Decode Health’s Post

The next #CRISPR screening milestone was just achieved. Congratulations to Britt Adamson and team for developing a PE platform for CRISPR screening. As usual - a few selected takeaways from the study: 👉 A Prime Editing Platform: Development and evaluation of a PE2 prime editing platform for PE editing, using Cas9(H840A) and prime editing guide RNAs (pegRNAs). 👉 Continuous Accumulation of Intended Edits: Stable expression of PE components results in the continuous accumulation of precise edits over time, with minimal observation of unwanted byproducts. 👉 MMR Deficiency Enhances PE Efficiency: Loss of the MMR pathway (MLH1 ko) enhances PE efficiency, especially for certain edits, highlighting the importance of cellular context in PE outcomes. 👉 Large-Scale & Phenoytpe-based Screening: Optimized PE enables variant screening at a massive scale, as demonstrated by the evaluation of thousands of edits intended to generate premature nonsense codons in essential genes. 👉 Impact of pegRNA Design Features: Design features of epegRNAs, such as PBS and RTT length, edit position, and targeted gene location, influence phenotype induction. This underscores the importance of careful library design considerations in PE experiments. Maybe the more the better in this case. 🤔 👉 Chromatin Context Influence: As predicted by ePRIDICT scores, chromatin context affects phenotype induction, with epegRNAs targeting regions with favorable chromatin contexts showing increased phenotype induction rates. 👉 Specificity of Phenotypes: PE phenotypes are highly specific, as demonstrated by the low activity and low correlations between replicates of synonymous control epegRNAs. This highlights the precision of PE screening outcomes. 👉 👉 👉 Ultimately, PE is a powerful tool for precise genome engineering with applications in functional genomics, therapeutic development, and biotechnological research. Further optimization and understanding of PE mechanisms remain, however, needed to enhance its versatility and efficacy in unbiased screening applications. Pls find the full text here: https://lnkd.in/eFEbVUSt

Drug discovery options at Qiagen. Could be worth a look regarding thesis projects. I know first hand that Qiagen partners accept students for internships and thesis projects.

Are you interested in exploring the role of AI/ML in data management and integration to identify mechanisms of action and facilitate drug discovery? Genomics in Drug Discovery ONLINE, the latest three-part webinar series, will provide you with examples of how to best leverage AI/ML, genomic diversity and functional genomic tools to catapult your drug discovery efforts towards the clinic. Join us on Thursday 7th March at 3pm BST / 4pm CET/ 10 EST for the first webinar in the series, to engage in live Q & A sessions with the speakers and stay up to date on the latest breakthroughs and advancements in Genomics in Drug Discovery. Registering for the webinar series will give you access to the following presentations: --- Webinar 1: Revolutionising Genomics through AI in Drug Discovery  Talk 1: Data Integration Using Machine Learning Models to Identify the Mechanism of Action of Drugs  - Harpreet Saini, Senior Director, Bioinformatics, Astex Pharmaceuticals    Talk 2: Omics Data Management and Analytics  - Felix Francis, PhD, Senior Manager, Data Management and Analytics, Takeda --- Webinar 2: Tackling Genetic Diversity through Translational Models & Patient Stratification for Effective Target Discovery Talk 1: Developing & Defining Translatable in-vitro Models & Indication Relevant Assays    - Emma Laing, Director, Computational Biology, GSK   Talk 2: Patient Stratification for Driving Gene Signature Selection for Precision Oncology  - Dipti Thakkar, Vice President, Target Biology & Pharmacology, Hummingbird Bioscience Talk 3: Pulse RNA Library Screens as a Method of Target Discovery in Myeloid Malignancies  - Jamshid Sorouri Khorashad, Honorary Senior Lecturer & Scientific Consultant, Imperial College London --- Webinar 3: Decoding Success Using Functional Genomics in Drug Discovery Talk 1: Application of CRISPR-Cas9 for iPSC-Models in Drug Discovery  - Matteo Martufi, Associate Director, GSK    Talk 2: Using Pooled CRISPR Screening to Drive Oncology Drug Discovery  - David Walter, Group Leader Functional Genomics, Cancer Research Horizons   Talk 3: Population Level Omics for Precision Medicine  - Ryan Dhindsa, MD, PhD, Assistant Professor, PI, Baylor College of Medicine --- By registering for today's webinar, you'll also gain access to all three webinars in the series. Click the link for more details and to register now: https://hubs.la/Q02mGJXl0 #DrugDiscovery #AI

