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Solanaceae pangenomes are coming of graphical age to bring heritability back

aBIOTECH 2022年第4期3卷 233-236页

作者： Björn Usadel Institute for Biological Data Science CEPLASHeinrich Heine UniversityDüsseldorfGermany IBG-4 Bioinformatics BioSCForschungszentrum JülichJülichGermany

Two recent articles describe a pangenome of potato and a graph-based pangenome for tomato,*** latter improves our understanding of the tomato genomics architecture even further and the use of this graph-based pangenom... 详细信息

关键词： Tomato Potato Pangenome Graph-based pangenome Heritability

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A Hydrodynamic Analysis of Water System in Dadahup Swamp Irrigation Area 1

A Hydrodynamic Analysis of Water System in Dadahup Swamp Irr...

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1st International Conference on Computer science and Artificial Intelligence, ICCSAI 2021

作者： Purboseno, Sentot Suparyanto, Teddy Hidayat, Alam Ahmad Pardamean, Bens Institute of Agriculture STIPER Department of Agricultural Engineering Yogyakarta55283 Indonesia Bina Nusantara University Bioinformatics and Data Science Research Center Jakarta11480 Indonesia Bina Nusantara University Bioinformatics and Data Science Research Center BINUS Graduate Program-Master of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9781728133331

Dadahup Swamp Irrigation Area (DIR) in Kapuas Regency, Central Kalimantan is developed for agricultural activities to provide food security after the pandemic. The water system consists of various channels, gates, and a flood embankment structure, which is also connected to three different rivers: Barito River, Mangkatif River, and Kapuas Mumpung River. To quantify flood risk assessments in the area, we perform a hydrodynamic analysis using the unsteady flow method via HEC-RAS software version 4.1.0. We use geometrical profiles from topography measurements and the outer boundary of the Dadahup DIR as input for the hydrologic model. Our results estimate the maximum surface water level from both cross and longitudinal sections in the channels and find that several points near the flood embankment area are prone to flood events. Our modeling approach provides a preliminary assessment for the local government to formulate a flood mitigation plan and policy in the area. © 2021 IEEE.

关键词： Rivers

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Transcriptome analysis of archived tumors by Visium, GeoMx DSP, and Chromium reveals patient heterogeneity

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Nature Communications 2025年第1期16卷 1-18页

作者： Dong, Yixing Saglietti, Chiara Bayard, Quentin Espin Perez, Almudena Carpentier, Sabrina Buszta, Daria Tissot, Stephanie Dubois, Rémy Kamburov, Atanas Kang, Senbai Haignere, Carla Sarkis, Rita Andre, Sylvie Alexandre Gaveta, Marina Lopez Lastra, Silvia Piazzon, Nathalie Santos, Rita von Loga, Katharina Hoffmann, Caroline Coukos, George Peters, Solange Soumelis, Vassili Durand, Eric Yves de Leval, Laurence Gottardo, Raphael Homicsko, Krisztian Madissoon, Elo Biomedical Data Science Center Lausanne University Hospital University of Lausanne Lausanne Switzerland Institute of Pathology Department of Laboratory Medicine and Pathology Lausanne University Hospital and University of Lausanne Lausanne Switzerland Owkin Paris France Department of Oncology Lausanne University Hospital Swiss Cancer Center Leman Lausanne Switzerland Ludwig Insitute for Cancer Research Lausanne Switzerland Agora Translational Research Center Lausanne Switzerland Swiss Institute of Bioinformatics Lausanne Switzerland School of Life Sciences Ecole Polytechnique Fédérale de Lausanne Switzerland

Recent advancements in probe-based, full-transcriptome technologies for FFPE tissues, such as Visium CytAssist, Chromium Flex, and GeoMx DSP, enable analysis of archival samples, facilitating the generation of data from extensive cohorts. However, these methods can be labor-intensive and costly, requiring informed selection based on research objectives. We compare these methods on FFPE tumor samples in Breast, NSCLC and DLBCL showing 1) good-quality, highly reproducible data from all methods;2) GeoMx data containing cell mixtures despite marker-based preselection;3) Visium and Chromium outperform GeoMx in discovering tumor heterogeneity and potential drug targets. We recommend the use of Visium and Chromium for high-throughput and discovery projects, while the manually more challenging GeoMx platform with targeted regions remains valuable for specialized questions. © The Author(s) 2025.

