Revolutionary Genomics Tools

Synthetic biology is - quite literally - our future. A goundbreaking new biological foundation model Evo2 achieves state-of-the-art prediction of genetic variation impacts and generates coherent genome sequences, spanning all domains of life. A diverse team from leading research institutions including Arc Institute Stanford University NVIDIA University of California, Berkeley trained the model on 9.3 trillion DNA base pairs and has fully shared all code, parameters, and data. A few highlights from the paper (link in comments) 🔬 Zero-shot prediction achieves state-of-the-art accuracy in genetic variant interpretation. Evo 2 can predict the functional consequences of genetic mutations across all domains of life without specialized training. It surpasses existing models in assessing the pathogenicity of both coding and noncoding variants, including BRCA1 cancer-linked mutations. This generalist capability suggests Evo 2 could revolutionize genetic disease research, reducing reliance on expensive, manually curated datasets. 🛠 Genome-scale generation paves the way for synthetic life design. Evo 2 can generate full-length genome sequences with realistic structure and function, including mitochondrial genomes, bacterial chromosomes, and yeast DNA. Unlike prior models, Evo 2 ensures natural sequence coherence, improving synthetic biology applications like engineered microbes or artificial organelles. This sets the stage for programmable biology at an unprecedented scale. 🧬 Unprecedented long-context understanding revolutionizes genomic analysis. Evo 2 operates with a context window of up to 1 million nucleotides—far beyond the capabilities of previous models—allowing it to analyze genomic features across vast distances. This ability enables it to accurately identify regulatory elements, exon-intron boundaries, and structural components critical for understanding genome function. Its long-context recall is a major breakthrough for interpreting complex biological sequences. 🎛 Inference-time search enables controllable epigenomic design. Evo 2’s generative abilities extend beyond raw DNA sequence to epigenomic features, allowing researchers to design sequences with specific chromatin accessibility patterns. This approach successfully encoded Morse code messages into synthetic epigenomes, demonstrating a new method for controlling gene regulation via AI. This could lead to breakthroughs in gene therapy and epigenetic engineering. 🔮 Future potential: Toward AI-driven biological design and virtual cell modeling. Evo 2 represents a major leap toward AI-powered genomic engineering. Future iterations could integrate additional biological layers—such as transcriptomics and proteomics—to create virtual cell models that simulate complex cellular behaviors. This could revolutionize drug discovery, genetic therapy, and even synthetic life creation.

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

Identifying cancer-related mutations accurately is a critical step in precision medicine. Today, we’ve published new research in Nature Biotechnology on 🧬DeepSomatic🧬, an AI-powered tool that uses machine learning to identify genetic variants, or mutations, in cancer cells more accurately than current methods. This work is aimed at helping researchers pinpoint what's driving a cancer and informing more effective treatment plans. Somatic variant detection is an integral part of cancer genomics analysis. While most methods have focused on short-read sequencing, long-read technologies offer potential advantages to discover variants in the hardest to sequence parts of the genome. 🧬 About the model:  DeepSomatic was rigorously trained on high-confidence data, a feat made possible by working with our partners at UC Santa Cruz. The model is capable of accurately differentiating actual genetic cancer variants from the technical artifacts introduced during sample preservation, addressing a critical hurdle in early detection. 🧬 Superior Accuracy and Clinical Impact:  DeepSomatic consistently outperformed other tools across all major sequencing platforms. It shows major improvements in identifying complex insertions and deletions (Indels). Furthermore, in a new study with partners at Children's Mercy, DeepSomatic successfully found ten small variants in pediatric leukemia cells that were missed by other tools. 🧬 Flexible and Broad Use:  The model is flexible, working across all major sequencing platforms, and can be applied to both tumor-normal and challenging tumor-only samples, extending its utility for complex cancer types. 🧬 Open Access:  We are making DeepSomatic and the CASTLE dataset openly available to the research community. DeepSomatic is the most recent addition to our 10-year journey developing open source methods for geneticists to study the genomes of humans, plants, and animals. We are excited to see how researchers and drug manufacturers will use these resources to develop more effective, personalized treatments for cancer patients. The ability to accurately identify these subtle genetic drivers is key to unlocking new therapies. More in our blog authored by Kishwar Shafin and Andrew Carroll: https://goo.gle/4n23gIB   Read the full article in Nature Biotechnology: https://lnkd.in/drxii8fz

We’ve long needed faster, more scalable tools to evolve proteins for research and therapeutic applications. In a new paper published in Science today, we report on a collaboration between Christian Diercks' team and my lab to develop T7-ORACLE—a continuous evolution system that dramatically speeds up and simplifies the process of engineering proteins with new functions. T7-ORACLE builds on orthogonal replication systems like OrthoRep and EcORep, but with greater speed, flexibility, and ease of use. This is like giving evolution a fast-forward button, allowing researchers to continuously and precisely evolve virtually any protein inside E. coli, without damaging their genome, and on a timescale of days instead of months. The platform has broad implications for drug discovery, diagnostics, and synthetic biology. We can now combine rational protein design with continuous evolution to rapidly engineer therapeutic proteins for cancer, neurodegeneration, and essentially any other disease area. https://lnkd.in/ggtdHrsy

