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arXiv 2024年

作者： Lee, Junhyeok Oh, Yujin Lee, Dahyoun Joh, Hyon Keun Sohn, Chul-Ho Baik, Sung Hyun Jung, Cheol Kyu Park, Jung Hyun Choi, Kyu Sung Kim, Byung-Hoon Ye, Jong Chul Interdisciplinary Program in Cancer Biology Seoul National University College of Medicine Seoul Korea Republic of Harvard Medical School BostonMA United States Department of Biomedical Systems Informatics Yonsei University College of Medicine Seoul Korea Republic of Department of Medicine The Graduate School of Yonsei University Seoul Korea Republic of Department of Radiology Seoul National University College of Medicine Seoul Korea Republic of Department of Radiology Seoul National University Hospital Seoul Korea Republic of Department of Radiology Seoul National University Bundang Hospital Seongnam Korea Republic of Department of Radiology Seoul Metropolitan Government Seoul National University Boramae Medical Center Seoul Korea Republic of Department of Psychiatry Yonsei University College of Medicine Seoul Korea Republic of Institute of Behavioral Sciences in Medicine Yonsei University College of Medicine Seoul Korea Republic of Daejeon Korea Republic of

Acute ischemic stroke (AIS) requires time-critical management, with hours of delayed intervention leading to an irreversible disability of the patient. Since diffusion weighted imaging (DWI) using the magnetic resonance image (MRI) plays a crucial role in the detection of AIS, automated prediction of AIS from DWI has been a research topic of clinical importance. While text radiology reports contain the most relevant clinical information from the image findings, the difficulty of mapping across different modalities has limited the factuality of conventional direct DWI-to-report generation methods. Here, we propose paired image-domain retrieval and text-domain augmentation (PIRTA), a cross-modal retrieval-augmented generation (RAG) framework for providing clinician-interpretative AIS radiology reports with improved factuality. PIRTA mitigates the need for learning cross-modal mapping, which poses difficulty in image-to-text generation, by casting the cross-modal mapping problem as an in-domain retrieval of similar DWI images that have paired ground-truth text radiology reports. By exploiting the retrieved radiology reports to augment the report generation process of the query image, we show by experiments with extensive in-house and public datasets that PIRTA can accurately retrieve relevant reports from 3D DWI images. This approach enables the generation of radiology reports with significantly higher accuracy compared to direct image-to-text generation using state-of-the-art multimodal language models. Copyright © 2024, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

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Democratising Knowledge Representation with BioCypher

arXiv

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arXiv 2022年

作者： Lobentanzer, Sebastian Aloy, Patrick Baumbach, Jan Bohar, Balazs Charoentong, Pornpimol Danhauser, Katharina Doğan, Tunca Dreo, Johann Dunham, Ian Fernandez-Torras, Adrià Gyori, Benjamin M. Hartung, Michael Hoyt, Charles Tapley Klein, Christoph Korcsmaros, Tamas Maier, Andreas Mann, Matthias Ochoa, David Pareja-Lorente, Elena Popp, Ferdinand Preusse, Martin Probul, Niklas Schwikowski, Benno Sen, Bünyamin Strauss, Maximilian T. Turei, Denes Ulusoy, Erva Heidrun Wodke, Judith Andrea Saez-Rodriguez, Julio Heidelberg University Faculty of Medicine Heidelberg University Hospital Institute for Computational Biomedicine Bioquant Heidelberg Germany The Barcelona Institute of Science and Technology Catalonia Barcelona Spain Catalonia Barcelona Spain Institute for Computational Systems Biology University of Hamburg Germany Earlham Institute Norwich United Kingdom Biological Research Centre Szeged Hungary Heidelberg University Im Neuenheimer Feld 267 Heidelberg69120 Germany Im Neuenheimer Feld 460 Heidelberg69120 Germany Department of Pediatrics Dr. von Hauner Children’s Hospital University Hospital LMU Munich Germany Biological Data Science Lab Department of Computer Engineering Hacettepe University Ankara Turkey Department of Bioinformatics Graduate School of Health Sciences Hacettepe University Ankara Turkey Computational Systems Biomedicine Lab Department of Computational Biology Institut Pasteur Université Paris Cité Paris France Bioinformatics and Biostatistics Hub Institut Pasteur Université Paris Cité Paris France Wellcome Genome Campus Cambridgeshire HinxtonCB10 1SD United Kingdom Open Targets Wellcome Genome Campus Cambridgeshire HinxtonCB10 1SD United Kingdom Laboratory of Systems Pharmacology Harvard Medical School Boston United States Imperial College London London United Kingdom Quadram Institute Bioscience Norwich United Kingdom Proteomics Program Novo Nordisk Foundation Centre for Protein Research University of Copenhagen Copenhagen Denmark Department of Proteomics and Signal Transduction Max Planck Institute of Biochemistry Martinsried Germany Im Neuenheimer Feld 460 Heidelberg69120 Germany Neuherberg Germany Medical Informatics Laboratory University Medicine Greifswald Germany

