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This innovative research on DNA data storage offers a glimpse into a future where data storage is both high-density and cost-effective, and also accessible to individuals. Imagine encoding your personal data directly onto DNA templates without the need for complex synthesis! 🔹 What potential applications do you see for distributed DNA data storage? 🔹 How could this approach impact data privacy and ownership? Share your thoughts below ⬇️.

🧬⚡💧Revolutionizing Data Storage with DNA Technology💧⚡🧬 🧬Storing data in DNA is a fascinating and innovative concept that leverages the natural properties of DNA molecules to encode vast amounts of information. Here's a concise explanation of how it works: 1. DNA Structure: DNA is composed of four chemical bases: adenine (A), cytosine (C), guanine (G), and thymine (T). These bases can be thought of as the "letters" that make up the genetic code. 2. Encoding Data: To store data in DNA, scientists convert binary data (0s and 1s) into sequences of these four bases. For example, a binary "0" might be represented by adenine (A) or cytosine (C), and a binary "1" by guanine (G) or thymine (T). 3. Synthesis: Once the data is encoded, the corresponding DNA sequence is synthesized in a laboratory. This involves creating a strand of DNA with the specific sequence of bases that represent the data. 4. Storage: DNA is incredibly dense and stable, allowing it to store massive amounts of data in a very small volume. For instance, all the data in the world could theoretically fit into a volume smaller than a sugar cube. 5. Reading Data: To retrieve the stored data, the DNA is sequenced, which means determining the order of the bases. This sequence is then decoded back into binary data, and finally into the original information. 🧬DNA data storage has several exciting practical applications: 1. Archival Storage: Museums and libraries can use DNA to preserve historical, cultural, and scientific data for centuries. 2. Medical Records: Hospitals can store vast amounts of patient data securely and durably. 3. Data Centers: DNA offers a compact, energy-efficient alternative for large data repositories. 4. Personal Data Storage: Individuals can store personal data like photos and documents in a tiny vial of DNA. 5. Scientific Research: Researchers can store and share large datasets, facilitating collaboration in genomics and bioinformatics. 6. Entertainment Industry: DNA can archive movies, music, and digital content for future generations. 7. Space Exploration: DNA's durability makes it ideal for storing data on long-duration space missions. DNA data storage offers a sustainable and compact solution to our growing data storage needs. Recent advancements, like microchips that grow multiple DNA strands in parallel, have made the process more practical and scalable, significantly boosting storage and retrieval efficiency. ⚡🧬💧 #DNADataStorage #FutureOfData #TechInnovation #SustainableStorage #Bioinformatics #DigitalPreservation #MedicalRecords #SpaceTech #DataCenters #PersonalData #EntertainmentArchiving

DNA: An Unconventional Data Storage Solution for future | Aakash Khurana Nature's code, rewritten for the digital age. DNA is unlocking a new era of data storage, promising unprecedented density and longevity.🧬 #DNAstorage #biotech #datascience #futuretech #innovation #sustainabletech #DNA #research That's absolutely correct! DNA, the molecule that carries genetic information, is emerging as a groundbreaking medium for data storage. Why DNA for Data Storage? * Incredible Density: DNA can store vast amounts of data in a tiny space. A single gram of DNA can hold the equivalent of roughly 215 petabytes of data! * Longevity: DNA is incredibly durable, with a potential lifespan of hundreds or even thousands of years under the right conditions. * Efficiency: Once written, DNA doesn't require any energy to maintain the stored data. How Does it Work? 1. Encoding: Digital data is converted into a genetic code of A, T, C, and G (the building blocks of DNA). 2. Synthesis: Synthetic DNA strands are created based on the encoded genetic sequence. 3. Storage: The DNA molecules can be stored in a dry, cold environment. 4. Retrieval: When needed, the DNA is sequenced to recover the original data. Challenges and Future Potential While DNA data storage holds immense promise, there are still challenges to overcome: * Cost: Currently, synthesizing and sequencing DNA is expensive. * Speed: Reading and writing data to DNA is significantly slower than traditional storage methods. * Error Correction: DNA sequencing can introduce errors, so robust error correction methods are essential. Potential Applications The potential applications of DNA data storage are vast and exciting. Some of the most promising areas include: * Long-term archival storage: DNA's durability makes it ideal for preserving critical data, such as historical records, medical images, or scientific research data for centuries. * Data centers: As data generation continues to explode, DNA-based data centers could offer unprecedented storage capacity and energy efficiency. * Digital preservation: Cultural heritage, such as art, music, and literature, could be preserved in DNA for future generations. * Space exploration: Due to its compact size and durability, DNA could be used to store data on long-term space missions. Despite these challenges, ongoing research and development are rapidly advancing this field. With continued progress, DNA data storage could revolutionize how we manage and preserve information for generations to come. It's important to note that the field is rapidly evolving, and new companies and partnerships are emerging regularly. For more info: https://lnkd.in/g2kkYp4f) https://lnkd.in/g3kuEuNH.

