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Publisher Correction: Integrating genomics and metabolomics for scalable non-ribosomal peptide discovery

Nature communications 2021年第1期12卷 4318页

作者： Bahar Behsaz Edna Bode Alexey Gurevich Yan-Ni Shi Florian Grundmann Deepa Acharya Andrés Mauricio Caraballo-Rodríguez Amina Bouslimani Morgan Panitchpakdi Annabell Linck Changhui Guan Julia Oh Pieter C Dorrestein Helge B Bode Pavel A Pevzner Hosein Mohimani Bioinformatics and Systems Biology Program University of California San Diego La Jolla CA USA. Center for Microbiome Innovation University of California at San Diego La Jolla CA USA. Computational Biology Department School of Computer Science Carnegie Mellon University Pittsburgh PA USA. Molecular Biotechnology Department of Biosciences Goethe University Frankfurt Frankfurt am Main Germany. Center for Algorithmic Biotechnology Institute of Translational Biomedicine St. Petersburg State University St Petersburg Russia. Tiny Earth Chemistry Hub University of Wisconsin-Madison Madison WI USA. Collaborative Mass Spectrometry Innovation Center Skaggs School of Pharmacy and Pharmaceutical Sciences University of California San Diego La Jolla CA USA. The Jackson Laboratory of Medical Genomics Farmington CT USA. Molecular Biotechnology Department of Biosciences Goethe University Frankfurt Frankfurt am Main Germany. helge.bode@mpi-marburg.mpg.de. Buchmann Institute for Molecular Life Sciences (BMLS) Goethe University Frankfurt & Senckenberg Research Institute Frankfurt am Main Germany. helge.bode@mpi-marburg.mpg.de. Max-Planck-Institute for Terrestrial Microbiology Department for Natural Products in Organismic Interactions Marburg Germany. helge.bode@mpi-marburg.mpg.de. Department of Computer Science and Engineering University of California San Diego La Jolla CA USA. ppevzner@ucsd.edu. Computational Biology Department School of Computer Science Carnegie Mellon University Pittsburgh PA USA. hoseinm@andrew.cmu.edu.

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"RETRACTED ARTICLE: Vibrio parahaemolyticus RhsP represents a widespread group of pro-effectors for type VI secretion systems

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Nature Communications 2018年第1期9卷 1-14页

作者： Nan Jiang Le Tang Ruiqiang Xie Zhi Li Brianne Burkinshaw Xiaoye Liang Dylan Sosa L. Aravind Tao Dong Dapeng Zhang Jun Zheng Faculty of Health Sciences University of Macau Macau SAR China Department of Ecosystem and Public Health University of Calgary Calgary Canada Department of Biochemistry and Molecular Biology University of Calgary Calgary Canada Snyder Institute for Chronic Diseases University of Calgary Calgary Canada State Key Laboratory of Microbial Metabolism Joint International Laboratory on Metabolic and Developmental Sciences School of Life Sciences and Biotechnology Shanghai Jiao Tong University Shanghai China Program of Bioinformatics and Computational Biology College of Arts and Sciences Saint Louis University Saint Louis USA National Center for Biotechnology Information National Library of Medicine National Institutes of Health Bethesda USA Department of Biology College of Arts and Sciences Saint Louis University Saint Louis USA

Type VI secretion systems (T6SSs) translocate effector proteins, such as Rhs toxins, to eukaryotic cells or prokaryotic competitors. All T6SS Rhs-type effectors characterized thus far contain a PAAR motif or a similar structure. Here, we describe a T6SS-dependent delivery mechanism for a subset of Rhs proteins that lack a PAAR motif. We show that the N-terminal Rhs domain of protein RhsP (or VP1517) fromVibrio parahaemolyticusinhibits the activity of the C-terminal DNase domain. Upon auto-proteolysis, the Rhs fragment remains inside the cells, and the C-terminal region interacts with PAAR2 and is secreted by T6SS2; therefore, RhsP acts as a pro-effector. Furthermore, we show that RhsP contributes to the control of certain “social cheaters” (opaRmutants). Genes encoding proteins with similar Rhs and PAAR-interacting domains, but diverse C-terminal regions, are widely distributed amongVibriospecies.

