Genetic Code Deciphering Technologies

❓ ONT, Illumina & MGI – What’s the Difference? 🔬 Next-Generation Sequencing (NGS) allows scientists to read genetic code by sequencing millions (or billions) of DNA fragments in parallel. Let’s explore some key platforms: 1️⃣ Illumina 1) Sample & Library Preparation: DNA/RNA is purified, fragmented, and ligated with adapters containing cluster recognition sites (bind to specific spots on the flow cell), index sequences (identify the sample), and primer binding sites. NEBNext UltraExpress® FS DNA Library Prep Kit https://lnkd.in/dMjcZphg is widely used for high-quality library preparation. 2) Cluster Generation: The flow cell has oligonucleotides complementary to the adapters, allowing fragments to bind. A PCR-like process (bridge amplification) forms clusters. Multiple copies of the strand ensure that the fluorescent signal during sequencing will be strong enough. 3) Sequencing: Fluorescently labeled nucleotides (G,C,A,T) with terminators bind one at a time to all single strands in the cluster (at any given moment, only one type of nucleotide binds, emitting a specific color). A camera records fluorescence to identify nucleotides. Terminator groups are cleaved to allow the next cycle. 4) Reverse Strand Sequencing: Index sequences are read, the reverse strand is synthesized and sequencing is repeated. 5) Data Analysis: Low-quality reads are filtered, and sequences are aligned. 2️⃣ MGI 1) Sample & Library Preparation: DNA is fragmented, ligated with adapters, and circularized into ssCirDNA. NEBNext® FS DNA Library Prep Kit for MGI® https://lnkd.in/dQMtgbNd provides a reliable solution for generating high-complexity libraries with optimized workflow. 2) DNB Generation by Rolling Circle Amplification: ssCirDNA acts as a template for continuous amplification, forming dense DNA Nanoballs (DNBs) with multiple copies of the sequence. 3) Loading DNBs: DNBs bind to specific spots on the flow cell. 4) Sequencing: Fluorescently labeled nucleotides with terminators bind one at a time to all sequences in the DNBs simultaneously. A camera records fluorescence to identify each nucleotide. Terminators are cleaved to allow the next cycle. 3️⃣ Oxford Nanopore Technologies 1) Sample & Library Preparation: DNA/RNA is extracted, purified, and ligated with motor protein adapters. 2) Loading the Flow Cell: The library is added to a flow cell containing thousands of nanopores. 3) Sequencing: The motor protein unzips the DNA, guiding it through the nanopore one base at a time. Each nucleotide disrupts the ionic current in a unique way, producing a signal used to determine the sequence. 4) Base Calling & Data Analysis: Signals are converted into nucleotide sequences, followed by read alignment and error correction. #NGS #Sequencing #Genomics #Bioinformatics #Illumina #Nanopore #MGI #Biotech

𝟗𝟖% 𝐨𝐟 𝐘𝐨𝐮𝐫 𝐆𝐞𝐧𝐨𝐦𝐞 𝐖𝐚𝐬 𝐈𝐠𝐧𝐨𝐫𝐞𝐝. 𝐔𝐧𝐭𝐢𝐥 𝐍𝐨𝐰 What if we could read the genome like a story — not just in fragments, but as a whole, with rhythm, meaning, and twist endings? Google just brought us one step closer. Introducing AlphaGenome — DeepMind and Google’s newest AI model that predicts how your DNA is read, regulated, and sometimes... misinterpreted. But first — a quick decode: DNA is made of 4 letters — A, T, C, G — the alphabet of life. These 3 billion letters tell your cells what to do. But they’re not read linearly like a book — they fold and loop in 3D, bringing distant parts together to turn genes on or off. This folding is everything. A mutation buried 100,000 letters away could still influence gene activity — just because folding brought it into the “wrong neighborhood.” That’s where AlphaGenome shines. It reads up to 1 million DNA letters at a time — enough to catch complex folding patterns and regulatory cues in one shot. “But wait — we have 3 billion letters!” Yes — and like reading a novel one chapter at a time, AlphaGenome moves across the genome in overlapping tiles, decoding each “functional neighborhood” in high detail. And here’s what it does inside each tile: - Predicts where genes start, stop, splice, and fold - Estimates RNA expression and protein-binding activity - Identifies how a tiny mutation might ripple across the system - Models splicing errors that cause rare diseases - Spots cancer-driving mutations in “non-coding” DNA — the 98% we used to overlook Previous models (like Enformer) had to trade off resolution vs. context. AlphaGenome offers both. It’s like watching your genome in high-def, panoramic, slow-mo… at the same time. Now available via API for non-commercial research. Not for clinical use — yet. But a massive step toward understanding how life truly runs under the hood. Sometimes, it’s not about what the DNA says — but where it folds… where it pauses… and what happens in the quiet. AlphaGenome teaches us something deeper: That meaning doesn’t always lie in the loudest signals. Often, it’s in the 98% we ignore. The background. The regulation. The timing. Life, too, is like that. It’s not just what you do. It’s when, how, and in what context you show up. The pauses between the notes matter. The unseen structure holds the story. And maybe… that’s where the real change begins Amit Saxena Ajay Nandgaonkar Suchitaa Paatil Sanju S Anju Goel Taruna Anand #AlphaGenome #GoogleDeepMind #GenomicsExplained #FutureOfHealth #VariantEffectPrediction #SyntheticBiology #PrecisionMedicine #DNAMagic #AIinHealthcare #AI #AccessForAll

Big news for open science today 🧬 Google DeepMind just released AlphaGenome on Hugging Face - AI models that read DNA and predict what it actually does in our cells. Why does this matter? 98% of our DNA doesn't code for proteins, yet mutations in these "dark" regions cause diseases we still don't understand. AlphaGenome will help researchers virtually test millions of genetic variants to find the ones that matter, without running every experiment in a lab. And now, it's openly available for the research community to use and build upon! Huge congrats to the team for making this work accessible 👏🙇 Models, paper and The New York Times article by Carl Zimmer below.