8th International Workshop on SEMANTIC WEB SOLUTIONS FOR LARGE-SCALE BIOMEDICAL DATA ANALYTICS (SeWeBMeDA-2025) co- event with Extended Semantic Web Conference https://lnkd.in/dwDHdMKD IMPORTANT DATES Submission deadline: March 6, 2025   Workshop days: 1st/ 2nd of June 2025   TOPICS of interest Generative & conversational AI applications in healthcare & life sciences New technologies & exploitation of existing ones in Linked Data, Semantic Web & Large Language Models (LLMs).   AI including Neurosymbolic AI in health care & life science. Dataspaces, Datawarehouse & Database Solutions/ applications in Healthcare & life sciences.  Techniques for analyzing data in the life sciences, medicine & health care Integration, analysis & data use in pursuit of challenges in the life sciences, medicine & health Large scale biomedical data curation, integration & processing Knowledge representation & knowledge discovery for biomedical data Data & metadata publishing, profiling & new datasets in biomedical & life sciences Question answering over biomedical & life science Linked Data, Ontologies & Knowledge Graphs Querying & federating data over heterogeneous data sources Biomedical ontology creation, mapping/ matching/ translation/ reconciliation & data visualization Machine learning with biomedical knowledge graphs Virtual & Augmented Reality in Biomedical/ Life Science education Risks & opportunities of using Semantic Web technologies in Healthcare Cleaning, quality assurance & provenance of data, services & processes in Life Science Knowledge Graphs & Relational Learning for Life Sciences Biomedical data quality assessment & improvement From Semantics to Explanations in bio medicine & life science Data streams, Internet of Things, mobile platforms, cloud environment in life science Text analysis, text mining & reasoning using semantic technologies New technologies & exploitation of existing ones in Linked Data & Semantic Web Social, ethical & moral issues publishing & consuming biomedical & life sciences data JOURNAL OF BIOMEDICAL SEMANTICS Top selected manuscripts will be invited for submitting paper for the special call at "Journal of Biomedical Semantics" SUBMISSIONS: Full papers (up to 15 pages): Presenting novel scientific research pertaining to topics relevant for workshop topics. Short papers (up to 8-10 pages): New system & Dataset descriptions, relevant to the topics of interest. Demo/Poster papers (up to 4 pages): Describe a demo or poster of a