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Learning binary latent variable models: A tensor eigenpair approach

arXiv

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arXiv 2018年

作者： Jaffe, Ariel Weiss, Roi Carmi, Shai Kluger, Yuval Nadler, Boaz Dept. of Computer Science and Applied Mathematics Weizmann Institute of Science Rehovot7610001 Israel Braun School of Public Health and Community Medicine Hebrew University of Jerusalem Jerusalem9112102 Israel Interdepartmental Program in Computational Biology and Bioinformatics Yale University New HavenCT06511 United States Dept. of Pathology Yale Cancer Center Yale University School of Medicine New HavenCT06520 United States Program of Applied Mathematics Yale University New HavenCT06511 United States

Latent variable models with hidden binary units appear in various applications. Learning such models, in particular in the presence of noise, is a challenging computational problem. In this paper we propose a novel spectral approach to this problem, based on the eigenvectors of both the second order moment matrix and third order moment tensor of the observed data. We prove that under mild non-degeneracy conditions, our method consistently estimates the model parameters at the optimal parametric rate. Our tensor-based method generalizes previous orthogonal tensor decomposition approaches, where the hidden units were assumed to be either statistically independent or mutually exclusive. We illustrate the consistency of our method on simulated data and demonstrate its usefulness in learning a common model for population mixtures in genetics. Copyright © 2018, The Authors. All rights reserved.

关键词： Tensors

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Correction to: Genes associated with survival of female bovine blastocysts produced in vivo

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Cell and tissue research 2020年第3期382卷 679页

作者： A M Zolini J Block M B Rabaglino G Rincon M Hoelker J J Bromfield D Salilew-Wondim P J Hansen Department of Animal Sciences D.H. Barron Reproductive and Perinatal Biology Research Program and Genetics Institute University of Florida PO Box 110910 Gainesville FL 32611-0910 USA. Zoetis Inc. Kalamazoo MI 49007 USA. Instituto de Investigación en Ciencias de La Salud CONICET Córdoba Argentina. Quantitative Genetics Bioinformatics and Computational Biology Group Department of Applied Mathematics and Computer Science Technical University of Denmark Kemitorvet 2800 Kgs. Lyngby Denmark. Institute of Animal Science Animal Breeding and Husbandry University of Bonn Bonn Germany. Teaching and Research Station Frankenforst Faculty of Agriculture University of Bonn Königswinter Germany. Center of Integrated Dairy Research University of Bonn Bonn Germany. Department of Animal Sciences D.H. Barron Reproductive and Perinatal Biology Research Program and Genetics Institute University of Florida PO Box 110910 Gainesville FL 32611-0910 USA. pjhansen@ufl.edu.

The first error is on page 5. A sentence lists two genes as SCNA1A and SCNA2A but they should be SCN1A and SCN2A.

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Using LICHeE and BAMSE for Reconstructing Cancer Phylogenetic Trees

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Current Protocols in bioinformatics 2018年第1期62卷 e49-e49页

作者： Ricketts, Camir Popic, Victoria Toosi, Hosein Hajirasouliha, Iman Tri-I Computational Biology & Medicine Graduate Program Cornell University New York NY United States Institute for Computational Biomedicine Department of Physiology and Biophysics Weill Cornell Medicine New York NY United States Illumina Inc. Illumina Mission Bay San Francisco CA United States Department of Bioinformatics Institute for Biochemistry and Biophysics University of Tehran Iran Englander Institute for Precision Medicine The Meyer Cancer Center Weill Cornell Medicine New York United States

The reconstruction of cancer phylogeny trees and quantifying the evolution of the disease is a challenging task. LICHeE and BAMSE are two computational tools designed and implemented recently for this purpose. They both utilize estimated variant allele fraction of somatic mutations across multiple samples to infer the most likely cancer phylogenies. This unit provides extensive guidelines for installing and running both LICHeE and BAMSE. © 2018 by John Wiley & Sons, Inc. © 2018 John Wiley & Sons, Inc.

