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Targeting cancer associated fibroblasts in pancreatic ductal adenocarcinoma: Identification and characterization of therapeutic candidates

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Pancreatology 2015年第3期15卷 S51-S52页

作者： Magdolna Djurec Osvaldo Graña David Pisano Raúl Rabadán Carmen Guerra Mariano Barbacid Molecular Oncology Centro Nacional de Investigaciones Oncológicas (CNIO) E-28029 Madrid Spain Biotechnology Program Centro Nacional de Investigaciones Oncológicas (CNIO) E-28029 Madrid Spain Center for Computational Biology and Bioinformatics Columbia University College of Physicians and Surgeons New York NY 10032 USA

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'Isotopo' a database application for facile analysis and management of mass isotopomer data

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Database : the journal of biological databases and curation 2014年第2014期2014卷 1-8页

作者： Ahmed, Zeeshan Zeeshan, Saman Huber, Claudia Hensel, Michael Schomburg, Dietmar Münch, Richard Eylert, Eva Eisenreich, Wolfgang Dandekar, Thomas Department of Bioinformatics Biocenter University of Würzburg Am Hubland Department of Neurobiology and Genetics Biocenter University of Wuerzburg Am Hubland Institute of Molecular and Translational Therapeutic Strategies Hannover Medical School Lehrstuhl für Biochemie Center of Isotopologue Profiling Technische Universität München Division of Microbiology University of Osnabrück Department of Bioinformatics and Biochemistry Technical University Braunschweig Institute for Microbiology Biozentrum Technical University Braunschweig Germany and Computational biology and structures program European Molecular Biology Laboratory Germany Department of Bioinformatics Biocenter University of Würzburg Am Hubland Department of Neurobiology and Genetics Biocenter University of Wuerzburg Am Hubland Institute of Molecular and Translational Therapeutic Strategies Hannover Medical School Lehrstuhl für Biochemie Center of Isotopologue Profiling Technische Universität München Division of Microbiology University of Osnabrück Department of Bioinformatics and Biochemistry Technical University Braunschweig Institute for Microbiology Biozentrum Technical University Braunschweig Germany and Computational biology and structures program European Molecular Biology Laboratory 97074 Wuerzburg 97074 Wuerzburg Carl-Neuberg-Str. 1 Lichtenbergstraße 4 Barbarastraße 11 Gebäude 36 49076 Osnabrück Langer Kamp 19B 2. Obergeschoss Spielmannstraße 7 Meyerhofstr. 1 97074 Wuerzburg 97074 Wuerzburg Carl-Neuberg-Str. 1 Lichtenbergstraße 4 Barbarastraße 11 Gebäude 36 49076 Osnabrück Langer Kamp 19B 2. Obergeschoss Spielmannstraße 7 Braunschweig 38106 Hanover Germany Department of Bioinformatics Biocenter University of Würzburg Am Hubland Department of Neurobiology and Genetics Biocenter University of Wuerzburg Am Hubland Institute of Molecular and Translational Therapeutic Strategies Hannover Medical School Lehrstuhl für Biochemie Center of Isotopologue Profiling Technische Univer

UNLABELLED: The composition of stable-isotope labelled isotopologues/isotopomers in metabolic products can be measured by mass spectrometry and supports the analysis of pathways and fluxes. As a prerequisite, the original mass spectra have to be processed, managed and stored to rapidly calculate, analyse and compare isotopomer enrichments to study, for instance, bacterial metabolism in infection. For such applications, we provide here the database application 'Isotopo'. This software package includes (i) a database to store and process isotopomer data, (ii) a parser to upload and translate different data formats for such data and (iii) an improved application to process and convert signal intensities from mass spectra of (13)C-labelled metabolites such as tertbutyldimethylsilyl-derivatives of amino acids. Relative mass intensities and isotopomer distributions are calculated applying a partial least square method with iterative refinement for high precision data. The data output includes formats such as graphs for overall enrichments in amino acids. The package is user-friendly for easy and robust data management of multiple experiments.;AVAILABILITY: The 'Isotopo' software is available at the following web link (section Download): http://***/bioinformatics/isotopo/. The package contains three additional files: software executable setup (installer), one data set file (discussed in this article) and one excel file (which can be used to convert data from excel to '.iso' format). The 'Isotopo' software is compatible only with the Microsoft Windows operating system.;DATABASE URL: http://***/bioinformatics/isotopo/. © The Author(s) 2014. Published by Oxford University Press.

