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BEGIN:VEVENT
DTSTART;TZID=Australia/Sydney:20250526T130000
DTEND;TZID=Australia/Sydney:20250526T140000
DTSTAMP:20260409T090924
CREATED:20250514T042721Z
LAST-MODIFIED:20250630T032509Z
UID:3759-1748264400-1748268000@spds.sydney.edu.au
SUMMARY:Systems or sub-systems biology? Which can best help us understand the cell?
DESCRIPTION:Judith and David Coffey SeminarProf Marc Wilkins\, UNSW\n\n\n\nThis event was held in person and online. \n\n\n\n\n\n\n\n\n\n\n\nThe promise of systems biology is that the study of a cell\, via its components and their interactions\, will define and reveal emergent properties of that system. The use of large-scale ‘omics techniques is essential for this\, however many such techniques still cannot measure all relevant biomolecules and their interactions. This leaves us with an incomplete and sometimes shallow understanding. This talk will describe our research into two\, tractable sub-systems of the cell where we have sought to fully define their components\, interactions and aspects of function. \n\n\n\nFirst\, I will describe our work to fully define the ‘protein methylation network’ of a eukaryotic cell. Here\, we asked is it possible to identify all instances of a post-translational modification in a eukaryote? We also asked\, can we prove it? We then asked can we define all enzymes\, in this case methytransferases\, that are responsible for this modification – and in doing so construct a complete sub-system in the cell. We have been successful in addressing the above\, which has allowed a range of emergent properties of this network to be discovered. \n\n\n\nSecondly\, I will describe our work to understand how two regulatory subsystems in the cell – the signalling system and the histone methylation system – actually connect. In yeast\, four enzymes methylate histones and four can demethylate histones. They do so with remarkable precision and dynamism\, helping define regions of the genome to be transcribed. We asked are these enzymes phosphorylated\, and to what degree? We also asked are phosphosites associated with specific cellular responses; in effect do certain phosphorylation events on the histone methylating enzymes direct them to change chromatin in certain parts of the genome? We additionally asked can we find which kinases are responsible; thus connecting the sensing / signalling system with that of chromatin regulation? This story is less complete than that above\, but is compelling! \n\n\n\n\n\n\nSubscribe to our seminar mailing list\n\n\n\n\n→\n\n\n\n\n\n\n\nFind out more about the Statistical Bioinformatics seminar series\n\n\n\n\n\n→\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nProf Marc Wilkins\n\n\n\nMarc is a professor of systems biology and is deputy dean (research and enterprise) in the faculty of science at UNSW. He has had a longstanding interest in understanding cells at a systems level\, and finding ways to do that. He defined the concept of the proteome\, coined the term\, and has directed entities such as the Systems Biology Initiative (SBI) and the Ramaciotti Centre for Genomics (2011-2022). He is currently the UNSW node leader in the MACSYS ARC Centre of Excellence\, which has the goal of building mathematical and computational models of whole cells. \n\n\n\nMarc has a career output of >280 publications in proteomics\, especially concerning protein post-translational modifications\, and in genomics\, transcriptomics and biological networks. In industry\, Prof. Wilkins co-founded two biotechnology companies\, both which took products to market and which were ASX-listed. Proteome Systems had a focus on proteomic technology development and its application to biodiscovery. Regeneus (now Cambium Bio) developed cell-based therapies for the treatment of inflammatory conditions.
URL:https://spds.sydney.edu.au/event/systems-or-sub-systems-biology-which-can-best-help-us-understand-the-cell/
LOCATION:Mackenzie Room\, Level 6\, Charles Perkins Centre\, University of Sydney\, Johns Hopkins Drive\, University of Sydney\, NSW\, 2006\, Australia
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Australia/Sydney:20250519T130000
DTEND;TZID=Australia/Sydney:20250519T140000
DTSTAMP:20260409T090924
CREATED:20250507T041753Z
LAST-MODIFIED:20250630T032526Z
UID:3688-1747659600-1747663200@spds.sydney.edu.au
SUMMARY:The 7TM family: are template-based models better than AlphaFold models?
DESCRIPTION:Judith and David Coffey SeminarProf Shoba Ranganathan\, Macquarie University\n\n\n\nThis event was held in person and online. \n\n\n\n\n\n\n\n\n\n\n\nInsect odorant and gustatory receptors (ORs/GRs) are 7-transmembrane-domain (7TM) ion channels essential for the survival of insects. ORs play a key role in many insect behaviours\, including foraging\, pollination\, social interactions\, and recognizing prey and enemies. ORs are also the target of biocontrol of insect pests and disease vectors. Ors are therefore highly specialized and bear very little sequence similarity\, even within the same species. These receptors are “upside-down” compared to G-protein coupled receptors (GPCRs)\, and function without any accessory G proteins to let calcium ions in upon ligand binding. To design or identify novel volatile attractant or repellent chemicals\, a knowledge of the 3D structure of these inverted topology 7TM receptors is essential. With the rise of AlphaFold\, currently in its third avatar\, we explored if detailed template-based modelling (TBM) using the few experimental insect OR structures available is still required\, instead of a quick AI-generated AlphaFold3 (AF3) model. Using all Ors from the genomes of two economically important Australian fruit fly pest species\, we show that 7TM receptors still need step-by-step TBM rather than AF3 models. \n\n\n\n\n\n\nSubscribe to our seminar mailing list\n\n\n\n\n→\n\n\n\n\n\n\n\nFind out more about the Statistical Bioinformatics seminar series\n\n\n\n\n\n→\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nProf Shoba Ranganathan\n\n\n\nShoba Ranganathan is an Honorary Professor of Bioinformatics in Applied Biosciences\, Macquarie University. She was the first Chair of Bioinformatics in Australia (2004-22). She has held research and academic positions in India\, USA\, Singapore and Australia as well as a consultancy in industry. Shoba’s research addresses several key areas of bioinformatics to understand biological systems using computational approaches. Her group has achieved both experience and expertise in different aspects of computational biology\, ranging from metabolites and small molecules to biochemical networks\, pathway analysis and computational systems biology. She has authored as well as edited several books in as well as contributed several articles to the 1st edition of this Encyclopedia. She was awarded the 2023 Outstanding Contributions to the International Society for Computational Biology (ISCB) Award. She is an Honorary Senior Fellow of the Australian Society for Bioinformatics and Computational Biology since 2018\, an ISCB Fellow since 2023 and an Asia Association for Artificial Intelligence Fellow since 2024.
