Le Cao lab

🌊 Major milestone for ocean science! 🌊 Our team’s study comprehensively mapping the planktonic microbiome of the Great Barrier Reef is published today in Nature, and featured in The Guardian! 🗞️✨ To protect an ecosystem as complex as the Great Barrier Reef, we first have to understand the microscopic life driving its health. By analyzing seawater across 48 reefs, our multi-institutional team built the Great Barrier Reef Microbial Genomes Database (GBR-MGD): 🦠 580+ bacterial & archaeal species new to science 🧬 360,000+ distinct marine viruses 🌿 Chromosome-level genomes of key reef microalgae 🛡️ A comprehensive baseline to track reef health and predict responses to environmental stress 📊 Cutting through the data noise with mixOmics MINT method Analyzing multi-omic data sampled across thousands of kilometers and multiple seasons generates an overwhelming amount of information. Our biggest analytical hurdle was to separate true, underlying biological patterns from spatial and temporal noise. On the analytical front, Marko Terzin and our team at Le Cao lab (Faculty of Science, Melbourne Integrative Genomics) leveraged our mixOmics MINT method to integrate complex multi-omic datasets while explicitly controlling for geographical and seasonal variability. This statistical approach allowed us to isolate robust microbial signatures that reflect ecosystem dynamics across the entire reef system, providing a reliable framework for future monitoring. A massive effort by an extraordinary team across The University of Queensland Australian Institute of Marine Science, University of Melbourne, James Cook University, and University of Tasmania! 🥂 📖 Read the Nature paper: https://lnkd.in/gecgH6kF 📰 Read The Guardian feature: https://lnkd.in/gmDxD2_Q Co-authors: Steven J. Robbins, PhD | Marko Terzin | Katherine Dougan | Julian Zaugg | Sara Bell | Patrick Laffy | Pam Engelberts | Kim-Anh Lê Cao | Renee Gruber | @Nicole S. Webster | David Bourne | Philip Hugenholtz | Yun Kit YEOH

Hiring: Data Analyst & Research Fellow, Integrative and Quantum Omics (Melbourne, Australia) How do we unravel the complexity of biological systems? In the Le Cao lab, we believe the answer lies at the intersection of advanced statistics, multi-omics integration, and the emerging frontier of quantum computing. We are looking for two driven individuals to join our team based at Melbourne Integrative Genomics (MIG) to push the boundaries of computational biology. Both positions are full-time, fixed-term roles for 2 years. 🔬 The roles - Data Analyst (Omics): You will conduct end-to-end analytics on diverse datasets (multi-omics, microbiome, bulk and single cell). - Postdoctoral Research Fellow (Quantum Omics): Working within the ARC Centre of Excellence in Quantum Biotechnology (QUBIC), you will spearhead method development at the exciting intersection of quantum computing and integrative omics. 🎯 Who this suits - For the Analyst: A detail-oriented expert in R and Python with a Master’s degree in Bioinformatics or Statistics and a passion for reproducible research - For the Fellow: A highly motivated PhD with expertise in both omics data analysis and quantum computing methods who wants to lead innovative research. - Both roles: Collaborative spirits with an active GitHub profile showcasing their code. - Visa sponsorship is available for the Research Fellow position only; current valid Australian working rights are required for the Data Analyst role. 🌍 What you gain - Competitive Compensation plus 17% superannuation. - World-Class Environment: Based in the Parkville precinct, the highest concentration of biological and medical research in Australia. - Professional development opportunities, and access to a vibrant community of 50+ researchers at MIG. If you are ready to unlock your career potential in a world-class team, apply via the links below! 👇 📅 Applications close  6 April 2026. 11:55 PM; Melbourne time zone. - Data analyst: https://lnkd.in/gCUSqzWm - Research Fellow: https://lnkd.in/g6Wmhbgc 📧 Email me if you want to check the suitability of the role. Please share widely through your network!

Check out our new software paper on integrating >= 3 omics data using #DIVAS 😃 When integrating >= 3 omics modalities, a common challenge is: there are plenty of methods that tell you what are the modes of variation shared by all of them; there are some methods that tell you what are the modes of variation individual to each of them; but what about the modes of variation that are only partially shared? 🧐 Partially shared variations are important because not all omics are qually informative. You might, for example, expect more biological variations shared between RNAs and proteins, but comparatively less shared between proteins and metabolomics. If you use conventional methods you might miss out those partially shared variations. DIVAS uses a matrix factorisation approach that does a comprehensive search for significant modes of variations among all combinations of omics types: jointly shared, partially shared and individual. Its data-driven algorithm requires very minimal manual tuning. You don't need to specify any number of components! 💪 The DIVAS R package project is led by Yinuo Sun, with strong support from the original developers J. S. Marron and his team at UNC Chapel Hill. Corresponding authors Kim-Anh Lê Cao & Jiadong Mao https://lnkd.in/ghCaBfVu #DataIntegration #multiomics #DataFussion #Bioinformatics #Statistics

