diff --git a/_data/events.yml b/_data/events.yml index 1580803..73063a4 100644 --- a/_data/events.yml +++ b/_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 on ABLeS and infrastructure, as well as 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 diff --git a/_data/news.yml b/_data/news.yml index 619d179..0089689 100644 --- a/_data/news.yml +++ b/_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 diff --git a/participants/aaa_aithm.md b/participants/aaa_aithm.md new file mode 100644 index 0000000..9077132 --- /dev/null +++ b/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, , + +## 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? + +This work is supported through the Production Bioinformatics scheme provided by ABLeS. The support includes storage and compute allocation. + +## 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. + +
+ +> _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._ diff --git a/participants/aithm.md b/participants/aithm.md new file mode 100644 index 0000000..d0b74d7 --- /dev/null +++ b/participants/aithm.md @@ -0,0 +1,44 @@ +--- +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, , + +## 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? + + +This work is supported through the Production Bioinformatics scheme provided by ABLeS. The support includes storage and compute allocation. + +## 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. + +
+ +> _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._ diff --git a/participants/flindcomp.md b/participants/flindcomp.md new file mode 100644 index 0000000..3001193 --- /dev/null +++ b/participants/flindcomp.md @@ -0,0 +1,40 @@ +--- +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, + +## 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? + + +This work is supported through the Production Bioinformatics scheme provided by ABLeS. The support includes storage and compute allocation. + +## 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. + +
+ +> _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._ diff --git a/participants/food_wine_ua.md b/participants/food_wine_ua.md new file mode 100644 index 0000000..b8e03cd --- /dev/null +++ b/participants/food_wine_ua.md @@ -0,0 +1,94 @@ +--- +title: School of Agriculture, Food and Wine, The University of Adelaide +description: Quantify how drought (rainfed) vs irrigation shapes the functional capacity of wheat-field soil microbiomes at global scale, and then “zoom in” on a deep-sequenced Australian transect to resolve fine-grained pathways, genomes (MAGs), and ecological indicators. +toc: false +type: ABLeS Participant +--- + +## Project title + +Global Functional Shifts in Wheat-Field Soils Under Drought vs Irrigation + +## Collaborators and funding + +[School of Agriculture, Food and Wine](https://set.adelaide.edu.au/agriculture-food-wine/), The University of Adelaide, Urrbrae, SA, Australia + +## Contact(s) + +- Jiayu Li, School of Agriculture, Food and Wine, The University of Adelaide, + +## Project description and aims + +1) Project summary +Goal. Quantify how drought (rainfed) vs irrigation shapes the functional capacity of wheat-field soil microbiomes at global scale, then zoom in on a deep-sequenced Australian transect to resolve fine-grained pathways, genomes (MAGs), and ecological indicators. +Datasets. +Global set: ~300 bulk-soil metagenomes (each ~20 Gb) from wheat fields worldwide (rainfed vs irrigated), plus amplicons (16S/ITS) where available. +Australian transect: 68 bulk-soil samples spanning west→east; each ~40 Gb metagenome (+ matched amplicons). +Overall approach. Harmonise metadata and processing; perform function- and genome-resolved metagenomics; build drought/irrigation classifiers and indicator gene panels; validate patterns with the Australian transect; release an open resource (MAGs, gene catalogues, reproducible workflows). +2) Aims & testable hypotheses +**Aim 1** Global functional contrasts. +Quantify differences in metabolic potential between rainfed vs irrigated wheat soils. +H1: Droughted (rainfed) sites show enrichment of osmoprotection (e.g., trehalose/betaine), EPS/aggregate formation, ROS mitigation, and water-use efficiency-linked nitrogen cycling routes; irrigated sites show higher denitrification potential and copiotrophic traits. +**Aim 2** Genome-resolved ecology. +Recover and compare MAGs and strain populations associated with drought vs irrigation. +H2: Distinct DefenceBiome-like consortia (e.g., Actinobacteria, Streptomyces, Microbacteriaceae) increase in drought and encode exudate-responsive transporters and stress regulons. +**Aim 3** Indicator panels & predictive models. +Develop gene/pathway and MAG indicator sets that predict water regime and agronomic context across continents. +H3: A small panel of functions (≤50 KOs/CAZymes) plus a handful of sentinel MAGs will classify water regime with high accuracy (AUC > 0.85) after controlling for soil/climate covariates. +**Aim 4** Australian transect validation & mechanistic resolution. +Use the 68-site transect to (i) validate global signatures; (ii) resolve fine-grained pathway variants, strain microdiversity, and ecological thresholds along rainfall/irrigation gradients. +3) Significance & impact +- Agronomic relevance: actionable microbiome indicators for drought-smart management, informing irrigation scheduling, residue/cover practices, and microbial amendments. +- Scientific advance: links host water regime → exudates → microbial functions/MAGs, uniting community and genome-resolved views at continental scale. +- Community resource: openly released MAG catalogue, non-redundant gene set, and reproducible pipelines, enabling re-use in wheat microbiome, soil health, and climate-adaptation studies. +4) Planned analyses +- 4.1 Data audit & harmonisation + - Inventory all cohorts; standardise metadata schema (soil chem/texture, climate, management, cultivar, water regime, geography). + - Define inclusion criteria; handle licence/consent; map to MIxS-Soil fields. +Deliverable: harmonised metadata table; PRISMA-style flow diagram. +- 4.2 Read processing & profiling + - QC: FastQC/MultiQC → adapter/quality trimming → host (wheat) removal. + - Taxon/function profiling (reads): Kraken2/Bracken (or centrifuge) + HUMAnN/eggNOG-mapper for KO/EC pathways + - ShortBRED for targeted marker sets (e.g., osmolyte genes). +Deliverable: per-sample taxon and functional tables + QC reports. +- 4.3 Assembly & gene catalogues + - Strategy chosen per cohort (single/co-assembly by eco-region); MEGAHIT/metaSPAdes with k-mer sweeps; protein prediction (Prodigal). + - Non-redundant gene catalogue via MMseqs2; function annotation (KEGG/KO, EC, COG, CAZy, antiSMASH/BGCs); resistome where relevant. +Deliverable: gene catalogue (FASTA + annotation TSV), abundance matrices. +- 4.4 MAG reconstruction & curation + - Binning with MetaBAT2, CONCOCT, VAMB; refinement (DASTool); quality via CheckM2; dereplication (dRep); taxonomy (GTDB-Tk). + - Abundance/coverage via coverM; SNP/strain tracking (inStrain). +Deliverable: dereplicated MAG set (≥50% comp, ≤10% contam; high-quality subset ≥90/≤5), per-MAG metadata. +- 4.5 Pathway & network ecology + - Targeted pathways: osmolytes (proline/betaine/trehalose), EPS, antioxidant systems, nitrogen (nitrification, denitrification, DNRA), sulfur shunts, phosphorus solubilisation, ACC deaminase, transporters for key exudates. + - Co-occurrence & guild inference (FastSpar/SpiecEasi) → stability/robustness tests. +Deliverable: pathway differential analyses, guild maps, effect sizes. +- 4.6 Statistics & causal controls + - Compositional methods (ALDEx2/ANCOM-BC) with covariate control (soil pH, texture, organic C, MAP/MAT, continent, cultivar) via mixed-effects models. + - Counterfactual checks (matching/propensity) to mitigate irrigation vs region confounding. +Deliverable: adjusted global contrasts with uncertainty quantification. +- 4.7 Predictive modelling & indicators + - ML (elastic net / random forest / XGBoost) to predict water regime and agronomic outcomes from functions + MAGs; nested CV and external validation (Australian transect). + - Derive minimal indicator panels for field diagnostics. +Deliverable: classifiers (AUC/PR curves), top features, portable panels. +- 4.8 Australian transect deep-dive + - High-resolution assembly, strain-level microdiversity, fine-scale pathway variants; change-point analyses along rainfall/irrigation; link to soil properties. +Deliverable: Australia-focused paper validating global signals. + + +## How is ABLeS supporting this work? + +This work is supported through the Production Bioinformatics scheme provided by ABLeS. The support includes storage and compute allocation. + +## Expected outputs enabled by participation in ABLeS + +- D1. Global non-redundant gene catalogue for wheat-field soils (KO/EC/CAZy/BGC). +- D2. Curated MAG set with habitat preferences and trait annotations. +- D3. Indicator panels (genes/MAGs) and validated predictive models of water regime. +- D4. Reproducible Nextflow workflows and environment recipes (Apptainer). +- D5. High impact papers: (i) global functional contrasts; (ii) genome-resolved validation on the Australian transect; (iii) workflow/methods. +- D6. FAIR-compliant data/metadata releases with DOIs. + +