URL:https://spds.sydney.edu.au/event/the-7tm-family-are-template-based-models-better-than-alphafold-models/
LOCATION:Mackenzie Room\, Level 6\, Charles Perkins Centre\, University of Sydney\, Johns Hopkins Drive\, University of Sydney\, Sydney\, 2006\, Australia
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Australia/Sydney:20250512T130000
DTEND;TZID=Australia/Sydney:20250512T140000
DTSTAMP:20260409T090924
CREATED:20250324T010319Z
LAST-MODIFIED:20250630T032541Z
UID:3055-1747054800-1747058400@spds.sydney.edu.au
SUMMARY:BANKSY unifies cell typing and tissue domain segmentation for scalable spatial omics data analysis
DESCRIPTION:Statistical Bioinformatics SeminarDr Vipul Singhal\, Head of Computational Biology\, Integrated Biosciences\, Inc\n\n\n\n\n\n\n\n\n\n\n\n\n\nA core property of solid tissue is the spatial arrangement of cell types into stereotypical spatial patterns. These cells can be investigated with spatial omics technologies to reveal both their omics features (transcriptomes\, proteomes\, etc)\, and their spatial coordinates. Because a cell’s state can be influenced by interactions with other cells\, it is informative to cluster cells using their omics signatures as well as their spatial relationships. We present BANKSY (Singhal et al.\, Nat. Genetics\, 2024)\, an algorithm with R and Python implementations that identifies both cell types and tissue domains from spatially-resolved -omics data. It does so by embedding cells in a product space of their own and neighbourhood omics features. BANKSY revealed niche-dependent cell states in the mouse brain\, and outperformed competing methods on domain segmentation and cell-typing benchmarks. BANKSY can also be used for quality control of spatial transcriptomics data and for spatially aware batch correction. Critically\, it is substantially faster and more scalable than existing methods\, enabling the processing of datasets with millions of cells. BANKSY comes in both Python and R implementations\, and works with major single cell frameworks like SingleCellExperiment\, Seurat\, and Scanpy. \n\n\n\n\n\n\nSubscribe to our seminar mailing list\n\n\n\n\n→\n\n\n\n\n\n\n\nFind out more about the Statistical Bioinformatics seminar series\n\n\n\n\n\n→\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nDr Vipul Singhal\n\n\n\nDr. Singhal is a computational biologist at Integrated Biosciences in Redwood City\, CA\, where he uses systems biology and machine learning to explore cellular responses to drugs and other perturbations. Previously\, he worked at the Genome Institute of Singapore\, developing algorithms to analyze spatial gene expression data in Dr. Kok Hao Chen’s lab. He earned his PhD in Bioengineering from Caltech\, focusing on tools for designing genetic circuits\, and his undergraduate degree in Electrical Engineering from Imperial College London. Outside work\, he enjoys snowboarding and climbing.
URL:https://spds.sydney.edu.au/event/banksy-unifies-cell-typing-and-tissue-domain-segmentation-for-scalable-spatial-omics-data-analysis/
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Australia/Sydney:20250505T130000
DTEND;TZID=Australia/Sydney:20250505T140000
DTSTAMP:20260409T090924
CREATED:20250417T055319Z
LAST-MODIFIED:20250630T032301Z
UID:3569-1746450000-1746453600@spds.sydney.edu.au
SUMMARY:Vitessce framework for interactive visualization of single-cell data and its applications
DESCRIPTION:Statistical Bioinformatics SeminarMark Keller\, PhD student at Harvard Medical School\n\n\n\n\n\n\n\n\n\n\n\nWe introduce Vitessce to address the need for a scalable\, interactive\, and extensible framework that supports visualization of spatial and multimodal single-cell data. Its modular architecture\, compatibility with multiple file formats\, and support for coordinated multiple views enable researchers to integrate previously disconnected data modalities and view them with a single tool. The adoption of Vitessce by multiple data portals\, publication-associated websites\, and commercial products underscores its utility. This framework also supports multiple projects in which we have applied Vitessce for comparative\, spatial\, and epigenomic data visualization\, which will be highlighted in this presentation. \n\n\n\n\n\n\nSubscribe to our seminar mailing list\n\n\n\n\n→\n\n\n\n\n\n\n\nFind out more about the Statistical Bioinformatics seminar series\n\n\n\n\n\n→\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nMark Keller\n\n\n\nMark Keller is a PhD student in the Bioinformatics and Integrative Genomics program at Harvard Medical School\, advised by Professor Nils Gehlenborg. His research interests include developing visualization tools for single-cell data.
URL:https://spds.sydney.edu.au/event/vitessce-framework-for-interactive-visualization-of-single-cell-data-and-its-applications/
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Australia/Sydney:20250428T130000
DTEND;TZID=Australia/Sydney:20250428T140000
DTSTAMP:20260409T090924
CREATED:20250403T231421Z
LAST-MODIFIED:20250509T062934Z
UID:3206-1745845200-1745848800@spds.sydney.edu.au
SUMMARY:Spatially resolved mapping of cells associated with human complex traits
DESCRIPTION:Liyang Song\, Westlake University\n\n\n\n\n\n\n\n\n\nDepicting spatial distributions of disease-relevant cells is crucial for understanding disease pathology. In our recent work\, we developed a method\, gsMap\, that integrates spatial transcriptomics (ST) data with genome-wide association study (GWAS) summary statistics to map cells to human complex traits\, including diseases\, in a spatially resolved manner. Using embryonic ST datasets covering 25 organs\, we benchmarked gsMap through simulation and by corroborating known trait-associated cells or regions in various organs. Applying gsMap to brain ST data\, we revealed that the spatial distribution of glutamatergic neurons (glu-neurons) associated with schizophrenia more closely resembles that for cognitive traits than that for mood traits\, such as depression. The schizophrenia-associated glu-neurons were distributed near the dorsal hippocampus\, with upregulated calcium signaling and regulation genes\, while the depression-associated glu-neurons were distributed near the deep medial prefrontal cortex\, with upregulated neuroplasticity and psychiatric drug target genes. \n\n\n\n\n\n\nSubscribe to our seminar mailing list\n\n\n\n\n→\n\n\n\n\n\n\n\nFind out more about the Statistical Bioinformatics seminar series\n\n\n\n\n\n→\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nLiyang Song\n\n\n\nLiyang Song is a fourth-year Ph.D. student in Statistical Genetics at Westlake University\, advised by Dr. Jian Yang. His research focuses on the intersection of human genetics\, statistics\, deep learning\, and spatial omics\, with applications to medicine. He is dedicated to developing efficient statistical methods and software tools that enable robust genetic and scientific discoveries by integrating genetic data with multi-omics datasets.