💡 Job Opportunity Alert!! 💡 The Metabolomics Australia node at the University of Melbourne is looking a talented individual with a passion for metabolomics to join our team. This bioinformatics role will focus on building robust pipelines, analyzing and interpreting high-dimensional metabolomics data, and collaborating closely with other team members and researchers to generate amazing data. Key skills: • Metabolomics data analysis (LC–MS/GC–MS) • Bioinformatics/statistical workflows (R, Python, or similar) • Experience with multi-omics data is a plus If you’re excited about applying computational methods to cutting-edge biological questions, we’d love to hear from you. We particularly encourage individuals with strong mass spectrometry experience, who have developed computational skills along the way to apply. Please note that we are unable to provide visa sponsorship, so candidates will have to already possess Australian working rights. #metabolomics #bioinformatics #massspec https://lnkd.in/gc6wkQYg

⭐  Single-omics first — integration must ADD value, not dilute it  ⭐ More omics data is not automatically better. Before combining layers, you need to know which ones actually carry a discriminative signal. We use PLS-Discriminant Analysis (PLS-DA) to classify samples into predefined groups and to evaluate cross-validated discriminative performance for each omics layer on its own. 1️⃣  ESTABLISH A BASELINE   •  run plsda() on each dataset individually   •  use perf() to assess cross-validated classification error   •  a strong layer (e.g. low error / ~80% accuracy) becomes the baseline   •  any integrated model must beat this baseline OR deliver additional, interpretable biology to be worth the effort 2️⃣  IDENTIFY WEAK LAYERS - noise dilutes the signal   •  if a layer shows poor class separation (discrimination) and high error, treat it as weak   •  decide: does this layer add interpretable biological structure, or just noise?   •  only integrate weak layers if they clearly strengthen the overall model - otherwise they can sink your integration performance 🏃🏽  ACTION STEPS (using mixOmics) • plsda() on each dataset individually • perf() to assess cross-validated error • compare baselines before deciding which layers to integrate 🔑  GOLDEN RULE More omics ≠ better. Integration is an investment – only integrate layers that strengthen the signal and the biology ✒️  COMMENT Do you always check your single-omics baselines first, or jump straight to the "whole"? ♥️  Like and follow to learn more 🔥  Get multi-omics tips in your inbox: https://mixOmics.pro ——— 💫 Part 4/6 of our "Building a Robust Omics Study" series. 📢 Next week in Part 5: True integration vs. simple correlation. mixOmics Pro | mixOmics | Le Cao lab | Kim-Anh Lê Cao | Mike Rennie ——— #multiOmics #omics #omicsIntegration #bioinformatics #computationalBiology #dataScience #lifeSciences #biotech #systemsBiology #metabolomics #proteomics #transcriptomics #microbiome #PhDLife #scienceCommunication #RStats #LeCaoLab #mixOmics #mixOmicsPro ———

⚠️  STOP ⚠️ PCA first ➔ because integration amplifies errors‼️ 🚨 Run PCA on each omics layer BEFORE combining datasets 1️⃣  OUTLIERS   •  Drifting samples = either biology or technical failure   •  Keep biological outliers, remove technical ones 2️⃣  A BATCH EFFECT   •  If samples cluster by date/lab/technician instead of treatment, your “signal” may be a workflow artifact   •  Correct batch effects before integrating 3️⃣  RELEVANCE   •  A layer with no structure only dilutes the integrated model   •  If it’s noise, exclude it 🏃🏽 ACTION STEPS (using mixOmics):   •  Run PCA (unsupervised) to reveal raw structure   •  Colour by treatment to check biological grouping   •  Colour by batch/date to expose technical artefacts 🔑 GOLDEN RULE  Integrate only layers that strengthen the signal — otherwise, integration makes things worse ✒️ COMMENT What’s the strangest batch effect you’ve seen? 🔥 UPDATES Get multi-omics tips in your inbox: https://mixOmics.pro — 💫 Part 3/6 of our "Building a Robust Omics Study" series 📢 Next week: Why single-omics analysis always comes first mixOmics Pro | mixOmics  |  Le Cao lab  |  Kim-Anh Lê Cao | Mike Rennie — #multiOmics #Omics #omicsIntegration #Bioinformatics #computationalBiology #dataScience #LifeSciences #Biotech #systemsBiology #metabolomics #proteomics #transcriptomics #microbiome #PhDLife #scienceCommunication #RStats #LeCaoLab #mixOmics #mixOmicsPro —