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Worm Perturb-Seq: massively parallel whole-animal RNAi and RNA-seq

Nature Communications 2025年第1期16卷 1-21页

作者： Hefei Zhang Xuhang Li Shivani Nanda Albertha J. M. Walhout dongyuan Song Jingyi Jessica Li Onur Yukselen Alper Kucukural Manuel Garber Department of Systems Biology University of Massachusetts Chan Medical School Worcester MA USA Bioinformatics Interdepartmental Ph.D. Program University of California Los Angeles CA USA Department of Statistics and Data Science Department of Biostatistics Department of Computational Medicine and Department of Human Genetics University of California Los Angeles CA USA Via Scientific Inc. Cambridge MA USA Department of Genomics and Computational Biology University of Massachusetts Chan Medical School Worcester MA USA

Transcriptomes provide highly informative molecular phenotypes that, combined with gene perturbation, can connect genotype to phenotype. An ultimate goal is to perturb every gene and measure transcriptome changes, however, this is challenging, especially in whole animals. Here, we present ‘Worm Perturb-Seq (WPS)’, a method that provides high-resolution RNA-sequencing profiles for hundreds of replicate perturbations at a time in living animals. WPS introduces multiple experimental advances combining strengths of Caenhorhabditis elegans genetics and multiplexed RNA-sequencing with a novel analytical framework, EmpirdE. EmpirdE leverages the unique power of large transcriptomic datasets and improves statistical rigor by using gene-specific empirical null distributions to identify dEGs. We apply WPS to 103 nuclear hormone receptors (NHRs) and find a striking ‘pairwise modularity’ in which pairs of NHRs regulate shared target genes. We envision the advances of WPS to be useful not only for C. elegans, but broadly for other models, including human cells.

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Multi-objective design of synthetic biological circuits

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IFAC-PapersOnLine 2017年第1期50卷 9871-9876页

作者： Lormeau C. Rybiński M. Stelling J. Department of Biosystems Science and Engineering ETH Zurich SIB Swiss Institute of Bioinformatics Life Science Zurich Ph.D. program ”Systems Biology” Basel 4058 Switzerland Department of Biosystems Science and Engineering ETH Zurich SIB Swiss Institute of Bioinformatics Basel 4058 Switzerland

Computational methods enable the design of synthetic biological circuits demonstrating a specific dynamic behavior. Current methods are based on the assembly of parts characterized in different contexts, which often fail to operate as predicted when combined. Here we introduce a circuit design method that compensates for parts uncertainty by identifying circuit topologies whose behavior is robust to variations in parameters. Our heuristic topological filtering approach efficiently yields robust circuit designs in a Bayesian framework, and enables to reliably assess trade-offs between performance, robustness, and experimental feasibility, thus increasing the probability of success of circuit implementation. © 2017

关键词： Biocybernetics Computer-aided circuit design Multiobjective optimisations Robustness Synthetic biology

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bioinformatics

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IEEE Potentials 2004年第1期23卷 24-27页

作者： doom, Travis Raymer, Michael Krane, dan IEEE Wright State University United States Department of Computer Science and Engineering Graduate Faculty in Biomedical Sciences Dayton OH United States WSU Biomedical Sciences Ph.D. Program Forensic Bioinformatics Inc. United States Bioinformatics Research Group Computer Science Department Wright State University United States

No abstract available

关键词： bioinformatics Biology computing Biological information theory data analysis Computational biology Proteins Biochemistry Sequences Genomics Large-scale systems

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An integrative approach revealing the landscape of long noncoding RNAs in human brain

An integrative approach revealing the landscape of long nonc...

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2016 International Conference on Computational Science and Computational Intelligence, CSCI 2016

作者： Li, dan Yang, William Arthur, Carolyn Yoshigoe, Kenji Liu, Jun Tong, Weida Yang, Mary MidSouth Bioinformatics Center and Joint Bioinformatics Ph.D. Program University of Arkansas at Little Rock University of Arkansas for Medical Sciences 2801 S. University Avenue Little RockAR72204 United States Harvard T. H. Chan School of Public Health Harvard University One Oxford Street CambridgeMA02138 United States United States Food and Drug Adminstration National Center for Toxicological Research 3900 NCTR Road JeffersonAR72079 United States Department of Informatics Science MidSouth Bioinformatics Center and Joint Bioinformatics Ph.D. Program University of Arkansas at Little Rock University of Arkansas for Medical Sciences 2801 S. Univ. Avenue Little RockAR72204 United States