关键词： cancer phylogeny deep sequencing next-generation sequencing somatic variations tumor heterogeneity

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Publisher Correction: Integrating yeast chemical genomics and mammalian cell pathway analysis

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Acta pharmacologica Sinica 2020年第5期41卷 729页

作者： Fu-Lai Zhou Sheena C Li Yue Zhu Wan-Jing Guo Li-Jun Shao Justin Nelson Scott Simpkins De-Hua Yang Qing Liu Yoko Yashiroda Jin-Biao Xu Yao-Yue Fan Jian-Min Yue Minoru Yoshida Tian Xia Chad L Myers Charles Boone Ming-Wei Wang The National Center for Drug Screening and the CAS Key Laboratory of Receptor Research Shanghai Institute of Materia Medica Chinese Academy of Sciences (CAS) Shanghai 201203 China. University of Chinese Academy of Sciences Beijing 100049 China. RIKEN Center for Sustainable Resource Science Wako Saitama 3510198 Japan. Bioinformatics and Computational Biology Program University of Minnesota-Twin Cities Minneapolis Minnesota 55455 USA. The State Key Laboratory of Drug Research Shanghai Institute of Materia Medica Chinese Academy of Sciences Shanghai 201203 China. Department of Biology The University of Tokyo Bunkyo-ku Tokyo 1138657 Japan. Collaborative Research for Innovative Microbiology The University of Tokyo Bunkyo-ku Tokyo 1138657 Japan. Department of Electronics and Information Engineering Huazhong University of Science and Technology Wuhan 430074 China. Bioinformatics and Computational Biology Program University of Minnesota-Twin Cities Minneapolis Minnesota 55455 USA. chadm@umn.edu. RIKEN Center for Sustainable Resource Science Wako Saitama 3510198 Japan. charlie.boone@utoronto.ca. Donnelly Centre and Department of Molecular Genetics University of Toronto Ontario M5S 3E1 Canada. charlie.boone@utoronto.ca. The National Center for Drug Screening and the CAS Key Laboratory of Receptor Research Shanghai Institute of Materia Medica Chinese Academy of Sciences (CAS) Shanghai 201203 China. mwwang@***. University of Chinese Academy of Sciences Beijing 100049 China. mwwang@***.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

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Ten simple rules for reproducible research in jupyter notebooks

arXiv

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arXiv 2018年

作者： Rule, Adam Birmingham, Amanda Zuniga, Cristal Altintas, Ilkay Huang, Shih-Cheng Knight, Rob Moshiri, Niema Nguyen, Mai H. Rosenthal, Sara Brin Pérez, Fernando Rose, Peter W. Design Lab UC San Diego San DiegoCA United States Center for Computational Biology and Bioinformatics UC San Diego San DiegoCA United States Department of Pediatrics UC San Diego San DiegoCA United States Data Science Hub San Diego Supercomputer Center UC San Diego San DiegoCA United States Departments of Bioengineering and Computer Science and Engineering Center for Microbiome Innovation UC San Diego San DiegoCA United States Bioinformatics and Systems Biology Graduate Program UC San Diego San DiegoCA United States Department of Statistics Berkeley Institute for Data Science UC Berkeley Lawrence Berkeley National Laboratory BerkeleyCA United States Biomedical Informatics Graduate Program Stanford University StanfordCA United States

Reproducibility of computational studies is a hallmark of scientific methodology. It enables researchers to build with confidence on the methods and findings of others, reuse and extend computational pipelines, and thereby drive scientific progress. Since many experimental studies rely on computational analyses, biologists need guidance on how to set up and document reproducible data analyses or simulations. In this paper, we address several questions about reproducibility. For example, what are the technical and non-technical barriers to reproducible computational studies? What opportunities and challenges do computational notebooks offer to overcome some of these barriers? What tools are available and how can they be used effectively? We have developed a set of rules to serve as a guide to scientists with a specific focus on computational notebook systems, such as Jupyter Notebooks, which have become a tool of choice for many applications. Notebooks combine detailed workflows with narrative text and visualization of results. Combined with software repositories and open source licensing, notebooks are powerful tools for transparent, collaborative, reproducible, and reusable data analyses. Copyright © 2018, The Authors. All rights reserved.

关键词： Digital storage

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High-depth African genomes inform human migration and health (vol 586, pg 741, 2020)

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NATURE 2021年第7856期592卷 E26-E26页