URL:https://spds.sydney.edu.au/event/spatially-resolved-mapping-of-cells-associated-with-human-complex-traits/
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Australia/Sydney:20250414T130000
DTEND;TZID=Australia/Sydney:20250414T140000
DTSTAMP:20260409T090924
CREATED:20250404T031759Z
LAST-MODIFIED:20250509T063228Z
UID:3253-1744635600-1744639200@spds.sydney.edu.au
SUMMARY:RNA polymerase II at histone genes predicts outcome in human cancer
DESCRIPTION:Dr Ye Zheng\, University of Texas MD Anderson Cancer Center\n\n\n\n\n\n\n\n\n\n\n\n\n\nGenome-wide hypertranscription is common in human cancer and predicts poor prognosis. To understand how hypertranscription might drive cancer\, we applied our formalin-fixed paraffin-embedded (FFPE)–cleavage under targeted accessible chromatin method for mapping RNA polymerase II (RNAPII) genome-wide in FFPE sections. We demonstrate global RNAPII elevations in mouse gliomas and assorted human tumors in small clinical samples and discover regional elevations corresponding to de novo HER2 amplifications punctuated by likely selective sweeps. RNAPII occupancy at S-phase-dependent histone genes correlated with WHO grade in meningiomas\, accurately predicted rapid recurrence\, and corresponded to whole-arm chromosome losses. Elevated RNAPII at histone genes in meningiomas and diverse breast cancers is consistent with histone production being rate-limiting for S-phase progression and histone gene hypertranscription driving overproliferation and aneuploidy in cancer\, with general implications for precision oncology. \n\n\n\n\n\n\nSubscribe to our seminar mailing list\n\n\n\n\n→\n\n\n\n\n\n\n\nFind out more about the Statistical Bioinformatics seminar series\n\n\n\n\n\n→\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nDr Ye Zheng\n\n\n\nYe Zheng is a tenure-track Assistant Professor in the Bioinformatics and Computational Biology Department of the University of Texas MD Anderson Cancer Center and an NIH/NHGRI K99/R00 fellow. Dr. Zheng received her postdoctoral training at the Fred Hutchinson Cancer Center from both molecular biology and quantitative modeling perspectives mentored by Dr. Steven Henikoff. Before her postdoctoral training\, Dr. Zheng received a Ph.D. in Statistics from the University of Wisconsin-Madison under the supervision of Dr. Sündüz Keleş.  At MD Anderson Cancer Center\, Dr. Zheng leads a hybrid research group. Her quantitative research group is dedicated to the statistical modeling and computational pipeline development using bulk and single-cell transcriptomics\, proteomics\, epigenomics\, and 3D genomics data to address biological and clinical challenges. Her wet lab specializes in the epigenomic profiling of the Formalin-Fixed\, Paraffin-Embedded (FFPE) samples.
URL:https://spds.sydney.edu.au/event/rna-polymerase-ii-at-histone-genes-predicts-outcome-in-human-cancer/
CATEGORIES:Videos
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Australia/Sydney:20250407T130000
DTEND;TZID=Australia/Sydney:20250407T140000
DTSTAMP:20260409T090924
CREATED:20250110T033554Z
LAST-MODIFIED:20250509T062211Z
UID:1324-1744030800-1744034400@spds.sydney.edu.au
SUMMARY:Toward a cell-type-specific understanding of complex diseases
DESCRIPTION:Statistical Bioinformatics SeminarDr Boxiang Liu\, National University of Singapore\n\n\n\n\n\n\n\n\n\n\n\n\n\nHigh-throughput genotyping and sequencing have led to the discovery of thousands of disease-associated variants. Because most of these variants lie in non-coding regions\, their functional mechanisms remain unclear. To identify genetic effects underlying complex diseases\, it has become increasingly important to investigate the proper cell types and contexts. We demonstrate the power of cell-type-specific assays for three complex diseases.  \n\n\n\nCoronary artery disease (CAD) is the leading cause of death globally. Approximately 40 – 60% of CAD severity can be attributed to genetic factors. GWAS meta-analyses have uncovered more than 100 significant loci\, but most are difficult to interpret because they reside in non-coding regions. We found that coronary artery smooth muscle-specific genetic regulatory mechanisms are highly enriched in CAD GWAS signals. By jointly analyzing eQTL and GWAS datasets\, we identified five risk genes. TCF21 and SMAD3 were subsequently validated by single-cell analysis in atherosclerotic mouse models. Age-related macular degeneration is one of the leading causes of blindness in elderlies. It has been estimated that genetic factors explain 45% – 70% of the variation in the severity of age-related macular degeneration. Retinal pigment epithelium (RPE) is vital in ocular development but is underrepresented in genetic regulation studies.  \n\n\n\nBy jointly analyzing RPE eQTL and AMD GWAS\, we identified several risk genes including RDH5. In particular\, we found that the eQTL regulatory SNP also regulates splicing. Experimental validation confirms that the minor allele leads to aberrant splicing and subsequently RNA non-sense-mediated decay. This result revealed the genetic mechanism of RDH5 regulation and confirmed RDH5 as a risk gene for age-related macular degeneration\, making it a potential target for drug development. Autoimmune diseases are a group of illnesses that are individually rare but collectively affect 5% of the population. Leveraging ~1M PBMC single cells from the Asian Immune Diversity Atlas (AIDA)\, we showcase our recent results on leveraging single-cell sQTLs to disentangle autoimmune diseases. \n\n\n\n\n\n\nSubscribe to our seminar mailing list\n\n\n\n\n→\n\n\n\n\n\n\n\nFind out more about the Statistical Bioinformatics seminar series\n\n\n\n\n\n→\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nDr Boxiang Liu\n\n\n\nBoxiang Liu obtained BA degree in Biophysics from Illinois Wesleyan University\, a MS degree in Statistics and a PhD degree in Bioinformatics from Stanford University. He was a research leader at Baidu Research USA and joined the National University of Singapore as an Assistant Professor in 2022. His research group specializes in genetic regulation of molecular traits (QTLs) and single-cell multi-omics. He is the winner of President’s Award in Natural Sciences and Mathematics\, Stanford University CEHG fellowship\, Charles B. Carrington Memorial Award\, and the Chinese Government Outstanding Overseas Ph.D. Students. His research group focuses on using computational and statistical tools to understand the genetics of complex human diseases\, with the long-term goal of validating known and identifying novel drug targets.
URL:https://spds.sydney.edu.au/event/toward-a-cell-type-specific-understanding-of-complex-diseases/
CATEGORIES:Videos
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Australia/Sydney:20250331T130000
DTEND;TZID=Australia/Sydney:20250331T140000
DTSTAMP:20260409T090924
CREATED:20250324T225435Z
LAST-MODIFIED:20250627T014800Z
UID:3073-1743426000-1743429600@spds.sydney.edu.au
SUMMARY:Revisiting quality control\, normalisation\, and spatially variable gene calling in CosMx WTx
DESCRIPTION:Dr Dharmesh Bhuva\, University of Queensland \n\n\n\n\n\n\n\n\n\nSpatially resolved molecular measurements have revolutionised the study of disease systems\, providing an unprecedented resolution and throughput of molecular measurements. The opportunity posed by such data requires a new set of tools to unlock its true potential. Initial uptake of this data saw the repurposing of computational tools developed for single-cell RNA-seq\, however\, new studies are showing the need for a different paradigm when analysing spatial measurements. While measurements across cells are relatively independent in single-cell RNA-seq\, they are spatially autocorrelated in spatial RNA-seq. I will begin by demonstrating the presence of such autocorrelation and show that single-cell inspired normalisation strategies are detrimental to spatial datasets. Next\, I will present some of our ideas on quality control of such data\, focusing on the CosMx platform. I will then present our SpaNorm model for normalisation of spatial molecular measurements and demonstrate extensions of this model that allow SVG calling and a GLM-PCA approximation. While the ideas presented in this talk have been assessed in CosMx data\, they should be applicable across other imaging-based spatial transcriptomics platforms. \n\n\n\n\n\n\nSubscribe to our seminar mailing list\n\n\n\n\n→\n\n\n\n\n\n\n\nFind out more about the Statistical Bioinformatics seminar series\n\n\n\n\n\n→\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nDr Dharmesh Bhuva\n\n\n\nDr Dharmesh D Bhuva is an early-career computational systems biologist who is passionate about understanding how complex systems of gene regulation and signalling lead to diverse molecular phenotypes in healthy and diseased tissues. He completed his PhD in 2020 at the University of Melbourne and WEHI where he developed new systems biology approaches to study molecular function and gene regulation in cancer. He then undertook his post-doctoral studies at the world-renowned WEHI Bioinformatics division\, where he embarked on developing novel approaches to study cancer tissues using spatial molecular data. In 2023\, he joined the computational systems oncology division at the South Australian Immunogenomics Cancer Institute (SAiGENCI) to continue his cutting-edge research in developing computational approaches to study tissue architecture. Dr. Bhuva has recently been awarded a MRFF grant and a NHMRC investigator grant (EL1) to identify spatial biomarkers in cancer systems\, work which he will undertake at the Frazer Institute.