+ +> _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._ \ No newline at end of file diff --git a/participants/horse_fua.md b/participants/horse_fua.md new file mode 100644 index 0000000..70a1aae --- /dev/null +++ b/participants/horse_fua.md @@ -0,0 +1,48 @@ +--- +title: Federation University Australia +description: Molecular Dynamics Studies of Horse Prion Protein S167D-Variant and Wild-Type, and Other PrPC Proteins and PrPScs. +toc: false +type: ABLeS Participant +--- + +## Project title + +Molecular Dynamics Studies of Horse Prion Protein S167D-Variant and Wild-Type + +## Collaborators and funding + +[Federation University Australia](https://www.federation.edu.au/) + +## Contact(s) + +- Jiapu Zhang, Federation University Australia, + +## Project description and aims + +Prion diseases, also called transmissible spongiform encephalopathies (TSEs), are fatal neurodegenerative diseases characterised by the accumulation of an abnormal prion protein isoform (PrPSc: rich in β-sheets—about 30% α-helix and 43% β-sheet), which is converted from the normal prion protein (PrPC: predominantly α-helical—about 42% α-helix and 3% β-sheet). However, prion disease has not been reported in horses up to now; therefore, horses are known to be a species resistant to prion diseases. Residue S167 in horses has been cited as a critical protective residue for encoding PrP conformational stability in prion-resistance. According to the “protein-only” hypothesis, PrPSc is responsible for both the spongiform degeneration of the brain and disease transmissibility. Thus, understanding the conformational dynamics of PrPSc from PrPC is key to developing effective therapies. This project focuses on molecular dynamics (MD) studies on the horse PrP S167D mutant (S167D-Variant) and Wild Type (WT), in order to understand their conformational dynamics. My WT MD results in this article (Zoonotic Dis. 2024, 4(3):187-200) have confirmed that the single amino acid differences at position 167 might influence the overall protein structures of the WT. + +In my article "Zhang J (2024) Molecular Dynamics and Optimization Studies of Horse Prion Protein Wild Type and Its S167D Mutant. Zoonotic Dis. 2024, 4(3):187-200; https://doi.org/10.3390/zoonoticdis4030017" (denoted as "the Article" in this small project), the investigator has not finished many aspects of the MD simulations: + +(i) the S167D-Variant should be done, because as it is reported by the Article "this article reports no results from the MD simulations for the mutant", "Various secondary structural statistics for the mutant simulations at different temperatures and pH conditions, and the changes in the H-bond network caused by the mutation with MD simulations, should be presented"; + +(ii) the WT should be done MD simulations furthermore from the 30 ns; because as it is reported by the Article "MD simulations on a timescale (30 ns) much shorter than the one we required to observe significant protein conformational change; it is therefore still unclear how probative these simulations are of the biological process we are trying our best to model", "a longer MD timescale is still needed to understand further. The simulations are very short (30 ns) to verify structural change. The simulation time should be increased and the conformational changes should be noted as soon as the computing resources are available from NCI Australia"; + +(iii) the MD should be done for the interactions of WT with the solvent and the ions, ligands binding, as it is reported in the Article "the free energy calculations are a research direction for this article to investigate the effects/contributions of ions such as Cu2+ and of solvents such as water. This should be highlighted as a future research direction for the author". + +**Project background:** + +Prion diseases are incurable neurodegenerative diseases caused by aberrant conformations of the prion protein (PrP). Many animals develop similar diseases, but rabbits, dogs, and horses show unusual resistance to prion diseases This resistance could be due to protective changes in the sequence of