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Publisher Correction: Comprehensive molecular characterization of mitochondrial genomes in human cancers

Nature genetics 2023年第5期55卷 893页

作者： Yuan Yuan Young Seok Ju Youngwook Kim Jun Li Yumeng Wang Christopher J Yoon Yang Yang Inigo Martincorena Chad J Creighton John N Weinstein Yanxun Xu Leng Han Hyung-Lae Kim Hidewaki Nakagawa Keunchil Park Peter J Campbell Han Liang Department of Bioinformatics and Computational Biology The University of Texas MD Anderson Cancer Center Houston TX USA. Cancer Genome Project Wellcome Trust Sanger Institute Hinxton UK. Graduate School of Medical Science and Engineering Korea Advanced Institute of Science and Technology Daejeon Korea. Department of Health Science and Technology Samsung Advanced Institute for Health Science and Technology Sungkyunkwan University School of Medicine Seoul Korea. Quantitative and Computational Biosciences Graduate Program Baylor College of Medicine Houston TX USA. Division of Biostatistics The University of Texas Health Science Center at Houston School of Public Health Houston TX USA. Department of Medicine and Dan L. Duncan Cancer Center Division of Biostatistics Baylor College of Medicine Houston TX USA. Department of Systems Biology The University of Texas MD Anderson Cancer Center Houston TX USA. Department of Applied Mathematics and Statistics Johns Hopkins University Baltimore MD USA. Department of Biochemistry and Molecular Biology The University of Texas Health Science Center at Houston McGovern Medical School Houston TX USA. Department of Biochemistry Ewha Womans University School of Medicine Seoul Korea. Laboratory for Cancer Genomics RIKEN Center for Integrative Medical Sciences Yokohama Japan. Division of Hematology/Oncology Samsung Medical Center Sungkyunkwan University School of Medicine Seoul Korea. kpark@skku.edu. Cancer Genome Project Wellcome Trust Sanger Institute Hinxton UK. pc8@sanger.ac.uk. Cambridge University Hospitals NHS Foundation Trust Cambridge UK. pc8@sanger.ac.uk. Department of Bioinformatics and Computational Biology The University of Texas MD Anderson Cancer Center Houston TX USA. hliang1@***. Quantitative and Computational Biosciences Graduate Program Baylor College of Medicine Houston TX USA. hliang1@***. Department of Systems Biology The University of Texas MD Anderson Cancer Center Houston TX USA. hliang1@mdanderson

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Manifold learning-based methods for analyzing single-cell RNA-sequencing data

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Current Opinion in Systems biology 2018年 7卷 36-46页

作者： Moon, Kevin R. Stanley, Jay S. Burkhardt, Daniel van Dijk, David Wolf, Guy Krishnaswamy, Smita Department of Genetics Yale University New Haven CT United States Applied Mathematics Program Yale University New Haven CT United States Computational Biology and Bioinformatics Program Yale University New Haven CT United States Department of Computer Science Yale University New Haven CT United States

Recent advances in single-cell RNA sequencing technologies enable deep insights into cellular development, gene regulation, and phenotypic diversity by measuring gene expression for thousands of cells in a single experiment. While these technologies hold great potential for improving our understanding of cellular states and progression, they also pose new challenges and require advanced mathematical and algorithmic tools to extract underlying biological signals. In this review, we cover one of the most promising avenues of research into unlocking the potential of scRNA-seq data: the field of manifold learning, and the related manifold assumption in data analysis. Manifold learning provides a powerful structure for algorithmic approaches to process the data, extract its dynamics, and infer patterns in it. In particular, we cover manifold learning-based methods for denoising the data, revealing gene interactions, extracting pseudotime progressions with model fitting, visualizing the cellular state space via dimensionality reduction, and clustering the data. © 2017 Published by Elsevier Ltd.