tool on the workshop topics. Position Paper (up to 6 - 8 pages). European/ International Project Showcase (up to 6-8 pages): Introduce & disseminate the findings from ongoing &/or recently finished project. Work-In progress (up to 5-10 pages): Incomplete results/ trends & receive feedback from experts in the area. Papers should be submitted via EasyChair: https://lnkd.in/dhDb6A7w ORGANISERS & CONTACT INFORMATION ( sewebmeda@gmail.com  ) Dr. Ali Hasnain

𝐓𝐡𝐫𝐢𝐥𝐥𝐞𝐝 𝐭𝐨 𝐒𝐡𝐚𝐫𝐞 𝐌𝐲 𝐉𝐨𝐮𝐫𝐧𝐞𝐲 𝐢𝐧 𝐃𝐚𝐭𝐚 𝐒𝐜𝐢𝐞𝐧𝐜𝐞, 𝐂𝐡𝐞𝐦𝐢𝐬𝐭𝐫𝐲, 𝐚𝐧𝐝 𝐂𝐡𝐞𝐦𝐢𝐧𝐟𝐨𝐫𝐦𝐚𝐭𝐢𝐜𝐬!  I’m excited to announce that I’ve completed the Data Science in Chemistry and Cheminformatics course! This 5.5-hour course was a deep dive into the fascinating intersection of data science and chemistry, but it became so much more than just a learning session—it was a passion project. 𝐇𝐞𝐫𝐞’𝐬 𝐰𝐡𝐚𝐭 𝐈 𝐞𝐱𝐩𝐥𝐨𝐫𝐞𝐝 𝐚𝐧𝐝 𝐦𝐚𝐬𝐭𝐞𝐫𝐞𝐝: 𝐊𝐞𝐲 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠𝐬: 1. 𝘛𝘩𝘦 𝘶𝘴𝘦 𝘰𝘧 𝘋𝘢𝘵𝘢 𝘚𝘤𝘪𝘦𝘯𝘤𝘦 𝘪𝘯 𝘊𝘩𝘦𝘮𝘪𝘴𝘵𝘳𝘺 𝘵𝘰 𝘴𝘰𝘭𝘷𝘦 𝘳𝘦𝘢𝘭-𝘸𝘰𝘳𝘭𝘥 𝘱𝘳𝘰𝘣𝘭𝘦𝘮𝘴. 2. 𝘚𝘵𝘳𝘶𝘤𝘵𝘶𝘳𝘦 𝘙𝘦𝘱𝘳𝘦𝘴𝘦𝘯𝘵𝘢𝘵𝘪𝘰𝘯 𝘢𝘯𝘥 𝘚𝘔𝘐𝘓𝘌𝘚 (𝘚𝘪𝘮𝘱𝘭𝘪𝘧𝘪𝘦𝘥 𝘔𝘰𝘭𝘦𝘤𝘶𝘭𝘢𝘳 𝘐𝘯𝘱𝘶𝘵 𝘓𝘪𝘯𝘦 𝘌𝘯𝘵𝘳𝘺 𝘚𝘺𝘴𝘵𝘦𝘮). 3. 𝘊𝘰𝘯𝘷𝘦𝘳𝘵𝘪𝘯𝘨 𝘣𝘦𝘵𝘸𝘦𝘦𝘯 𝘷𝘢𝘳𝘪𝘰𝘶𝘴 𝘊𝘩𝘦𝘮𝘪𝘤𝘢𝘭 𝘍𝘪𝘭𝘦 𝘍𝘰𝘳𝘮𝘢𝘵𝘴. 4. 𝘊𝘩𝘦𝘮𝘪𝘯𝘧𝘰𝘳𝘮𝘢𝘵𝘪𝘤𝘴 𝘸𝘪𝘵𝘩 𝘙𝘋𝘒𝘪𝘵 𝘢𝘯𝘥 𝘷𝘪𝘴𝘶𝘢𝘭𝘪𝘻𝘢𝘵𝘪𝘰𝘯 𝘶𝘴𝘪𝘯𝘨 𝘱𝘺3𝘋𝘮𝘰𝘭. 5. 𝘐𝘯𝘵𝘦𝘳𝘢𝘤𝘵𝘪𝘯𝘨 𝘸𝘪𝘵𝘩 𝘕𝘰𝘵𝘦𝘣𝘰𝘰𝘬 (𝘑𝘶𝘱𝘺𝘵𝘦𝘳 𝘕𝘰𝘵𝘦𝘣𝘰𝘰𝘬) 6. 𝘓𝘦𝘷𝘦𝘳𝘢𝘨𝘪𝘯𝘨 𝘔𝘰𝘭𝘦𝘤𝘶𝘭𝘦 𝘋𝘢𝘵𝘢𝘣𝘢𝘴𝘦𝘴 𝘧𝘰𝘳 𝘤𝘩𝘦𝘮𝘪𝘤𝘢𝘭 𝘪𝘯𝘧𝘰𝘳𝘮𝘢𝘵𝘪𝘰𝘯. 7. 𝘋𝘢𝘵𝘢 𝘚𝘤𝘳𝘢𝘱𝘪𝘯𝘨 𝘧𝘳𝘰𝘮 𝘤𝘰𝘮𝘱𝘰𝘶𝘯𝘥 𝘥𝘢𝘵𝘢𝘣𝘢𝘴𝘦𝘴 𝘶𝘴𝘪𝘯𝘨 𝘗𝘺𝘵𝘩𝘰𝘯 𝘧𝘰𝘳 𝘳𝘦𝘢𝘭-𝘸𝘰𝘳𝘭𝘥 𝘢𝘱𝘱𝘭𝘪𝘤𝘢𝘵𝘪𝘰𝘯𝘴. 8. 𝘝𝘪𝘴𝘶𝘢𝘭𝘪𝘻𝘪𝘯𝘨 𝘢𝘯𝘥 𝘮𝘢𝘯𝘪𝘱𝘶𝘭𝘢𝘵𝘪𝘯𝘨 𝘮𝘰𝘭𝘦𝘤𝘶𝘭𝘢𝘳 𝘴𝘵𝘳𝘶𝘤𝘵𝘶𝘳𝘦𝘴 𝘸𝘪𝘵𝘩 𝘗𝘺𝘵𝘩𝘰𝘯. 9. 𝘉𝘶𝘪𝘭𝘥𝘪𝘯𝘨 𝘥𝘢𝘵𝘢𝘴𝘦𝘵𝘴 𝘸𝘪𝘵𝘩 𝘙𝘋𝘒𝘪𝘵 𝘧𝘰𝘳 𝘤𝘰𝘮𝘱𝘶𝘵𝘢𝘵𝘪𝘰𝘯𝘢𝘭 𝘱𝘳𝘰𝘫𝘦𝘤𝘵𝘴. 𝘒𝘦𝘺 𝘓𝘦𝘢𝘳𝘯𝘪𝘯𝘨𝘴: Hands-On Practice: While the course was concise, I went far beyond its scope by diving into projects, solving complex problems, and practicing consistently. This wasn’t just learning—it was passion in action. The field of computational chemistry excites me in ways that never feel like “work.” Skills I Honed Along the Way: Problem-solving by tackling real-world cheminformatics challenges. Project management and time management, ensuring I made the most of every learning opportunity. Applying theoretical knowledge to practical, real-world projects. Real-World Impact: This field allows me to contribute to cutting-edge advancements in drug discovery, material science, and environmental solutions—areas that I’m deeply passionate about. I’m eager to take this knowledge further, collaborate on exciting projects, and continue building solutions at the intersection of chemistry, data science, and technology. If you’re in the world of cheminformatics, data