Independent Data Lab

𝗘𝘅𝗰𝗶𝘁𝗶𝗻𝗴 𝗮𝗱𝘃𝗮𝗻𝗰𝗲𝗺𝗲𝗻𝘁 𝗶𝗻 𝘀𝗶𝗻𝗴𝗹𝗲-𝗰𝗲𝗹𝗹 𝗶𝗺𝗺𝘂𝗻𝗲 𝗱𝗮𝘁𝗮 𝗮𝗻𝗮𝗹𝘆𝘀𝗶𝘀! 🧬 Integrating single-cell RNA sequencing (#scRNAseq) and adaptive immune receptor sequencing (#scVDJseq) is critical for understanding  immune cells (T or B cells)  development. However, until now, R users lacked a dedicated ability for this advanced analysis. Introducing #dandelionR, a new R-based solution inspired by the #Python ability Dandelion. This ability enables: ✅ Construction of a VDJ feature space to #integrate scRNA-seq and scVDJ-seq data. ✅ Trajectory analysis using diffusion maps and absorbing Markov chains. With dandelionR, researchers and bioinformaticians using #R can now explore immune repertoire trajectories with greater precision, paving the way for groundbreaking discoveries in immunology and beyond. 📄 Publication in #bioRxiv: https://lnkd.in/e8JxfnCC 🔗 Source code on #GitHub: Compiled by: Hassiba Belahbib #Bioinformatics #Immunology #scRNAseq #Innovation #DataIntegration #DataAnalysis #ComputationalBiology (image credit: Pixabay)

𝗦𝗶𝗺𝗽𝗹𝗶𝗳𝘆𝗶𝗻𝗴 𝗣𝗵𝘆𝗹𝗼𝗴𝗲𝗻𝗲𝘁𝗶𝗰 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀 𝗙𝗼𝗿 𝗕𝗶𝗼𝗹𝗼𝗴𝗶𝘀𝘁 𝘄𝗶𝘁𝗵 𝗖𝗮𝗺𝗜𝗧𝗿𝗲𝗲 ! Introducing #CamITree, a powerful new ability designed to streamline phylogenetic analysis for #viral and #mitochondrial genomes. This user-friendly desktop software integrates key abilities like MAFFT, IQ-TREE2, and MrBayes into a single, automated workflow, making complex analyses faster and more #accessible. 𝗪𝗵𝘆 𝗖𝗮𝗺𝗜𝗧𝗿𝗲𝗲 𝗦𝘁𝗮𝗻𝗱𝘀 𝗢𝘂𝘁: 𝘼𝙡𝙡-𝙞𝙣-𝙊𝙣𝙚 𝙒𝙤𝙧𝙠𝙛𝙡𝙤𝙬: From sequence alignment to tree estimation (#ML & #BI methods), everything is automated. 𝙐𝙨𝙚𝙧-𝙁𝙧𝙞𝙚𝙣𝙙𝙡𝙮: No advanced computational skills needed, perfect for all researchers. 𝙀𝙛𝙛𝙞𝙘𝙞𝙚𝙣𝙩 & 𝙎𝙚𝙘𝙪𝙧𝙚: Parallel processing for faster results, with data privacy ensured as a desktop app. Customizable: Modify runtime parameters and save configurations for future use. 𝙒𝙝𝙤’𝙨 𝙄𝙩 𝙁𝙤𝙧? Researchers working on small genomes (e.g., #viruses, #mitochondria) or anyone seeking a streamlined, efficient, and secure solution for evolutionary studies. 📚 More details in the BMC Bioinformatics paper (2025): https://lnkd.in/dcuPKNFs 🔗 Available for free on #𝗪𝗶𝗻𝗱𝗼𝘄𝘀! Download on #GitHub: https://lnkd.in/dREy8eBx Compiled by: Hassiba Belahbib 👉 Follow Independent Data Lab for more new bioinformtatics tools ! #Phylogenetics #Bioinformatics #EvolutionaryBiology #GenomeAnalysis #MitochondrialGenomes #ViralGenomes #PhylogeneticTrees #ResearchAbilities #DataAnalysis