ISBN: (纸本)9781509055104

Long noncoding RNAs (lncRNAs) function as regulators and play critical roles in diverse biological processes, however, the majority of lncRNAs are not characterized, and their roles in regulation remain to be elucidated. Present RNA-seq assembly approaches are insufficient to identify complete full-length transcripts and often reveal excessive amount of single-exon lncRNAs, many of them tend to be the fragments of transcripts. Here we developed an integrated approach that combined the results from reference-guided and de novo assembly to systematically identify lncRNAs. Experiments on simulated data and real RNA-seq data showed that our method effectively yielded a more comprehensive set of lncRNAs. We applied the method on 299 human brain RNA-seq samples with a total of 4.5 billion raw reads. The results yield a comprehensive lncRNAs mapping in human brain, allowing discovery of novel lncRNAs and further function annotation. © 2016 IEEE.

关键词： RNA

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Connexins and deafness: From molecules to disease

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Seminars in Hearing 2006年第3期27卷 148-159页

作者： Azaiez, Hela Van Camp, Guy Smith, Richard J. H. Department of Otolaryngology-Head and Neck Surgery University of Iowa Hospitals and Clinics Iowa City IA United States Interdepartmental Ph.D. Program in Genetics University of Iowa Hospitals and Clinics Iowa City IA United States Department of Medical Genetics University of Antwerp Antwerp Belgium Department of Otolaryngology Iowa City IA 52240 120 Hawkins Drive United States

Gap junctions are important structures in cell communication. Mutations in connexins, the building blocks of gap junctions, are involved in several human disorders including skin disease, peripheral neuropathy, cataracts, and deafness, the most common of all sensory deficits. Mutations in five connexin-encoding genes-GJB1, GJB2, GJB3, GJB6, and GJA1-are linked to deafness. GJB2 is of particular importance since mutations in this gene are the most common cause of congenital autosomal recessive nonsyndromic deafness. The severity of deafness caused by mutations in GJB2 varies from mild to profound as a function of the type of mutation. Persons who segregate two mutations of GJB2 that result in no protein product (also known as truncating mutations) are more likely to have severe-to-profound hearing loss than are persons with one truncating mutation and one missense mutation (a mutation that results in the generation of an abnormal protein product). Persons carrying two missense mutations have the mildest degree of GJB2-related deafness. Understanding these phenotype-genotype relationships and the limitations of GJB2 mutation screening is of major importance for genetic counseling. Copyright © 2006 by Thieme Medical Publishers, Inc.

关键词： Clinical application Connexins deafness dFNB1 Genotype-phenotype

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Enhancing healthcare AI stability with edge computing and machine learning for extubation prediction

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Scientific Reports 2025年第1期15卷 1-11页

作者： Kuo-Yang Huang Ching-Hsiung Lin Che-Liang Chung Ying-Lin Hsu Huang-Chi Chen Ming-Hwarng Horng Ching-Sen Liu Jia-Lang Xu Division of Chest Medicine Department of Internal Medicine Changhua Christian Hospital Changhua Taiwan Institute of Genomics and Bioinformatics National Chung Hsing University Taichung Taiwan Ph.D. Program in Medical Biotechnology National Chung Hsing University Taichung Taiwan Division of Chest Medicine Department of Internal Medicine Yuanlin Christian Hospital Changhua Taiwan Institute of Statistics National Chung Hsing University Taichung Taiwan Department of Applied Mathematics National Chung Hsing University Taichung Taiwan Department of Computer Science and Information Engineering Chaoyang University of Technology Taichung Taiwan

The advancement of the Internet of Medical Things (IoMT) has revolutionized data acquisition and processing in critical care settings. Given the pivotal role of ventilators, accurately predicting extubation outcomes is essential to optimize patient care. This study presents an edge computing-based framework that incorporates machine learning algorithms to predict ventilator extubation success using real-time data collected directly from ventilators. The system was deployed on edge devices to enable on-site inference with minimal latency. Among the evaluated models, Random Forest and XGBoost, the latter demonstrated superior predictive performance under both holdout and tenfold cross-validation schemes. Notably, the edge-based architecture reduced server data transmissions by 83.33%, while improving system stability, resilience, and sustainability. This paper details the model evaluation and demonstrates the feasibility and efficiency of edge intelligence in ventilator weaning decision support.

