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Publisher Correction: Protein-altering variants associated with body mass index implicate pathways that control energy intake and expenditure in obesity

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Nature genetics 2018年第5期50卷 766-767页

作者： Valérie Turcot Yingchang Lu Heather M Highland Claudia Schurmann Anne E Justice Rebecca S Fine Jonathan P Bradfield Tõnu Esko Ayush Giri Mariaelisa Graff Xiuqing Guo Audrey E Hendricks Tugce Karaderi Adelheid Lempradl Adam E Locke Anubha Mahajan Eirini Marouli Suthesh Sivapalaratnam Kristin L Young Tamuno Alfred Mary F Feitosa Nicholas G D Masca Alisa K Manning Carolina Medina-Gomez Poorva Mudgal Maggie C Y Ng Alex P Reiner Sailaja Vedantam Sara M Willems Thomas W Winkler Gonçalo Abecasis Katja K Aben Dewan S Alam Sameer E Alharthi Matthew Allison Philippe Amouyel Folkert W Asselbergs Paul L Auer Beverley Balkau Lia E Bang Inês Barroso Lisa Bastarache Marianne Benn Sven Bergmann Lawrence F Bielak Matthias Blüher Michael Boehnke Heiner Boeing Eric Boerwinkle Carsten A Böger Jette Bork-Jensen Michiel L Bots Erwin P Bottinger Donald W Bowden Ivan Brandslund Gerome Breen Murray H Brilliant Linda Broer Marco Brumat Amber A Burt Adam S Butterworth Peter T Campbell Stefania Cappellani David J Carey Eulalia Catamo Mark J Caulfield John C Chambers Daniel I Chasman Yii-Der I Chen Rajiv Chowdhury Cramer Christensen Audrey Y Chu Massimiliano Cocca Francis S Collins James P Cook Janie Corley Jordi Corominas Galbany Amanda J Cox David S Crosslin Gabriel Cuellar-Partida Angela D'Eustacchio John Danesh Gail Davies Paul I W Bakker Mark C H Groot Renée Mutsert Ian J Deary George Dedoussis Ellen W Demerath Martin Heijer Anneke I Hollander Hester M Ruijter Joe G Dennis Josh C Denny Emanuele Di Angelantonio Fotios Drenos Mengmeng Du Marie-Pierre Dubé Alison M Dunning Douglas F Easton Todd L Edwards David Ellinghaus Patrick T Ellinor Paul Elliott Evangelos Evangelou Aliki-Eleni Farmaki I Sadaf Farooqi Jessica D Faul Sascha Fauser Shuang Feng Ele Ferrannini Jean Ferrieres Jose C Florez Ian Ford Myriam Fornage Oscar H Franco Andre Franke Paul W Franks Nele Friedrich Ruth Frikke-Schmidt Tessel E Galesloot Wei Gan Ilaria Gandin Paolo Gasparini Jane Gibson Vilmantas Giedraitis Anette P Gjesing Penny Gordon-Larsen Mathias Gorski Hans-Jörgen Montreal Heart Institute Université de Montréal Montreal Quebec Canada. Division of Epidemiology Department of Medicine Vanderbilt-Ingram Cancer Center Vanderbilt Epidemiology Center Vanderbilt University School of Medicine Nashville TN USA. Charles Bronfman Institute for Personalized Medicine Icahn School of Medicine at Mount Sinai New York NY USA. Genetics of Obesity and Related Metabolic Traits Program Icahn School of Medicine at Mount Sinai New York NY USA. Department of Epidemiology University of North Carolina Chapel Hill NC USA. Human Genetics Center University of Texas School of Public Health University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences University of Texas Health Science Center at Houston Houston TX USA. Broad Institute of MIT and Harvard Cambridge MA USA. Department of Genetics Harvard Medical School Boston MA USA. Division of Endocrinology and Center for Basic and Translational Obesity Research Boston Children's Hospital Boston MA USA. Center for Applied Genomics Division of Human Genetics Children's Hospital of Philadelphia Philadelphia PA USA. Quantinuum Research LLC San Diego CA USA. Estonian Genome Center University of Tartu Tartu Estonia. Division of Epidemiology Department of Medicine Institute for Medicine and Public Health Vanderbilt Genetics Institute Vanderbilt University Nashville TN USA. Institute for Translational Genomics and Population Sciences LABioMed at Harbor-UCLA Medical Center Torrance CA USA. Wellcome Trust Sanger Institute Hinxton UK. Department of Mathematical and Statistical Sciences University of Colorado Denver CO USA. Wellcome Trust Centre for Human Genetics University of Oxford Oxford UK. Department of Biological Sciences Faculty of Arts and Sciences Eastern Mediterranean University Famagusta Cyprus. Max Planck Institute of Immunobiology and Epigenetics Freiburg Germany. Department of Biostatistics and Center for Statistical Genetics University of M

In the version of this article originally published, one of the two authors with the name Wei Zhao was omitted from the author list and the affiliations for both authors were assigned to the single Wei Zhao in the author list. In addition, the ORCID for Wei Zhao (Department of Biostatistics and Epidemiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA) was incorrectly assigned to author Wei Zhou. The errors have been corrected in the HTML and PDF versions of the article.