URL:https://spds.sydney.edu.au/event/revisiting-quality-control-normalisation-and-spatially-variable-gene-calling-in-cosmx-wtx/
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Australia/Sydney:20250324T130000
DTEND;TZID=Australia/Sydney:20250324T140000
DTSTAMP:20260409T090924
CREATED:20250312T011044Z
LAST-MODIFIED:20250509T062218Z
UID:2919-1742821200-1742824800@spds.sydney.edu.au
SUMMARY:Towards Generalist AI Models in Pathology: The Unique Role of Molecular Data
DESCRIPTION:Statistical Bioinformatics SeminarDr Guillaume Jaume\, Harvard Medical School\n\n\n\n\n\n\n\n\n\n\n\n\n\nHow can we develop generalist AI models for pathology? How can we leverage these models for better diagnosis\, prognosis\, response-to-treatment prediction\, and biomarker discovery? Foundation models have taken the field of computational pathology by storm—bringing a whole new perspective on AI model development\, training\, and evaluation. Whole-slide image classification now largely relies on pretrained “patch encoders”\, such as UNI\, and increasingly relies on “slide encoders”\, such as Threads. Multimodal learning\, in particular based on morphomolecular data\, emerges as a critical component for training and evaluating these models. In this talk\, I will present our recent works in this direction: (1) HEST (NeurIPS’24) for joint analysis of spatial transcriptomics and histology\, and (2) Tangle (CVPR’24)\, Madeleine (ECCV’24) and Threads (in review) for molecular-guided slide representation learning. I will close by sharing my perspective on the potential future direction of the field. \n\n\n\n\n\n\nSubscribe to our seminar mailing list\n\n\n\n\n→\n\n\n\n\n\n\n\nFind out more about the Statistical Bioinformatics seminar series\n\n\n\n\n\n→\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nDr Guillaume Jaume\n\n\n\nGuillaume is a 3rd-year postdoctoral researcher at Harvard Medical School and Brigham & Women’s Boston Hospital in the group of Prof. Faisal Mahmood. He obtained his Ph.D. in Electrical and Electronic Engineering from EPFL in collaboration with IBM Research and ETH Zurich in 2022. Guillaume’s research focuses on computational pathology to integrate AI tools into the clinical and research facets of pathology. His research involves two main objectives: first\, enhancing the representation learning of tissue by developing general-purpose foundation models for pathology and oncology; and second\, integrating AI tools in drug development to improve drug safety assessment\, detect toxicity\, and discover safety biomarkers.
URL:https://spds.sydney.edu.au/event/towards-generalist-ai-models-in-pathology-the-unique-role-of-molecular-data/
CATEGORIES:Videos
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Australia/Sydney:20250317T130000
DTEND;TZID=Australia/Sydney:20250317T140000
DTSTAMP:20260409T090924
CREATED:20250226T014312Z
LAST-MODIFIED:20250509T062231Z
UID:2608-1742216400-1742220000@spds.sydney.edu.au
SUMMARY:Multi-state evolutionary model quantifies tumour cellular plasticity
DESCRIPTION:Statistical Bioinformatics SeminarDr Gladys Poon\, HKU\n\n\n\n\n\n\n\n\n\n\n\n\n\nCell-state transition dynamics are important in many diseases in cancer\, transitions among distinct cell states can affect treatment effectiveness and metastasis. Cancer evolutionary studies based on phylogenetics often assume weak or no selection – especially in the recent past – to estimate effective population sizes and evolutionary forces across a relatively long time span. However\, this assumption is violated during tumour growth when cells rapidly proliferate and outcompete one another under strong selection pressures.We adopt a stochastic modelling approach where cells divide according to a birth-death branching process and couple their fates with a Markov model for phenotypic transitions. Both genotype and phenotype are simultaneously inherited by the next generation. Phylogenies are constructed for a subsampled population of cells and coupled cell state information at a single time-point is used to estimate transition rates. We show that it is possible to determine phenotypic transition dynamics for specific population trajectories by ‘fine-graining’ node depth levels. This feature is crucial for addressing the proliferation of tumours which are mixtures of clones with different selection advantages.We pair our analysis with cell state annotations derived specifically for single-cell RNAsequencing (scRNAseq) data to define evolutionary relatedness between cell phenotypes. We then apply our computational framework to published metastatic pancreatic cancer phylogenies reconstructed using CRISPR-based lineage tracing in mice\, where scRNAseq information is available for each leaf. By comparing across metastatic locations in a single mouse\, we are able to reveal changes in selection pressures and cell phenotypic transition rates during metastatic progression. Inferred cell state transitions are supported by inferred RNA velocities. \n\n\n\n\n\nThis is an online event held via Zoom.\n\n\n\n\nSubscribe to our seminar mailing list\n\n\n\n\n→\n\n\n\n\n\n\n\nFind out more about the Statistical Bioinformatics seminar series\n\n\n\n\n\n→\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nDr Gladys Poon\n\n\n\nDr Gladys Poon completed both her undergraduate (Physics) and postgraduate (PhD in Oncology) studies at the University of Cambridge. During her time in the Blundell lab\, Gladys used population genetics to quantify the expected mutation burden and unknown drivers in human cancers\, particularly in acute myeloid leukemia. She also combined newly gained experimental expertise with her quantitative background to interrogate clonal evolutionary patterns in leukemic bone marrow\, revealing levels of positive selection during the human lifespan. Her research bridged between mathematics\, cancer genomics and computational modelling. \n\n\n\nGladys is now with the SMA lab at HKU – working on cellular plasticity in hepatocellular carcinoma. This lab uses lineage tracing experiments in mice and modelling to understand the role of cellular plasticity during tumor evolution.