作者： Choudhury, Ananyo Aron, Shaun Botigue, Laura R. Sengupta, Dhriti Botha, Gerrit Bensellak, Taoufik Wells, Gordon Kumuthini, Judit Shriner, Daniel Fakim, Yasmina J. Ghoorah, Anisah W. Dareng, Eileen Odia, Trust Falola, Oluwadamilare Adebiyi, Ezekiel Hazelhurst, Scott Mazandu, Gaston Nyangiri, Oscar A. Mbiyavanga, Mamana Benkahla, Alia Kassim, Samar K. Mulder, Nicola Adebamowo, Sally N. Chimusa, Emile R. Muzny, Donna Metcalf, Ginger Gibbs, Richard A. Matovu, Enock Bucheton, Bruno Hertz-Fowler, Christiane Koffi, Mathurin Macleod, Annette Mumba-Ngoyi, Dieudonne Noyes, Harry Nyangiri, Oscar A. Simo, Gustave Simuunza, Martin Rotimi, Charles Ramsay, Michele Choudhury, Ananyo Choudhury, Ananyo Aron, Shaun Botigue, Laura Sengupta, Dhriti Botha, Gerrit Bensellak, Taoufik Wells, Gordon Kumuthini, Judit Shriner, Daniel Fakim, Yasmina J. Ghoorah, Anisah W. Dareng, Eileen Odia, Trust Falola, Oluwadamilare Adebiyi, Ezekiel Hazelhurst, Scott Mazandu, Gaston Nyangiri, Oscar A. Mbiyavanga, Mamana Benkahla, Alia Kassim, Samar K. Mulder, Nicola Adebamowo, Sally N. Chimusa, Emile R. Rotimi, Charles Ramsay, Michele Adeyemo, Adebowale A. Lombard, Zane Hanchard, Neil A. Adebamowo, Clement Agongo, Godfred Boua, Romuald P. Oduro, Abraham Sorgho, Hermann Landoure, Guida Cisse, Lassana Diarra, Salimata Samassekou, Oumar Anabwani, Gabriel Matshaba, Mogomotsi Joloba, Moses Kekitiinwa, Adeodata Mardon, Graeme Mpoloka, Sununguko W. Kyobe, Samuel Mlotshwa, Busisiwe Mwesigwa, Savannah Retshabile, Gaone Williams, Lesedi Wonkam, Ambroise Moussa, Ahmed Adu, Dwomoa Ojo, Akinlolu Burke, David Salako, Babatunde O. Matovu, Enock Bucheton, Bruno Hertz-Fowler, Christiane Koffi, Mathurin Macleod, Annette Mumba-Ngoyi, Dieudonne Noyes, Harry Nyangiri, Oscar A. Simo, Gustave Simuunza, Martin Awadalla, Philip Bruat, Vanessa Gbeha, Elias Adeyemo, Adebowale A. Lombard, Zane Hanchard, Neil A. Sydney Brenner Institute for Molecular Bioscience Faculty of Health Sciences University of the Witwatersrand Johannesburg South Africa School of Electrical and Information Engineering University of the Witwatersrand Johannesburg South Africa Division of Human Genetics National Health Laboratory Service and School of Pathology Faculty of Health Sciences University of the Witwatersrand Johannesburg South Africa Navrongo Health Research Centre Navrongo Ghana Clinical Research Unit of Nanoro Institut de Recherche en Sciences de la Sante Bobo-Dioulasso Burkina Faso Center for Research in Agricultural Genomics (CRAG) Plant and Animal Genomics Program CSIC-IRTA-UAB-UB Barcelona Spain Computational Biology Division and H3ABioNet Department of Integrative Biomedical Sciences IDM University of Cape Town Cape Town South Africa System and Data Engineering Team Abdelmalek Essaadi University ENSA Tangier Morocco Centre for Proteomic and Genomic Research (CPGR) Cape Town South Africa South African National Bioinformatics Institute University of the Western Cape Bellville South Africa Africa Health Research Institute Durban South Africa Center for Research on Genomics and Global Health National Human Genome Research Institute National Institutes of Health Bethesda MD USA Department of Agriculture and Food Science Faculty of Agriculture University of Mauritius Reduit Mauritius Department of Digital Technologies Faculty of Information Communication & Digital Technologies University of Mauritius Reduit Mauritius Department of Public Health and Primary Care University of Cambridge Cambridge UK Institute of Human Virology Nigeria Abuja Nigeria Department of Epidemiology and Public Health University of Maryland School of Medicine University of Maryland Baltimore Baltimore MD USA University of Maryland Greenebaum Comprehensive Cancer Center University of Maryland School of Medicine University of Maryland Baltimore Baltimore MD USA Institute of Human Virology and Greeneba

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Author Correction: Conserved and divergent gene regulatory programs of the mammalian neocortex

