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Elephantid Genomes Reveal the Molecular Bases of Woolly Mammoth Adaptations to the Arctic
Article Elephantid Genomes Reveal the Molecular Bases of Woolly Mammoth Adaptations to the Arctic Graphical Abstract Authors Vincent J. Lynch, Oscar C. Bedoya-Reina, Aakrosh Ratan, ..., George H. Perry, Webb Miller, Stephan C. Schuster Correspondence [email protected] (V.J.L.), [email protected] (W.M.) In Brief Lynch et al. sequence complete genomes from three Asian elephants and two woolly mammoths and identify amino acid changes unique to woolly mammoths. Woolly-mammoth-specific amino acid changes underlie cold- adapted traits in mammoths, including small ears, thick fur, and altered temperature sensation. Highlights d Complete genomes of three Asian elephants and two woolly mammoths were sequenced d Mammoth-specific amino acid changes were found in 1,642 protein-coding genes d Genes with mammoth-specific changes are associated with adaptation to extreme cold d An amino acid change in TRPV3 may have altered temperature sensation in mammoths Lynch et al., 2015, Cell Reports 12, 217–228 July 14, 2015 ª2015 The Authors http://dx.doi.org/10.1016/j.celrep.2015.06.027 Cell Reports Article Elephantid Genomes Reveal the Molecular Bases of Woolly Mammoth Adaptations to the Arctic Vincent J. Lynch,1,* Oscar C. Bedoya-Reina,2,4 Aakrosh Ratan,2,5 Michael Sulak,1 Daniela I. Drautz-Moses,2,6 George H. Perry,3 Webb Miller,2,* and Stephan C. Schuster2,6 1Department of Human Genetics, The University of Chicago, 920 East 58th Street, CLSC 319C, Chicago, IL 60637, USA 2Center for Comparative Genomics and Bioinformatics, Pennsylvania State University, 506B -
Ontology-Based Methods for Analyzing Life Science Data
Habilitation a` Diriger des Recherches pr´esent´ee par Olivier Dameron Ontology-based methods for analyzing life science data Soutenue publiquement le 11 janvier 2016 devant le jury compos´ede Anita Burgun Professeur, Universit´eRen´eDescartes Paris Examinatrice Marie-Dominique Devignes Charg´eede recherches CNRS, LORIA Nancy Examinatrice Michel Dumontier Associate professor, Stanford University USA Rapporteur Christine Froidevaux Professeur, Universit´eParis Sud Rapporteure Fabien Gandon Directeur de recherches, Inria Sophia-Antipolis Rapporteur Anne Siegel Directrice de recherches CNRS, IRISA Rennes Examinatrice Alexandre Termier Professeur, Universit´ede Rennes 1 Examinateur 2 Contents 1 Introduction 9 1.1 Context ......................................... 10 1.2 Challenges . 11 1.3 Summary of the contributions . 14 1.4 Organization of the manuscript . 18 2 Reasoning based on hierarchies 21 2.1 Principle......................................... 21 2.1.1 RDF for describing data . 21 2.1.2 RDFS for describing types . 24 2.1.3 RDFS entailments . 26 2.1.4 Typical uses of RDFS entailments in life science . 26 2.1.5 Synthesis . 30 2.2 Case study: integrating diseases and pathways . 31 2.2.1 Context . 31 2.2.2 Objective . 32 2.2.3 Linking pathways and diseases using GO, KO and SNOMED-CT . 32 2.2.4 Querying associated diseases and pathways . 33 2.3 Methodology: Web services composition . 39 2.3.1 Context . 39 2.3.2 Objective . 40 2.3.3 Semantic compatibility of services parameters . 40 2.3.4 Algorithm for pairing services parameters . 40 2.4 Application: ontology-based query expansion with GO2PUB . 43 2.4.1 Context . 43 2.4.2 Objective . -
2020 Program Book
PROGRAM BOOK Note that TAGC was cancelled and held online with a different schedule and program. This document serves as a record of the original program designed for the in-person meeting. April 22–26, 2020 Gaylord National Resort & Convention Center Metro Washington, DC TABLE OF CONTENTS About the GSA ........................................................................................................................................................ 3 Conference Organizers ...........................................................................................................................................4 General Information ...............................................................................................................................................7 Mobile App ....................................................................................................................................................7 Registration, Badges, and Pre-ordered T-shirts .............................................................................................7 Oral Presenters: Speaker Ready Room - Camellia 4.......................................................................................7 Poster Sessions and Exhibits - Prince George’s Exhibition Hall ......................................................................7 GSA Central - Booth 520 ................................................................................................................................8 Internet Access ..............................................................................................................................................8 -