Standardising the representation of biomedical knowledge among all researchers is an insurmountable task, hindering the effectiveness of many computational methods. To facilitate harmonisation and interoperability despite this fundamental challenge, we propose to standardise the framework of knowledge graph creation instead. We implement this standardisation in BioCypher, a FAIR (findable, accessible, interoperable, reusable) framework to transparently build biomedical knowledge graphs while preserving provenances of the source data. Mapping the knowledge onto biomedical ontologies helps to balance the needs for harmonisation, human and machine readability, and ease of use and accessibility to nonspecialist researchers. We demonstrate the usefulness of this framework on a variety of use cases, from maintenance of task-specific knowledge stores, to interoperability between biomedical domains, to on-demand building of task-specific knowledge graphs for federated learning. BioCypher (https://***) frees up valuable developer time;we encourage further development and usage by the community. © 2022, CC BY-NC-ND.

关键词： Ontology

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Geographic and age variations in mutational processes in colorectal cancer

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Nature 2025年 1-11页

作者： Marcos Díaz-Gay Mariya Kazachkova Ammal Abbasi Christopher D. Steele Raviteja Vangara Erik N. Bergstrom Azhar Khandekar Burçak Otlu Ludmil B. Alexandrov Wellington dos Santos Sergey Senkin Behnoush Abedi-Ardekani Ana Carolina de Carvalho Thomas Cattiaux Ricardo Cortez Cardoso Penha Valérie Gaborieau Priscilia Chopard Sandra Perdomo Paul Brennan Sarah Moody Jingwei Wang Stephen Fitzgerald Saamin Cheema Calli Latimer Jon W. Teague Laura Humphreys Michael R. Stratton Christine Carreira Anush Mukeriya David Zaridze Riley Cox Monique Albert Larry Phouthavongsy Steven Gallinger Reza Malekzadeh Ahmadreza Niavarani Marko Miladinov Katarina Erić Sasa Milosavljevic Suleeporn Sangrajrang Maria Paula Curado Samuel Aguiar Rui Manuel Reis Monise Tadin Reis Luis Gustavo Romagnolo Denise Peixoto Guimarães Ivana Holcatova Jaroslav Kalvach Carlos Alberto Vaccaro Tamara Alejandra Piñero Beata Świątkowska Jolanta Lissowska Katarzyna Roszkowska-Purska Antonio Huertas-Salgado Tatsuhiro Shibata Satoshi Shiba Surasak Sangkhathat Taned Chitapanarux Gholamreza Roshandel Patricia Ashton-Prolla Daniel C. Damin Francine Hehn de Oliveira Trevor D. Lawley Department of Cellular and Molecular Medicine University of California San Diego La Jolla CA USA Department of Bioengineering University of California San Diego La Jolla CA USA Moores Cancer Center University of California San Diego La Jolla CA USA Digital Genomics Group Structural Biology Program Spanish National Cancer Research Center (CNIO) Madrid Spain Biomedical Sciences Graduate Program University of California San Diego La Jolla CA USA Division of Cancer Epidemiology and Genetics National Cancer Institute Bethesda MD USA Department of Health Informatics Graduate School of Informatics Middle East Technical University Ankara Turkey Sanford Stem Cell Institute University of California San Diego La Jolla CA USA Genomic Epidemiology Branch International Agency for Research on Cancer (IARC/WHO) Lyon France Cancer Ageing and Somatic Mutation Wellcome Sanger Institute Cambridge UK Evidence Synthesis and Classification Branch International Agency for Research on Cancer (IARC/WHO) Lyon France Clinical Epidemiology N. N. Blokhin National Medical Research Centre of Oncology Moscow Russia Ontario Tumour Bank Ontario Institute for Cancer Research Toronto Ontario Canada Centre for Biodiversity Genomics University of Guelph Guelph Ontario Canada Lunenfeld-Tanenbaum Research Institute Sinai Health System Toronto Ontario Canada Digestive Oncology Research Center Digestive Disease Research Institute Tehran University of Medical Sciences Tehran Iran Clinic for Digestive Surgery—First Surgical Clinic University Clinical Centre of Serbia Belgrade Serbia Department of Pathology University Clinical Centre of Serbia Belgrade Serbia International Organization for Cancer Prevention and Research Belgrade Serbia National Cancer Institute Bangkok Thailand Department of Epidemiology A. C. Camargo Cancer Center Sao Paulo Brazil Colon Cancer Reference Center A. C. Camargo Cancer Center Sao Paulo Brazil Molecular Oncology Research Center Barretos Cancer Hospital

