Code Ocean

🏅 Introducing the Code Ocean Games! 🏅 Olympic athletes are gifted with different skills. A winning relay team needs a strong starter, a sprinter, a cut-in, and an anchor, each with unique abilities, but together, they can achieve gold. Just like an Olympic team, it takes a group of open-source tools and cloud technology to help scientists do great research. Meet our computational Olympians that achieve collaborative, reproducible bioinformatics for R&D. 🌟 Our All-Star Roster 🌟 🏊♀️ Capsules: Reproducibility triathlon 🤾 Lineage Graphs: Result provenance track and field 🥎 Collections: Organizational softball 🤸 FAIRR Data Architecture: Data gymnastics 🏃 Starter Environments: New environment sprint Join us as we celebrate the synergy of these tools that make it easy for computational scientists to work in the cloud, and with each other #ComputationalScience #Bioinformatics #ResearchAndDevelopment #Olympics2024

How much time do you or your team spend deciphering legacy code, navigating unorganized codebases, or managing the tedious process of sharing analyses? Probably WAY too much. Wasted time, along with the complexities of multi-omics data analysis can significantly slow down the part that matters most… the science that changes lives. On August 22nd, we’re hosting a session on transforming your multi-omics research with reproducible environments. Some topics we’ll cover: 🔹 Handling diverse data types, standardizing methods, and managing varying analysis pipelines. 🔹 Dealing with leftover code, unorganized tools, and difficulties collaborating on analysis. 🔹 Establishing standardized workflows, ensuring documentation and transparency. 🔹 Real-world examples of improved reproducibility in multi-omics research. We hope you can join us! #biotech #multiomics #bioinformatics

🚀 Exciting news for researchers and developers who want to automate their Code Ocean workflows! 🚀 We have released Python SDK, which makes it easier than ever to leverage the full functionality of the Code Ocean Public API in Python. Now you can seamlessly implement scalable workflows without having to interact with the UI or AWS. What are some benefits of the SDK for Code Ocean? 🦾 Automate Capsules & Pipelines: Once your Capsules and Pipelines are ready for production, automate your workflows while maintaining lineage, reproducibility and traceability. 📊 Create, Search, and Share Assets: Explore the possibilities our SDK can provide by scripting your analysis according to your own needs. We wrote the SDK with convenience in mind to offload the heavy lifting to us! 🧪 Concurrency: Leverage common libraries to process thousands, even millions of samples at once! Check it out and see how we can help streamline your computational research. #dev #biotech #compbio #python

Have you subscribed to Code Ocean's monthly newsletter yet? In this month's edition: Should your IT team start running your computational research? The role of research IT/engineering and computational scientists are distinct but complementary. Computational scientists shouldn’t be burdened with IT tasks—they need to focus on the work they do best - making scientific discoveries. Finding a "unicorn" who can do it all is rare and unsustainable. Instead, align the right roles for optimal impact. Haven't subscribed to our newsletter yet? Sign up to receive a monthly Code Ocean newsletter, packed with exclusive updates on: - Industry trends - Upcoming events and webinars - Compbio news - And more!

🌟 Your inside look of the latest foundational models in biotech and pharma 🌟 To keep up with the rapid acceleration of model development, we’ve created an interactive foundational model map for finding the foundational models for specific use cases. In the map, you can toggle between: - 7 key domains - 59 foundational models - 837 use cases Key domains we’ve covered: - Genomics and genetic analysis - Epigenetics - Single-cell and multi-omics analysis - Molecular structures and interactions - Discovery and biomedical applications - Imaging This map lets you organize foundational models by input and output types, such as molecules, sequences, and structure tables. You can utilize it to map use cases directly to the relevant models, providing a practical approach beyond mere semantic definitions of domains. Try it out yourself! 👇 https://lnkd.in/eQHwfhhV #biotech #research #dataanalytics

True ✅ or false ⛔: computational work should be reproducible if the data is FAIR. FALSE ⛔: Many people in the industry recognize the principles of FAIR (findable, accessible, interoperable, and reusable) data, but there's often a false expectation that FAIR data is reproducible. The reality: FAIR principles should be applied to analytics to ensure computational reproducibility. Simon Adar recently shared his suggestion for adaptations from FAIR Data to FAIR Analytics: 🔎 Findable: analysis and supplementary materials have sufficiently rich metadata and a unique and persistent identifier. ♿ Accessible: metadata and analysis are understandable to humans and machines. Analysis is deposited in a trusted repository. 🧳 Interoperable: analysis can move and still operate between machines, people and different systems. 💡 Reusable: analysis can be used with trust, confidence, and without troubleshooting with new data, code or updated packages. There’s also accurate information on provenance. Interoperability and reusability are only possible if you have guaranteed computational reproducibility. ♻️ Reproducible: ALL data, code and computing environment available in such a way that the computations can be executed again with identical results! FAIR data = FAIR analytics = reproducibility Three very different components, but all equally important. #biotech #bioinformatics  #dataanalytics #research

Start running nf-core pipelines in Code Ocean! ⭐ Get instant access to a curated set of best practice Nextflow pipelines ⭐ No painful software installation or command line experience needed ⭐ Easily customize runs with Code Ocean’s App Panel Getting started with nf-core typically requires software setup, command line installations, extensive documentation and troubleshooting. With Code Ocean, just import your nf-core pipeline of choice, set your parameters, and click RUN. It’s that easy! Check it out in the demo!

💥 Congratulations to our customer, Flagship Pioneering, raising $3.6 billion to support the creation and development of 25 new companies focused on human health, sustainability, and AI. Over the past two decades, they've driven innovation and growth in over 100 biotech companies, pioneering original science and machine learning platforms to create technologies that change lives. We’re excited to see how Flagship will further push the boundaries of science. Read the full press release 👇 https://lnkd.in/d9NPX3MV #Biotech #Innovation #Sustainability #AI #Healthcare

Two examples of foundational models that are game-changers in biotech and pharma 🧬: While many foundational models hold great potential with hundreds of valuable downstream applications, there are two models that have gained noticeable interest within the biotech community. Meet AlphaFold 3 and LLaVa-Med. AlphaFold 3: great for protein structure prediction 🧠 - Enhanced training: Predicts protein structures from amino acid sequences and other components. - Drug discovery: Perfect for predicting bio-molecular interactions, such as drug locations within proteins. - Multimodal: Combines small molecules, sequences, and templates to produce detailed complex structures. - Fusion module: Increases accuracy and supports multi-molecular structures. LLaVA-Med - great for medical imaging analysis 🩻 - Dual-modal analysis: Analyzes multiple images and text, allowing detailed inquiries about medical images. - Practical applications: Identifies and details things like implanted devices in patients. - Fine-tuned for medicine: Trained to understand medical jargon and provide insights from medical images. These are just two examples of the many promising foundational models in #biotech and #pharma, barely scratching the surface of their potential in scientific #research and medical diagnostics.

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