome sequencing if required. Variant files will be normalised, quality-filtered, and harmonised to a common reference genome. To identify genetic modifiers of AAA susceptibility, we will computationally extract loci where alleles are identical within the AAA-permissive strains and within the resistant strains, but differ between the two groups. The resulting variants will be annotated for predicted functional impact, genomic context and regulatory datasets to prioritise biologically meaningful loci. Candidate biomarkers will then be validated in vitro using genetic material from AAA patients stored in the Queensland Research Centre for Peripheral Vascular Disease Biobank. 
+
+The analysis pipeline requires access to R and RStudio, with the ability to install packages and Bioconductor dependencies. Some steps may be more efficiently run in or require Python for analysis, requiring Ubuntu OS and pip also.
+
+## How is ABLeS supporting this work?
+
+
+
+## Expected outputs enabled by participation in ABLeS
+
+The analysis will also uncover candidate genomic and transcriptomic biomarkers to improve risk prediction, diagnosis, and inform therapeutic development.
+
+<br/>
+
+> _These details have been provided by project members at project initiation. For more information on the project, please consult the contact(s) or project links above._

participants/aithm.md

@@ -0,0 +1,43 @@
+---
+title: Queensland Research Centre for Peripheral Vascular Disease, AITHM, James Cook University
+description: This project is an integrative meta-analysis of preexisting scRNA-seq datasets from humans with abdominal aortic aneurysm (AAA) and mouse models of AAA.
+toc: false
+type: ABLeS Participant
+---
+
+## Project title
+
+Using single-cell transcriptomics and genomic biomarkers to drive abdominal aortic aneurysm risk prediction, diagnosis and therapeutic interventions.
+
+## Contact(s)
+
+- Kristen Barratt, JCU, <[email protected]>,  <[email protected]>
+
+## Project description and aims
+
+- Background and context:
+Abdominal aortic aneurysm (AAA) occurs due to progressive weakening and expansion of the abdominal aorta. It affects 4-5% of the population 1,2, with males >60 years particularly at risk 3. Despite decades of pre-clinical research aimed at identifying diagnostic biomarkers and drug interventions that limit or prevent AAA growth, the most effective clinical strategies remain early detection and imaging surveillance of small, asymptomatic AAAs. Surgical repair is performed only for large or symptomatic AAA 3. Even with emergency surgical repair, the mortality rate of ruptured AAA remains 76-90% 4–6. These realities underscore the need for new approaches that can advance our mechanistic understanding of AAA and guide therapeutic development.
+In this project we will conduct an integrative meta-analysis of publicly available human and mouse single-cell RNA-sequencing (scRNA-seq) AAA datasets to generate a comprehensive analysis of AAA at the transcriptomic level. This will give us new insight into the pathophysiology of AAA, identify novel therapuetic targets, and allow us to recommend the which AAA mouse model recapitulates human AAA the best.  
+
+- Aim: Perform an integrative meta-analysis of publicly available human and mouse single-cell RNA-sequencing AAA datasets
+In our recent systematic review of publicly available human and mouse AAA scRNA-seq datasets, we found substantial methodological heterogeneity between studies, including that most sequenced only a single sample per group. This calls into question the results that each study obtained due to limited statistical power. To address this, we will perform an integrative meta-analysis of all eligible datasets, treating individual sequencing samples as ‘pseudo-bulks’ to enable robust statistical analyses. This approach will allow us to identify consistent differentially expressed genes across datasets, detect and characterise previously unrecognised cell types in aortic and aneurysm tissue, and compare mouse AAA models to human disease at the cellular and molecular level. By collating, harmonising, and re-analysing all publicly available datasets across species, we aim to generate a unified, high-resolution map of aneurysm development.
+
+- Methodology: We identified thirteen single-cell RNA sequencing (scRNA-seq) datasets from mouse and human AAA studies in public repositories (e.g., GEO) based on defined inclusion and exclusion criteria. Processed count matrices will be downloaded and analysed using the Seurat pipeline in R. Each dataset will undergo quality control (filtering by mitochondrial %, feature and transcript counts, and removal of doublets and ambient RNA), followed by preliminary cell-type annotation. Datasets will then be integrated by species using Seurat’s Harmony workflow. This is the most computationally intensive step in the pipeline, involving anchor identification and PCA-based dimensionality reduction. The resulting integrated datasets (~60–70 GB each) will be further processed to produce a unified UMAP embedding, re-annotated by cell type, and subdivided for downstream analyses including differential expression, cell proportion analysis, gene set enrichment, and RNA velocity modelling. Comparative analyses will identify conserved and divergent molecular and cellular features between human and mouse AAA datasets.
+
+- Resource requirements:
+The analyses proposed involves large datasets and computationally intensive steps that cannot be run solely on a standard desktop computer. While some preprocessing and exploratory analyses will be conducted locally, the HPC will be used for steps that exceed local memory or processing capacity.
+The Harmony integration of the single-cell datasets is highly memory-intensive and must be run on a single core with substantial RAM. Downstream analyses, including RNA velocity, differential gene expression, and UMAP embedding, are less memory-intensive but benefit from multi-core processing. The analysis pipeline requires access to R and RStudio, with the ability to install packages such as Seurat and Bioconductor dependencies (e.g. scDblFinder, SingleCellExperiment, DOSE, clusterProfiler, DESeq2, etc). Some steps are more efficiently run in or require Python for analysis (CellPhone and Velocyto), requiring Ubuntu OS and pip also.
+
+## How is ABLeS supporting this work?
+
+
+
+## Expected outputs enabled by participation in ABLeS
+
+This project will generate a high-resolution transcriptomic map of AAA, revealing new mechanistic insights into disease development. It will identify conserved and divergent cell types and pathways between human AAA and mouse models, guiding the choice of appropriate preclinical models. The analysis will also uncover candidate genomic and transcriptomic biomarkers to improve risk prediction, diagnosis, and inform therapeutic development.
+
+Using ABLeS and the NCI HPC will allow us to publish the integrative metanalysis in a relatively high impact journal for our field (similar papers have been published in ATVB, IF of 10). Analysis code will be made publicly available in online repositories. The datasets being used are preexisting so will not be re-published. 
+
+<br/>
+
+> _These details have been provided by project members at project initiation. For more information on the project, please consult the contact(s) or project links above._