Incidence rates of colorectal cancer vary geographically and have changed over time1. Notably, in the past two decades, the incidence of early-onset colorectal cancer, which affects individuals below 50 years of age, has doubled in many countries2,3,4,5. The reasons for this increase are unknown. Here we investigate whether mutational processes contribute to geographic and age-related differences by examining 981 colorectal cancer genomes from 11 countries. No major differences were found in microsatellite-unstable cancers, but variations in mutation burden and signatures were observed in the 802 microsatellite-stable cases. Multiple signatures, most with unknown aetiologies, exhibited varying prevalence in Argentina, Brazil, Colombia, Russia and Thailand, indicating geographically diverse levels of mutagenic exposure. Signatures SBS88 and ID18, caused by the bacteria-produced mutagen colibactin6,7, had higher mutation loads in countries with higher colorectal cancer incidence rates. SBS88 and ID18 were also enriched in early-onset colorectal cancers, being 3.3 times more common in individuals who were diagnosed before 40 years of age than in those over 70 years of age, and were imprinted early during colorectal cancer development. Colibactin exposure was further linked toAPCdriver mutations, with ID18 being responsible for about 25% ofAPCdriver indels in colibactin-positive cases. This study reveals geographic and age-related variations in colorectal cancer mutational processes, and suggests that mutagenic exposure to colibactin-producing bacteria in early life may contribute to the increasing incidence of early-onset colorectal cancer.

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scDrug+: predicting drug-responses using single-cell transcriptomics and molecular structure

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Biomedicine & pharmacotherapy = Biomedecine & pharmacotherapie 2024年 177卷 117070页

作者： Yih-Yun Sun Chiao-Yu Hsieh Jian-Hung Wen Tzu-Yang Tseng Jia-Hsin Huang Yen-Jen Oyang Hsuan-Cheng Huang Hsueh-Fen Juan Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University Taiwan Taiwan AI Labs Taipei 10351 Taiwan. Taiwan AI Labs Taipei 10351 Taiwan Institute of Biomedical Informatics National Yang Ming Chiao Tung University Taipei 11221 Taiwan. Department of Life Science National Taiwan University Taipei 106 Taiwan. Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University Taiwan. Institute of Biomedical Informatics National Yang Ming Chiao Tung University Taipei 11221 Taiwan. Electronic address: hsuancheng@nycu.edu.tw. Department of Life Science National Taiwan University Taipei 106 Taiwan Center for Computational and Systems Biology National Taiwan University Taipei 106 Taiwan Center for Advanced Computing and Imaging in Biomedicine National Taiwan University Taipei 106 Taiwan. Electronic address: yukijuan@ntu.edu.tw.

Predicting drug responses based on individual transcriptomic profiles holds promise for refining prognosis and advancing precision medicine. Although many studies have endeavored to predict the responses of known drugs to novel transcriptomic profiles, research into predicting responses for newly discovered drugs remains sparse. In this study, we introduce scDrug+, a comprehensive pipeline that seamlessly integrates single-cell analysis with drug-response prediction. Importantly, scDrug+ is equipped to predict the response of new drugs by analyzing their molecular structures. The open-source tool is available as a Docker container, ensuring ease of deployment and reproducibility. It can be accessed at https://***/ailabstw/scDrugplus.