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Ex vitro rooting of minicutting of Carica papaya L. single shoot from tissue culture

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In Vitro Cellular and developmental Biology - Plant 2023年第3期59卷 393-400页

作者： Chen, Yu-Ting Yang, Tun-Han Yu, Tsong-Ann Ph.D. Program in Medical Biotechnology National Chung Hsing University 145 Xingda Rd. South Dist Taichung City 402 Taiwan Institute of Genomics and Bioinformatics National Chung Hsing University 145 Xingda Rd. South Dist Taichung City 402 Taiwan Department of Biomedical Sciences Da-Yeh University 168 University Rd Changhua County Dacun Township 515 Taiwan

Papaya (Carica papaya L.) is an important economic crop. An efficient and low-cost propagation ex vitro process is required for commercial papaya plantlet production. In this study, a propagation process of papaya was established by minicutting and evaluating parameters affecting survival. Individual shoots (1.5 cm) were cut from papaya multiple shoots in vitro as the minicuttings. After dipping in 400.0 mg L⁻¹ indole-3-butyric acid (IBA), they were planted into the nursery pro trays with 104 holes containing vermiculite, perlite, and King Root® substrate at 1:1:1 ratio, and then covered with a lid to keep the ratio of humidity (RH) at 100% for root development. After a 3-wk treatment in a growth chamber (28°C), the rooting rate, root number, root length, and stem length were promoted to 91.1%, 2.9, 3.71 cm, and 1.88 cm, respectively. Combining the treatment with 400 mg L⁻¹ IBA and 1/2 Murashige and Skoog (MS) salt solution supplemented at the 14th day after cutting gives the best growth potential for papaya plantlets, especially increasing root length to 4.55 cm. It also effectively enhances the survival rate of plantlets to 93.3%. This process saves the rooting initiation medium and time cost and avoids the complicated procedure of cleaning medium from root surface, which results in root damage. Under this minicutting process, the papaya plantlets thrived and were optimally suited to field cultivation. © 2023, The Society for In Vitro Biology.

关键词： Ex vitro Minicutting Papaya rooting Survival rate

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Remote access to neurosurgical ICU physiological data using the world wide web

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Studies in Health Technology and Informatics 1996年 29卷 242-249页

作者： Nenov, Valeriy Klopp, John Division of Neurosurgery University of California at Los Angeles School of Medicine Los Angeles CA 90024 United States Computer Science Department United States Interdepartmental Neuroscience Ph.D. Program University of California at Los Angeles Los Angeles CA 90024 United States

There is a significant demand by physicians and clinical researchers for remote access to continuously acquired physiological patient data. Until recently such access was technically unfeasible. However, with the recent development of Internet-based World Wide Web (WWW) client/server applications and underlying communication protocols, there is now a real possibility for the development of costeffective, platform independent solutions to this problem. We have devised a way using existing WWW tools and minimal startup costs to provide access to current as well as previously acquired physiological patient data. physicians and clinical researchers can obtain access to these data through personal computers located in the office, at home or even through portable computers while traveling to conferences or while on vacation. © The authors 1996.

关键词： Hospital data processing

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Automating Care by Self-maintainability for Full Laboratory Automation