participants/flindcomp.md

@@ -0,0 +1,39 @@
+---
+title: Computational Multi-Omics, Flinders University.
+description: The gut-brain axis is a bidirectional communication network linking neural function with the gut microenvironment and is increasingly implicated in neuropsychiatric and neurodegenerative conditions. We are conducting a large scale meta analysis of thousands of human metagenomes to identify consistent disease associated signatures.
+toc: false
+type: ABLeS Participant
+---
+
+## Project title
+
+Disentangling the gut-brain axis through meta-analyses
+
+## Collaborators and funding
+
+Work is funded through an NHMRC Investigator grant.
+
+## Contact(s)
+
+- Feargal Ryan, Flinders University, <[email protected]>
+
+## Project description and aims
+
+This project aims to disentangle gut–brain axis signals by conducting large-scale meta-analyses across thousands of publicly available shotgun metagenomes. Our primary objectives are to:
+- Taxonomic profiling of shotgun metagenomes to establish baseline microbial composition.
+- Taxon set enrichment analysis (TaxSEA) to detect coordinated shifts in functionally related microbial groups.
+- De novo assembly–based epitope mining to identify microbial proteins with potential cross-reactivity to host neural or immune epitopes.
+- Gene family–level testing to assess functional shifts beyond taxonomy, improving biological interpretability.
+- Standardised multi-cohort pipelines for reproducible, scalable meta-analyses.
+
+## How is ABLeS supporting this work?
+
+
+
+## Expected outputs enabled by participation in ABLeS
+
+All outputs will be published in open-access journals as mandated  by our primary funding body, the NHMRC. Any software or pipelines developed will be open source, shared via my lab’s GitHub and deposited in community repositories (e.g. Bioconductor), as we have done previously. Currently, the scale of datasets required particularly for the cross-reactive epitope pipeline is a major barrier on Flinders University infrastructure. This pipeline was initially developed through a Pawsey Preparatory Access Scheme, and ABLeS access is essential to extend it to the full dataset of thousands of samples. 
+
+<br/>
+
+> _These details have been provided by project members at project initiation. For more information on the project, please consult the contact(s) or project links above._