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Calibrated Langevin-dynamics simulations of intrinsically disordered proteins

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Physical Review E 2014年第4期90卷 042709-042709页

作者： W. Wendell Smith Po-Yi Ho Corey S. O'Hern Department of Physics Yale University New Haven Connecticut 06520-8120 USA Integrated Graduate Program in Physical and Engineering Biology Yale University New Haven Connecticut 06520-8114 USA Department of Mechanical Engineering and Materials Science Yale University New Haven Connecticut 06520-8286 USA Department of Applied Physics Yale University New Haven Connecticut 06520-8267 USA Program in Computational Biology and Bioinformatics Yale University New Haven Connecticut 06520 USA

We perform extensive coarse-grained (CG) Langevin dynamics simulations of intrinsically disordered proteins (IDPs), which possess fluctuating conformational statistics between that for excluded volume random walks and collapsed globules. Our CG model includes repulsive steric, attractive hydrophobic, and electrostatic interactions between residues and is calibrated to a large collection of single-molecule fluorescence resonance energy transfer data on the interresidue separations for 36 pairs of residues in five IDPs: α−, β−, and γ-synuclein, the microtubule-associated protein τ, and prothymosin α. We find that our CG model is able to recapitulate the average interresidue separations regardless of the choice of the hydrophobicity scale, which shows that our calibrated model can robustly capture the conformational dynamics of IDPs. We then employ our model to study the scaling of the radius of gyration with chemical distance in 11 known IDPs. We identify a strong correlation between the distance to the dividing line between folded proteins and IDPs in the mean charge and hydrophobicity space and the scaling exponent of the radius of gyration with chemical distance along the protein.

关键词： LANGEVIN equations SIMULATION methods & models CONFORMATIONAL analysis MICROTUBULE-associated proteins HYDROPHOBIC compounds

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A penalized linear mixed model for genomic prediction using pedigree structures

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BMC proceedings 2014年第SUPPL 1 GENETIC ANALYSIS WORKSHOP 18VANESSA OLMO期8卷 S67页

作者： Can Yang Cong Li Mengjie Chen Xiaowei Chen Lin Hou Hongyu Zhao Department of Biostatistics Yale School of Public Health New Haven CT 06520 USA. Program in Computational Biology and Bioinformatics Yale University New Haven CT 06520 USA. Department of Biostatistics Yale School of Public Health New Haven CT 06520 USA

Genetic Analysis Workshop 18 provided a platform for evaluating genomic prediction power based on single-nucleotide polymorphisms from single-nucleotide polymorphism array data and sequencing data. Also, Genetic Analysis Workshop 18 provided a diverse pedigree structure to be explored in prediction. In this study, we attempted to combine pedigree information with single-nucleotide polymorphism data to predict systolic blood pressure. Our results suggested that the prediction power based on pedigree information only could be unsatisfactory. Using additional information such as single-nucleotide polymorphism genotypes would improve prediction accuracy. In particular, the improvement can be significant when there exist a few single-nucleotide polymorphisms with relatively larger effect sizes. We also compared the prediction performance based on genome-wide association study data (ie, common variants) and sequencing data (ie, common variants plus low-frequency variants). The experimental result showed that inclusion of low frequency variants could not lead to improvement of prediction accuracy.

关键词： Genomic Prediction Pedigree Information GWAS Data Genetic Analysis Workshop Pedigree Structure

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Incorporating gene annotations as node metadata to improve network centrality measures for better node ranking 13

Incorporating gene annotations as node metadata to improve n...

