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Predicting regional somatic mutation rates using DNA motifs

PLoS Computational Biology 2023年第10 October期19卷 e1011536-e1011536页

作者： Liu, Cong Wang, Zengmiao Wang, Jun Liu, Chengyu Wang, Mengchi Ngo, Vu Wang, Wei Department of Chemistry and Biochemistry University of California San Diego La Jolla CA United States State Key Laboratory of Remote Sensing Science Center for Global Change and Public Health Faculty of Geographical Science Beijing Normal University Beijing China Bioinformatics and Systems Biology Graduate Program University of California San Diego La Jolla CA United States Department of Cellular and Molecular Medicine University of California San Diego La Jolla CA United States

How the locus-specificity of epigenetic modifications is regulated remains an unanswered question. A contributing mechanism is that epigenetic enzymes are recruited to specific loci by DNA binding factors recognizing particular sequence motifs (referred to as epi-motifs). Using these motifs to predict biological outputs depending on local epigenetic state such as somatic mutation rates would confirm their functionality. Here, we used DNA motifs including known TF motifs and epi-motifs as a surrogate of epigenetic signals to predict somatic mutation rates in 13 cancers at an average 23kbp resolution. We implemented an interpretable neural network model, called contextual regression, to successfully learn the universal relationship between mutations and DNA motifs, and uncovered motifs that are most impactful on the regional mutation rates such as TP53 and epi-motifs associated with H3K9me3. Furthermore, we identified genomic regions with significantly higher mutation rates than the expected values in each individual tumor and demonstrated that such cancer-related regions can accurately predict cancer types. Interestingly, we found that the same mutation signatures often have different contributions to cancer-related and cancer-independent regions, and we also identified the motifs with the most contribution to each mutation signature. © 2023 Liu et al.

关键词： Neural network models

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Latent Regression and Ordination Risk of Infectious Disease and Climate

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Procedia Computer Science 2021年 179卷 25-32页

作者： Rezzy Eko Caraka Rung Ching Chen Youngjo Lee Prana Ugiana Gio Arif Budiarto Bens Pardamean College of Informatics Chaoyang University of Technology 168 Jifong East Road Wufong Dist. Taichung City 41349 Taiwan (R.O.C.). Department of Statistics College of Natural Science Seoul National University Gwanak-gu Seoul 08826 Korea Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Department of Mathematics Universitas Sumatera Utara Medan 20222 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Indonesia.

Global warming arising from climate change can increase the spread of deadly diseases. Effort is needed to develop a set of policies for the government to stem or reduce health risks from global warming. The purpose of this paper is to examine more detail and comprehensively about the relationship among climate and event disease count in Taiwan using the partial least square latent regression model. The results obtained that of the 17 types of diseases in Taiwan, that has the most significant loading factor is Amoebiasis, Malaria and Chikungunya. At the same time, climate variables that have the biggest most significant factor are Number day with max temp more than 30, Number day Temp more than 25, and Rainfall PH. Cronbach’s Alpha infectious disease 0.9696 and climate 0.2813. At the same time, the value of Dillon Goldstein’s rho infectious disease 0.974 and climate 0.6404, respectively.

关键词： Disease Counts Climate Latent PLS Regression

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Evaluation of drug repositioning by molecular docking of pharmaceutical resources available in the Brazilian healthcare system against SARS-CoV-2

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Informatics in Medicine Unlocked 2021年 23卷 100539页

作者： Grahl, Matheus V.C. Alcará, Allan M. Perin, Ana Paula A. Moro, Carlo F. Pinto, Éderson S.M. Feltes, Bruno C. Ghilardi, Isadora M. Rodrigues, Felipe V.F. Dorn, Marcio da Costa, Jaderson C. Norberto de Souza, Osmar Ligabue-Braun, Rodrigo Brain Institute of Rio Grande do Sul (BraIns) Pontifical Catholic University of Rio Grande do Sul Porto Alegre Brazil Graduate Program in Medicine and Health Sciences Pontifical Catholic University of Rio Grande do Sul Porto Alegre Brazil Graduate Program in Medicine Pediatrics and Child Health Pontifical Catholic University of Rio Grande do Sul Porto Alegre Brazil Graduate Program in Cellular and Molecular Biology Center of Biotechnology Federal University of Rio Grande do Sul RS Brazil Laboratory of Structural Bioinformatics and Computational Biology Institute of Informatics Federal University of Rio Grande do Sul RS Brazil Laboratory of Immunobiology and Immunogenetics Institute of Biosciences Federal University of Rio Grande do Sul RS Brazil School of Technology Pontifical Catholic University of Rio Grande do Sul Porto Alegre Brazil Department of Pharmacosciences and Graduate Program in Biosciences (PPGBio) Federal University of Health Sciences of Porto Alegre Porto Alegre Brazil

