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Addendum to “Hypothesis: a glycoprotein-degradation complex formed by protein–protein interaction involves cytoplasmic peptide: N -glycanase” [Biochem. Biophys. Res. Commun. 302 (2003) 1–5]

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Biochemical and Biophysical Research Communications 2003年第2期303卷 732-732页

作者： Tadashi Suzuki William J Lennarz Undergraduate Program for Bioinformatics and Systems Biology and Department of Biophysics and Biochemistry Graduate School of Science University of Tokyo and PRESTO Japan Science and Technology Corporation (JST) Hongo 7-3-1 Bunkyo-ku Tokyo 113-0033 Japan Department of Biochemistry and Cell Biology and the Institute for Cell and Developmental Biology State University of New York at Stony Brook Stony Brook NY 11794-5215 USA

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Shotgun Sequencing of Southern Thailand Traditional Sweet Pickled Mango During the Production Process

SSRN

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SSRN 2023年

作者： Indrati, Niken Sirisarn, Wanchat Nuanpirom, Jiratchaya Yodsawat, Prasert Wanna, Warapond Prombutara, Pinidphon Phonsatta, Natthaporn Panya, Atikorn Maneerat, Suppasil Poungsombat, Patcha Khoomrung, Sakda Sumpavapol, Punnanee Food Microbiology and Safety Laboratory Food Science and Technology Program Faculty of Agro-Industry Prince of Songkla University Songkhla90110 Thailand Center of Genomics and Bioinformatics Research Faculty of Science Prince of Songkla University Songkhla90110 Thailand Omics Sciences and Bioinformatics Center Faculty of Science Chulalongkorn University Bangkok10330 Thailand Thailand Science Park Pathumthani12120 Thailand Center of Excellence in Innovative Biotechnology for Sustainable Utilization of Bioresources Faculty of Agro-Industry Prince of Songkla University Songkhla90110 Thailand Metabolomics and Systems Biology Department of Biochemistry Faculty of Medicine Siriraj Hospital Mahidol University Bangkok10700 Thailand Siriraj Metabolomics and Phenomics Center Faculty of Medicine Siriraj Hospital Mahidol University Bangkok10700 Thailand Faculty of Science Mahidol University Bangkok10400 Thailand Faculty of Science and Technology Songkhla Rajabhat University Songkhla90000 Thailand

Southern Thailand's traditional sweet pickled mango, known as Ma-Muang Bao Chae-Im (MBC), holds an important place in the local community. Several immersion processes, including brining and submersion in a hypertonic sugar solution, have been employed in the MBC production process. The occurrence of microbial succession during the production process affects its flavor due to the produced metabolites. However, microbial succession during the production process has received inadequate research. Therefore, this study aimed to (i) understand the microbiome alteration and (ii) investigate the functional profiling (metabolic pathways) during MBC production process. Through shotgun metagenomics, the microbial succession was shifting from bacteria in raw mango to fungi in MBC, with Ascomycota as the dominant phylum and Hanseniaspora as the dominant genus in MBC. The abundance of biosynthesis pathways was in line with the occurrence of microbial growth throughout the production process and prolonged storage. Polymyxin resistance was found as a significant pathway during brining 1 (B1-RM) and brining 2 (B2-B1). Mixed acid fermentation pathway and glyoxylate cycle were suggested to have a role in the aroma development of MBC by synthesizing metabolites related to the acidic and alcoholic aroma. © 2023, The Authors. All rights reserved.

关键词： Brining

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Quantum processor-inspired machine learning in the biomedical sciences

arXiv

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arXiv 2019年

作者： Li, Richard Y. Gujja, Sharvari Bajaj, Sweta R. Gamel, Omar E. Cilfone, Nicholas Gulcher, Jeffrey R. Lidar, Daniel A. Chittenden, Thomas W. Department of Chemistry University of Southern California Los AngelesCA United States Computational Biology and Bioinformatics Program Department of Biological Sciences University of Southern California Los AngelesCA United States Center for Quantum Information Science & Technology University of Southern California Los AngelesCA United States Computational Statistics and Bioinformatics Group Advanced Artificial Intelligence Research Laboratory Genuity Science BostonMA United States Complex Biological Systems Alliance MedfordMA United States Cancer Genetics Group Genuity Science BostonMA United States Department of Electrical Engineering University of Southern California Los AngelesCA United States Department of Physics and Astronomy University of Southern California Los AngelesCA United States Division of Genetics and Genomics Boston Children's Hospital Harvard Medical School BostonMA United States