URL:https://spds.sydney.edu.au/event/multi-state-evolutionary-model-quantifies-tumour-cellular-plasticity/
CATEGORIES:Videos
ATTACH;FMTTYPE=image/jpeg:https://spds.sydney.edu.au/wp-content/uploads/2025/01/Complex-systems-1-scaled.jpeg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Australia/Sydney:20250310T130000
DTEND;TZID=Australia/Sydney:20250310T140000
DTSTAMP:20260409T090924
CREATED:20250219T030630Z
LAST-MODIFIED:20250509T062239Z
UID:2471-1741611600-1741615200@spds.sydney.edu.au
SUMMARY:Explaining the asynchrony of aging through cell population dynamics
DESCRIPTION:Statistical Bioinformatics SeminarDr Ming Yang\, HKUST\n\n\n\n\n\n\n\n\n\n\n\n\n\nOrgans and tissues age at different rates within a single individual. Such asynchrony in aging has been widely observed at multiple levels\, from functional hallmarks\, such as anatomical structures and physiological processes\, to molecular endophenotypes\, such as the transcriptome and metabolome. However\, we lack a conceptual framework to understand why some components age faster than others. Just as demographic models explain why aging evolves\, here we test the hypothesis that demographic differences among cell types\, determined by cell-specific differences in turnover rate\, can explain why the transcriptome shows signs of aging in some cell types but not others. Through analysis of mouse single-cell transcriptome data across diverse tissues and ages\, we find that cellular age explains a large proportion of the variation in the age-related increase in transcriptome variance. We further show that long-lived cells are characterized by relatively high expression of genes associated with proteostasis and that the transcriptome of long-lived cells shows greater evolutionary constraint than short-lived cells. In contrast\, in short-lived cell types\, the transcriptome is enriched for genes associated with DNA repair. Based on these observations\, we develop a novel heuristic model that explains how and why aging rates differ among cell types. \n\n\n\n\n\nThis is an online event held via Zoom.\n\n\n\n\nSubscribe to our seminar mailing list\n\n\n\n\n→\n\n\n\n\n\n\n\nFind out more about the Statistical Bioinformatics seminar series\n\n\n\n\n\n→\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nDr Ming Yang\n\n\n\nMing Yang is a Research Assistant Professor in the Division of Life Science at the Hong Kong University of Science and Technology (HKUST). She earned her Ph.D. in Biochemistry and Molecular Biology from Sun Yat-sen University in 2017\, with a training background in population genetics and bioinformatics. From 2017 to 2022\, she was a Postdoctoral Research Scientist at the University of Washington School of Medicine\, and later promoted to an Acting Instructor. In 2024\, she joined HKUST\, focusing on the application of computational and systems biology in aging and aging-related diseases.
URL:https://spds.sydney.edu.au/event/explaining-the-asynchrony-of-aging-through-cell-population-dynamics/
CATEGORIES:Videos
ATTACH;FMTTYPE=image/jpeg:https://spds.sydney.edu.au/wp-content/uploads/2025/01/Complex-systems-1-scaled.jpeg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Australia/Sydney:20250303T130000
DTEND;TZID=Australia/Sydney:20250303T140000
DTSTAMP:20260409T090924
CREATED:20250219T032342Z
LAST-MODIFIED:20250509T062247Z
UID:2478-1741006800-1741010400@spds.sydney.edu.au
SUMMARY:Zero-Shot Foundation Model for a Universal Gene Expression Atlas of Human Tissue: Unveiling Clinically Relevant Cell States and Disease-Specific Spatial Niches
DESCRIPTION:Statistical Bioinformatics SeminarDr Xiaomeng Wan\, HKUST\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe rapid accumulation of single-cell datasets from diverse organs and tissues presents significant opportunities for understanding complex diseases\, yet challenges remain in effectively analyzing this wealth of information and further leveraging it to various data types\, including spatial transcriptomics (ST) and bulk RNA-seq datasets. Here\, we introduce UniGeneX\, a generative single-cell foundation model designed to reconstruct a universal gene expression profile from extensive transcriptomic data. UniGeneX minimizes batch effects while preserving biological variability\, enabling the identification of shared gene programs across tumor samples. By providing consistent cell type labels and leveraging biological patterns from training data\, UniGeneX facilitates the discovery of disease-specific cell niches in spatial and key cell states associated with clinical outcomes. Our model addresses existing limitations in current single-cell foundation models by focusing on a universal gene expression framework rather than merely learning embeddings for downstream tasks. We demonstrate the effectiveness of UniGeneX in characterizing disease-relevant cell states in glioma and idiopathic pulmonary fibrosis (IPF)\, ultimately advancing our understanding of the mechanisms underlying complex diseases. \n\n\n\n\n\nThis was an online event held via Zoom.\n\n\n\n\nSubscribe to our seminar mailing list\n\n\n\n\n→\n\n\n\n\n\n\n\nFind out more about the Statistical Bioinformatics seminar series\n\n\n\n\n\n→\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nDr Xiaomeng Wan\n\n\n\nDr Xiaomeng Wan is currently a Postdoctoral Associate in the Department of Mathematics at the Hong Kong University of Science and Technology (HKUST)\, under the guidance of Prof Can Yang. She earned her PhD from HKUST under the mentorship of Prof Can Yang. Her research centres on statistical machine learning and deep learning\, particularly exploring their applications in the analysis of transcriptomics datasets.
URL:https://spds.sydney.edu.au/event/zero-shot-foundation-model-for-a-universal-gene-expression-atlas-of-human-tissue-unveiling-clinically-relevant-cell-states-and-disease-specific-spatial-niches/
CATEGORIES:Videos
ATTACH;FMTTYPE=image/jpeg:https://spds.sydney.edu.au/wp-content/uploads/2025/01/Complex-systems-1-scaled.jpeg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Australia/Sydney:20250224T130000
DTEND;TZID=Australia/Sydney:20250224T140000
DTSTAMP:20260409T090924
CREATED:20241126T005558Z
LAST-MODIFIED:20250509T062253Z
UID:1227-1740402000-1740405600@spds.sydney.edu.au
SUMMARY:Computational Approaches in Functional Genomics: Understanding Gene Regulation and Development
DESCRIPTION:Statistical Bioinformatics SeminarDr Luca Pinello\, Harvard Medical School\n\n\n\n\n\n\n\n\n\n\n\nIn this talk\, Dr Luca Pinello will present an overview of his lab’s recent work at the intersection of CRISPR genome editing\, single-cell omics assays\, and generative AI to elucidate gene regulation. His lab’s research focuses on developing computational methods to investigate genotype-phenotype relationships at high resolution\, infer dynamic gene regulatory networks\, and integrate multi-omics data. By leveraging state-of-the-art technologies and advanced computational approaches\, his lab aims to uncover the complexities of cellular heterogeneity\, identify key regulatory elements and transcription factors\, and understand the interplay between chromatin structure and function in normal development and disease. \n\n\n\n\n\nThis is an online event held via Zoom.\n\n\n\n\nSubscribe to our seminar mailing list\n\n\n\n\n→\n\n\n\n\n\n\n\nFind out more about the Statistical Bioinformatics seminar series\n\n\n\n\n\n→\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n\n\n\n\n\n\n\n\n\n\n\n\n\n \n\n\n\n\n\nDr Luca Pinello\n\n\n\nLuca Pinello is a computational biologist and leader in developing computational methods for functional genomics\, genome editing and single cell technologies. He holds a Ph.D. in Mathematics and Computer Science from University of Palermo\, Italy. He is currently an Associate Pathologist at Massachusetts General Hospital (MGH) and an Associate Professor of Pathology at Harvard Medical School. He is also part of the MGH Center for Cancer Research and an Associate Member of the BROAD Institute of MIT and Harvard. He has developed several foundational computational tools in the field of genome editing for the design (CRISPRme\, CRISPRitz\, PrimeDesign)\, quantification (CRISPResso 1 and 2)\, and analyses of coding and non-coding tiling screens (CRISPRO\, CRISPR-SURF). He was awarded one of the first NIH R35 Genomic Innovator Awards\, a prestigious grant supporting highly innovative researchers working on important problems in genomics. 