In 2020 SARS-CoV-2 reached pandemic status, reaching Brazil in mid-February. As of now, no specific drugs for treating the disease are available. In this work, the possibility of interaction between SARS-CoV-2 viral proteins (open and closed spike protein, isolate spike protein RBD, NSP 10, NSP 16, main protease, and RdRp polymerase) and multiple molecules is addressed through the repositioning of drugs available for the treatment of other diseases that are approved by the FDA and covered by SUS, the Brazilian Public Health System. Three different docking software were used, followed by a unification of the results by independent evaluation. Afterwards, the chemical interactions of the compounds with the targets were inspected via molecular dynamics and analyzed. The results point to a potential effectiveness of Penciclovir, Ribavirin, and Zanamivir, from a set of 48 potential candidates. They may also be multi-target drugs, showing high affinity with more than one viral protein. Further in vitro and in vivo validation is required to assess the suitability of repositioning the proposed drugs for COVID-19. © 2021 The Authors

关键词： COVID19 Drug repositioning Molecular docking SARS-CoV-2 SUS

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Ramadhan short-term electric load: A hybrid model of cycle spinning wavelet and group method data handling (CSW-GMDH)

IAENG International Journal of Computer Science

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IAENG International Journal of Computer Science 2019年第4期46卷 670页

作者： Caraka, Rezzy Eko Chen, Rung Ching Toharudin, Toni Pardamean, Bens Bakar, Sakhinah Abu Yasin, Hasbi College of Informatics Chaoyang University of Technology Taichung City41349 Taiwan Department of Statistics Padjadjaran University Bandung Indonesia College of Informatics Chaoyang University of Technology Taichung City41349 Indonesia Bioinformatics Data Science Research Center Bina Nusantara University Indonesia BINUS Graduate Program-Master of Computer Science Bina Nusantara University. Indonesia School of Mathematical Sciences FST The National University of Malaysia Malaysia Department of Statistics Diponegoro University Semarang Indonesia

In general, performing a nonlinearity time series analysis in the modeling of data can reach a robust and increase the quality of the results. Wavelet methods have successfully been applied in a great variety of applications for modeling also forecasting. Wavelet Transform divided into two categories. There is continuous wavelet (CWT) and a discrete wavelet transform (DWT). Cycle spinning unlike the discrete wavelet transform (DWT), is highly redundant, non-orthogonal, also defined naturally for all sample sizes. There is a Group Method of Data Handling (GMDH) algorithm, which is a multivariate analysis method can be used in modeling and identifying uncertainty on linear also nonlinearity systems. In this paper, we aim to explain the combination of A-Trous wavelet transforms applied on cycle spinning and group method of data handling (GMDH) in data of short-term electric load holy month of Ramadhan from 2014 to 2015. © 2019, International Association of Engineers.

关键词： Discrete wavelet transforms

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High Resolution Imaging of Virus in Liquid Droplets: The Application of Cryo-TEM Methodology to Improve Liquid-phase TEM Imaging of Biological Materials

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Microscopy and Microanalysis 2021年第S2期27卷 19-20页

作者： Madeline J. Dukes GM Jonaid William J. Dearnaley Michael A. Casasanta Deborah F. Kelly Protochips Inc.Morrisville NC USA. Bioinformatics and Genomics Graduate Program Huck Institutes of the Life Sciences Pennsylvania State University University Park PA USA. Department of Biomedical Engineering Pennsylvania State University University Park PA USA. Materials Research Institute Pennsylvania State University University Park PA USA. Center for Structural Oncology Pennsylvania State University University Park PA16802 USA.

