Sayonika M.’s Post

📢 We're happy to share new #PANCAIM article recently published in #Bioinformatics journal by PANCAIM Partners Tero Aittokallio & John Zobolas and their collaborators from Oslo University Hospital! 📚Their research focuses on identifying #genomic, #molecular, and #clinical markers crucial for predicting patient #survival, paving the way for personalized disease management strategies. 🔍The article addresses the pressing need for prognostic markers by leveraging modern omics technologies. Authors used a combination of genomics, epigenomics, transcriptomics, proteomics, and metabolomics to understand their impact on survival prognosis. 💡The authors introduce a comprehensive survival analysis workflow tailored for high-dimensional omics data to navigate through the complexities and extract meaningful insights. Their approach utilizes Cox-type penalized regressions and hierarchical Bayesian models to identify survival-associated features and validate survival models effectively. Curious to dive deeper? 1️⃣ Read the article open access here: https://lnkd.in/eGA5D7EV 2️⃣ Check out the step-by-step R tutorial using The Cancer Genome Atlas survival and omics data, which is a valuable resource for researchers seeking to implement and evaluate survival models in their studies: https://lnkd.in/enVnQbj6 #SurvivalAnalysis #OmicsData #PersonalizedMedicine #ResearchImpact

Discover the cutting-edge world of single-cell sequencing! 🧬 Follow the link below to delve deeper into this emerging technique, revolutionizing genomics, transcriptomics, and epigenomics at the cellular level. Since its inception in 2009, single-cell transcriptome sequencing has led to a plethora of methodologies, opening doors to exploring complex biological phenomena without preconceived notions. Take the immune system, for example—an enigmatic network of cells operating in diverse pathophysiological pathways. Thanks to platforms like 10x Genomics, we can now unravel this complexity like never before. Click the link to learn more! https://lnkd.in/diCDEhMY

📢Glad to share the following paper, published in the The Journal of Molecular Diagnostics (Elsevier), as a result of the joint effort between our Bioinformatics group and the Molecular Diagnostics of the Institute of Pathology in Erlangen, Germany! 🔍We describe a machine learning approach to support the diagnosis of CIC::DUX4 rearrangements in blue round cell sarcomas. 🌈Special thanks to my supervisor Dr Fulvia Ferrazzi and my Professor Florian Haller, and congratulations to Dewi Schlieben with whom I had the pleasure of sharing my first autorship! #bioinformatics #functionalgenomics #machinelearning #sarcoma

In the realm of modern biomedical research, our understanding of disease processes has evolved immensely, thanks to the remarkable advancements in multiomics and lipidomics. Multiomics combines genomics, proteomics, metabolomics, and more, providing a holistic view of biological systems. These cutting‑edge technologies have enabled us to delve deeper into the intricacies of various diseases, offering once unimaginable insights. This eBook serves as a comprehensive exploration of these transformative approaches and their pivotal role in identifying therapeutic targets and understanding drug responses across a spectrum of ailments. https://lnkd.in/gpYfiXG5

💪 Exploring the Role of ncRNA in Muscle Growth! Here is a new #PrePrint that uncovers how non-coding RNA (ncRNA) contributes to muscle growth! Since neither ncRNA nor multinucleated adult human skeletal muscle are ideally studied using sequencing technologies, researchers employed a customized RNA profiling pipeline, #MERFISH technology, and quantitative network modeling to study cell-type-specific ncRNA responses during muscle growth at scale. Key Findings: - 110 ncRNAs were modulated in vivo, with novel hypertrophy-linked ncRNAs identified - Identified few known hypertrophy-related ncRNAs, including CYTOR & numerous novel hypertrophy linked ncRNAs like PPP1CB-DT, EEF1A1P24 and TMSB4XP8 - Discovered that hypertrophy linked MYREM has a specific myonuclear expression pattern in vivo using spatial transcriptomic probes This study reveals the crucial role of ncRNAs in muscle growth, providing insight into their biological role. 📖 Read the full preprint: https://hubs.ly/Q02L6NTB0 #ncRNA #MuscleGrowth #Hypertrophy #SpatialTranscriptomics #MERFISH #Biology #Research