URL:https://spds.sydney.edu.au/event/statistical-bioinformatics-seminar-series/
CATEGORIES:Videos
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Australia/Sydney:20241216T130000
DTEND;TZID=Australia/Sydney:20241216T140000
DTSTAMP:20260409T090924
CREATED:20241128T003907Z
LAST-MODIFIED:20250110T033323Z
UID:1244-1734354000-1734357600@spds.sydney.edu.au
SUMMARY:Incorporating experimental medicine into single-cell multi-omics
DESCRIPTION:Special Statistical Bioinformatics Seminar\n\n\n\n\n\nDr Jacqueline Siu\, Kennedy Institute of Rheumatology\, University of Oxford \n\n\n\n \n\n\n\n\n\nAbout the seminar: Coordinated global efforts to create a comprehensive map of the human body using single-cell technologies has opened up avenues in computational biology and AI. However\, there remains a pressing need to increase the data diversity from the ancestral diversity of participants to increasing the types of immune perturbations captured. This talk will focus on incorporating single-cell multi-omics to different experimental medicine approaches–such as deceased organ donors\, vaccine and xenogeneic-antigen challenge trials–in order to understand healthy immune responses in lymphoid organs. \n\n\n\nAbout the speaker: Jacqueline Siu received her PhD at the University of Cambridge where she combined bioinformatics and experimental medicine to understand the function and differentiation of homeostatic human B cells in matched secondary lymphoid organs. To extend her interest in human tissue immunity especially after perturbation\, Jacqueline joined the labs of Professor Mark Coles/ Professor Calliope Dendrou as a postdoctoral fellow\, where she explored the influenza vaccine response of human axillary lymph nodes. In 2023\, Jacqueline received the Wellcome Trust Early Career award to continue her interest in integrating human challenge models and single-cell genomics to understand early responding human B cells and their impact on long-lived antibodies.
URL:https://spds.sydney.edu.au/event/statistical-bioinformatics-seminar-judith-david-coffey-invited-speaker-special-seminar/
LOCATION:Mackenzie Room\, Level 6\, Charles Perkins Centre\, University of Sydney\, Johns Hopkins Drive\, University of Sydney\, NSW\, 2006\, Australia
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Australia/Sydney:20241213T140000
DTEND;TZID=Australia/Sydney:20241213T150000
DTSTAMP:20260409T090924
CREATED:20241127T231618Z
LAST-MODIFIED:20250509T064329Z
UID:1236-1734098400-1734102000@spds.sydney.edu.au
SUMMARY:Veridical data science and alignment in medical AI
DESCRIPTION:Professor Bin Yu\, Statistics\, EECS\, Center for Computational Biology\, Simons Institute for the Theory of Computing\, UC Berkeley \n\n\n\n \n\n\n\n\n\n\n\n\n\n\n\n\nAbout the seminar\n\n\n\nAlignment and trust are crucial for the successful integration of AI in healthcare including digital twin projects\, a field involving diverse stakeholders such as medical personnel\, patients\, administrators\, public health officials\, and taxpayers\, all of whom influence how these concepts are defined. This talk presents a series of collaborative medical case studies where AI algorithms progressively become\, from transparency to more opaque thus with increasing difficulty of alignment assessment. These range from tree-based methods for trauma diagnosis\, to LLM-based emergency department co-pilot\, and mechanistic circuits for structured data extraction from pathology reports. They are guided by Veridical Data Science (VDS) principles—Predictability\, Computability\, and Stability (PCS)—for the goal of building trust and interpretability\, enabling doctors to assess alignment. The talk concludes with a discussion on applying VDS to digital twins and medical foundation models and next steps for evaluating AI algorithm alignment in healthcare. \n\n\n\n\n\n\n\n\n\nAbout Bin Yu\n\n\n\nBin Yu is CDSS Chancellor’s Distinguished Professor in Statistics\, EECS\, and Computational Biology\, and Scientific Advisor at the Simons Institute for the Theory of Computing\,  all at UC Berkeley. Her research focuses on the practice and theory of statistical machine learning\, veridical data science\, and solving interdisciplinary data problems in neuroscience\, genomics\, and precision medicine. She and her team have developed algorithms such as iterative random forests (iRF)\, stability-driven NMF\, and adaptive wavelet distillation (AWD) from deep learning models. She is a member of the National Academy of Sciences and of the American Academy of Arts and Sciences. She was a Guggenheim Fellow\, IMS President\, and delivered the IMS Rietz and Wald Lectures and Distinguished Achievement Award and Lecture (formerly Fisher Lecture) of COPSS. She holds an Honorary Doctorate from The University of Lausanne
URL:https://spds.sydney.edu.au/event/veridical-data-science-and-alignment-in-medical-ai/
LOCATION:Lecture Theatre 173\, Carslaw Building (F07.01.173)
ATTACH;FMTTYPE=image/jpeg:https://spds.sydney.edu.au/wp-content/uploads/2024/11/BinYu-photo22.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Australia/Sydney:20241125T130000
DTEND;TZID=Australia/Sydney:20241125T140000
DTSTAMP:20260409T090924
CREATED:20241104T062549Z
LAST-MODIFIED:20250110T033504Z
UID:749-1732539600-1732543200@spds.sydney.edu.au
SUMMARY:Indigenous Australian genomes show deep population structure and abundant novel variation
DESCRIPTION:Special Judith and David Coffey Seminar\n\n\n\n\n\n \n\n\n\nProf Stephen Leslie\, The University of MelbourneAbstract: Without the inclusion of diverse genetic ancestries in reference resources\, inequity in research and clinical practice risks being entrenched. A handful ethnicities have been the focus of genomic research to date\, and Indigenous Australians are virtually absent from global reference panels and genomic analyses. The National Centre for Indigenous Genomics (NCIG) has collected genomic data from four Indigenous Australian communities from distinct regions of Australia.  This is the largest sample of Indigenous Australian whole genomes to date\, made possible by careful engagement and consultation with the communities\, setting new standards for best practice in Indigenous research. In this talk I will show analyses of the genomes of 159 individuals from these four communities\, investigating patterns of genetic variation and diversity within this dataset\, as well as the causes and consequences of these patterns.  \n\n\n\nWe discover substantial uncharacterised genetic variation in Indigenous Australians when compared to worldwide populations\, with implications for the utility of global reference panels and databases for conducting research within these communities.  We show the variation is shaped by exceptionally strong population structure across Australia and explore its features and the factors that have caused such structure. Our analysis emphasizes the distinctiveness of Indigenous Australians in a worldwide context with consequences for the design and implementation of genomic research and clinical practice in Indigenous communities.   \n\n\n\nAbout the speaker: Prof. Stephen Leslie is a statistician working in the field of mathematical genetics.  He is currently Professor of Statistical Genomics in the School of Mathematics and Statistics\, and Melbourne Integrative Genomics\, at the University of Melbourne.  Stephen has made key contributions to detecting\, understanding\, and controlling for population differences in genetic data\, including his landmark papers on fine-scale population genetic differentiation in the UK\, and more recently for Indigenous Australians.  He also developed the first methods for typing HLA and KIR alleles (major immune-associated gene families) from SNP data\, making these important variants available for large datasets world-wide\, including as part of the main data release for the UK Biobank.  Stephen has won several major awards for his work including the Woodward Medal of the University of Melbourne\, and the Moran Medal of the Australian Academy of Science. \n\n\n\nStephen’s research focuses on understanding the association of population genetic variation and history/demography; uncovering the relationship of immune-associated loci and autoimmune disease; and performing statistically rigorous analyses of the relationship of genetic variants to disease. 