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Cell-cycle phenotyping with conditional random fields: A case study in Saccharomyces cerevisiae

Cell-cycle phenotyping with conditional random fields: A cas...

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IEEE International Symposium on Biomedical Imaging

作者： Michael B. Mayhew Alexander J. Hartemink Program in Computational Biology & Bioinformatics Duke University Durham NC USA Department of Computer Science Program in Computational Biology & Bioinformatics Duke University Durham NC USA

ISBN: (纸本)9781467364560

High-resolution, multimodal microscopy grants an intimate view of the inner workings of cells. Complex processes like cell division can be monitored with microscope images, assuming identification of cells and their cell-cycle markers: cellular structures indicative of cell-cycle progress. Here, we explore how spatial relationships between these markers can facilitate their identification. We grew and synchronized Saccharomyces cerevisiae cell cultures and then acquired multimodal image data as the cells proceeded through the cell cycle. We trained a conditional random field model to capture pixel-level spatial relationships among three different cell-cycle markers observable in our images. We observed good predictive performance of this pixel-level model on three held-out test images, and performance improved when we used marker-level information from our training data to prune model predictions. Our results support the use of conditional random fields in bioimage labeling and encourage the use of as much multiscale information as available in training data when identifying cell-cycle markers.

关键词： Labeling Predictive models Biological system modeling Training data Microscopy Data models

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Fast and accurate calculation of protein depth by Euclidean distance transform

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17th Annual International Conference on Research in computational Molecular biology, RECOMB 2013

作者： Xu, Dong Li, Hua Zhang, Yang Bioinformatics and Systems Biology Program Sanford-Burnham Medical Research Institute United States Department of Computational Medicine and Bioinformatics University of Michigan United States Integration Application Center Institute of Computing Technology Chinese Academy of Sciences China

ISBN: (纸本)9783642371943

The depth of each atom/residue in a protein structure is a key attribution that has been widely used in protein structure modeling and function annotation. However, the accurate calculation of depth is time consuming. Here, we propose to use the Euclidean distance transform (EDT) to calculate the depth, which conveniently converts the protein structure to a 3D gray-scale image with each pixel labeling the minimum distance of the pixel to the surface of the molecule (i.e. the depth). We tested the proposed EDT method on a set of 261 non-redundant protein structures. The data show that the EDT method is 2.6 times faster than the widely used method by Chakravarty and Varadarajan. The depth value by EDT method is also highly accurate, which is almost identical to the depth calculated by exhaustive search (Pearson's correlation coefficient≈1). We believe the EDT-based depth calculation program can be used as an efficient tool to assist the studies of protein fold recognition and structure-based function annotation. © 2013 Springer-Verlag.

关键词： Proteins

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Erratum to: A survey of best practices for RNA-seq data analysis

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Genome biology 2016年第1期17卷 181页

作者： Ana Conesa Pedro Madrigal Sonia Tarazona David Gomez-Cabrero Alejandra Cervera Andrew McPherson Michal Wojciech Szcześniak Daniel J Gaffney Laura L Elo Xuegong Zhang Ali Mortazavi Institute for Food and Agricultural Sciences Department of Microbiology and Cell Science University of Florida Gainesville FL 32603 USA. aconesa@ufl.edu. Centro de Investigación Príncipe Felipe Genomics of Gene Expression Laboratory 46012 Valencia Spain. aconesa@ufl.edu. Wellcome Trust Sanger Institute Wellcome Trust Genome Campus Hinxton Cambridge CB10 1SA UK. pm12@sanger.ac.uk. Wellcome Trust-Medical Research Council Cambridge Stem Cell Institute Anne McLaren Laboratory for Regenerative Medicine Department of Surgery University of Cambridge Cambridge CB2 0SZ UK. pm12@sanger.ac.uk. Centro de Investigación Príncipe Felipe Genomics of Gene Expression Laboratory 46012 Valencia Spain. Department of Applied Statistics Operations Research and Quality Universidad Politécnica de Valencia 46020 Valencia Spain. Unit of Computational Medicine Department of Medicine Karolinska Institutet Karolinska University Hospital 171 77 Stockholm Sweden. Center for Molecular Medicine Karolinska Institutet 17177 Stockholm Sweden. Unit of Clinical Epidemiology Department of Medicine Karolinska University Hospital L8 17176 Stockholm Sweden. Science for Life Laboratory 17121 Solna Sweden. Systems Biology Laboratory Institute of Biomedicine and Genome-Scale Biology Research Program University of Helsinki 00014 Helsinki Finland. School of Computing Science Simon Fraser University Burnaby V5A 1S6BC Canada. Department of Bioinformatics Institute of Molecular Biology and Biotechnology Adam Mickiewicz University in Poznań 61-614 Poznań Poland. Wellcome Trust Sanger Institute Wellcome Trust Genome Campus Hinxton Cambridge CB10 1SA UK. Turku Centre for Biotechnology University of Turku and Åbo Akademi University FI-20520 Turku Finland. Key Lab of Bioinformatics/Bioinformatics Division TNLIST and Department of Automation Tsinghua University Beijing 100084 China. School of Life Sciences Tsinghua University Beijing 100084 China. Department of Developmental and Cell Biolo