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Deciphering the unique dynamic activation pathway in a G protein-coupled receptor enables unveiling biased signaling and identifying cryptic allosteric sites in conformational intermediates

arXiv

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arXiv 2025年

作者： Fan, Jigang Zhu, Chunhao Lan, Xiaobing Zhuang, Haiming Li, Mingyu Zhang, Jian Lu, Shaoyong Medicinal Chemistry and Bioinformatics Center Shanghai Jiao Tong University School of Medicine Shanghai200025 China College of Pharmacy Ningxia Medical University Ningxia Hui Autonomous Region Yinchuan750004 China Center for Data Science Peking University Beijing100871 China

Neurotensin receptor 1 (NTSR1), a member of the Class A G protein-coupled receptor superfamily, plays an important role in modulating dopaminergic neuronal activity and eliciting opioid-independent analgesia. Recent studies suggest that promoting β-arrestin-biased signaling in NTSR1 may diminish drugs of abuse, such as psychostimulants, thereby offering a potential avenue for treating human addiction-related disorders. In this study, we utilized a novel computational and experimental approach that combined nudged elastic band-based molecular dynamics simulations, Markov state models, temporal communication network analysis, site-directed mutagenesis, and conformational biosensors, to explore the intricate mechanisms underlying NTSR1 activation and biased signaling. Our study reveals a dynamic stepwise transition mechanism and activated transmission network associated with NTSR1 activation. It also yields valuable insights into the complex interplay between the unique polar network, non-conserved ion locks, and aromatic clusters in NTSR1 signaling. Moreover, we identified a cryptic allosteric site located in the intracellular region of the receptor that exists in an intermediate state within the activation pathway. Collectively, these findings contribute to a more profound understanding of NTSR1 activation and biased signaling at the atomic level, thereby providing a potential strategy for the development of NTSR1 allosteric modulators in the realm of G protein-coupled receptor biology, biophysics, and medicine. © 2025, CC BY-NC-SA.

关键词： Markov processes

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Unveiling roles of non-coding RNAs in cancer through advanced technologies

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Quantitative Biology 2025年第4期13卷 e70005-e70005页

作者： Wang, Runhao Han, Leng Brown Center for Immunotherapy Indiana University School of Medicine Indianapolis IN United States Department of Biostatistics and Health Data Science Indiana University School of Medicine Indianapolis IN United States Center for Computational Biology and Bioinformatics Indiana University School of Medicine Indianapolis IN United States

Non-coding RNAs (ncRNAs) have emerged as key regulators in tumorigenesis. In this perspective, we briefly review the significance of ncRNA in cancer biology and highlight recent technological advancements in characterization of ncRNA in cancer research. Specifically, we discuss how these advanced approaches, such as Patho-DBiT, CRISPR screens, and snoKARR-seq, hold the potential to revolutionize ncRNA research by offering comprehensive insights into their spatial expression patterns and functional roles. © 2025 The Author(s). Quantitative Biology published by John Wiley & Sons Australia, Ltd on behalf of Higher Education Press.

关键词： cancer CRISPR screens ncRNAs patho-DBiT snoKARR-seq spatial transcriptomics

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Biomedical data and AI

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science China Life sciences 2025年第5期68卷 1536-1540页