Think of Evo 2 as a “foundation model” for genomics - a bit like how general-purpose AI language models can read and generate text, but here the “text” is the DNA and RNA of hundreds of thousands of species. Evo 2 has been trained on an enormous dataset of 9.3 trillion genetic letters (known as base pairs), spanning everything from bacteria to plants to animals. It has 40 billion parameters (with a smaller 7 billion-parameter version also available), making it one of the largest biological AI models ever built. Crucially, it’s not locked away behind corporate doors. Evo 2 is completely open-source, meaning anyone with an internet connection can examine the model code, inspect the training data, and even adapt the model to their own needs. This open-access approach is rare at this level and is designed to encourage collaboration across the global scientific community. Key Capabilities - Reading Massive Sequences: Evo 2 can process up to 1 million DNA letters in a single run, far more than most AI models can handle. This enables it to pick up on long-range interactions within a genome - important details that can be missed when only smaller fragments of DNA are examined. - Predicting Genetic Variations: By analyzing how a gene might be affected by a mutation, Evo 2 can offer insights into whether a change is likely to be harmful or benign. This could dramatically speed up genetic research and diagnostics - tasks that might currently take months in a lab could potentially be done in seconds. - Generating New Sequences: Evo 2 doesn’t just read existing genetic code; it can also propose brand-new DNA or RNA sequences. Imagine being able to design entire microbial genomes for a specific purpose, such as producing a sustainable biofuel or decomposing pollutants more efficiently. - Integrating DNA, RNA, and Protein Insight: Because it learned from such a broad spectrum of organisms, Evo 2 can bridge the gaps between DNA, RNA, and the proteins they encode. This might help researchers spot how a slight tweak in a protein’s genetic recipe could change its shape or function, offering possibilities for targeted therapies or advanced biotech applications. Evo 2’s rapid mutation analysis could help how we pinpoint disease-causing gene variations, accelerating rare disease diagnosis and giving doctors a clearer view of potential treatment strategies. Could this open-source approach help us tackle pressing issues faster? And what guardrails do we need so that rewriting the code of life truly benefits everyone? #innovation #technology #future #management #startups

3 days ago, scientists released what they say is the biggest-ever artificial-intelligence (#AI) model for #biology. “The model — which was trained on 128,000 #genomes spanning the tree of life, from humans to single-celled bacteria and archaea — can write whole #chromosomes and small genomes from scratch. It can also make sense of existing #DNA, including hard-to-interpret ‘non-coding’ gene variants that are linked to disease[1]." Evo-2 is freely available.   ✅#My2_cents The three major institutions involved were the Stanford University, NVIDIA – which makes the AI computer chips and software to run it – and the Arc Institute, a biomedical research nonprofit that is itself a collaboration among Stanford, the University of California, Berkeley, and the University of California, San Francisco.   ➡️IMHO, the timing of the release of Evo-2 is in view of NVIDIA's recent share price drop (due to doubts about the need for NVIDIA hardware) a, let's say, stroke of good fortune.   One of the co-leader, Stanford’s Brian Hie, explained the matter very simply in the Stanford Report:   „All life is encoded in DNA using just four chemicals, known as nucleotides. These complex molecules are abbreviated using the letters A, C, G, and T. The human genome, at 3 billion nucleotides long, is just a string of these four letters. Now, if you imagine DNA as the characters in a book that is 3 billion letters long, the individual genes are the words. They are spelled differently. Some have more letters than others. And they have different purposes and meanings – that is, they have different functions.   ➡️With AI, we can search for patterns in all that code and use it to predict what the next nucleotide in the sequence is likely to be. ➡️In this way, Evo 2 is able to generate – to write – new genetic code that has never existed before.   With Evo 2, you can enter a sequence of up to 1 million nucleotides. The million-nucleotide window in biology is important, as it allows us to explore long-distance interactions between two or more genes that may not be physically close to one another on the DNA molecule.   The longer context window could allow us to spot connections between these long-distance collaborators that we wouldn’t even know about with a shorter window[2].”   ➡️Large language models hold significant promise for interpreting biological sequence data. They can predict the effects of small DNA variations on an organism’s fitness, generate realistic genome-length sequences, and design novel biological systems. This includes laboratory validation of synthetic CRISPR systems.   ➡️The successor of Evo-1 marks another substantial leap forward in our ability to understand and engineer biological systems across various modalities and levels of complexity[3].   The preprint manuscript can be downloaded from the Arc Institute website[4] or from the biorxiv pre-print server[5]. 🆔References check my 1st comment   20250222—10742