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2013 4th ACM Conference on bioinformatics, computational biology and Biomedical Informatics, ACM-BCB 2013

作者： Mistry, Divya Dickerson, Julie Department of Electrical and Computer Engineering Bioinformatics and Computational Biology Program Iowa State University Ames IA United States

ISBN: (纸本)9781450324342

Network centrality measures allow ranking of nodes and edges based on their importance to the network topology. Closeness centrality [1] and shortest path betweenness centrality [2] are two of the most popular and well-utilized centrality measures that have provided good results [3,4,5,6]. Both of these centralities rely exclusively on topological features of the network [7] to calculate node importance. We propose an improvement to these path length based centrality measures that incorporate nodespecific metadata to provide biologically relevant node ranking. We choose gene annotations and gene ontology (GO) evidences as our metadata to highlight the new approach. Application of the newly proposed centrality measures to synthetic networks, and pathogen infected barley's gene co-expression networks resulted in a significantly better prioritization of the nodes. We compared our results against unmodified centrality measures applied to the same networks. Our proposed improvements provide a new avenue for tailoring centrality measures for biological networks, and hold great potential for further improvement of random walk based [8] and motif-based centrality [9] measures. Copyright © 2007 by the Association for Computing Machinery.

关键词： Metadata

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computational approaches to understanding protein aggregation in neurodegeneration

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Journal of Molecular Cell biology 2014年第2期8卷 104-115页

作者： Rachel L. Redler David Shirvanyants Onur Dagliyan Feng Ding Doo Nam Kim Pradeep Kota Elizabeth A. Proctor Srinivas Ramachandran Arpit Tandon Nikolay V. Dokholyan Department of Biochemistry and Biophysics University of North Carolina at Chapel Hill Chapel Hill NC USA Program in Cellular and Molecular Biophysics University of North Carolina at Chapel Hill Chapel Hill NC USA Curriculum in Bioinformatics and Computational Biology University of North Carolina at Chapel Hill Chapel Hill NC USA Center for Computational and Systems Biology University of NorthCarolina at Chapel Hill chapel Hill NC USA Department of Physics and Astronomy Ctemson University Ctemson SC USA Cellular Growth Mechanisms Section Laboratory of Cell and Developmental Signaling Frederick National Laboratory for Cancer Research Frederick MD USA Division of Basic Sciences Fred Hutchison Cancer Research Center Seatt{e WA USA

The generation of toxic non-native protein conformers has emerged as a unifying thread among disorders such as Alzheimer＇s disease, Parkinson＇s disease, and amyotrophic lateral sclerosis. Atomic-level detail regarding dynamical changes that facilitate protein aggre- gation, as well as the structural features of large-scale ordered aggregates and soluble non-native oligomers, would contribute signifi- cantly to current understanding of these complex phenomena and offer potential strategies for inhibiting formation of cytotoxic species. However, experimental limitations often preclude the acquisition of high-resolution structural and mechanistic information for aggregating systems. computational methods, particularly those combine both aU-atom and coarse-grained simulations to cover a wide range of time and length scales, have thus emerged as crucial tools for investigating protein aggregation. Here we review the current state of computational methodology for the study of protein self-assembly, with a focus on the application of these methods toward understanding of protein aggregates in human neurodegenerative disorders.

关键词： protein aggregation molecular dynamics protein folding neurodegeneration

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Using dnase digestion data to accurately identify transcription factor binding sites 18

Using dnase digestion data to accurately identify transcript...

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18th Pacific Symposium on Biocomputing, PSB 2013

作者： Luo, Kaixuan Hartemink, Alexander J. Program in Computational Biology and Bioinformatics Duke University DurhamNC27708 United States Department of Computer Science Duke University DurhamNC27708 United States

Identifying binding sites of transcription factors (TFs) is a key task in deciphering transcriptional regulation. ChIP-based methods are used to survey the genomic locations of a single TF in each experiment. But methods combining DNase digestion data with TF binding specificity information could potentially be used to survey the locations of many TFs in the same experiment, provided such methods permit reasonable levels of sensitivity and specificity. Here, we present a simple such method that outperforms a leading recent method, centipede, marginally in human but dramatically in yeast (average auROC across 20 TFs increases from 74% to 94%). Our method is based on logistic regression and thus benefits from supervision, but we show that partially and completely unsupervised variants perform nearly as well. Because the number of parameters in our method is at least an order of magnitude smaller than centipede, we dub it millipede.

关键词： Transcription factors

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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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