the corresponding PrP in each animal. Structural studies have identified S174, S167 and D159 as the key protective residues in rabbit, horse and dog PrP, respectively. But no systemic MD studies currently support the protective activity of these residues, especially for the horse PrP residue S167. Experimental laboratory results revealed that expression of horse PrP-S167D (which carries a substitution for the equivalent residue in the PrP of hamsters, a species that is susceptible to prion diseases) shows high toxicity in behavioural and anatomical assays. Thus, this project aims to carry out an MD study of the horse PrP wild-type (WT) NMR structure 2KU4.pdb and the S167D mutant (hereafter, mutant) homology structure (constructed by me). We will present in this project useful protective bioinformatics of S167 and discuss the structural features that make horse PrP more stable. The findings of this project might contribute to the development of drugs/compounds that stabilize the PrP structure and prevent the formation of toxic conformations of prion diseases. + +Here, we detail the central topic on PrP more in this brief introduction. Unlike bacteria and viruses which are based on DNA and RNA, prions are unique as disease-causing agents since they are misfolded proteins. Prion contains no nucleic acids, and it is a misshapen or conformationally changed protein that acts like an infectious agent. Prion diseases are called “protein structural conformational” diseases. Normal prion protein is denoted as PrPC and diseased infectious prion is denoted as PrPSc. PrPC is predominant in α-helices, but PrPSc is rich in β-sheets in the form of amyloid fibrils. PrPC is a normal protein found on the membranes of cell, including several blood components of which platelets constitute the largest reservoir in humans. Several topological forms exist; one cell-surface form anchored via glycolipid and two transmembrane forms. The normal protein has a complex function, which continues to be investigated at present; the cleavage of PrPC in peripheral nerves causes the activation of myelin repair in Schwann cells, PrPC regulates cell death, PrPC may have a function in the maintenance of long-term memory, PrPC may play roles in innate immunity and stem cell renewal, etc. PrPC binds Cu2+ ions with high affinity; the significance of this property is not clear, but it is presumed to relate to the protein’s structure or function. PrPC is not sedimentable, meaning it cannot be separated by centrifuging techniques. PrPC is readily digested by proteinase K and can be liberated from the cell surface by the enzyme phosphainositide phospholipase C, which cleaves the glycophosphatidylinositol glycoplipid anchor. PrPC plays an important role in cell–cell adhesion and intracellular signaling in vivo and may therefore be involved in cell–cell communication in the brain. PrPSc always causes prion disease. Several highly infectious, brain-derived PrPSc structures have been discovered by cryo-EM; another brain-derived fibril structure isolated from humans with the prion disease GSS syndrome has also been determined. Often, PrPSc is bound to cellular membranes, presumably via its array of glycolipid anchors; however, sometimes the fibres are dissociated from membranes and accumulate outside of cells in the form of plaques. S167 in PrPC is a protective residue and generates a more compact and stable structure in the C-terminal subdomain of the PrPC global domain. + +## How is ABLeS supporting this work? + +This work is supported through the Production Bioinformatics scheme provided by ABLeS. The support includes storage and compute allocation. + +## Expected outputs enabled by participation in ABLeS + +The research results will be published by a Springer Nature monograph or several journals' articles. + +