关键词： Drug-responses Machine learning Precision medicine Single-cell transcriptomics

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Spatial and temporal dynamics of ATP synthase from mitochondria toward the cell surface

Communications biology

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Communications biology 2023年第1期6卷 427页

作者： Yi-Wen Chang T Tony Yang Min-Chun Chen Y-Geh Liaw Chieh-Fan Yin Xiu-Qi Lin-Yan Ting-Yu Huang Jen-Tzu Hou Yi-Hsuan Hung Chia-Lang Hsu Hsuan-Cheng Huang Hsueh-Fen Juan Department of Life Science Institute of Molecular and Cellular Biology National Taiwan University Taipei 106 Taiwan. Department of Electrical Engineering National Taiwan University Taipei 106 Taiwan. Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University Taipei 106 Taiwan. Department of Medical Research National Taiwan University Hospital Taipei 100 Taiwan. Institute of Biomedical Informatics National Yang Ming Chiao Tung University Taipei 112 Taiwan. hsuancheng@nycu.edu.tw. Department of Life Science Institute of Molecular and Cellular Biology National Taiwan University Taipei 106 Taiwan. yukijuan@ntu.edu.tw. Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University Taipei 106 Taiwan. yukijuan@ntu.edu.tw. Center for Computational and Systems Biology National Taiwan University Taipei 106 Taiwan. yukijuan@ntu.edu.tw.

Ectopic ATP synthase complex (eATP synthase), located on cancer cell surface, has been reported to possess catalytic activity that facilitates the generation of ATP in the extracellular environment to establish a suitable microenvironment and to be a potential target for cancer therapy. However, the mechanism of intracellular ATP synthase complex transport remains unclear. Using a combination of spatial proteomics, interaction proteomics, and transcriptomics analyses, we find ATP synthase complex is first assembled in the mitochondria and subsequently delivered to the cell surface along the microtubule via the interplay of dynamin-related protein 1 (DRP1) and kinesin family member 5B (KIF5B). We further demonstrate that the mitochondrial membrane fuses to the plasma membrane in turn to anchor ATP syntheses on the cell surface using super-resolution imaging and real-time fusion assay in live cells. Our results provide a blueprint of eATP synthase trafficking and contribute to the understanding of the dynamics of tumor progression.

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Inferring spatial transcriptomics markers from whole slide images to characterize metastasis-related spatial heterogeneity of colorectal tumors: A pilot study

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Journal of Pathology informatics 2023年 14卷 100308-100308页

作者： Fatemi, Michael Feng, Eric Sharma, Cyril Azher, Zarif Goel, Tarushii Ramwala, Ojas Palisoul, Scott M. Barney, Rachael E. Perreard, Laurent Kolling, Fred W. Salas, Lucas A. Christensen, Brock C. Tsongalis, Gregory J. Vaickus, Louis J. Levy, Joshua J. Department of Computer Science University of Virginia Charlottesville VA United States Thomas Jefferson High School for Science and Technology Alexandria VA United States Department of Computer Science Purdue University West Lafayette IN United States Department of Computer Science Massachusetts Institute of Technology Cambridge MA United States Department of Biomedical Informatics and Medical Education University of Washington Seattle WA United States Emerging Diagnostic and Investigative Technologies Department of Pathology and Laboratory Medicine Dartmouth Health NH United States Dartmouth Cancer Center NH United States Department of Epidemiology Dartmouth College Geisel School of Medicine Hanover NH United States Department of Molecular and Systems Biology Dartmouth College Geisel School of Medicine Hanover NH United States Integrative Neuroscience at Dartmouth (IND) graduate program Dartmouth College Geisel School of Medicine Hanover NH United States Department of Community and Family Medicine Dartmouth College Geisel School of Medicine Hanover NH United States Department of Dermatology Dartmouth Health NH United States Program in Quantitative Biomedical Sciences Dartmouth College Geisel School of Medicine Hanover NH United States