关键词： Data mining Gene regulatory networks Imputation Manifold learning Pseudotime trends Single cell RNA sequencing

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Using physical features of protein core packing to distinguish real proteins from decoys

arXiv

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arXiv 2020年

作者： Grigas, Alex T. Mei, Zhe Treado, John D. Levine, Zachary A. Regan, Lynne O'Hern, Corey S. Graduate Program in Computational Biology and Bioinformatics Yale University New HavenCT06520 United States Integrated Graduate Program in Physical and Engineering Biology Yale University New HavenCT06520 United States Department of Chemistry Yale University New HavenCT06520 United States Department of Mechanical Engineering and Materials Science Yale University New HavenCT06520 United States Department of Pathology Yale University New HavenCT06520 United States Department of Molecular Biophysics and Biochemistry Yale University New HavenCT06520 United States Institute of Quantitative Biology Biochemistry and Biotechnology Centre for Synthetic and Systems Biology School of Biological Sciences University of Edinburgh Department of Physics Yale University New HavenCT06520 United States Department of Applied Physics Yale University New HavenCT06520 United States

The ability to consistently distinguish real protein structures from computationally generated model decoys is not yet a solved problem. One route to distinguish real protein structures from decoys is to delineate the important physical features that specify a real protein. For example, it has long been appreciated that the hydrophobic cores of proteins contribute signifficantly to their stability. As a dataset of decoys to compare with real protein structures, we studied submissions to the bi-annual CASP competition (speciffically CASP11, 12, and 13), in which researchers attempt to predict the structure of a protein only knowing its amino acid sequence. Our analysis reveals that many of the submissions possess cores that do not recapitulate the features that define real proteins. In particular, the model structures appear more densely packed (because of energetically unfavorable atomic overlaps), contain too few residues in the core, and have improper distributions of hydrophobic residues throughout the structure. Based on these observations, we developed a deep learning method, which incorporates key physical features of protein cores, to predict how well a computational model recapitulates the real protein structure without knowledge of the structure of the target sequence. By identifying the important features of protein structure, our method is able to rank decoys from the CASP competitions equally well, if not better than, state-of-the-art methods that incorporate many additional features. Copyright © 2020, The Authors. All rights reserved.

关键词： Proteins

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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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Compressed diffusion

arXiv

引用

arXiv 2019年

作者： Gigante, Scott Stanley, Jay S. Vu, Ngan van Dijk, David Moon, Kevin R. Wolf, Guy Krishnaswamy, Smita Computational Biology and Bioinformatics Program Department of Computer Science Department of Genetics Yale University New HavenCT United States Department of Mathematics and Statistics Utah State University LoganUT United States Department of Mathematics and Statistics Université de Montréal MontéalQC Canada

Diffusion maps are a commonly used kernel-based method for manifold learning, which can reveal intrinsic structures in data and embed them in low dimensions. However, as with most kernel methods, its implementation requires a heavy computational load, reaching up to cubic complexity in the number of data points. This limits its usability in modern data analysis. Here, we present a new approach to computing the diffusion geometry, and related embeddings, from a compressed diffusion process between data regions rather than data points. Our construction is based on an adaptation of the previously proposed measure-based Gaussian correlation (MGC) kernel that robustly captures the local geometry around data points. We use this MGC kernel to efficiently compress diffusion relations from pointwise to data region resolution. Finally, a spectral embedding of the data regions provides coordinates that are used to interpolate and approximate the pointwise diffusion map embedding of data. We analyze theoretical connections between our construction and the original diffusion geometry of diffusion maps, and demonstrate the utility of our method in analyzing big datasets, where it outperforms competing approaches. Copyright © 2019, The Authors. All rights reserved.

关键词： Embeddings

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Reliable uncertainty estimate for antibiotic resistance classification with Stochastic Gradient Langevin Dynamics

arXiv

引用

arXiv 2018年

作者： Hamid, Md-Nafiz Friedberg, Iddo Bioinformatics and Computational Biology Program Department of Veterinary Microbiology and Preventive Medicine Iowa State University AmesIA United States

Antibiotic resistance monitoring is of paramount importance in the face of this ongoing global epidemic. Deep learning models trained with traditional optimization algorithms (e.g. Adam, SGD) provide poor posterior estimates when tested against out-of-distribution (OoD) antibiotic resistant/non-resistant genes. In this paper, we introduce a deep learning model trained with Stochastic Gradient Langevin Dynamics (SGLD) to classify antibiotic resistant genes. The model provides better uncertainty estimates when tested against OoD data compared to traditional optimization methods such as Adam. Copyright © 2018, The Authors. All rights reserved.

