Human Genome Mapping Innovations

You're looking at the clearest picture of the human genome ever made. We’ve known the sequence of the human genome for over 20 years — but until now, we couldn’t actually see how it works inside living cells. That’s changed. In a groundbreaking study, scientists from Oxford's Radcliffe Department of Medicine have captured the most detailed map yet of the genome’s structure — down to a single base pair. Using a new technique called MCC ultra, they’ve revealed how DNA physically folds, bends, and loops inside the cell to control which genes are switched on or off. It’s not just the genetic code that matters. It’s how that code is arranged in 3D space. Inside every cell, about 6 feet (2 meters) of DNA is crammed into a space smaller than a tenth of a millimeter. This DNA doesn’t lie flat — it loops and coils, bringing distant parts into contact. These looping structures act like switches: some bring genes to the surface so they’re read, others bury them deep to keep them silent. Until now, researchers could only see these structures at low resolution. The new method captures them at single-letter precision, revealing how control regions — the noncoding parts of the genome — physically interact with the genes they regulate. That matters, because more than 90% of disease-linked genetic changes occur outside the genes themselves, in these regulatory switches. The study proposes a new model: electromagnetic forces help DNA form “islands” of gene activity — clusters of loops that turn specific genes on or off. Understanding this architecture opens up new ways to study heart disease, cancer, and autoimmune disorders — and even find new drug targets. Learn more: "Oxford scientists capture genome’s structure in unprecedented detail." University of Oxford, 2025. 📸Credit: Radcliffe Department of Medicine

When the Human Genome Project was declared "complete" in 2003, ~8% of the human genome was still missing. Since then, a global community of scientists has worked tirelessly to fill those gaps, culminating in the first telomere-to-telomere (T2T) human genome — a remarkable feat achieved using multiple sequencing technologies. Oxford Nanopore then introduced the first ‘nanopore-only’ T2T protocol, allowing complete genome assembly on a single device. However, that approach required a combination of ultra-long reads, 6B4 duplex reads (for homopolymer correction), and simplex reads — which, while powerful, came at a higher cost for customers. Now, things just got even better. In collaboration with the team of Heng Li (Harvard Medical School), the Machine Learning team at Oxford Nanopore Technologies, just published a new preprint that showcases a significant advancement in T2T assembly: 📄 https://lnkd.in/esjPExJq This study introduces hifiasm-ONT, a fast and accurate error correction algorithm that uses read phasing to overcome recurrent errors in simplex reads — enabling near T2T assemblies without the need for ultra-long or 6B4 reads. This is a major leap forward in making complete genome assembly more accessible, scalable, and cost-effective — and a testament to the power of collaboration and innovation. Congratulations to everyone involved! Heng Li, Mike Vella, Sean McKenzie, @katherine R Lawrence, Rhydian Windsor #T2T #HumanGenome #LongReadSequencing #OxfordNanopore #Genomics #Hifiasm #MachineLearning #Innovation #Research

The UK Biobank has completed whole-genome sequencing (WGS) for nearly half a million participants, marking a significant milestone in human genetics research. This dataset represents the largest publicly available WGS resource to date. Methods: The whole genomes of 490,640 UKB participants were sequenced to an average coverage of 32.5× (with at least 23.5× per individual) using Illumina NovaSeq 6000 sequencing machines. The research team employed multiple bioinformatics pipelines including GraphTyper and DRAGEN to identify genetic variants across five ancestral groups: European (93.5%), African, South Asian, East Asian, and Ashkenazi Jewish populations. Results: The sequencing effort identified approximately 1.5 billion variants (comprising SNPs, insertion–deletion (indel) variants and SVs). Key findings include: - Although most associations with disease traits were primarily observed in individuals of European ancestries, strong or novel signals were also identified in individuals of African and Asian ancestries - Discovery of rare variants previously missed by other technologies, particularly in untranslated regions (UTRs) - Identification of clinically actionable variants in 14.8% more individuals compared to previous methods - Novel disease associations, including rare frameshift variants linked to cataracts and other conditions Conclusions: This dataset, representing a large collection of whole-genome sequencing data that is available to the UK Biobank research community, will enable advances of our understanding of the human genome, facilitate the discovery of diagnostics and therapeutics with higher efficacy and improved safety profile, and enable precision medicine strategies. The resource particularly enhances our ability to study rare genetic variation, which is crucial for understanding disease mechanisms and developing targeted therapies. Importantly, while the dataset is predominantly European, it includes the largest WGS cohort of South Asian ancestry available to date, helping to address disparities in genomic research. Exciting new publication fromThe UK Biobank Whole-Genome Sequencing Consortium