URL:https://spds.sydney.edu.au/event/special-judith-and-david-coffey-seminar-prof-stephen-leslie/
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Australia/Sydney:20241107T080000
DTEND;TZID=Australia/Sydney:20241108T170000
DTSTAMP:20260409T090924
CREATED:20241028T004420Z
LAST-MODIFIED:20241028T004515Z
UID:606-1730966400-1731085200@spds.sydney.edu.au
SUMMARY:BioCAsia 2024
DESCRIPTION:The 2024 Bioconductor Asia conference aims to bring together researchers and scientists to exchange scientific knowledge and foster collaboration within the bioinformatics community both across the Asia-Pacific region and globally. The conference will also offer a series of hands-on workshops on R and Bioconductor\, aimed at advancing education and training in computational biomedical sciences. Visit the website to find out more https://biocasia2024.bioconductor.org/
URL:https://spds.sydney.edu.au/event/biocasia-2024/
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Australia/Sydney:20241104T080000
DTEND;TZID=Australia/Sydney:20241106T170000
DTSTAMP:20260409T090924
CREATED:20241027T234614Z
LAST-MODIFIED:20241027T235810Z
UID:598-1730707200-1730912400@spds.sydney.edu.au
SUMMARY:ABACBS Conference
DESCRIPTION:The ninth annual Australian Bioinformatics and Computational Biology Society Conference will be hosted at the University of Sydney 4-6 November 2024. Our centre director\, research leaders and ECRs are members of the convening committee. Visit the conference website for full details https://www.abacbs.org/conference2024/home
URL:https://spds.sydney.edu.au/event/abacbs-conference/
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Australia/Sydney:20241104T080000
DTEND;TZID=Australia/Sydney:20241104T170000
DTSTAMP:20260409T090924
CREATED:20241028T004554Z
LAST-MODIFIED:20241028T004555Z
UID:603-1730707200-1730739600@spds.sydney.edu.au
SUMMARY:COMBINE Student Symposium
DESCRIPTION:The COMBINE Student Symposium is back in 2024! Visit the symposium website for all the details https://www.combine.org.au/symp/symposium-2024/
URL:https://spds.sydney.edu.au/event/combine-student-symposium/
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Australia/Sydney:20241018T080000
DTEND;TZID=Australia/Sydney:20241018T170000
DTSTAMP:20260409T090924
CREATED:20251030T234700Z
LAST-MODIFIED:20241025T031617Z
UID:288-1729238400-1729270800@spds.sydney.edu.au
SUMMARY:Weekly Seminar - Week 1
DESCRIPTION:Statistical Bioinformatics Seminar\n\n\n\n\n\n\n\nCaption here\n\n\n\n\n\nLorem ipsum dolor sit amet\, consectetur adipiscing elit. Sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam\, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur. Excepteur sint occaecat cupidatat non proident\, sunt in culpa qui officia deserunt mollit anim id est laborum. \n\n\n\nCurabitur pretium tincidunt lacus. Nulla gravida orci a odio. Nullam varius\, turpis et commodo pharetra\, est eros bibendum elit\, nec luctus magna felis sollicitudin mauris. Integer in mauris eu nibh euismod gravi…
URL:https://spds.sydney.edu.au/event/weekly-seminar-week-1/
LOCATION:Eastern Avenue Lecture Theatre\, Eastern Avenue - The University of Sydney\, Camperdown\, NSW\, 2006
CATEGORIES:Weekly Seminars
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Australia/Sydney:20241014T130000
DTEND;TZID=Australia/Sydney:20241014T140000
DTSTAMP:20260409T090924
CREATED:20241025T031405Z
LAST-MODIFIED:20241025T031405Z
UID:351-1728910800-1728914400@spds.sydney.edu.au
SUMMARY:Harnessing the power of AI in biotechnology\, personalised medicine\, drug development and beyond
DESCRIPTION:Statistical Bioinformatics Seminar: Judith and David Coffey Speaker\n\n\n\nSpeaker: Prof David Ascher (University of Queensland) \n\n\n\nAbstract: We have developed a comprehensive computational platform that uses graph-based signatures to represent the wild-type environment of a residue in order to predict the structural and functional effects of mutations. This platform has been used to explore the effects of genetic disease and drug resistance mutations on protein folding\, stability\, dynamics and interactions\, and their links to mutational tolerance and phenotypes. Mutations leading to larger molecular consequences\, tended to be rarer\, and needed the presence of compensatory mutations balancing these fitness costs to become fixed in a population. \n\n\n\nWe have now successfully clinically translated methods that use insights on the 3D effects of mutations to guide patient risk management in genetic diseases\, and in the pre-emptive detection of drug resistance mutations in tuberculosis (rifampicin and pyrazinamide resistance). It has also been applied as part of drug development pipelines to guide design of drugs less prone to resistance. \n\n\n\nThis work has highlighted that structural bioinformatics tools\, when applied in a systematic\, integrated way\, can provide a powerful and scalable approach for predicting structural and functional consequences of mutations in order to reveal molecular mechanisms leading to clinical and experimental phenotypes. These computational tools are freely available (http://biosig.unimelb.edu.au/biosig/tools). \n\n\n\nAbout the speaker: David Ascher is Deputy Associate Dean of Research and Director of Biotechnology Program at The University of Queensland\, Deputy Director of the Australian Centre for Ecogenomics and Head of the Computational Biology and Clinical Informatics laboratory at the Baker Institute. David’s research focus is in modelling biological data to gain insight into fundamental biological processes. One of his primary research interests has been developing tools to unravel the link between genotype and phenotype\, using computational and experimental approaches to understand the effects of mutations on protein structure and function. His group has developed a platform of 65 widely used programs for assessing the molecular consequences of coding variants. This platform has now been successfully translated into clinical use to guide the diagnosis\, management and treatment of a number of hereditary diseases\, rare cancers and drug resistant infections\, and into industry as part of drug development pipelines to guide design of safer and more effective therapeutics.