science, or computational chemistry—let’s connect! I’d love to discuss ideas, collaborate, or simply learn from your experiences. Huge thanks to Khadija Anwar who shared this opportunity with me and Mohamed EL Soudy who taught very diligently and very professionally. He has a strong grip on the subject. #DataScience #Chemoinformatics #ComputationalChemistry #ChemistryWithPython #RDKit #ProfessionalGrowth

I really like this article from Djork-Arné Clevert, Benjamin Haibe-Kains, Lovisa Holmberg-Schiavone and Matthieu Schapira in Nature Portfolio that provides #DataScience guidelines for organizations engaged in early-stage #Drug #RnD Here are some of the takeaways: 📌 Integration of Data Science in Drug Discovery: The article outlines the importance of integrating data science into the early stages of drug discovery, emphasizing that robust data management and the use of artificial intelligence (#AI) can significantly accelerate the discovery and optimization of new drugs. 📌 Centralized Data Management: It advocates for the adoption of centralized database architectures and standardized #vocabularies across laboratories to ensure that data generated is interoperable and easily integrated into high-value datasets (sounds a lot like FAIR 😎 ... which is a good thing) 📌 Enhanced Data Recording through Automation: The article highlights the potential of lab automation and the use of electronic lab notebooks (#ELNs) to push the boundaries of data recording, ensuring that even the most detailed #metadata is captured and made available for #datamining. 📌 Best Practices for Data Archiving and Dissemination: Emphasis is placed on the need for transparent and reproducible data processing, structuring and documenting data effectively, and the use of #cloud-based systems to manage large, #multimodal datasets for easier dissemination and long-term usability. 📌 Collaboration and Open Science: The article calls for a collaborative approach where experimentalists and data scientists work as a unified team, encouraging the adoption of open science principles to enhance data sharing. https://lnkd.in/e4cjv6cD