𝗜𝗻𝘁𝗲𝗿𝗲𝘀𝘁𝗶𝗻𝗴 𝘀𝘁𝗮𝘁𝘀 𝗼𝗻 𝘁𝗵𝗲 𝗔𝗜 𝗶𝗻 𝗟𝗶𝗳𝗲 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗠𝗮𝗿𝗸𝗲𝘁 𝗳𝗼𝗿 𝘁𝗵𝗲 𝗻𝗲𝘅𝘁 𝟵 𝘆𝗲𝗮𝗿𝘀 Market Growth Projections The US AI in life science analytics market is projected to grow from with a CAGR of 11.3% - see the plot below. #MarketGrowth #LifeScience #Analytics Driving Factors Increased adoption of AI technologies in pharmaceutical and biotechnology research is a key driver of this growth. #AIAdoption #PharmaResearch #Biotech Applications of AI AI is utilized to analyze complex datasets, accelerating drug discovery and development processes. #DrugDiscovery #DataAnalysis #AIApplications Regulatory Support Regulatory bodies like the FDA are supporting AI integration in drug development by providing clear guidelines. #RegulatorySupport #FDA #DrugDevelopment Are you ready to keep up with AI challenges? Source: https://lnkd.in/e94xV-ep

𝗜𝗗𝗟 𝗠𝘂𝗹𝘁𝗶-𝗢𝗺𝗶𝗰𝘀 𝗗𝗮𝘆 𝗦𝘁𝗿𝗲𝗮𝗺𝗹𝗶𝗻𝗶𝗻𝗴 𝗠𝘂𝗹𝘁𝗶-𝗢𝗺𝗶𝗰 𝗗𝗮𝘁𝗮 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁 𝘄𝗶𝘁𝗵 𝗢𝗺𝗶𝗹𝗮𝘆𝗲𝗿𝘀: 𝗔 𝗣𝘆𝘁𝗵𝗼𝗻 𝗦𝗼𝗹𝘂𝘁𝗶𝗼𝗻 𝗳𝗼𝗿 𝗕𝗶𝗼𝗶𝗻𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗰 #Omilayers, a Python package designed to simplify the management of multi-omic data. This package encapsulates SQLite and DuckDB databases, allowing efficient data management without the need for complex SQL queries. Omilayers provides a simplified application programming interface (API) that facilitates the storage, retrieval, and modification of multi-omic data, which is crucial for bioinformatics analysis.  The study shows that DuckDB outperforms SQLite in terms of performance and storage efficiency, although SQLite may be preferred for frequent, small transactions. Omilayers is particularly useful for analyses requiring robust data management while remaining accessible to users unfamiliar with databases. 📚 Read more in this #BMC Bioinformatics paper: https://lnkd.in/eBweez7F 🔗 Available on #GitHub: https://lnkd.in/eCZtznB5 / https://lnkd.in/e2JZkuAS 💻 Usage instructions here: https://lnkd.in/ePtTw-Kv Compiled by Hassiba Belahbib 👉 Follow Independent Data Lab for more omics news! #MultiOmics #DataManagement #Python #Bioinformatics #DuckDB #SQLite #Omilayers #BigData #Genomics #Proteomics