作者： Hao Xu Shibo Zhou Zefeng Zhu Vincenzo Vitelli Liangyi Chen Ziwei Dai Ning Yang Luhua Lai Shengyong Yang Sergey Ovchinnikov Zhuoran Qiao Sirui Liu Chen Song Jianfeng Pei Han Wen Jianfeng Feng Yaoyao Zhang Zhengwei Xie Yang-Yu Liu Zhiyuan Li Fulai Jin Hao Li Mohammad Lotfollahi Xuegong Zhang Ge Yang Shihua Zhang Ge Gao Pulin Li Qi Liu Jing-Dong Jackie Han Peking-Tsinghua Center for Life Sciences (CLS) Academy for Advanced Interdisciplinary StudiesPeking University Center for Quantitative Biology (CQB) Academy for Advanced Interdisciplinary StudiesPeking University Peking University-Tsinghua University-National Institute of Biological Sciences Joint Graduate Program Academy for Advanced Interdisciplinary StudiesPeking University Department of Physics University of Chicago School of Life Sciences Southern University of Science and Technology Peking University Chengdu Academy for Advanced Interdisciplinary Biotechnologies College of Chemistry and Molecular Engineering Peking University Department of Biotherapy Cancer Center and State Key Laboratory of BiotherapyWest China HospitalSichuan University Department of Biology Massachusetts Institute of Technology Lambic Therapeutics Inc. Changping Laboratory Al for Science Institute Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence Fudan University Department of Obstetrics and Gynecology West China Second University HospitalSichuan University Peking University International Cancer Institute and Peking University-Yunnan Baiyao International Medical Institute and State Key Laboratory of Natural and Biomimetic Drugs Department of Molecular and Cellular PharmacologySchool of Pharmaceutical SciencesPeking University Health Science CenterPeking University Channing Division of Network Medicine Department of MedicineBrigham and Women's Hospital and Harvard Medical School Center for Artificial Intelligence and Modeling the Carl R.Woese Institute for Genomic BiologyUniversity of Illinois Urbana-Champaign Department of Genetics and Genome Sciences School of Medicine and Department of Computer and Data Sciences and Department of Population and Quantitative Health SciencesCase Western Reserve University Department of Biochemistry and Biophysics University of California Sanger Institute Department of Automation Tsinghua University State Key Laboratory of Multimodal Artificial Intelligence Systems I

The development of artificial intelligence(AI) and the mining of biomedical data complement each other. From the direct use of computer vision results to analyze medical images for disease screening, to now integrating biological knowledge into models and even accelerating the development of new AI based on biological discoveries, the boundaries of both are constantly expanding, and their connections are becoming closer.

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A bioinformatics Analysis and Homology 3D Modeling of Dihydroxy-orotate-dehydrogenase of Plasmodium ovale

A Bioinformatics Analysis and Homology 3D Modeling of Dihydr...

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2023 International Conference on Informatics, Multimedia, Cyber and Information Systems, ICIMCIS 2023

作者： Nirwantono, Rudi Assidqi, Khoirunnisa Hidayat, Alam Ahmad Pebrianto Trinugroho, Joko Pardamean, Bens Bina Nusantara University Faculty of Engineering Bioinformatics and Data Science Research Center Biotechnology Department Jakarta11480 Indonesia Bina Nusantara University Faculty of Engineering Food Biotechnology Research Center Biotechnology Department Jakarta11480 Indonesia Bina Nusantara University School of Computer Science Bioinformatics and Data Science Research Center Mathematics Departement Jakarta11480 Indonesia Bina Nusantara University Bioinformatics and Data Science Research Center Jakarta11480 Indonesia Bina Nusantara University Binus Graduate Program - Computer Science Program Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350307306

The infection of Plasmodium vivax is relatively less virulent than the deathliest Plasmodium falciparum. However, it still can lead to a fatal case and often induces recurring malaria due to dormant parasites in the liver. Thus, the research to study the drug to treat Plasmodium vivax is essential, where the enzyme dihydroorotate dehydrogenase (DHODH) has recently become a new drug target. However, the drug-enzyme interaction study has only recently been conducted in Plasmodium falciparum (pfDHODH) due to the lack of the 3D structure of enzyme DHODH from Plasmodium vivax that is crucial for the study. Therefore, this study aimed to perform the modelling study of Plasmodium vivax DHODH (PvDHODH) to create a 3D structure as a basis for drug-protein interaction study in upcoming studies. Sequence pvDHODH (Accession: SCO68359.1) was used in homology modelling using Modeller 10.2 with the crystal structure of DHODH from DHODH (Accession: 7KZY) as a template. Overall, the model generated from the homology modelling was considered a good model, and the 3D structure was close to the native state according to several parameters, including DOPE score, GA341 score, QMEAN4 value, 3D-1D score, ERRAT2 score, and ProSA score. Ramachandran plot also revealed that almost all amino acids were distributed in the desirable and allowed area (99.1%), and only 0.9% were in generously allowed regions. Superimposition of the model with the template also indicated that the model has an almost similar structure and amino acids positioning, including in the binding pocket and active site. Therefore, the model can be used for downstream analysis in drug-protein interaction studies. Nevertheless, some improvements can still be performed to upgrade the quality of the model by deleting unaligned residues on the C-terminal and realigning the residues at the doubting site. © 2023 IEEE.