Over 150 000 Americans are diagnosed with colorectal cancer (CRC) every year, and annually over 50 000 individuals will die from CRC, necessitating improvements in screening, prognostication, disease management, and therapeutic options. Tumor metastasis is the primary factor related to the risk of recurrence and mortality. Yet, screening for nodal and distant metastasis is costly, and invasive and incomplete resection may hamper adequate assessment. Signatures of the tumor-immune microenvironment (TIME) at the primary site can provide valuable insights into the aggressiveness of the tumor and the effectiveness of various treatment options. Spatially resolved transcriptomics technologies offer an unprecedented characterization of TIME through high multiplexing, yet their scope is constrained by cost. Meanwhile, it has long been suspected that histological, cytological, and macroarchitectural tissue characteristics correlate well with molecular information (e.g., gene expression). Thus, a method for predicting transcriptomics data through inference of RNA patterns from whole slide images (WSI) is a key step in studying metastasis at scale. In this work, we collected tissue from 4 stage-III (pT3) matched colorectal cancer patients for spatial transcriptomics profiling. The Visium spatial transcriptomics (ST) assay was used to measure transcript abundance for 17 943 genes at up to 5000 55-micron (i.e., 1–10 cells) spots per patient sampled in a honeycomb pattern, co-registered with hematoxylin and eosin (H&E) stained WSI. The Visium ST assay can measure expression at these spots through tissue permeabilization of mRNAs, which are captured through spatially (i.e., x–y positional coordinates) barcoded, gene specific oligo probes. WSI subimages were extracted around each co-registered Visium spot and were used to predict the expression at these spots using machine learning models. We prototyped and compared several convolutional, transformer, and graph convolutional neural

关键词： Colorectal cancer Deep learning Graph neural network Histomorphology Spatial transcriptomics Transformers

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Erratum for Verzi et al., Intestinal Master Transcription Factor CDX2 Controls Chromatin Access for Partner Transcription Factor Binding

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Mol Cell Biol 2014年第2期35卷 496-496页

作者： Michael P. Verzi Hyunjin Shin Adrianna K. San Roman X. Shirley Liu Ramesh A. Shivdasani Department of Medical Oncology Dana-Farber Cancer Institute Boston Massachusetts USA Departments of Medicine Brigham and Women's Hospital and Harvard Medical School Boston Massachusetts USA Department of Genetics Rutgers The State University of New Jersey New Brunswick New Jersey USA Department of Biostatistics and Computational Biology Dana-Farber Cancer Institute Boston Massachusetts USA Graduate Program in Biological and Biomedical Sciences Harvard Medical School Boston Massachusetts USA

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Spatial and multiomics analysis of human and mouse lung adenocarcinoma precursors reveals TIM-3 as a putative target for precancer interception

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Cancer cell 2025年

作者： Bo Zhu Pingjun Chen Muhammad Aminu Jian-Rong Li Junya Fujimoto Yanhua Tian Lingzhi Hong Hong Chen Xin Hu Chenyang Li Natalie Vokes Andre L Moreira Don L Gibbons Luisa M Solis Soto Edwin Roger Parra Cuentas Ou Shi Songhui Diao Jie Ye Frank R Rojas Eduardo Vilar Anirban Maitra Ken Chen Nicolas Navin Monique Nilsson Beibei Huang Simon Heeke Jianhua Zhang Cara L Haymaker Vamsidhar Velcheti Daniel H Sterman Veena Kochat William I Padron Ludmil B Alexandrov Zhubo Wei Xiuning Le Linghua Wang Junya Fukuoka J Jack Lee Ignacio I Wistuba Harvey I Pass Mark Davis Samir Hannash Chao Cheng Steven Dubinett Avrum Spira Kunal Rai Scott M Lippman P Andrew Futreal John V Heymach Alexandre Reuben Jia Wu Jianjun Zhang Department of Thoracic/Head and Neck Medical Oncology The University of Texas MD Anderson Cancer Center Houston TX USA Department of Genomic Medicine The University of Texas MD Anderson Cancer Center Houston TX USA. Institute for Data Science in Oncology The University of Texas MD Anderson Cancer Center Houston TX USA Department of Translational Molecular Pathology The University of Texas MD Anderson Cancer Center Houston TX USA. Department of Imaging Physics The University of Texas MD Anderson Cancer Center Houston TX USA. Department of Medicine Baylor College of Medicine Houston TX USA. Clinical Research Center in Hiroshima Hiroshima University Hospital Hiroshima Japan. Department of Thoracic/Head and Neck Medical Oncology The University of Texas MD Anderson Cancer Center Houston TX USA. Department of Pathology NYU Langone Health New York NY USA. Department of Clinical Cancer Prevention The University of Texas MD Anderson Cancer Center Houston TX USA. Department of Translational Molecular Pathology and Sheikn Ahmed Center for Pancreatic Cancer Research The University of Texas MD Anderson Cancer Center Houston TX USA. Department of Bioinformatics and Computational Biology The University of Texas MD Anderson Cancer Center Houston TX USA. Department of Systems Biology The University of Texas MD Anderson Cancer Center Houston TX USA. Department of Cancer Systems Imaging The University of Texas MD Anderson Cancer Center Houston TX USA. Department of Medicine NYU Grossman School of Medicine New York NY USA. Department of Medicine NYU Grossman School of Medicine New York NY USA Cardiothoracic Surgery NYU Grossman School of Medicine New York NY USA. Department of Cellular and Molecular Medicine UC San Diego La Jolla CA USA. Department of Pathology Informatics Nagasaki University Graduate School of Biomedical Sciences Nagasaki Japan. Department of Biostatistics The University of Texas MD Anderson Cancer Center Houston TX USA. Department of Car