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Hypostatic jammed packings of frictionless nonspherical particles

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Physical Review E 2018年第1期97卷 012909-012909页

作者： Kyle VanderWerf Weiwei Jin Mark D. Shattuck Corey S. O'Hern Department of Physics Yale University New Haven Connecticut 06520 USA Department of Mechanics and Engineering Science Peking University Beijing 100871 China Department of Mechanical Engineering & Materials Science Yale University New Haven Connecticut 06520 USA Benjamin Levich Institute and Physics Department The City College of New York New York New York 10031 USA Department of Applied Physics Yale University New Haven Connecticut 06520 USA Graduate Program in Computational Biology and Bioinformatics Yale University New Haven Connecticut 06520 USA

We perform computational studies of static packings of a variety of nonspherical particles including circulo-lines, circulo-polygons, ellipses, asymmetric dimers, dumbbells, and others to determine which shapes form packings with fewer contacts than degrees of freedom (hypostatic packings) and which have equal numbers of contacts and degrees of freedom (isostatic packings), and to understand why hypostatic packings of nonspherical particles can be mechanically stable despite having fewer contacts than that predicted from naive constraint counting. To generate highly accurate force- and torque-balanced packings of circulo-lines and cir-polygons, we developed an interparticle potential that gives continuous forces and torques as a function of the particle coordinates. We show that the packing fraction and coordination number at jamming onset obey a masterlike form for all of the nonspherical particle packings we studied when plotted versus the particle asphericity A, which is proportional to the ratio of the squared perimeter to the area of the particle. Further, the eigenvalue spectra of the dynamical matrix for packings of different particle shapes collapse when plotted at the same A. For hypostatic packings of nonspherical particles, we verify that the number of “quartic” modes along which the potential energy increases as the fourth power of the perturbation amplitude matches the number of missing contacts relative to the isostatic value. We show that the fourth derivatives of the total potential energy in the directions of the quartic modes remain nonzero as the pressure of the packings is decreased to zero. In addition, we calculate the principal curvatures of the inequality constraints for each contact in circulo-line packings and identify specific types of contacts with inequality constraints that possess convex curvature. These contacts can constrain multiple degrees of freedom and allow hypostatic packings of nonspherical particles to be mechanically stable.

关键词： Granular materials Granular packing Jamming

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New candidate genes potentially involved in Zika virus teratogenesis

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Computers in Biology and Medicine 2024年 173卷 108259-108259页

作者： Ferrão Maciel-Fiuza, Miriãn Rengel, Bruna Duarte Wachholz, Gabriela Elis do Amaral Gomes, Julia de Oliveira, Maikel Rosa Kowalski, Thayne Woycinck Roehe, Paulo Michel Luiz Vianna, Fernanda Sales Schüler-Faccini, Lavínia Mayer, Fabiana Quoos Varela, Ana Paula Muterle Fraga, Lucas Rosa Graduate Program in Genetics and Molecular Biology Universidade Federal do Rio Grande do Sul Porto Alegre Brazil Instituto Nacional de Genética Médica Populacional Porto Alegre Brazil Genomics Medicine Laboratory Center of Experimental Research Hospital de Clínicas de Porto Alegre Porto Alegre Brazil Department of Morphological Sciences Institute of Health Sciences Universidade Federal do Rio Grande do Sul Porto Alegre Brazil Graduate Program in Medicine: Medical Sciences Universidade Federal do Rio Grande do Sul Porto Alegre Brazil Teratogen Information System Medical Genetics Service Hospital de Clínicas de Porto Alegre Porto Alegre Brazil Bioinformatics Core Hospital de Clínicas de Porto Alegre Porto Alegre Brazil Centro Universitário CESUCA Cachoeirinha Brazil Department of Microbiology Immunology and Parasitology Institute of Health Sciences Universidade Federal do Rio Grande do Sul Porto Alegre Brazil Graduate Program in Molecular and Cellular Biology Biotechnology Center Universidade Federal do Rio Grande do Sul Porto Alegre Brazil Graduate Program in Biosciences Universidade Federal de Ciências da Saúde de Porto Alegre Porto Alegre Brazil

Despite efforts to elucidate Zika virus (ZIKV) teratogenesis, still several issues remain unresolved, particularly on the molecular mechanisms behind the pathogenesis of Congenital Zika Syndrome (CZS). To answer this question, we used bioinformatics tools, animal experiments and human gene expression analysis to investigate genes related to brain development potentially involved in CZS. Searches in databases for genes related to brain development and CZS were performed, and a protein interaction network was created. The expression of these genes was analyzed in a CZS animal model and secondary gene expression analysis (DGE) was performed in human cells exposed to ZIKV. A total of 2610 genes were identified in the databases, of which 1013 were connected. By applying centrality statistics of the global network, 36 candidate genes were identified, which, after selection resulted in nine genes. Gene expression analysis revealed distinctive expression patterns for PRKDC, PCNA, ATM, SMC3 as well as for FGF8 and SHH in the CZS model. Furthermore, DGE analysis altered expression of ATM, PRKDC, PCNA. In conclusion, systems biology are helpful tools to identify candidate genes to be validated in vitro and in vivo. PRKDC, PCNA, ATM, SMC3, FGF8 and SHH have altered expression in ZIKV-induced brain malformations. © Elsevier Ltd

关键词： Viruses

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An automatic nuclei segmentation method based on deep convolutional neural networks for histopathology images

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BMC biomedical engineering 2019年第1期1卷 24页

作者： Hwejin Jung Bilal Lodhi Jaewoo Kang Department of Computer Science and Engineering Korea University Seoul Republic of Korea. Interdisciplinary Graduate Program in Bioinformatics Korea University Seoul Republic of Korea.