关键词： Amino acids

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Deep learning for oil palm fruit ripeness classification with densenet 6

Deep learning for oil palm fruit ripeness classification wit...

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6th International Conference on Information Management and Technology, ICIMTech 2021

作者： Herman, Herman Cenggoro, Tjeng Wawan Susanto, Albert Pardamean, Bens Bina Nusantara University BINUS Graduate Program - Master of Computer Science Computer Science Department Jakarta11480 Indonesia Bina Nusantara University School of Computer Science Bioinformatics and Data Science Research Center Computer Science Department Jakarta11480 Indonesia Bina Nusantara University BINUS Graduate Program - Master of Computer Science Program Bioinformatics and Data Science Research Center Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9781665449373

Oil palm fruit farming is one of the most leading agriculture industries in the South East Asia region. Unfortunately, most of the harvesting method is still done through manual labor. Multiple research has been conducted to help farmers automatically detect the ripeness level of the oil palm fruit. We conducted an experiment of detecting the ripeness level of oil palm fruit by using one of the state-of-the-art computer vision approaches, which was deep learning with a Convolutional Neural Network (CNN). The dataset used consists of 7 levels of ripeness with 400 images of oil palm fruits. The models used in this test scenario were both AlexNet and DenseNet. The result of this study showed that DenseNet has been proven to outperform AlexNet by 8.5% in terms of accuracy and 8% in F1 score. © 2021 IEEE.

关键词： Computer vision

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Spatial Confidence Regions for Excursion Sets with False Discovery Rate Control

arXiv

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arXiv 2025年

作者： Ryu, Howon Maullin-Sapey, Thomas Schwartzman, Armin Davenport, Samuel Division of Biostatistics and Bioinformatics University of California San Diego San DiegoCA United States Big Data Institute Nuffield Department of Population Health University of Oxford Oxford United Kingdom School of Mathematics University of Bristol Bristol United Kingdom Halıcıoğlu Data Science Institute University of California San Diego San DiegoCA United States

Identifying areas where the signal is prominent is an important task in image analysis, with particular applications in brain mapping. In this work, we develop confidence regions for spatial excursion sets above and below a given level. We achieve this by treating the confidence procedure as a testing problem at the given level, allowing control of the False Discovery Rate (FDR). Methods are developed to control the FDR, separately for positive and negative excursions, as well as jointly over both. Furthermore, power is increased by incorporating a two-stage adaptive procedure. Simulation results with various signals show that our confidence regions successfully control the FDR under the nominal α level. We showcase our methods with an application to functional magnetic resonance imaging (fMRI) data from the Human Connectome Project illustrating the improvement in statistical power over existing approaches. © 2025, CC BY-NC-SA.

关键词： Image analysis

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A methodology for hierarchical classification of semantic answer types of questions

A methodology for hierarchical classification of semantic an...

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2020 SeMantic AnsweR Type Prediction Task, SMART 2020 at 2020 Semantic Web Challenge, ISWC 2020

作者： Ammar, Ammar Mehryar, Shervin Celebi, Remzi Department of Bioinformatics BiGCaT NUTRIM Maastricht University Netherlands University of Toronto Canada Institute of Data Science Maastricht University Maastricht Netherlands

Question answering systems have recently been integrated with many smart devices and search engines. Answer type prediction plays an important role in question answering systems as it can help filter irrelevant results and improve overall search and retrieval performance. Here, we present our approach for answer type prediction using the datasets provided for the International Semantic Web Conference (ISWC 2020) SMART Task Challenge. Predicting granular answer types for a question from a big knowledge graph is a greater challenge due to the large number of possible classes. Thus, we propose a 3-step approach to tackle the challenge task. We start with building a classifier that predicts the category of the types and build another classifier just for resource types. The latter model will predict the most general (frequent) type for each question, ignoring type hierarchy. We use a multi-class text classification algorithm built-in fastai library for these two models. The models’ accuracies are 0.95 and 0.73 for category and generic type classification respectively in the validation set (20% randomly chosen samples) of the DBPedia dataset. Next, we train a third classifier to find more specific types (sub-classes) for each question based on the previous general predicted types. We achieve 0.62 and 0.61 using NDCG@5 and NDCG@10 metrics respectively for the test set. Copyright © 2020 for this paper by its authors.

关键词： Search engines

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