I’ve begun to wonder if the wet-lab will be digitized faster than dry-lab operation, at least in computational chemistry. I promise this isn’t pure click bait and there is actually quite a bit of truth to it. Computational chemistry kind of has a blind spot for embracing the full spectrum of digitization like the wet-lab or bioinformatics fields are doing through cloud-native automated workflows, ELNs, LIMS, and other R&D data platforms. CompChem should be light years ahead of pretty much every other discipline as this is one of the earliest applications of computers. The CHARMM molecular dynamics engine is over 40 years old and Gaussian is over 50! For so long the core bottlenecks to effective CompChem was sheer compute. In my view the parallel improvements in hardware have basically “solved” this problem, and indeed CompChemists were eager to jump onto GPU compute and make great use of it. I remember running Folding@Home as a teenager on my new Playstation 3 (which I still use!). This probably culminates in one of the most impressive computer engineering achievements which is the development of the special purpose MD chips from DESRES. However, I think this extreme competency in high-performance computing has led practitioners to focus more on trees and less on the forest. For instance, in today’s world there is a massive pool of relatively inexpensive MD-capable hardware available on the cloud but relatively few methods or applications that are designed and engineered to take advantage of this distributed architecture. My bias is towards ensemble based methods with loose coupling between replicas, but there are so many untapped opportunities. Furthermore, most CompChem software does a terrible job of handling I/O efficiently. When its running by itself with an (expensive) high-performance filesystem hooked up you can get away with this, but as you scale this up and hook it up to more complex workflows efficiently handling data becomes essential. Another common blind spot I see is around data management. Again CompChem has always had “big data” problems before it was even a word. But what I’m referring to is more about data organization, cleanliness, and generally working towards FAIR principles. While the wet-lab is working towards completely digitized processes via sensor aggregation, LIMS, and ELNs, the CompChem field is still emailing files and spreadsheets around! Nevermind the fact that the file format landscape is complete chaos, where receiving a PDB is as informative as receiving a ‘TXT’ file sometimes. In some of our discussions we’ve heard of CompChemists keeping paper lab notebooks not that long ago! I’m optimistic because CompChem obviously can become much more “digitized” than any wet-lab can. All of the annoying data entry an ELN will never fully remove can be made invisible in CompChem.