关键词： Stochastic systems

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Gene Co-expression Network analysis of Lung Squamous Cell Carcinoma data

arXiv

引用

arXiv 2018年

作者： Hamid, Md-Nafiz Bioinformatics and Computational Biology program Department of Veterinary Microbiology and Preventive Medicine Iowa State University AmesIA United States

We performed a gene co-expression analysis on Lung Squamous Cell Carcinoma data to find modules (groups) of genes that may highly impact the growth of these type of tumors. Additionally, we used cancer survival data to relate modules to prognostic significance in terms of survival time. Analysis on RNA-seq data revealed modules which are significant in gene enrichment analysis. Specifically, two genes - RFC4 and ECT2 - have been found to be significant in terms of survival time. We also performed a second gene co-expression analysis on a second dataset of microarray data, and many significant genes found in this analysis could also be found in the RNA-seq data implying that these genes might indeed play a crucial role in Lung Squamous Cancer. All the R code for the analysis can be found at: https://***/nafizh/Gene_ coexpression_analysis_lung_cancer Copyright © 2018, The Authors. All rights reserved.

关键词： Genes

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Rank Projection Trees for Multilevel Neural Network Interpretation

arXiv

引用

arXiv 2018年

作者： Warrell, Jonathan Mohsen, Hussein Gerstein, Mark Program in Computational Biology and Bioinformatics Yale University Molecular Biophysics and Biochemistry Yale University Department of Computer Science Yale University

A variety of methods have been proposed for interpreting nodes in deep neural networks, which typically involve scoring nodes at lower layers with respect to their effects on the output of higher-layer nodes (where lower and higher layers are closer to the input and output layers, respectively). However, we may be interested in picking out a prioritized collection of subsets of the inputs across a range of scales according to their importance for an output node, and not simply a prioritized ranking across the inputs as singletons. Such a situation may arise in biological applications, for instance, where we are interested in epistatic effects between groups of genes in determining a trait of interest. Here, we outline a flexible framework which may be used to generate multiscale network interpretations, using any previously defined scoring function. We demonstrate the ability of our method to pick out biologically important genes and gene sets in the domains of cancer and psychiatric genomics. Copyright © 2018, The Authors. All rights reserved.

关键词： Genes

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Perspective on recent developments and challenges in regulatory and systems genomics

arXiv

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arXiv 2024年

作者： Zeiltinger, Julia Roy, Sushmita Ay, Ferhat Mathelier, Anthony Medina-Rivera, Alejandra Mahony, Shaun Sinha, Saurabh Ernst, Jason Stowers Institute for Medical Research Kansas CityMO64112 United States Department of Pathology & Laboratory Medicine The University of Kansas Medical Center Kansas CityKS66160 United States Department of Biostatistics and Medical Informatics University of Wisconsin-Madison Madison53715 United States Wisconsin Institute for Discovery University of Wisconsin-Madison Madison53715 United States Centers for Autoimmunity Inflammation and Cancer Immunotherapy La Jolla Institute for Immunology La Jolla CA United States Bioinformatics and Systems Biology Program University of California La Jolla San DiegoCA United States Department of Pediatrics University of California La Jolla San DiegoCA United States Nordic EMBL Partnership University of Oslo Oslo0318 Norway Department of Medical Genetics Institute of Clinical Medicine University of Oslo Oslo University Hospital Oslo Norway Department of Pharmacy University of Oslo Oslo Norway Laboratorio Internacional de Investigación sobre el Genoma Humano Universidad Nacional Autónoma de México Campus Juriquilla Blvd. Juriquilla 3001 Santiago de Querétaro76230 Mexico Center for Eukaryotic Gene Regulation Department of Biochemistry and Molecular Biology The Pennsylvania State University University ParkPA16802 United States Walter H. Coulter Department of Biomedical Engineering Georgia Institute of Technology AtlantaGA United States H. Milton Stewart School of Industrial and Systems Engineering Georgia Institute of Technology AtlantaGA United States Department of Biological Chemistry University of California Los Angeles Los AngelesCA90095 United States Computer Science Department University of California Los Angeles Los AngelesCA90095 United States Department of Computational Medicine University of California Los Angeles Los AngelesCA90095 United States Eli and Edythe Broad Center of Regenerative Medicine Stem Cell Research University of California Los Angeles Los AngelesCA90095 United States