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Nature 2024年第7996期625卷 E26页

作者： Nathan R Zemke Ethan J Armand Wenliang Wang Seoyeon Lee Jingtian Zhou Yang Eric Li Hanqing Liu Wei Tian Joseph R Nery Rosa G Castanon Anna Bartlett Julia K Osteen Daofeng Li Xiaoyu Zhuo Vincent Xu Lei Chang Keyi Dong Hannah S Indralingam Jonathan A Rink Yang Xie Michael Miller Fenna M Krienen Qiangge Zhang Naz Taskin Jonathan Ting Guoping Feng Steven A McCarroll Edward M Callaway Ting Wang Ed S Lein M Margarita Behrens Joseph R Ecker Bing Ren Department of Cellular and Molecular Medicine University of California San Diego School of Medicine La Jolla CA USA. Center for Epigenomics University of California San Diego School of Medicine La Jolla CA USA. Bioinformatics and Systems Biology Program University of California San Diego La Jolla CA USA. Genomic Analysis Laboratory The Salk Institute for Biological Studies La Jolla CA USA. Division of Biological Sciences University of California San Diego La Jolla CA USA. Computational Neurobiology Laboratory The Salk Institute for Biological Studies La Jolla CA USA. Department of Genetics The Edison Family Center for Genome Sciences & Systems Biology Washington University School of Medicine St Louis MO USA. Princeton Neuroscience Institute Princeton University Princeton NJ USA. Department of Genetics Harvard Medical School Boston USA. Stanley Center for Psychiatric Research Broad Institute of MIT and Harvard Cambridge MA USA. McGovern Institute for Brain Research Department of Brain and Cognitive Sciences Massachusetts Institute of Technology Cambridge MA USA. Allen Institute for Brain Science Seattle WA USA. Systems Neurobiology Laboratories The Salk Institute for Biological Studies La Jolla CA USA. Department of Neurosciences University of California San Diego La Jolla CA USA. McDonnell Genome Institute Washington University School of Medicine St. Louis MO USA. Department of Neurological Surgery University of Washington Seattle WA USA. Genomic Analysis Laboratory The Salk Institute for Biological Studies La Jolla CA USA. ecker@salk.edu. Howard Hughes Medical Institute The Salk Institute for Biological Studies La Jolla CA USA. ecker@salk.edu. Department of Cellular and Molecular Medicine University of California San Diego School of Medicine La Jolla CA USA. biren@ucsd.edu. Center for Epigenomics University of California San Diego School of Medicine La Jolla CA USA. biren@ucsd.edu. Institute of Genomic Medicine Moores Cancer C

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Void distributions reveal structural link between jammed packings and protein cores

arXiv

引用

arXiv 2018年

作者： Treado, John D. Mei, Zhe Regan, Lynne O'Hern, Corey S. Department of Mechanical Engineering & Materials Science Yale University New HavenCT06520 United States Integrated Graduate Program in Physical & Engineering Biology Yale University New HavenCT06520 United States Department of Chemistry Yale University New HavenCT06520 United States Department of Molecular Biophysics & Biochemistry Yale University New HavenCT06520 United States Department of Physics Yale University New HavenCT06520 United States Department of Applied Physics Yale University New HavenCT06520 United States Program in Computational Biology and Bioinformatics Yale University New HavenCT06520 United States

Dense packing of hydrophobic residues in the cores of globular proteins determines their stability. Recently, we have shown that protein cores possess packing fraction φ ≈ 0.56, which is the same as dense, random packing of amino acid-shaped particles. In this article, we compare the structural properties of protein cores and jammed packings of amino acid-shaped particles in much greater depth by measuring their local and connected void regions. We find that the distributions of surface Voronoi cell volumes and local porosities obey similar statistics in both systems. We also measure the probability that accessible, connected void regions percolate as a function of the size of a spherical probe particle and show that both systems possess the same critical probe size. By measuring the critical exponent τ that characterizes the size distribution of connected void clusters at the onset of percolation, we show that void percolation in packings of amino acid-shaped particles and protein cores belong to the same universality class, which is different from that for void percolation in jammed sphere packings. We propose that the connected void regions of proteins are a defining feature of proteins and can be used to differentiate experimentally observed proteins from decoy structures that are generated using computational protein design software. This work emphasizes that jammed packings of amino acid-shaped particles can serve as structural and mechanical analogs of protein cores, and could therefore be useful in modeling the response of protein cores to cavity-expanding and -reducing mutations. Copyright © 2018, The Authors. All rights reserved.

关键词： Amino acids

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