We are excited to share that our CEO, Emily Milsovic, and Armonica Technologies. Inc. were featured in CIOViews. Armonica's unique method detects essential missing epigenomic data that traditional genomics techniques cannot identify. This breakthrough will lead to enhanced therapeutics, diagnostics, and patient treatments. CIO Views, an esteemed international publication, showcases the success stories of business leaders to inspire C-level executives. https://lnkd.in/d_fMZQ7X hashtag #GenomicData hashtag #Innovation hashtag #Epigenomics hashtag #Therapeutics hashtag #Diagnostics hashtag #CIOViews

🎉 We are excited to launch our AI-derived biomedical knowledge base, QIAGEN Biomedical KB-AI, designed to accelerate data-driven drug discovery in the pharma and biotech industries. Learn more 👉 https://lnkd.in/e9WaMDtV ✅ QIAGEN Biomedical KB-AI contains over 640 million biomedical relationships, including gene, disease and drug causal relationships, to aid in data-driven drug discovery. ✅ AI-driven insights complement human-curated QIAGEN Biomedical KB-HD and help identify novel relationships between diseases, biological pathways and molecular interactions that may be missed by traditional methods. ✅ Workflow cuts down on hypothesis testing time and speeds up the identification of promising therapeutic avenues with clear, structured data.

Genomics Market : Use of genomics in specialized/personalized medicine Download PDF Brochure: -https://lnkd.in/dsrgnkZ7 Growth in the #personalizedmedicine market is set to offer an array of opportunities for players operating in the market. Various #pharmaceutical companies are increasingly investing in personalized medicine #research. Personalized medicine enables #medical practitioners to identify the exact needs of a #patient and then prescribe medicines and treatments accordingly. This approach guarantees better care outcomes. Moreover, it is expected that advancements in the field of gene therapy have the potential to replace traditional drugs and #surgeries by enabling doctors to treat a disorder by inserting a #gene into a patient’s #cell. The global genomics market in terms of revenue was estimated to be worth $46.2 billion in 2023 and is poised to reach $83.1 billion by 2028, growing at a CAGR of 12.4% from 2023 to 2028. #genomicsrevolution #dnainnovation #precisionmedicine #genomicresearch #bioinformatics #personalizedhealthcare #genomictesting #nextgensequencing #crisprtechnology #genomicdata #biotechadvances #genetherapy #pharmacogenomics #genomicmedicine #geneediting #precisiongenomics #omicstechnology #geneticdiscovery #genomicsolutions #bioinformaticstools

#ArtificialIntelligence in #Genomics Industry is projected to witness a CAGR of 39.2% during the period 2023-2032. This growth can be attributed to the rising focus on reducing turnaround time in drug discovery.

#ArtificialIntelligence in #Genomics Industry is projected to witness a CAGR of 39.2% during the period 2023-2032. This growth can be attributed to the rising focus on reducing turnaround time in drug discovery.

#ArtificialIntelligence in #Genomics Industry is projected to witness a CAGR of 39.2% during the period 2023-2032. This growth can be attributed to the rising focus on reducing turnaround time in drug discovery.

#ArtificialIntelligence in #Genomics Industry is projected to witness a CAGR of 39.2% during the period 2023-2032. This growth can be attributed to the rising focus on reducing turnaround time in drug discovery.