How tumor microenvironment shapes lung adenocarcinoma (LUAD) precancer evolution remains poorly understood. Spatial immune profiling of 114 human LUAD and LUAD precursors reveals a progressive increase of adaptive response and a relative decrease of innate immune response as LUAD precursors progress. The immune evasion features align the immune response patterns at various stages. TIM-3-high features are enriched in LUAD precancers, which decrease in later stages. Furthermore, single-cell RNA sequencing (scRNA-seq) and spatial immune and transcriptomics profiling of LUAD and LUAD precursor specimens from 5 mouse models validate high TIM-3 features in LUAD precancers. In vivo TIM-3 blockade at precancer stage, but not at advanced cancer stage, decreases tumor burden. Anti-TIM-3 treatment is associated with enhanced antigen presentation, T cell activation, and increased M1/M2 macrophage ratio. These results highlight the coordination of innate and adaptive immune response/evasion during LUAD precancer evolution and suggest TIM-3 as a potential target for LUAD precancer interception.

关键词： TIM-3 cancer prevention imaging mass cytometry immune landscape lung adenocarcinoma evolution precancer spatial single cell stage dependent

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SpliceAPP: an interactive web server to predict splicing errors arising from human mutations

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BMC genomics 2024年第1期25卷 600页

作者： Ang-Chu Huang Jia-Ying Su Yu-Jen Hung Hung-Lun Chiang Yi-Ting Chen Yen-Tsung Huang Chen-Hsin Albert Yu Hsin-Nan Lin Chien-Ling Lin Institute of Molecular Biology Academia Sinica No. 128 Sec. 2 Academia Road Nangang District Taipei City 115014 Taiwan. Genome and Systems Biology Degree Program Academia Sinica and National Taiwan University Taipei Taiwan. Institute of Statistical Science Academia Sinica Taipei Taiwan. Bioinformatics Program International Graduate Program Academia Sinica Taipei Taiwan. Institute of Biomedical Informatics National Yang Ming Chiao Tung University Taipei Taiwan. Institute of Molecular Biology Academia Sinica No. 128 Sec. 2 Academia Road Nangang District Taipei City 115014 Taiwan. arith@gate.sinica.edu.tw. Institute of Molecular Biology Academia Sinica No. 128 Sec. 2 Academia Road Nangang District Taipei City 115014 Taiwan. mbcllin@gate.sinica.edu.tw. Genome and Systems Biology Degree Program Academia Sinica and National Taiwan University Taipei Taiwan. mbcllin@gate.sinica.edu.tw. Bioinformatics Program International Graduate Program Academia Sinica Taipei Taiwan. mbcllin@gate.sinica.edu.tw.

BACKGROUND:Splicing variants are a major class of pathogenic mutations, with their severity equivalent to nonsense mutations. However, redundant and degenerate splicing signals hinder functional assessments of sequence variations within introns, particularly at branch sites. We have established a massively parallel splicing assay to assess the impact on splicing of 11,191 disease-relevant variants. Based on the experimental results, we then applied regression-based methods to identify factors determining splicing decisions and their respective weights. RESULTS:Our statistical modeling is highly sensitive, accurately annotating the splicing defects of near-exon intronic variants, outperforming state-of-the-art predictive tools. We have incorporated the algorithm and branchpoint information into a web-based tool, SpliceAPP, to provide an interactive application. This user-friendly website allows users to upload any genetic variants with genome coordinates (e.g., chr15 74,687,208 A G), and the tool will output predictions for splicing error scores and evaluate the impact on nearby splice sites. Additionally, users can query branch site information within the region of interest. CONCLUSIONS:In summary, SpliceAPP represents a pioneering approach to screening pathogenic intronic variants, contributing to the development of precision medicine. It also facilitates the annotation of splicing motifs. SpliceAPP is freely accessible using the link https://***/SpliceAPP . Source code can be downloaded at https://***/hsinnan75/SpliceAPP .