+ +> _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._ diff --git a/participants/jcurtin.md b/participants/jcurtin.md new file mode 100644 index 0000000..c2b1065 --- /dev/null +++ b/participants/jcurtin.md @@ -0,0 +1,53 @@ +--- +title: John Curtin School of Medical Research, ANU. +description: This project will generate processed data products (FASTQ, unaligned/aligned modBAM, and VCF) from the raw signal-level nanopore data of the 1KGP-ONT cohort replicated at NCI (de95), enabling downstream analyses of human genetic variation and base modifications. +toc: false +type: ABLeS Participant +--- + +## Project title + +Generation of processed data from 1KGP-ONT + +## Collaborators and funding + +**Collaborators:** + +- National Computational Infrastructure ([NCI Australia](https://nci.org.au/)) + +**Funding partners:** + +- ARC +- NHMRC +- MRFF +- NCMAS + +## Contact(s) + +- Eduardo Eyras, ANU, + +## Project description and aims + +This project aims to process the soon-to-be-released raw nanopore signal data from the 1000 Genomes Project ONT Long Read Sequencing Consortium (1KGP-ONT) Collection hosted at NCI (de95). These will include unaligned and aligned modBAM files, FASTQ, and VCFs, enabling reanalysis for variant detection, base modification studies, and other genomic investigations. + +ModBAM files will serve as the preferred processed data format, containing both read-level information and base modification annotations derived from POD5/BLOW5 inputs. The processing involves computationally intensive modified basecalling workflows that require GPU acceleration. + +The processed data will be made publicly available alongside the raw data via the NCI Data Catalogue, expanding access to high-quality ONT long-read resources for the genomics research community. This will accelerate research in population genomics, epigenomics, and human disease. + +## How is ABLeS supporting this work? + +This work is supported through the Production Bioinformatics scheme provided by ABLeS. The support includes storage and compute allocation. + +## Expected outputs enabled by participation in ABLeS + +It will enable the generation of processed datasets derived from the 1KGP-ONT data collection hosted at NCI (de95), including: + +1. FASTQ files for standard sequence analysis; +2. Unaligned and aligned modBAM files containing read-level and base modification information; +3. VCF files for variant detection + +These outputs will be stored and published via the NCI Data Catalogue alongside the existing 1KGP-ONT raw data, ensuring open access to the research community. Catalogue records will include DOIs, comprehensive metadata, and licensing information to make the data findable. + +
+ +> _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._ diff --git a/participants/parkinson_fua.md b/participants/parkinson_fua.md new file mode 100644 index 0000000..034f121 --- /dev/null +++ b/participants/parkinson_fua.md @@ -0,0 +1,38 @@ +--- +title: Federation University Australia +description: Computational Studies of Parkinson's Diseases, MD of alpha-synuclein WT and Variants. +toc: false +type: ABLeS Participant +--- + +## Project title + +Computational Studies of Parkinson's Diseases + +## Collaborators and funding + +[Federation University Australia](https://www.federation.edu.au/) + +## Contact(s) + +- Jiapu Zhang, Federation University Australia, + +## Project description and aims + +Parkinson’s disease (PD), the second most common neurodegenerative disorder in human, was first described as the "shaking palsy" in 1817 (now being the fastest growing neurodegenerative condition in the world) by the English doctor James Parkinson, is a long-term degenerative disorder of the central nervous system that mainly affects the motor system. As the disease worsens, non-motor symptoms become increasingly common. The symptoms generally come on slowly over time. Early in the disease, the most obvious are shaking, rigidity, slowness of movement, and difficulty with walking. Thinking and behavioral problems may also occur. Dementia becomes common in the advanced stages of the disease. Depression and anxiety are also common, occurring in more than a third of people with PD. Other symptoms include sensory, sleep, and emotional problems. The exact cause of these symptoms is not yet fully understood and currently there is no cure to PD. + +PD has no single cause, with links to several gene mutations as well as environmental influences. The cause of PD is generally unknown, but believed to involve genetic factors: those with a family member affected are more likely to get the disease themselves. Around 15% of individuals with PD have a first-degree relative who has the disease, and 5-10% of people with PD are known to have forms of the disease that occur because of a mutation in one of several specific genes. The role of genetic factors in the pathogenesis of PD has received increasing attention from scholars. Since the discovery of the first PD-causing gene alpha-synuclein SNCA in the late 1990s, at least 6 pathogenic genes have been associated with familial PD. Genetic