🌐 Beyond hg19 and hg38: Exploring the New Frontiers in Genomics with the Pangenome and T2T Genome 🌐 In a previous post, we delved into the differences between hg19 (GRCh37) and hg38 (GRCh38)—two widely used human genome assemblies that many bioinformaticians and researchers encounter regularly. While hg38 has improved upon hg19 by filling gaps, adding alternate loci, and enhancing alignment accuracy, the field of genomics is constantly evolving to represent human genetic diversity more comprehensively. Now, the human pangenome and telomere-to-telomere (T2T) genome are redefining the boundaries of human genetics, offering unprecedented insights into the diversity and completeness of our genetic code. 🔬 The Human Pangenome While both hg19 and hg38 were valuable in their time, they represent a limited portion of human diversity. The pangenome, developed by an NIH-funded consortium, brings us closer to a more inclusive reference by integrating genome sequences from 47 individuals with ancestries from around the world. This richer representation of human variation allows for more accurate identification of genetic variants across diverse populations—a crucial step toward equitable healthcare and research. 🧬 The Telomere-to-Telomere (T2T) Genome The T2T genome represents the first complete human genome, filling in previously uncharted regions like telomeres and centromeres that were missing in earlier assemblies. This landmark accomplishment gives us a fully mapped genome, offering deeper insights into complex genomic regions that influence aging, cell division, and disease. 📈 From hg19 and hg38 to Pangenome and T2T Just as we saw with the shift from hg19 to hg38, adopting the pangenome and T2T genomes in clinical and research workflows will take time and effort. Many legacy pipelines and tools still rely on hg19 for compatibility, and hg38 has become increasingly popular due to its improvements. Now, the pangenome and T2T genomes are the next frontier, aiming to provide even greater accuracy and inclusivity in human genetics. 🏥 Challenges and the Path Forward Despite their advantages, transitioning to these new references in clinical settings presents challenges. The reliance on older references like GRCh37/hg19 and regulatory hurdles make it difficult for new models like the pangenome to gain widespread use. However, as the genomics community works to adopt these updates, we’re paving the way for research that better reflects the diversity of the human population. 💡 What’s Next? As we move forward, it’s important to keep the conversation open about when and how to incorporate these advancements in our work. Whether you're sticking with hg19 or hg38 for compatibility or are ready to explore the pangenome and T2T, each reference genome serves a purpose—and together, they help us build a fuller understanding of human genetics. #Genomics #Pangenome #T2T #Bioinformatics #HumanGenetics #HealthcareInnovation #DiversityInScience

NotebookLM: "...[described is] a novel technique called nucleotide dependency analysis to enhance the interpretability of genomic language models (gLMs) and detect functional elements within DNA sequences. By quantifying how a single nucleotide substitution affects the predicted probability of another nucleotide, this method effectively uncovers functional relationships that existing gLM reconstruction methods often miss. The researchers demonstrate that these dependencies are superior at indicating the deleteriousness of genetic variants and can accurately map diverse genomic features, including regulatory motifs, interactions between distal elements like splice sites, and complex RNA secondary and tertiary structures, including pseudoknots, all in an alignment-free manner. Ultimately, dependency maps serve as a powerful new tool for dissecting the regulatory code and diagnosing the limitations of different gLM architectures and training data choices." From the source: "...we introduced nucleotide dependencies that quantify how nucleotide substitutions at one genomic position affect the likelihood of nucleotides at another position. This new metric appears as a general and effective approach to identifying functionally related nucleotides using gLMs. Nucleotide dependency maps reveal functional elements across various biological processes, including transcriptional, post-transcriptional regulatory elements, their interactions and RNA folding. Therefore, this new metric has implications across multiple areas of computational and genome biology." https://lnkd.in/ebVkQHp8

AMD, UNSW Sydney & Pawsey: Redefining Real-Time Genomics with Slorado A major milestone for open science and high-performance genomics. AMD, UNSW Sydney, and the Pawsey Supercomputing Research Centre have introduced Slorado — the world’s first fully open-source, real-time nanopore DNA basecaller designed for AMD GPUs and powered by the ROCm open software platform. This breakthrough removes long-standing vendor lock-in and dramatically accelerates genomic workflows, empowering researchers with speed, scale, and flexibility. 🔬 What Slorado Enables + Fully open-source basecalling pipeline for nanopore sequencing + Runs on AMD GPUs via ROCm and supports hybrid GPU environments + Scales across multi-GPU and HPC infrastructures + Delivers performance parity with proprietary alternatives while improving accessibility ⚡ Performance Highlights on Pawsey’s Setonix Supercomputer Powered by AMD Instinct GPUs: + Full human genome decoded in: + 2.3 hours on MI250X GPUs + Just 0.8 hours on next-gen MI300X GPUs + High-accuracy models (HAC & SUP) also show significant acceleration without compromising data quality This level of performance transforms what once took days into hours — or even minutes — enabling faster research cycles, real-time pathogen surveillance, and scalable population genomics. 🌍 Why This Matters ✅ Democratizes access to high-performance genomics ✅ Accelerates discovery and clinical research ✅ Strengthens reproducibility through open-source transparency ✅ Expands AMD’s role as a trusted platform for scientific computing and AI ✅ Bridges HPC, AI, and bioinformatics into a unified ecosystem Slorado is more than a tool — it’s a signal of where the future of genomics is heading: open, accelerated, and accessible at global scale. AMD continues to push the boundaries of what’s possible in scientific computing – from AI to genomics and beyond. 🔗 Explore more: https://lnkd.in/ghSRHX7S #AMD #Genomics #OpenScience #HPC #AIinHealthcare #ROCm #InstinctGPUs #Supercomputing #Innovation #Bioinformatics #FutureOfScience #AMDBrandAmbassador