This review introduced a handful of recent applications of nanopore sequencing technology, the list is by no means complete. The ability to sequence continuous long read assemblies sequences truly offers the potential of closing gaps in our knowledge of human genetics, transcriptomics, epigenetics, and infectious disease. In effect, this sequencing technology has the potential to permit the biochemical reconstitution of human disease processes. Interesting review!! #genomics #oxfordnanopore #sequencing #longreads #infectiousdiseases #transcriptomics #epigenetics #publichealth #diagnostic #biotechnology #humandiseases #science #betterhealth #NGS #technology

In recent years, technologies that allow scientists to study a person’s DNA at single-molecule resolution have vastly expanded our knowledge of the human genome, the microbiome, and the genetic basis of disease. With such a detailed view of DNA, it’s possible to see genetic variants and structural details that were simply undetectable with earlier sequencing technologies. However, today’s gold-standard methods for single-molecule analysis typically require at least 150,000 human cells—containing millions of individual DNA molecules. That means researchers can’t apply these tools when just a few thousand cells are available, such as in many tumor biopsies, limiting the scientific and clinical potential of the technologies. Now, researchers at Gladstone Institutes have developed two new tools for single-molecule analysis that slash the amount of DNA needed by 90 to 95 percent. Their work, published in the journal Nature Genetics, shows how these tools could allow scientists to address biological questions they were previously unable to answer. Read more: https://bit.ly/4bIUD0Z

We’re thrilled to share the recent publication of research aimed at leveraging #ML to expand the genetic landscape of MASLD. In our paper “Machine Learning Across Multiple Imaging and Biomarker Modalities in the UK Biobank Improves Genetic Discovery for Liver Fat Accumulation,” we leveraged ML to impute liver fat content across multiple imaging and biomarker-based modalities, followed by genome-wide association studies (GWAS), revealing novel insights into the genetic basis of liver fat accumulation. By further characterizing these GWAS associations, we demonstrated that a majority of the newly identified liver fat associations are likely clinically relevant to MASLD. Using advanced ML and imputation, we’ve enabled a series of large-scale GWASs in the UK Biobank to identify multiple genes that could potentially serve as therapeutic targets for MASLD. Read our new paper: https://lnkd.in/gsCSUSWZ Hari Somineni, Sumit Mukherjee, Ph.D., David (Didi) Amar, Jingwen Pei, Karl Guo, Dave Light, Kaitlin Flynn, Ph.D., Chris Probert, Tom Soare, Santhosh Satapati, Daphne Koller, David Lloyd, Colm O'Dushlaine

Multiomics sequencing: how to maximise the data generated from valuable research samples Understanding the mechanisms of disease requires complex data ranging from genomic data through to transcriptomics, epigenetics and proteomics. Legacy multiomic approaches require multiple platforms and complex processes, which are incompatible with disease research that utilises scarce tissue samples. Download the brochure to learn how multiomic nanopore sequencing can generate data on: - Genomics - Bulk and single-cell transcriptomics - Epigenetics - Proteomics - All on a single platform. What you’re missing matters: maximise the data generated from your valuable research samples. Download Brochure https://lnkd.in/gE4m8-nS Brought to you by Oxford Nanopore Technologies #Multiomics #Genomics #DiseaseResearch

I am very pleased to share my latest, lead author pre-print manuscript on the computational modeling and simulation of the uromicrobiome from 19 patients with recurrent UTIs. My colleagues and I leveraged a precision medicine approach by sequencing patient-specific metatranscriptomes (data on microbiome organism diversity and gene expression), to reconstruct and simulate models for each of the 19 women involved in the study: https://lnkd.in/dvGSspDb A major take away is that the E. coli which causes UTI symptoms, appears to have "friends" in this systems which support recurrence and resistance against typical treatment options. Through modeling microbial community metabolism, we also identified specific metabolites cross-feeding between E. coli and other pathogens which likely exacerbate pathology. Most interesting, these metabolites can act as future precision medicine targets to mitigate UTI pathology, without the application of antibiotics. I am grateful that this exciting precision medicine project was funded by Deutsche Forschungsgemeinschaft (DFG) - German Research Foundation Cluster of Excellence: 'Precision Medicine in Chronic Inflammation' with additional funding through Kiel University's Young Scientist Program to expand data on our cohort. #UTI #precisionmedicine #systemsbiology #microbiome #computationalbiology