Recent advances in high-throughput genomic technologies coupled with exponential increases in computer processing and memory have allowed us to interrogate the complex aberrant molecular underpinnings of human disease from a genome-wide perspective. While the deluge of genomic information is expected to increase, a bottleneck in conventional high-performance computing is rapidly approaching. Inspired in part by recent advances in physical quantum processors, we evaluated several unconventional machine learning (ML) strategies on actual human tumor data;namely "Ising-type" methods whose objective function is formulated identically to simulated annealing and quantum annealing, as well as a restricted Boltzmann machine. Here we show for the first time the efficacy of multiple Ising-type ML algorithms for classification of high-dimensional, multi-omics human cancer data from the Cancer Genome Atlas. To assess algorithm performance, we compared these classifiers to a variety of standard ML methods. Our results indicate the feasibility of using Ising-type ML to provide competitive classification of human cancer types and associated molecular subtypes and superior performance with smaller training datasets, thus providing compelling empirical evidence for the potential future application of unconventional computing approaches in the biomedical sciences. © 2019, CC BY.

关键词： Machine learning

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Computation of Microbial Ecosystems in Time and Space (COMETS): An open source collaborative platform for modeling ecosystems metabolism

arXiv

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arXiv 2020年

作者： Dukovski, Ilija Bajić, Djordje Chacón, Jeremy M. Quintin, Michael Vila, Jean C.C. Sulheim, Snorre Pacheco, Alan R. Bernstein, David B. Riehl, William J. Korolev, Kirill S. Sanchez, Alvaro Harcombe, William R. Segrè, Daniel Bioinformatics Program Boston University BostonMA United States Biological Design Center Boston University BostonMA United States Department of Ecology and Evolutionary Biology Yale University New HavenCT United States Microbial Sciences Institute Yale University West HavenCT United States Department of Ecology Evolution and Behavior University of Minnesota St. PaulMN United States BioTechnology Institute University of Minnesota St. PaulMN United States Department of Biotechnology and Food Science Norwegian University of Science and Technology Trondheim Norway Department of Biotechnology and Nanomedicine SINTEF Industry Trondheim Norway Environmental Genomics and Systems Biology Division Lawrence Berkeley National Laboratory BerkeleyCA United States Department of Biomedical Engineering Boston University BostonMA United States Department of Physics Boston University BostonMA United States Department of Biology Boston University BostonMA United States

Genome-scale stoichiometric modeling of metabolism has become a standard systems biology tool for modeling cellular physiology and growth. Extensions of this approach are also emerging as a valuable avenue for predicting, understanding and designing microbial communities. COMETS (Computation Of Microbial Ecosystems in Time and Space) was initially developed as an extension of dynamic flux balance analysis, which incorporates cellular and molecular diffusion, enabling simulations of multiple microbial species in spatially structured environments. Here we describe how to best use and apply the most recent version of this platform, COMETS 2, which incorporates a more accurate biophysical model of microbial biomass expansion upon growth, as well as several new biological simulation modules, including evolutionary dynamics and extracellular enzyme activity. COMETS 2 provides user-friendly Python and MATLAB interfaces compatible with the well-established COBRA models and methods, and comprehensive documentation and tutorials, facilitating the use of COMETS for researchers at all levels of expertise with metabolic simulations. This protocol provides a detailed guideline for installing, testing and applying COMETS 2 to different scenarios, with broad applicability to microbial communities across biomes and scales. Copyright © 2020, The Authors. All rights reserved.