𝗘𝗨 𝗖𝗼𝗺𝗺𝗶𝘁𝘀 €𝟮𝟬𝟬 𝗕𝗶𝗹𝗹𝗶𝗼𝗻 𝘁𝗼 𝗔𝗜 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁 - 𝗪𝗵𝗮𝘁 𝗱𝗼𝗲𝘀 𝗶𝘁 𝗺𝗲𝗮𝗻 𝗳𝗼𝗿 𝗦𝗠𝗘𝘀 𝗮𝗻𝗱 𝗕𝗶𝗼𝘁𝗲𝗰𝗵? - The European Union has announced the InvestAI initiative, aiming to mobilise €200 billion for artificial intelligence development. - This includes a €20 billion fund to establish AI gigafactories, each housing around 100,000 advanced AI chips. - The initiative seeks to position Europe as a leader in AI by enabling open, collaborative innovation and providing access to large-scale computing resources. 𝗜𝗺𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀 𝗳𝗼𝗿 𝗕𝗶𝗼𝘁𝗲𝗰𝗵 𝗮𝗻𝗱 𝗦𝗠𝗘𝘀 𝗶𝗻 𝗣𝗵𝗮𝗿𝗺𝗮: - Access to such AI infrastructure can give much needed resources to smaller companies that rely on such tech. - SMEs in the pharma sector may gain access to better AI tools, levelling the playing field with larger competitors. - Improved data analysis capabilities could lead to more personalised medicine approaches. 𝗪𝗶𝗹𝗹 𝗧𝗵𝗶𝘀 𝗛𝗲𝗹𝗽 𝗘𝘂𝗿𝗼𝗽𝗲 𝗖𝗮𝘁𝗰𝗵 𝗨𝗽 𝘄𝗶𝘁𝗵 𝘁𝗵𝗲 𝗨𝗦𝗔 𝗮𝗻𝗱 𝗖𝗵𝗶𝗻𝗮? - This is a good sized investment, but falls quite short of the recent offer the other day of nearly a 100 Billion USD for OpenAI. - Building gigafactories and pushing collaboration wont really reduce current gap with the USA and China. - Success will depend on effective implementation and continuous adaptation. Source: https://lnkd.in/eN6gCniX Image source: https://lnkd.in/eqxrYqik

𝗖𝗼𝗺𝗽𝗮𝗿𝗶𝘀𝗼𝗻 𝗼𝗳 𝘀𝗽𝗮𝘁𝗶𝗮𝗹 𝗴𝗲𝗻𝗲 𝗲𝘅𝗽𝗿𝗲𝘀𝘀𝗶𝗼𝗻 𝗽𝗿𝗲𝗱𝗶𝗰𝘁𝗶𝗼𝗻 𝗺𝗲𝘁𝗵𝗼𝗱𝘀 - 𝗲𝗺𝗲𝗿𝗴𝗶𝗻𝗴 𝘁𝗼𝗼𝗹𝘀 𝘂𝘀𝗶𝗻𝗴 𝗔𝗜 𝘁𝗼 𝗰𝗮𝗹𝗰𝘂𝗹𝗮𝘁𝗲 𝗮𝗽𝗽𝗿𝗼𝘅𝗶𝗺𝗮𝘁𝗲 𝗴𝗲𝗻𝗲 𝗹𝗲𝘃𝗲𝗹 𝗱𝗮𝘁𝗮 🧬 Benchmarking of 11 prediction methods A recent study published in Nature Communications conducted a comprehensive benchmarking of eleven methods used to predict spatial gene expression from histology images.  #genomics #spatialbiology #bioinformatics 🧪 Assessment across multiple datasets The evaluation encompassed various datasets to ensure robustness and generalisability of the findings, providing insights into the performance of each method across different tissue types.  #genomics #spatialbiology #bioinformatics 📊 Identification of strengths and limitations The study identified specific strengths and limitations of each method, offering guidance for researchers in selecting appropriate tools for their spatial gene expression analyses.  #genomics #spatialbiology #bioinformatics 🌐 Implications for translational research The findings help translational research, by aiding in the selection of suitable computational tools for spatial transcriptomics studies.  #genomics #spatialbiology #bioinformatics How can insights from such benchmarking studies allow non-AI experts of more accurate and efficient spatial gene expression prediction methods? What are the potential challenges in applying these methods to diverse tissue types and disease contexts? We can image that the gene level data output maybe limited. Can we take this one step further and predict the level of potential biomarker proteins from tissue?  Source: https://lnkd.in/eDfDrxaW Put together by Kaz de Souza Mateus Azim⚡💪🏽