Predicting how genetic variation affects phenotypic outcomes at the organismal, cellular, and molecular levels requires deciphering the cis-regulatory code, the sequence rules by which non-coding regions regulate genes. In this perspective, we discuss recent computational progress and challenges towards solving this fundamental problem. We describe how cis-regulatory elements are mapped and how their sequence rules can be learned and interpreted with sequence-to-function neural networks, with the goal of identifying genetic variants in human disease. We also discuss how studies of the 3D chromatin organization could help identifying long-range regulatory effects and how current methods for mapping gene regulatory networks could better describe biological processes. We point out current gaps in knowledge along with technical limitations and benchmarking challenges of computational methods. Finally, we discuss newly emerging technologies, such as spatial transcriptomics, and outline strategies for creating a more general model of the cis-regulatory code that is more broadly applicable across cell types and individuals. © 2024, CC BY-NC-SA.

关键词： Molecular docking

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Atomistic mechanisms underlying the activation of the G protein-coupled sweet receptor heterodimer by sugar alcohol recognition (vol 9, 10205, 2019)

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SCIENTIFIC REPORTS 2019年第1期9卷 1-11页

作者： Mahalapbutr, Panupong Darai, Nitchakan Panman, Wanwisa Opasmahakul, Aunchan Kungwan, Nawee Hannongbua, Supot Rungrotmongkol, Thanyada Structural and Computational Biology Research Unit Department of Biochemistry Faculty of Science Chulalongkorn University 10330 Bangkok Thailand Program in Biotechnology Faculty of Science Chulalongkorn University 10330 Bangkok Thailand Multidisciplinary Program of Petrochemistry and Polymer Science Faculty of Science Chulalongkorn University 10330 Bangkok Thailand Computational Chemistry Center of Excellent Department of Chemistry Faculty of Science Chulalongkorn University 10330 Bangkok Thailand Department of Chemistry Faculty of Science Chiang Mai University 50200 Chiang Mai Thailand Center of Excellence in Materials Science and Technology Chiang Mai University 50200 Chiang Mai Thailand Ph.D. Program in Bioinformatics and Computational Biology Faculty of Science Chulalongkorn University 10330 Bangkok Thailand Molecular Sensory Science Center Faculty of Science Chulalongkorn University 10330 Bangkok Thailand

The human T1R2-T1R3 sweet taste receptor (STR) plays an important role in recognizing various low-molecular-weight sweet-tasting sugars and proteins, resulting in the release of intracellular heterotrimeric G protein that in turn leads to the sweet taste perception. Xylitol and sorbitol, which are naturally occurring sugar alcohols (polyols) found in many fruits and vegetables, exhibit the potential caries-reducing effect and are widely used for diabetic patients as low-calorie sweeteners. In the present study, computational tools were applied to investigate the structural details of binary complexes formed between these two polyols and the T1R2-T1R3 heterodimeric STR. Principal component analysis revealed that the Venus flytrap domain (VFD) of T1R2 monomer was adapted by the induced-fit mechanism to accommodate the focused polyols, in which α-helical residues 233–268 moved significantly closer to stabilize ligands. This finding likely suggested that these structural transformations might be the important mechanisms underlying polyols-STR recognitions. The calculated free energies also supported the VFD of T1R2 monomer as the preferential binding site for such polyols, rather than T1R3 region, in accord with the lower number of accessible water molecules in the T1R2 pocket. The E302 amino acid residue in T1R2 was found to be the important recognition residue for polyols binding through a strongly formed hydrogen bond. Additionally, the binding affinity of xylitol toward the T1R2 monomer was significantly higher than that of sorbitol, making it a sweeter tasting molecule. [ABSTRACT FROM AUTHOR]

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