关键词： Human mutations LASSO regression RNA splicing Splicing variant prediction

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A systems biology approach uncovers novel disease mechanisms in age-related macular degeneration

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Cell Genomics 2023年第6期3卷 100302-100302页

作者： Orozco, Luz D. Owen, Leah A. Hofmann, Jeffrey Stockwell, Amy D. Tao, Jianhua Haller, Susan Mukundan, Vineeth T. Clarke, Christine Lund, Jessica Sridhar, Akshayalakshmi Mayba, Oleg Barr, Julie L. Zavala, Rylee A. Graves, Elijah C. Zhang, Charles Husami, Nadine Finley, Robert Au, Elizabeth Lillvis, John H. Farkas, Michael H. Shakoor, Akbar Sherva, Richard Kim, Ivana K. Kaminker, Joshua S. Townsend, Michael J. Farrer, Lindsay A. Yaspan, Brian L. Chen, Hsu-Hsin DeAngelis, Margaret M. Department of Bioinformatics and Computational Biology Genentech South San Francisco 94080 CA United States Department of Ophthalmology and Visual Sciences University of Utah School of Medicine The University of Utah Salt Lake City 84132 UT United States Department of Population Health Sciences University of Utah School of Medicine The University of Utah Salt Lake City 84132 UT United States Department of Obstetrics and Gynecology University of Utah School of Medicine The University of Utah Salt Lake City 84132 UT United States Department of Ophthalmology Ross Eye Institute Jacobs School of Medicine and Biomedical Sciences State University of New York University at Buffalo Buffalo 14203 NY United States Department of Pathology Genentech South San Francisco 94080 CA United States Department of Human Genetics Genentech South San Francisco 94080 CA United States Departments of Microchemistry Proteomics and Lipidomics Genentech South San Francisco 94080 CA United States Department of Human Pathobiology & OMNI Reverse Translation Genentech South San Francisco 94080 CA United States Neuroscience Graduate Program Jacobs School of Medicine and Biomedical Sciences State University of New York University at Buffalo Buffalo 14203 NY United States Department of Biochemistry Jacobs School of Medicine and Biomedical Sciences State University of New York University at Buffalo Buffalo 14203 NY United States Veterans Administration Western New York Healthcare System Buffalo 14212 NY United States Department of Medicine Biomedical Genetics Boston University School of Medicine Boston 02118 MA United States Retina Service Massachusetts Eye & Ear Department of Ophthalmology Harvard Medical School Boston 02114 MA United States Genetics Genomics and Bioinformatics Graduate Program Jacobs School of Medicine and Biomedical Sciences State University of New York University at Buffalo Buffalo 14203 NY United States

Age-related macular degeneration (AMD) is a leading cause of blindness, affecting 200 million people worldwide. To identify genes that could be targeted for treatment, we created a molecular atlas at different stages of AMD. Our resource is comprised of RNA sequencing (RNA-seq) and DNA methylation microarrays from bulk macular retinal pigment epithelium (RPE)/choroid of clinically phenotyped normal and AMD donor eyes (n = 85), single-nucleus RNA-seq (164,399 cells), and single-nucleus assay for transposase-accessible chromatin (ATAC)-seq (125,822 cells) from the retina, RPE, and choroid of 6 AMD and 7 control donors. We identified 23 genome-wide significant loci differentially methylated in AMD, over 1,000 differentially expressed genes across different disease stages, and an AMD Müller state distinct from normal or gliosis. Chromatin accessibility peaks in genome-wide association study (GWAS) loci revealed putative causal genes for AMD, including HTRA1 and C6orf223. Our systems biology approach uncovered molecular mechanisms underlying AMD, including regulators of WNT signaling, FRZB and TLE2, as mechanistic players in disease. © 2023 The Authors

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