🚀 𝗘𝘅𝗰𝗶𝘁𝗶𝗻𝗴 𝗔𝗱𝘃𝗮𝗻𝗰𝗲𝘀 𝗶𝗻 𝗣𝗿𝗼𝘁𝗲𝗶𝗻 𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻 𝗣𝗿𝗲𝗱𝗶𝗰𝘁𝗶𝗼𝗻! A very recent study published in BMC Bioinformatics introduces SEGT-GO, a cutting-edge method leveraging Graph Transformers and Explainable AI (XAI) to predict protein functions with unprecedented accuracy. Here's why this is a game-changer: 🔬 𝗧𝗵𝗲 𝗖𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲: While we have millions of protein sequences, understanding their functions remains a bottleneck. Traditional methods struggle to capture complex relationships in Protein-Protein Interaction (PPI) networks, especially for distant proteins. 💡 𝗧𝗵𝗲 𝗜𝗻𝗻𝗼𝘃𝗮𝘁𝗶𝗼𝗻: 𝗦𝗘𝗚𝗧-𝗚𝗢 𝘂𝘀𝗲𝘀: Multi-hop Neighborhood Serialization to map distant protein interactions into feature embeddings. SHAP (XAI Framework) to filter out noise and optimize predictions, ensuring better accuracy and interpretability. 📊 𝗧𝗵𝗲 𝗥𝗲𝘀𝘂𝗹𝘁𝘀: Outperforms existing methods like DeepGraphGO, especially on small datasets and cross-species predictions. Excels in predicting unseen protein functions, demonstrating strong generalization across species. 🌍 𝗪𝗵𝘆 𝗶𝘁'𝘀 𝗶𝗺𝗽𝗼𝗿𝘁𝗮𝗻𝘁: This breakthrough not only enhances our understanding of protein functions but also accelerates advancements in drug discovery, disease research, and biotechnology. For researchers and professionals in bioinformatics, SEGT-GO represents a significant leap forward in harnessing AI for biological insights. 📚 Read the full study here: https://lnkd.in/ewq-8biS 🔗 #GitHub code: https://lnkd.in/enj2bwZC Compiled by: Hassiba Belahbib 👉 Follow Independent Data Lab for more bioinformatics news #ProteinFunctionPrediction #Bioinformatics #GraphTransformers #ExplainableAI #DeepLearning #PPI #SEGTGO #DrugDiscovery #Biotechnology #ArtificialIntelligence #AI

DeepPrep: A New Tool for Faster Brain Imaging Analysis 🧠 Efficient Neuroimaging Pipeline DeepPrep is a new tool that speeds up brain imaging analysis, making it nice and tidy as well as scalable. This could significantly improve research in neuroscience. #Neuroimaging #DeepPrep 🛠️ Methodology DeepPrep uses deep learning algorithms and a workflow manager to create a computationally efficient and scalable neuroimaging pipeline. #DeepLearning #Neuroimaging ▶️ Broad utility image processing could be applied to other areas of medicine, such as cancer diagnostics, cardiovascular imaging, and rare disease research. Faster analysis means quicker diagnoses and more precise treatments, potentially benefiting NHS workflows and clinical research. #MedicalImaging #CancerResearch #Cardiology #RareDiseases #NHS 🧬 Bioinformatics in Biotech Bioinformatics Contract Research Organisations (CROs) such as Independent Data Lab can assist in processing large amounts of imaging data, enhancing the efficiency of studies like those using DeepPrep. #Bioinformatics #CROs Source:https://lnkd.in/e-f_t9Pu Put together by Kaz de Souza Mateus Azim⚡💪🏽