BACKGROUND:Since nuclei segmentation in histopathology images can provide key information for identifying the presence or stage of a disease, the images need to be assessed carefully. However, color variation in histopathology images, and various structures of nuclei are two major obstacles in accurately segmenting and analyzing histopathology images. Several machine learning methods heavily rely on hand-crafted features which have limitations due to manual thresholding. RESULTS:To obtain robust results, deep learning based methods have been proposed. Deep convolutional neural networks (DCNN) used for automatically extracting features from raw image data have been proven to achieve great performance. Inspired by such achievements, we propose a nuclei segmentation method based on DCNNs. To normalize the color of histopathology images, we use a deep convolutional Gaussian mixture color normalization model which is able to cluster pixels while considering the structures of nuclei. To segment nuclei, we use Mask R-CNN which achieves state-of-the-art object segmentation performance in the field of computer vision. In addition, we perform multiple inference as a post-processing step to boost segmentation performance. We evaluate our segmentation method on two different datasets. The first dataset consists of histopathology images of various organ while the other consists histopathology images of the same organ. Performance of our segmentation method is measured in various experimental setups at the object-level and the pixel-level. In addition, we compare the performance of our method with that of existing state-of-the-art methods. The experimental results show that our nuclei segmentation method outperforms the existing methods. CONCLUSIONS:We propose a nuclei segmentation method based on DCNNs for histopathology images. The proposed method which uses Mask R-CNN with color normalization and multiple inference post-processing provides robust nuclei segmentation results. O

关键词： Convolutional neural network Deep learning Histopathology Image analysis Nuclei segmentation

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COWID: an efficient cloud-based genomics workflow for scalable identification of SARS-COV-2

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Briefings in bioinformatics 2023年第5期24卷 bbad280-bbad280页

作者： Lim, Hendrick Gao-Min Fann, Yang C Lee, Yuan-Chii Gladys Graduate Institute of Biomedical Informatics College of Medical Science and Technology Taipei Medical University Taipei 11031 Taiwan Department of Medical Research Tzu Chi Hospital Indonesia Pantai Indah Kapuk Greater Jakarta 14470 Indonesia It and Bioinformatics Program Division of Intramural National Institute of Neurological Disorders and Stroke National Institutes of Health Bethesda 20892 MD United States

Implementing a specific cloud resource to analyze extensive genomic data on severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) poses a challenge when resources are limited. To overcome this, we repurposed a cloud platform initially designed for use in research on cancer genomics (https://***) to enable its use in research on SARS-CoV-2 to build Cloud Workflow for Viral and Variant Identification (COWID). COWID is a workflow based on the Common Workflow Language that realizes the full potential of sequencing technology for use in reliable SARS-CoV-2 identification and leverages cloud computing to achieve efficient parallelization. COWID outperformed other contemporary methods for identification by offering scalable identification and reliable variant findings with no false-positive results. COWID typically processed each sample of raw sequencing data within 5 min at a cost of only US$0.01. The COWID source code is publicly available (https://***/hendrick0403/COWID) and can be accessed on any computer with Internet access. COWID is designed to be user-friendly;it can be implemented without prior programming knowledge. Therefore, COWID is a time-efficient tool that can be used during a pandemic. © 2023 Published by Oxford University Press.

关键词： cloud repurposing cloud workflow COVID-19 Illumina sequencing parallel computation SARS-CoV-2

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Retraction Note: A proposal for Internet of Smart Home Things based on BCI system to aid patients with amyotrophic lateral sclerosis

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Neural Computing and Applications 2024年第28期36卷 17761-17761页

作者： de Oliveira Júnior, Wilson G. de Oliveira, Juliana M. Munoz, Roberto de Albuquerque, Victor Hugo C. Graduate Program in Applied Informatics Laboratory of Bioinformatics University of Fortaleza Fortaleza Brazil School of Informatics Engineering Universidad de Valparaíso Valparaíso Chile

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