🔬 Google’s DeepSomatic: AI for short- and long-read cancer genomics 🧬 Big step forward from Google Research — the new DeepSomatic model, just published in Nature Biotechnology, uses deep learning to identify somatic mutations (the DNA changes driving tumors) directly from sequencing data. 🧠 Why it matters: Accurately detecting tumor-specific mutations is key to precision oncology, but conventional tools often struggle across sequencing platforms and sample types. DeepSomatic applies the same AI principles behind DeepVariant to cancer genomes. 🚀 Highlights: - Works with short- and long-read sequencing (Illumina, PacBio, Nanopore) - Handles tumor–normal, tumor-only, and FFPE samples - Major improvement for indel detection — traditionally one of the hardest challenges - Released with a new benchmark dataset: CASTLE (Cancer Standards Long-read Evaluation) - Outperforms established tools like MuTect2, Strelka2, and ClairS 💡 While long-read sequencing is still rare in clinical oncology, tools like DeepSomatic signal a shift: AI + long-readscould soon deliver richer, more accurate tumor profiling for precision medicine. The model performs best with high-accuracy chemistries (PacBio HiFi or ONT duplex/Q20+), showing that modern long-read data can rival short-reads for small variant detection. 🔗 Links to the paper and blog are in the comments. #AI #Genomics #CancerResearch #DeepLearning #Bioinformatics #PrecisionOncology #LongReadSequencing #PacBio #OxfordNanopore #DeepSomatic #GoogleResearch #NatureBiotechnology

Researchers have been duct taping biology workflows together for decades. SciSpace just flipped the script. They built a BioMed Agent that takes you from idea to interpretation in a single reasoning chain. Not a chatbot. Not a toy. A system that thinks like a biological scientist. Modern biology is chaos. You search papers. Design constructs. Run omics. Analyze variants. Draft figures. All in different tools. All manually stitched. BioMed Agent pulls this into one unified intelligence. It is not a general assistant. It is a domain-built engine trained for molecular, cellular, and clinical reasoning. You ask biological questions. It responds with workflows, not text blurbs. I tried it on a cloning problem. One prompt defined the strategy, vector backbone, restriction sites, primers, and QC checks. It even flagged conflicts in the design automatically. That is real experimental planning, not autocomplete. Then I switched to immune profiling. I dropped in bulk RNA-seq from activated T cells. It processed counts, normalized data, ran differential expression, and surfaced pathway enrichments. A full analysis cycle in minutes, not days. Genomics is where it really shows depth. Feed it a variant list and phenotype notes. It prioritizes candidates, reads ClinVar logic, checks inheritance patterns, and scores pathogenicity. This is built for real cases, not conference demos. It also runs drug logic. You can compare inhibitors, predict ADMET liabilities, map pathway effects, and surface off-target risks in a single flow. It feels like the scaffolding of an integrated discovery engine. And then there is the illustration layer. Describe a mechanism, a signaling axis, or a clinical workflow. The agent creates clear, publication-grade diagrams that match your scientific intent. Need revisions? Remove a component. Add a molecular event. Change directionality. The system redraws the entire figure around your biological instructions. A design tool built on scientific logic, not clip art. For labs, clinics, and biotech teams, this unlocks something new: Experiment design, computational analysis, variant interpretation, and figure creation finally live in one place. It compresses timelines at every stage of discovery. If you want to feel what an integrated scientific agent actually is, here is early access: Try BioMed Agent → https://lnkd.in/gbtTHuGn Global: PPBIO20 (20% off monthly), PPBIO40 (40% off annual). India: PPBIO30 (30% off monthly & annual on Premium/Advanced). Give it your hardest prompt. Watch how it builds the reasoning chain you used to assemble by hand.

midway through AGBT. here's what has stood out so far. data is getting cheaper to generate, and the dimensionality of what you can measure is expanding. every major launch here has been a version of one or both of those. on the dimensionality side: stellaromics launched Pyxa - the first commercial platform for 3D spatial transcriptomics in intact tissue. every other spatial platform gives you a 2D slice and asks you to infer the rest. pyxa works on full volumes up to 100 micrometers thick. this is a meaningful shift. bruker launched paintscape, which does something i hadn't seen before: direct 3D visualization of the genome itself, in situ, in single cells. not gene expression — the physical organization of chromosomes. 419-plex now, 1000+ plex oncology panel later this year. vizgen didn't launch a product but previewed a pipeline clearly moving in the same direction - protein co-detection, thick tissue imaging, 3D spatial biology. the field is converging on the same problems. on the cost and throughput side: illumina launched trupath genome - library prep eliminated entirely, 16 genomes per day, $395 per run, 98% of genes fully phased, with coverage of genomic dark regions that standard short-read WGS misses. short-read just got meaningfully closer to long-read utility. ultima launched the UG200: half the footprint of the UG100, 2x output, half the runtime. UG200 Ultra runs 60,000+ 30x whole genomes per year on one machine. solaris 2.0 eliminates the ePCR instrument entirely. starts at $850,000, ships Q2. complete genomics started shipping the T7+: 48B reads across 4 flow cells in 24 hours, $100/genome at scale, $800,000 list price. everyone talks about genomic data growing exponentially, but too few talk about the actual scientists and companies making that trend possible. #AGBT