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ENERGY-BASED ARCHITECTURE FOR CLASSIFICATION OF PUBLICATION FIGURES

ENERGY-BASED ARCHITECTURE FOR CLASSIFICATION OF PUBLICATION ...

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Annual ORNL Biomedical Sciences and Engineering Conference on Collaborative Biomedical Innovations

作者： P. E. Barbano M. L. Nagy M. Krauthammer Department of Mathematics Yale University Program for Computational Biology and Bioinformatics Yale University

ISBN: (纸本)9781479921195

We present an implementation of the experimental and theoretical results obtained in the analysis of text and image content of biomedical publications. Particularly, we propose a novel optical recognition system using an adaptive algorithm for the classification and analysis of highly heterogeneous images in research papers. When compared with conventional algorithms, our technology substantially increases the probability of detection and classification of images buried in text or obscured by other images. We report successful testing of the new architecture using PubMed publications.

关键词： publications images ARCHITECTURE Classification of algorithms new buildings Taxonomy texts optical identification WWOX gene Image classification

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Capturing the Mechanical Unfolding Pathway of a Large Protein with Coiled‐Coil Probes†

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Angewandte Chemie 2014年第49期126卷

作者： Qing Li Zackary N. Scholl Piotr E. Marszalek Department of Mechanical Engineering and Material Sciences Duke University Durham NC 27708 (USA) Program in Computational Biology and Bioinformatics Duke University Durham NC 27708 (USA)

The folding behaviors and mechanisms of large multidomain proteins have remained largely uncharacterized, primarily because of the lack of appropriate research methods. To address these limitations, novel mechanical folding probes have been developed that are based on antiparallel coiled‐coil polypeptides. Such probes can be conveniently inserted at the DNA level, at different positions within the protein of interest where they minimally disturb the host protein structure. During single‐molecule force spectroscopy measurements, the forced unfolding of the probe captures the progress of the unfolding front through the host protein structure. This novel approach allows unfolding pathways of large proteins to be directly identified. As an example, this probe was used in a large multidomain protein with ten identical ankyrin repeats, and the unfolding pathway, its direction, and the order of sequential unfolding were unequivocally and precisely determined. This development facilitates the examination of the folding pathways of large proteins, which are predominant in the proteasomes of all organisms, but have thus far eluded study because of the technical limitations encountered when using traditional techniques.

关键词： Coiled‐Coil‐Strukturen Einzelmolekülspektroskopie Multidomänenproteine Proteinfaltung Proteinstrukturen

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1005-LBP

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Human Immunology 2014年第6期75卷 480-480页

作者： Sivasankaran, Adarsh Madbouly, Abeer Cherkassky, Vladimir Maiers, Martin Biomedical Informatics and Computational Biology University of Minnesota Rochester Rochester MN 55904 United States Bioinformatics Research National Marrow Donor Program Minneapolis MN 55413 United States