关键词： Microorganisms

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Prognostic Impacts and Dynamic Changes of Cohesin Complex Gene Mutations in De Novo Acute Myeloid Leukemia

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BLOOD 2017年 130卷 2582-2582页

作者： Tsai, Cheng-Hong Hou, Hsin-An Tang, Jih-Luh Kuo, Yuan-Yeh Chiu, Yu-Chiao Lin, Chien-Chin Lin, Liang-In Yao, Ming Li, Chi-Cheng Huang, Shang-Yi Ko, Bor-Sheng Hsu, Szu Chun Lin, Chien-Ting Wu, Shang-Ju Chen, Chien-Yuan Tsai, Woei Chou, Wen-Chien Tien, Hwei-Fang Tai-Cheng Stem Cell Therapy Center National Taiwan University Taipei Taiwan Division of Hematology Department of Internal Medicine National Taiwan University Hospital Taipei Taiwan Genome and Systems Biology Degree Program National Taiwan University Taipei Taiwan Graduate Institute of Oncology National Taiwan University Taipei Taiwan Graduate institute of Biomedical Electronics and Bioinformatics National Taiwan University Taipei Taiwan Department of Clinical Laboratory Sciences and Medical Biotechnology National Taiwan University Taipei Taiwan Department of Laboratory Medicine National Taiwan University Hospital Taipei Taiwan Division of Hematology Department of Laboratory Medicine National Taiwan University Hospital Taipei Taiwan

2582

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Publisher Correction: Predicting functional consequences of mutations using molecular interaction network features

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Human genetics 2022年第10期141卷 1593页

作者： Kivilcim Ozturk Hannah Carter Division of Medical Genetics Department of Medicine University of California San Diego La Jolla CA USA. Bioinformatics and Systems Biology Program University of California San Diego La Jolla CA USA. Division of Medical Genetics Department of Medicine University of California San Diego La Jolla CA USA. hkcarter@health.ucsd.edu. Bioinformatics and Systems Biology Program University of California San Diego La Jolla CA USA. hkcarter@health.ucsd.edu. Moores Cancer Center University of California San Diego La Jolla CA USA. hkcarter@health.ucsd.edu.

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Structural systems biology: from bacterial to cancer networks

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BMC Genomics 2014年第2期15卷 1-1页

作者： Adam Godzik Bioinformatics and Systems Biology Program Sanford-Burnham Medical Research Institute 10901 N. Torrey Pines Rd La Jolla CA 92037 USA Center of Excellence in Genomic Medicine Research King Abdulaziz University P.O. Box: 80216 Jeddah 21589 Kingdom of Saudi Arabia

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Author Correction: Single-cell multi-ome regression models identify functional and disease-associated enhancers and enable chromatin potential analysis

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Nature genetics 2024年第6期56卷 1319页

作者： Sneha Mitra Rohan Malik Wilfred Wong Afsana Rahman Alexander J Hartemink Yuri Pritykin Kushal K Dey Christina S Leslie Computational and Systems Biology Program Memorial Sloan Kettering Cancer Center New York City NY USA. Rye Country Day School Rye NY USA. Tri-Institutional Training Program in Computational Biology and Medicine New York City NY USA. Hunter College City University of New York New York City NY USA. Department of Computer Science Duke University Durham NC USA. Program in Computational Biology and Bioinformatics Duke University Durham NC USA. Center for Genomic and Computational Biology Duke University Durham NC USA. Department of Computer Science Princeton University Princeton NJ USA. Lewis-Sigler Institute for Integrative Genomics Princeton University Princeton NJ USA. Computational and Systems Biology Program Memorial Sloan Kettering Cancer Center New York City NY USA. deyk@***. Computational and Systems Biology Program Memorial Sloan Kettering Cancer Center New York City NY USA. lesliec@***.

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Erratum to: The real cost of sequencing: scaling computation to keep pace with data generation

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Genome biology 2016年第1期17卷 78页

作者： Paul Muir Shantao Li Shaoke Lou Daifeng Wang Daniel J Spakowicz Leonidas Salichos Jing Zhang George M Weinstock Farren Isaacs Joel Rozowsky Mark Gerstein Department of Molecular Cellular and Developmental Biology Yale University New Haven CT 06520 USA. Systems Biology Institute Yale University West Haven CT 06516 USA. Integrated Graduate Program in Physical and Engineering Biology Yale University New Haven CT 06520 USA. Program in Computational Biology and Bioinformatics Yale University New Haven CT 06520 USA. Department of Molecular Biophysics and Biochemistry Yale University New Haven CT 06520 USA. The Jackson Laboratory for Genomic Medicine Farmington CT 06032 USA. Program in Computational Biology and Bioinformatics Yale University New Haven CT 06520 USA. mark@***. Department of Molecular Biophysics and Biochemistry Yale University New Haven CT 06520 USA. mark@***. Department of Computer Science Yale University New Haven CT 06520 USA. mark@***.

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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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