Some useful insights here regarding #datamanagement practices in the lab From Sapio Sciences in Lab Manager: With the recent increase in automated and digital lab operations, laboratories of all kinds are facing increased challenges with siloed data, relying on disparate systems that hinder accessibility and usability. Adopting a science-aware approach that integrates data from various instruments and applications into a unified platform can enhance productivity. “If scientists want to get the most out of their data, and take advantage of revolutionary AI and ML models, they need to change the way they think about managing that data. It isn’t enough to simply consolidate it. If a scientist can’t then easily search, visualize, and analyze that data in a science-aware way, you’re ultimately going to be holding back the pace of your research efforts.” Read more about Sapio's vision for addressing these challenges here: https://lnkd.in/gPmZktBd Excited to learn more about the various approaches laboratories and services providers in the industry are taking to tackle rising challenges like data management and more @ Future Labs Live USA Download our prospectus to learn more about Future Labs Live US (October 30-31, Pennsylvania Convention Center) - https://lnkd.in/gbJWycYG #labautomation #labdatamanagement #labproductivity #laboperations #futureofthelab #innovation

🧬𝐈𝐬 𝐃𝐍𝐀 𝐭𝐡𝐞 𝐟𝐮𝐭𝐮𝐫𝐞 𝐨𝐟 𝐝𝐢𝐠𝐢𝐭𝐚𝐥 𝐝𝐚𝐭𝐚 𝐬𝐭𝐨𝐫𝐚𝐠𝐞?🧬 💡 A new article from Nina Notman and Chemistry World explores the potential of DNA as a long-term solution for preserving digital information. In 2013, Eric Schmidt, then CEO of Google, stated that global data generated every 2 days equaled that for all of human history up to 2003 - just a decade earlier! As of 2024, we are now producing 100 times more data than that and it is likely to increase a further fivefold over the next decade. The rise of the internet, social media and smart devices have fueled this data growth until now and the widespread use of AI is only expected to accelerate it. Current data storage technology will not be able to keep up with this current data explosion in terms of capacity and energy requirements. However, DNA offers extremely high data density coupled with almost zero need for energy for long-term storage. The article discusses the potential current challenges to developing DNA data storage but it is almost inevitable that it will become a critical method at least for long-term archiving in the next few years. NunaBio Limited are a company that makes DNA. We are keen to follow the work of the DNA Data Storage Alliance to see how this technology develops. #DNA #datastorage

🧬 What Does 1 TB of DNA Look Like? DNA isn’t just the blueprint of life—it’s nature’s most efficient data storage system, capable of holding an incredible amount of information in an unbelievably tiny space. If we imagine 1 TB of data stored in DNA, the visualization is simply mind-blowing. 📊 DNA vs. Modern Storage Systems Storage Density: DNA can store 215 petabytes (215 million GB) in a single gram. Comparatively:  A 1 TB hard drive weighs around 450 grams.  DNA can theoretically store the entire internet (estimated at 40 zettabytes) in just 40 grams of material.  Longevity: DNA remains stable for thousands of years, unlike traditional hard drives, which typically last 3-5 years, or SSDs, which last 5-10 years.  🌍 Real-World Applications and Trends 1️⃣ DNA Data Storage Research: Tech giants like Microsoft and Illumina are actively investing in DNA storage, with predictions that DNA will become a mainstream storage medium by 2030.  The global DNA data storage market is expected to grow at a CAGR of 65%, reaching $1.5 billion by 2032. 2️⃣ How Compact is 1 TB in DNA? If stretched, 1 TB of DNA data would span about 20,000 km, roughly half the circumference of the Earth!  In its natural form, DNA coils into a microscopic space within cells, highlighting its ultra-compact efficiency. 3️⃣ Data Preservation: DNA’s resilience to extreme conditions makes it ideal for preserving critical data, such as cultural archives or medical records, for future generations.  Recent breakthroughs allow DNA to encode data with 99.99% accuracy, a level unmatched by current digital technologies.  💡 Why DNA is the Ultimate Storage System Nature’s design has inspired next-gen computing and biotechnology, as DNA’s structure combines durability, density, and efficiency.  A single human cell contains 3 billion base pairs—equivalent to 1.5 GB of data—distributed across 46 chromosomes in every cell of our bodies.  🌟 Fun Fact: If we scaled the DNA in a single human body, it would stretch for over 10 billion miles, enough to reach Pluto and back. DNA storage is no longer just science fiction—it’s the future of data technology. Imagine a world where your entire digital life fits into a drop of liquid! 🧬✨         "Credits: 🌟  All write-up is done by me(P.S.Mahesh) after indepth research. All rights and credits for the video/visual presented are reserved for their respective owners. 📚 For attribution or content removal requests, please contact me. 📩