MIT just created Google Maps for cells! Medicine has been pretending the cell is a spreadsheet. It isn’t. It’s a city at rush hour; genes, proteins, and chromatin all pushing and pulling at once. MIT’s new AI framework is the first serious attempt to stop studying the pixels and finally watch the whole movie. It disentangles what each measurement uniquely captures versus what reflects the cell’s shared underlying state, giving researchers something we’ve never really had: a navigable map of cellular behavior, not a stack of disconnected snapshots. This matters because synthetic biology has hit a complexity wall. Writing a genetic circuit is easy; predicting how it ripples through chromatin, RNA, proteins, and morphology is where human intuition dies. This is the “Google Maps for cells” moment: a debugging tool for living systems. Drug developers can isolate true therapeutic signal from off-target noise. Synthetic biologists can see how engineered inserts collide with native machinery. Precision oncologists can track resistance as a moving, multi-omic target. The strategic shift is unavoidable: R&D moves from educated guesses to system-wide simulation before we ever build. But as biology becomes programmable, ethics can’t stay analog. Who gets access to longevity-grade medicine when failure costs drop toward zero? Research in comments.

We’re getting better at reading genes. Now we’re learning how to read them in 3D. A new study introduces a method to resolve signal overlap in spatial transcriptomics data, one of the biggest technical bottlenecks in mapping gene expression inside intact tissue. In dense biological samples, transcripts from neighboring cells often overlap, making it difficult to accurately assign signals to the correct cellular source. This blurring limits how precisely we can reconstruct tissue architecture. By improving how overlapping signals are separated computationally in three-dimensional space, researchers can generate far more accurate maps of how cells are organized in situ. This doesn’t just refine the data, it changes the reliability of downstream biological interpretation. For neuroscience, this is particularly significant. The brain is a tightly packed 3D network of gradients, microenvironments and dynamic cellular interactions. Circuit function, disease progression and developmental processes all depend on spatial context. If our spatial resolution is compromised, our models of brain function are incomplete. As biology moves from bulk averages toward high-resolution spatial systems, segmentation accuracy becomes foundational infrastructure, not a minor technical upgrade. Precision in three dimensions is what enables precision in understanding. Source: Nature Biotechnology, 2026 — “Identifying 3D signal overlaps in spatial transcriptomics data with ovrlpy.” #Neuroscience #SpatialTranscriptomics #SystemsBiology #Genomics #BrainResearch #Biotechnology #Innovation #Research

I am delighted to finally announce a preprint describing the Q100 project, where we finished the complete, diploid HG002 human genome to near-perfect accuracy. Building benchmarks is hard, unglamorous work, but the impact can be huge. Consider how much the Genome in a Bottle variant benchmarks have shaped the field of genomics over the past ~10 years. However, these mapping-based benchmarks omit about 15% of the genome. Assembling and annotating the complete, diploid HG002 genome from "T2T" allowed us to fill in those missing regions and explore the limitations of reference-based variant calling and benchmarking, especially within complex, segmentally duplicated regions that are often heterozygous. Short blog here w/ link to paper: https://lnkd.in/eW4m-qWq

🚀 Genomic Language Models: Transformers for the Language of Life Large language models have reshaped natural language processing—now, the same architectures are being applied to the genome. 📈 I’ve noticed that genomic language models are increasingly featured in the latest journal issues, reflecting the rapid momentum and growing interest in this technology. This new Nature Machine Intelligence review (March 2025) explores genome language models (gLMs), transformer-based deep learning systems trained on DNA sequences. Just as LLMs learn grammar and meaning in text, gLMs are beginning to uncover the regulatory grammar of the genome. 🧬 Applications of gLMs in genomics: - Identifying regulatory elements (promoters, enhancers, silencers) - Predicting gene expression and chromatin accessibility - Assessing the functional impact of genetic variants - Mapping 3D genome architecture and transcription factor binding Discovering functions of non-coding RNAs 🔑 Highlights from the review: - Why transformers? Their attention mechanism captures long-range DNA dependencies, crucial for understanding regulation. - Pretraining power: gLMs learn from massive unlabelled DNA, enabling zero- and few-shot predictions in biology. - Model families: From hybrid models like Enformer and Borzoi to transformer gLMs (DNABERT, Nucleotide Transformer, GENA-LM) and beyond (HyenaDNA, Evo). - Challenges ahead: Whole-chromosome modelling, curated datasets for long-range regulation, and better interpretability. 💡 Takeaway: Genomic language models are not just technical breakthroughs—they’re powerful tools for decoding how genes are regulated, why mutations matter, and how the genome shapes health and disease. 📄 See a link to the review in the comments below, or DM me for the full text. #Genomics #ArtificialIntelligence #MachineLearning #Transformers #GenomicLanguageModels #LargeLanguageModels #Bioinformatics #ComputationalBiology