𝐈𝐃𝐋 𝐌𝐮𝐥𝐭𝐢-𝐎𝐌𝐈𝐂𝐒 𝐃𝐚𝐲: "𝐑𝐞𝐯𝐨𝐥𝐮𝐭𝐢𝐨𝐧𝐢𝐳𝐢𝐧𝐠 𝐒𝐩𝐚𝐭𝐢𝐚𝐥 𝐁𝐢𝐨𝐥𝐨𝐠𝐲: 𝐒𝐈𝐌𝐎 𝐔𝐧𝐥𝐨𝐜𝐤𝐬 𝐌𝐮𝐥𝐭𝐢𝐦𝐨𝐝𝐚𝐥 𝐈𝐧𝐬𝐢𝐠𝐡𝐭𝐬 𝐟𝐨𝐫 𝐏𝐫𝐞𝐜𝐢𝐬𝐢𝐨𝐧 𝐌𝐞𝐝𝐢𝐜𝐢𝐧𝐞" #SIMO is an advanced computational tool that integrates spatial and #multiomics single-cell data. It aligns #spatial #transcriptomics with other modalities, like chromatin accessibility and DNA methylation, using probabilistic alignment and optimal transport algorithms to reconstruct spatial cell clusters and gene regulatory networks. 💡 𝗞𝗲𝘆 𝗛𝗶𝗴𝗵𝗹𝗶𝗴𝗵𝘁𝘀:    𝙄𝙣𝙣𝙤𝙫𝙖𝙩𝙞𝙤𝙣 𝙞𝙣 𝙎𝙥𝙖𝙩𝙞𝙖𝙡 𝘽𝙞𝙤𝙡𝙤𝙜𝙮: SIMO bridges the gap between spatial and single-cell omics, offering a comprehensive view of cellular organization and regulation in tissues.    𝘽𝙚𝙣𝙘𝙝𝙢𝙖𝙧𝙠𝙞𝙣𝙜 𝙀𝙭𝙘𝙚𝙡𝙡𝙚𝙣𝙘𝙚: SIMO outperforms existing tools in accuracy and robustness, especially in handling complex spatial patterns and integrating non-transcriptomic data.    𝘽𝙞𝙤𝙡𝙤𝙜𝙞𝙘𝙖𝙡 𝙄𝙣𝙨𝙞𝙜𝙝𝙩𝙨:        ✔️ In #mouse brain studies, SIMO revealed spatial heterogeneity in cortical layers and multimodal gene regulation.        ✔️ In #human myocardial infarction datasets, it uncovered spatial heterogeneity in cardiomyocytes and fibroblasts, identifying potential therapeutic targets for cardiac repair. 📍 𝗔𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀: SIMO is compatible with diverse omics data, making it a versatile tool for studying tissue physiology, #pathology, and #disease modeling. ✅ 𝗜𝗺𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀: Significant leap in spatial biology, enabling researchers to uncover previously inaccessible multimodal insights. It holds promise for advancing precision medicine by providing deeper understanding of tissue organization and potential therapeutic targets. 𝗙𝗶𝗴𝘂𝗿𝗲 𝟳 highlights SIMO's ability to map cell types in human myocardial infarction, revealing cardiomyocyte subpopulations with distinct spatial distributions. Cells near the infarct zone activate repair pathways, while distant cells maintain normal functions. By integrating #scATACseq data, SIMO uncovers chromatin accessibility linked to repair genes and highlights functional differences through pathway enrichment. This demonstrates SIMO's strength in uncovering spatial and functional heterogeneity in tissue repair. 📰 More details in the #Nature Communications paper: https://lnkd.in/esSZhcez ⛓️ SIMO toolkit is available at #GitHub: https://lnkd.in/eW5bGVf5 Compiled by: Hassiba Belahbib 👉 Follow Independent Data Lab to stay updated about bioinformatics tools ! #multiomics #singlecell #spatialbiology #spatialtranscriptomics #cellclusters #generegulatorynetworks #precisionmedicine #bioinformatics #computationalbiology #dataintegration #dataanalysis

DNA repair drugs market projected to reach £27.22 billion by 2029 📈 Market growth: tThe DNA repair drugs market is expected to reach £27.22 billion by 2029, with a compound annual growth rate of 20%. #MarketGrowth #Healthcare 🎯 Targeted therapies: the development of targeted therapies is a key factor driving this market expansion, giving specific treatment options for patients. #TargetedTherapies #MedicalAdvancements 🧬 Precision medicine: A focus on precision medicine is contributing to the rise in DNA repair drug development, tailoring treatments to individual genetic profiles. #PrecisionMedicine #Genomics 🧑🔬 Biotech and bioinformatics: The integration of bioinformatics in biotech research is enhancing the development of DNA repair drugs, leading to more effective treatments. #Bioinformatics #Biotech 🔬 Research methodology: The market analysis is based on current industry trends, growth drivers, and technological advancements in DNA repair therapies. #Research #HealthcareInsights Sources: https://lnkd.in/e8Wft7Cf https://lnkd.in/eaYVTPFK. Put together by Kaz de Souza Mateus Azim⚡💪🏽