factors are also only one of the factors in the pathogenesis of PD. alpha-synuclein (aS) is the main component of the Lewy bodies that accumulate in the brains of people with PD. Misfolding of the protein aS, which associates with presynaptic vesicles (aS is typically found in the presynaptic terminals of neurons, where it plays a role in synaptic transmission and synaptic vesicle transport), has been implicated in the molecular chain of events leading to PD. aS can exist in different conformations, including an unstructured or unfolded form in the cytosol and a helical form associated with lipid membranes. Part I of this book studies full-length wild-type aS proteins (aS 1-140) and their (about eight) mutants from the optimized molecular structure and Molecular Dynamics (MD) structural dynamics points of view. We will study the effects of point mutations and other factors on the distribution of conformers in aS. As we all know aS protein is not completely disordered. We may use variants of the protein that have been shown to have a range of effects in experimental animals or tissue culture. The most important is how point mutations can cause such a big influence on conformer distribution and in turn how can this lead to such a diversity of biological effects. In this project our systematic MD study of a wider range of mutants (including these at least 6 pathological mutants, A30P, E35K, 4E6K, H50Q, G51D, A53T, A53T-V40D-V74D, E57K) with experimentally determined conformer distributions and known biological properties would provide some answers. + +To do extensive MD-studies of the full length aS(1-140) WT and its at least 6 mutants, we will do under different force fields such as the ff03-AMBER force field, the DES-Amber force field, the a99SB-disp force field, the ff14SBonlysc force field, the GB99dms force field, the CHARMM36m force field, the SIRAH force field, etc. + +## How is ABLeS supporting this work? + +This work is supported through the Production Bioinformatics scheme provided by ABLeS. The support includes storage and compute allocation. + +## Expected outputs enabled by participation in ABLeS + +The research results will be published by a Springer Nature monograph or several journals' articles. + +
+ +> _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._ \ No newline at end of file diff --git a/participants/proteoforms.md b/participants/proteoforms.md new file mode 100644 index 0000000..01da686 --- /dev/null +++ b/participants/proteoforms.md @@ -0,0 +1,42 @@ +--- +title: Mass Spectrometry and Proteomics Ggroup - University of South Australia +description: This research focuses on long-read RNA sequencing and proteogenomics analysis to develop sample-specific protein databases from RNA-seq data. +toc: false +type: ABLeS Participant +--- + +## Project title + +Identification of Proteoforms correlating with chemoresistance using a proteoogenomics approach + +## Collaborators and funding + +- University of South Australia +- University of Adelaide + +## Contact(s) + +- Peter Hoffmann, Unisa, + +## Project description and aims + +This project integrates long-read RNA sequencing and mass spectrometry-based proteomics to uncover proteogenomic mechanisms driving chemoresistance in ovarian cancer. The goal is to detect and quantify isoforms and proteoforms that influence drug response and patient outcomes. + +Aims +- **Aim 1**: Generate long-read transcriptomic and proteomic data from chemo-sensitive and chemo-resistant ovarian cancer cell lines to build sample-specific protein databases. + +- **Aim 2**: Develop a proteogenomic pipeline for accurate isoform-level analysis and detection of low-abundance or non-canonical proteins. + +- **Aim 3**: Apply the workflow to patient samples to validate biomarkers and assess predictive value for treatment response. + +## How is ABLeS supporting this work? + +This work is supported through the Production Bioinformatics scheme provided by ABLeS. The support includes storage and compute allocation. + +## Expected outputs enabled by participation in ABLeS + +Expected outputs include generating custom protein databases using GenomeProt from long-read RNA-seq and proteomics data on ovarian cancer. These databases and analysis results will support proteogenomic research on chemoresistance and will be shared via public repositories and publications to promote reproducibility and discovery. + +
+ +> _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._