arXiv

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arXiv 2025年

作者： Ochiai, Koji Tahara-Arai, Yuya Kato, Akari Kaizu, Kazunari Kariyazaki, Hirokazu Umeno, Makoto Takahashi, Koichi Kanda, Genki N. Ozaki, Haruka Laboratory for Biologically Inspired Computing RIKEN Center for Biosystems Dynamics Research 6-7-1 Minatojima Minamimachi Chuo-ku Hyogo Kobe650-0047 Japan Bioinformatics Laboratory Institute of Medicine University of Tsukuba 1-1-1 Tennodai Ibaraki Tsukuba305-8577 Japan Ph.D. Program in Humanics School of Integrative and Global Majors University of Tsukuba 1-1-1 Tennodai Ibaraki Tsukuba305-8577 Japan Laboratory Automation Suppliers’ Association 2-1-8 Minatojima Minamimachi Chuo-ku Hyogo Kobe650-0047 Japan Cell Modeling and Simulation Group Exploratory Research Center on Life and Living Systems National Institutes of Natural Sciences 5-1 Higashiyama Myodaiji Aichi Okazaki444-8787 Japan Robotics Division YASKAWA Electric Corporation 2-1 Kurosaki Shiroishi Yahatanishi-ku Kitakyushu Fukuoka806-0004 Japan BAIKEIDO LLC 3-8-1 Asano Kokurakita-ku Kitakyushu Fukuoka802-0001 Japan Graduate School of Media and Governance Keio University 5322 Endo Fujisawa-shi Kanagawa252-0882 Japan Medical Research Laboratory Institute of Integrated Research Institute of Science Tokyo 1-5-45 Yushima Bunkyo-ku Tokyo113-8519 Japan Center for Artificial Intelligence Research University of Tsukuba 1-1-1 Tennodai Ibaraki Tsukuba305-8577 Japan

The automation of experiments in life sciences and chemistry has significantly advanced with the development of various instruments and AI technologies. However, achieving full laboratory automation, where experiments conceived by scientists are seamlessly executed in automated laboratories, remains a challenge. We identify the lack of automation in planning and operational tasks—critical human-managed processes collectively termed "care"—as a major barrier. Automating care is the key enabler for full laboratory automation. To address this, we propose the concept of self-maintainability (SeM): the ability of a laboratory system to autonomously adapt to internal and external disturbances, maintaining operational readiness akin to living cells. A SeM-enabled laboratory features autonomous recognition of its state, dynamic resource and information management, and adaptive responses to unexpected conditions. This shifts the planning and execution of experimental workflows, including scheduling and reagent allocation, from humans to the system. We present a conceptual framework for implementing SeM-enabled laboratories, comprising three modules—Requirement manager, Labware manager, and device manager—and a Central manager. SeM not only enables scientists to execute envisioned experiments seamlessly but also provides developers with a design concept that drives the technological innovations needed for full automation. Copyright © 2025, The Authors. All rights reserved.

关键词： Information management

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Based on deep feature extraction non-invasive bladder cancer prediction system 24

Based on deep feature extraction non-invasive bladder cancer...

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8th International Conference on Medical and Health Informatics, ICMHI 2024

作者： Lin, Shih-Huan Xu, Ya-Wen Chuang, Min-Ling Chu, Yen-Wei Ph.D. Program in Medical Biotechnology National Chung Hsing University Taichung Taiwan Institute of Genomics and Bioinformatics National Chung Hsing University Taichung Taiwan Department of Pathology Feng Yuan Hospital Ministry of Health and Welfare Taichung Taiwan Biotechnology Center National Chung Hsing University Taichung Taiwan Agricultural Biotechnology Center National Chung Hsing University Taichung Taiwan Institute of Molecular Biology National Chung Hsing University Taichung Taiwan Taichung Taiwan

ISBN: (纸本)9798400716874

Bladder cancer (BCa) ranks among the top ten most common cancers worldwide, and its five-year survival rate largely depends on early diagnosis. Given that 90% of BCa patients are over 55 years old, with an average age of 73 years. Therefore, a non-invasive early detection method is crucial for treatment and improving patient prognosis. To meet this need, our study leveraged non-invasive urine routine analysis as analytical data, combined with deep feature extraction, to achieve early detection and precise prediction, thereby alleviating the burden on middle-aged and elderly patients. We conducted urine routine analysis on 783 patients at Fengyuan Hospital, yielding promising preliminary results with accuracy (Acc), area under the curve (AUC), specificity (Sp), and sensitivity (Sn) values of 0.725, 0.800, 0.730, and 0.721, respectively. Furthermore, independent testing on 196 patients at MacKay Memorial Hospital provided stable validation, with AUC, Sp, and Sn values of 0.766, 0.606, and 0.831, respectively. These findings offer valuable insights for cancer detection, and the provided model can assist doctors in promoting early treatment plans, thereby improving patient outcomes and potentially saving lives. © 2024 Copyright owner author(s)

关键词： diseases

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