URL:https://spds.sydney.edu.au/event/harnessing-the-power-of-ai-in-biotechnology-personalised-medicine-drug-development-and-beyond/
LOCATION:Charles Perkins Centre\, University of Sydney\, Johns Hopkins Dr\, Sydney\, NSW\, 2050\, Australia
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Australia/Sydney:20240930T130000
DTEND;TZID=Australia/Sydney:20240930T140000
DTSTAMP:20260409T090924
CREATED:20241025T031405Z
LAST-MODIFIED:20241101T052902Z
UID:354-1727701200-1727704800@spds.sydney.edu.au
SUMMARY:Chatting About Pathology
DESCRIPTION:Statistical Bioinformatics Seminar\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nSpeaker: Dr Drew Williamson (Emory University) \n\n\n\nAbstract: Generative models such as ChatGPT have catapulted AI further into the mainstream than ever before\, while interested parties in healthcare have begun to test these models in medical settings. However\, these models are rarely purpose-built for medical applications and the majority of those that do exist are targeted at a broader segment of healthcare than any individual provider practices. I’ll discuss the design\, training\, and evaluation of PathChat\, our multimodal generative model created specifically for pathology. I’ll also discuss possible future directions in this line of inquiry. \n\n\n\nAbout the speaker: Drew Williamson completed a BA in Mathematics at Oberlin College\, an MD at Case Western Reserve University\, residency in Anatomic Pathology at Brigham & Women’s Hospital\, and fellowships in Molecular Genetic Pathology and Clinical Informatics at Mass General Brigham. This training was followed by postdoctoral research in the lab of Faisal Mahmood\, PhD at Brigham & Women’s Hospital and Harvard Medical School. A board certified pathologist\, Drew’s research focuses on the applications of deep learning-based methods to pathology data\, from histology images to genomics to\, most recently\, natural language. He is now an Assistant Professor in the department of Pathology & Laboratory Medicine at Emory University.
URL:https://spds.sydney.edu.au/event/chatting-about-pathology/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Australia/Sydney:20240422T130000
DTEND;TZID=Australia/Sydney:20240422T140000
DTSTAMP:20260409T090924
CREATED:20241025T031405Z
LAST-MODIFIED:20241108T045121Z
UID:344-1713790800-1713794400@spds.sydney.edu.au
SUMMARY:Systematic comparison of sequencing-based spatial transcriptomic methods with cadasSTre and SpatialBen
DESCRIPTION:Statistical Bioinformatics Seminar: Judith and David Coffey Speaker\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nSpeaker: Prof Matthew Ritchie (WEHI) \n\n\n\nAbstract: Sequencing-based Spatial Transcriptomics (sST) allows gene expression to be measured within complex tissue contexts. Although a wide array of sST technologies are currently available to researchers\, efforts to comprehensively benchmark different platforms are currently lacking. The inherent variability across technologies and datasets poses challenges in formulating standardized evaluation metrics. To address this\, we established a collection of reference tissues and regions characterized by well-defined histological architecture and other biological ground truth and used them to generate the cadasSTre and SpatialBench datasets that compare 11 sST methods. We highlight molecular diffusion as a variable parameter across different methods and tissues\, significantly impacting the effective resolution. Furthermore\, we observed that spatial transcriptomic data demonstrate unique attributes beyond merely adding a spatial axis to single-cell data\, including an enhanced ability to capture patterned rare cell states along with specific markers\, albeit being influenced by multiple factors including sequencing depth and resolution. For the 10X Visium platform\, we benchmarked the performance of different sample handling approaches after preprocessing\, explored spatially variable gene detection and the ability of clustering and cell deconvolution to identify expected cell types and tissue regions. Multi-sample differential expression analysis was able to recover known gene signatures related to biological sex or gene knockout. Our datasets and analyses serve as a practical guide for sST users and will be useful in future benchmarking studies. \n\n\n\nAbout the speaker: Professor Matt Ritchie has been at lab head at the WEHI for the past 11 years. His team develops analysis methods and open-source software tailored to new applications of genomic technology in biomedical research. In the single-cell and spatial biology field\, this work includes tools for data preprocessing (scPipe)\, benchmarking at scale (CellBench) and new protocols and analysis methods (FLAMES) for applying long-read sequencing to single-cell research. His most recent research is on developing benchmarking resources for sequencing-based spatial transcriptomics technologies (cadasSTre and SpatialBench). Matt completed his PhD on microarray data analysis at WEHI in 2005 under the supervision of Professor Gordon Smyth\, which was followed by a period of post-doctoral research at the EBI (Hinxton\, UK) and University of Cambridge before returning to WEHI as a Senior Research Officer in 2008. He is a keen advocate of open-source software\, having served on both the Technical Advisory Board and Community Advisory Board of the Bioconductor project.
URL:https://spds.sydney.edu.au/event/systematic-comparison-of-sequencing-based-spatial-transcriptomic-methods-with-cadasstre-and-spatialben/
LOCATION:Charles Perkins Centre\, University of Sydney\, Johns Hopkins Dr\, Sydney\, NSW\, 2050\, Australia
ATTACH;FMTTYPE=image/jpeg:https://spds.sydney.edu.au/wp-content/uploads/2024/10/nutriomics-scaled.jpg
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DTSTART;TZID=Australia/Sydney:20240415T130000
DTEND;TZID=Australia/Sydney:20240415T140000
DTSTAMP:20260409T090924
CREATED:20241025T031405Z
LAST-MODIFIED:20241025T031405Z
UID:347-1713186000-1713189600@spds.sydney.edu.au
SUMMARY:High dimensional tensor methods for multi-modal single cell genomics data
DESCRIPTION:Statistical Bioinformatics Seminar\n\n\n\nSpeaker: Kwangmoon Park (University of Wisconsin-Madison) \n\n\n\nAbstract: Emerging single cell technologies that simultaneously capture long-range interactions of genomic loci together with their DNA methylation levels are advancing our understanding of 3D genome structure and its interplay with the epigenome at the single cell level. While methods to analyze data from single cell high throughput chromatin conformation capture (scHi-C) experiments are maturing\, methods that can jointly analyze multiple modalities with scHi-C data are lacking. In this talk\, I present two tensor modeling frameworks: Muscle and SHOPS\, to jointly analyze 3D conformation and DNA methylation data measured at the single cell level. First\, I present Muscle\, a joint decomposition of Multiple single cell tensors. Muscle is a novel tensor decomposition method that can integrate the scHi-C and DNA methylation modalities with a direct interpretability. Next\, I introduce SHOPS\, Sparse Higher Order Partial Least Squares\, which provides an inference on the direct association between Hi-C and DNA methylation. SHOPS is a new tensor response regression method to simultaneously achieve denoising of the scHi-C tensor and selecting the most relevant methylation sites with dimension reduction. \n\n\n\nAbout the speaker: Kwangmoon Park is a Statistics Ph.D. Candidate at the University of Wisconsin-Madison. He is currently working on statistical genomics and high dimensional statistics with Professor Sündüz Keleş. Before joining UW-Madison\, he earned a master’s degree in Statistics at the Yonsei University in 2020. He earned a B.A. in Economics and Statistics at Yonsei University in Korea and studied Economics as an exchange student at Erasmus Universiteit Rotterdam in the Netherlands. Kwangmoon Park is mainly interested in questions related to understanding how genes are regulated by distal regions in the genome\, particularly by functional non-coding regions. For that purpose\, he develops statistical tools for analyzing High-dimensional genomic data\, including Hi-C and HiChIP\, and for linking diverse types of genomic or epigenomic data with better statistical interpretation. The statistical methodologies he works on are related to tensor factorization/regression and dimension reduction techniques\, including Partial Least Squares. \n\n\n\nJoin on Zoom: https://uni-sydney.zoom.us/j/84087321707
URL:https://spds.sydney.edu.au/event/high-dimensional-tensor-methods-for-multi-modal-single-cell-genomics-data/
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