Aim A Neural network (NN) to predict serologic specificity of HLA alleles was first introduced in 2003 by Maiers et al. The procedure involved several manual steps and prediction errors were estimated from an arbitrarily selected single partition of available labeled data. However, these might not reflect the true predictive capability of the model. Our aim was to provide an automated framework for the existing NN, estimate the true prediction accuracy and capability using a cross-validation model and compare with other predictive machine learning techniques. Methods An NN framework was created and validated in the R software suite to predict serologic specificities for HLA-A, B, C, DQB1, DRB1 and DPB1. We established a method to tune NN parameters using a grid technique with leave one out cross validation and compared the prediction accuracy of the NN with a k-Nearest Neighbors (kNN) technique. Results A concordance between the manual and automated NN of upwards of 96% was obtained for all loci, measured on an independent unlabelled test dataset. With parameter tuning techniques for the NN we have estimated the average prediction accuracy across all loci to be 86% .We also observed that kNN provide d better prediction accuracy of 90% (average). These numbers were slightly lower than the previous NN validation based on a single partition of the data, but still provide acceptable performance for both the NN and kNN. Conclusions Human involvement in NN model selection is both extremely time consuming and prone to errors. An automated framework minimizes the risks of over-training by monitoring variations in errors. Cross-validation techniques are known to provide better generalization error estimates. In this context, our reported error estimates maybe closer to the actual error rates one can achieve with similar models. We also demonstrate kNN outperforms the existing NN. This result should support efforts in finding relationships between alleles and associated serol

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CoreGenes3.5: A webserver for the determination of core genes from sets of viral and small bacterial genomes

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BMC Research Notes 2013年第1期6卷 1-4页

作者： Turner, Dann Reynolds, Darren Seto, Donald Mahadevan, Padmanabhan Centre for Research in Biosciences Faculty of Applied Sciences University of the West of England Bristol BS16 1QY United Kingdom Bioinformatics and Computational Biology Program School of Systems Biology George Mason University Manassas VA 20110 United States Department of Biology University of Tampa Tampa FL 33606 United States

Background: CoreGenes3.5 is a webserver that determines sets of core genes from viral and small bacterial genomes as an automated batch process. Previous versions of CoreGenes have been used to classify bacteriophage genomes and mine data from pathogen genomes. Findings. CoreGenes3.5 accepts as input GenBank accession numbers of genomes and performs iterative BLASTP analyses to output a set of core genes. After completion of the program run, the results can be either displayed in a new window for one pair of reference and query genomes or emailed to the user for multiple pairs of small genomes in tabular format. Conclusions: With the number of genomes sequenced increasing daily and interest in determining phylogenetic relationships, CoreGenes3.5 provides a user-friendly web interface for wet-bench biologists to process multiple small genomes for core gene determinations. CoreGenes3.5 is available at. © 2013 Turner et al.;licensee BioMed Central Ltd.

关键词： Bacteriophage Core genes Data mining Taxonomy Viral genomics

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Broadening the scope of nanopublications

Broadening the scope of nanopublications

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10th International Conference on The Semantic Web: Semantics and Big Data, ESWC 2013

作者： Kuhn, Tobias Barbano, Paolo Emilio Nagy, Mate Levente Krauthammer, Michael Department of Pathology Yale University New Haven United States Department of Sociology Particular of Modeling and Simulation ETH Zurich Switzerland Department of Mathematics Yale University United States Program for Computational Biology and Bioinformatics Yale University United States

ISBN: (纸本)9783642382871

In this paper, we present an approach for extending the existing concept of nanopublications-tiny entities of scientific results in RDF representation-to broaden their application range. The proposed extension uses English sentences to represent informal and underspecified scientific claims. These sentences follow a syntactic and semantic scheme that we call AIDA (Atomic, Independent, Declarative, Absolute), which provides a uniform and succinct representation of scientific assertions. Such AIDA nanopublications are compatible with the existing nanopublication concept and enjoy most of its advantages such as information sharing, interlinking of scientific findings, and detailed attribution, while being more flexible and applicable to a much wider range of scientific results. We show that users are able to create AIDA sentences for given scientific results quickly and at high quality, and that it is feasible to automatically extract and interlink AIDA nanopublications from existing unstructured data sources. To demonstrate our approach, a web-based interface is introduced, which also exemplifies the use of nanopublications for non-scientific content, including meta-nanopublications that describe other nanopublications. © 2013 Springer-Verlag Berlin Heidelberg.

关键词： Semantics

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Meeting report on RECOMB 2013 （the 17th Annual International Conference on Research in computational Molecular biology）

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Frontiers of Electrical and Electronic Engineering in China 2013年第2期8卷 175-181页

作者： Xuegong Zhang Fengzhu Sun MOE Key Laboratory of Bioinformatics and Bioinformatics Division/Center for Synthetic and Systems Biology TNLIST and Department of Automation Tsinghua University Beijing 100084 China Molecular and Computational Biology Program University of Southern California Los Angeles CA 90089 USA

RECOMB 2013 was successfully held in Tsinghua University, Beijing, China on April 7-10, 2013, hosted by the bioinformatics Division and Center for Synthetic and Systems biology, Tsinghua National Laboratory for Information Science and Technology （TNLIST）. A total of about 500 professionals from both academia and industry from 29 countries and regions attended the conference and its RECOMB-Seq satellite workshop after the main conference.

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