In a groundbreaking study, scientists have discovered a way to manipulate the very fabric of life by using light to reshape DNA strands. This innovative approach provides new insights into the material properties of chromosomes, unlocking potential advancements in understanding gene expression and developing treatments for genetic diseases. Chromatin, the material that makes up chromosomes, is a complex structure where long strands of DNA are wrapped tightly around proteins. Despite its compact nature, chromatin must unfurl in certain regions to allow cells to access and replicate genetic information. Some areas remain rigid and coiled, silencing genes, while others are flexible and accessible, facilitating gene expression. This duality has led scientists to question whether chromatin behaves like a solid, a liquid, or a hybrid of both. Answering this question is vital for advancements in disease treatment and cellular engineering. Chromatin’s material properties affect critical processes like transcription, replication, and genome protection. Yet, few tools exist to measure how chromatin reacts to forces or to study its physical characteristics at specific locations. A Princeton research team has now developed a revolutionary technique to address these gaps. The ViscoElastic Chromatin Tethering and ORganization (VECTOR) system enables precise manipulation of chromatin loci. By inducing synthetic liquid-like droplets within the cell’s nucleus using a specific wavelength of blue light, researchers can apply controlled forces to targeted DNA sequences. This process causes rapid, precise repositioning of DNA strands in just a few minutes. “Basically, we’ve turned droplets into little fingers that pluck on the genomic strings within living cells,” explained Cliff Brangwynne, a lead researcher on the project and director of Princeton’s Omenn-Darling Bioengineering Institute. https://lnkd.in/g-u9yG7s #light #wavelength #dna #bioengineering #dna #gene #expression #chromatin

Meet Evo, the DNA-trained AI that creates genomes from scratch ChatGPT-like model learns on its own to devise new proteins and genetic sequences Evo, a multimodal AI model, processes and generates genomic sequences using deep learning. Trained on millions of microbial genomes, it can predict the impact of mutations, generate realistic genome-length sequences, and design novel biological systems. This tool has been validated through the successful design and laboratory testing of synthetic CRISPR systems and transposons. Evo represents a significant advancement in computational biology, enabling a deeper understanding and engineering of life at multiple scales of complexity. The original article: https://lnkd.in/eu5T7WT5, along with a commentary: https://lnkd.in/eXbCM6JN, was published in Science. #genetics #genomics #precisionmedicine #genomicmedicine #DNA #RNA #protein #design #ai #syntheticbiology #omics #computationalbiology #bioengeneering #deeplearning #life #crispr #bioinformatics #microbiology #biomarkers #biotechnology #innovation #research #science #sciencecommunication

The collaboration between Stanford, NVIDIA, and the Arc Institute, brought together expertise in machine learning, computational biology, and experimental biology to developed Evo 2, a groundbreaking generative AI tool that marks a significant milestone in biology. This open-source tool can predict protein forms and functions from DNA across all domains of life, identify molecules useful for bioengineering and medicine, and run virtual experiments in minutes instead of years. Developed by a multi-institutional team, Evo 2 was trained on a comprehensive dataset including all known living species and even some extinct ones. The tool can process sequences up to 1 million nucleotides long, allowing researchers to explore long-distance interactions between genes that may not be physically close on DNA molecules. Evo 2 significantly expands upon its predecessor, Evo 1. While Evo 1 was trained on about 113,000 genomes of simpler life forms (prokaryotes), Evo 2 includes genomes of approximately 15,000 plants and animals (eukaryotes), including humans. This expansion increased the dataset from about 300 billion nucleotides to almost 9 trillion. Similar to how ChatGPT autocompletes text based on patterns, Evo 2 autocompletes DNA sequences. It can generate entirely new gene sequences or modify existing ones in ways that haven't occurred naturally in evolutionary history. The tool also includes machine learning models that predict how new sequences will function in real life, which can then be tested in labs using gene editing technologies like CRISPR. Researchers hope Evo 2 will have clinical significance by helping predict which mutations lead to specifc diseases such as Cancer or specific Rare Dieases and distinguishing between harmless genetic variations and pathogenic ones. It could also be used to design new genetic sequences with specific functions. #GenerativeAI #Biology #Evo2 #GeneticResearch #DNASequencing #StanfordResearch #BrianHie #OpenSource #Bioinformatics #ProteinFunction #MolecularBiology #AIinMedicine #GeneticPrediction #CRISPR #Bioengineering #Evolution #MachineLearning #MutationAnalysis #DiseaseResearch #DNAAutocomplete Source: www.stanford.edu Disclaimer: The opinion are mine and not of employer's