PREDICTD: Parallel Epigenomics Data Imputation with Cloud-Based Tensor Decomposition
bioRxiv preprint doi: https://doi.org/10.1101/123927; this version posted April 4, 2017. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC 4.0 International license. PREDICTD: PaRallel Epigenomics Data Imputation with Cloud-based Tensor Decomposition Timothy J. Durham Maxwell W. Libbrecht Department of Genome Sciences Department of Genome Sciences University of Washington University of Washington J. Jeffry Howbert Jeff Bilmes Department of Genome Sciences Department of Electrical Engineering University of Washington University of Washington William Stafford Noble Department of Genome Sciences Department of Computer Science and Engineering University of Washington April 4, 2017 Abstract The Encyclopedia of DNA Elements (ENCODE) and the Roadmap Epigenomics Project have produced thousands of data sets mapping the epigenome in hundreds of cell types. How- ever, the number of cell types remains too great to comprehensively map given current time and financial constraints. We present a method, PaRallel Epigenomics Data Imputation with Cloud-based Tensor Decomposition (PREDICTD), to address this issue by computationally im- puting missing experiments in collections of epigenomics experiments. PREDICTD leverages an intuitive and natural model called \tensor decomposition" to impute many experiments si- multaneously. Compared with the current state-of-the-art method, ChromImpute, PREDICTD produces lower overall mean squared error, and combining methods yields further improvement. We show that PREDICTD data can be used to investigate enhancer biology at non-coding human accelerated regions. PREDICTD provides reference imputed data sets and open-source software for investigating new cell types, and demonstrates the utility of tensor decomposition and cloud computing, two technologies increasingly applicable in bioinformatics. -
Functional Analysis of Somatic Mutations Affecting Receptor Tyrosine Kinase Family in Metastatic Colorectal Cancer
Author Manuscript Published OnlineFirst on March 29, 2019; DOI: 10.1158/1535-7163.MCT-18-0582 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Functional analysis of somatic mutations affecting receptor tyrosine kinase family in metastatic colorectal cancer Leslie Duplaquet1, Martin Figeac2, Frédéric Leprêtre2, Charline Frandemiche3,4, Céline Villenet2, Shéhérazade Sebda2, Nasrin Sarafan-Vasseur5, Mélanie Bénozène1, Audrey Vinchent1, Gautier Goormachtigh1, Laurence Wicquart6, Nathalie Rousseau3, Ludivine Beaussire5, Stéphanie Truant7, Pierre Michel8, Jean-Christophe Sabourin9, Françoise Galateau-Sallé10, Marie-Christine Copin1,6, Gérard Zalcman11, Yvan De Launoit1, Véronique Fafeur1 and David Tulasne1 1 Univ. Lille, CNRS, Institut Pasteur de Lille, UMR 8161 - M3T – Mechanisms of Tumorigenesis and Target Therapies, F-59000 Lille, France. 2 Univ. Lille, Plateau de génomique fonctionnelle et structurale, CHU Lille, F-59000 Lille, France 3 TCBN - Tumorothèque Caen Basse-Normandie, F-14000 Caen, France. 4 Réseau Régional de Cancérologie – OncoBasseNormandie – F14000 Caen – France. 5 Normandie Univ, UNIROUEN, Inserm U1245, IRON group, Rouen University Hospital, Normandy Centre for Genomic and Personalized Medicine, F-76000 Rouen, France. 6 Tumorothèque du C2RC de Lille, F-59037 Lille, France. 7 Department of Digestive Surgery and Transplantation, CHU Lille, Univ Lille, 2 Avenue Oscar Lambret, 59037, Lille Cedex, France. 8 Department of hepato-gastroenterology, Rouen University Hospital, Normandie Univ, UNIROUEN, Inserm U1245, IRON group, F-76000 Rouen, France. 9 Department of Pathology, Normandy University, INSERM 1245, Rouen University Hospital, F 76 000 Rouen, France. 10 Department of Pathology, MESOPATH-MESOBANK, Centre León Bérard, Lyon, France. 11 Thoracic Oncology Department, CIC1425/CLIP2 Paris-Nord, Hôpital Bichat-Claude Bernard, Paris, France. -
Daniel Aalberts Scott Aa
PLOS Computational Biology would like to thank all those who reviewed on behalf of the journal in 2015: Daniel Aalberts Jeff Alstott Benjamin Audit Scott Aaronson Christian Althaus Charles Auffray Henry Abarbanel Benjamin Althouse Jean-Christophe Augustin James Abbas Russ Altman Robert Austin Craig Abbey Eduardo Altmann Bruno Averbeck Hermann Aberle Philipp Altrock Ferhat Ay Robert Abramovitch Vikram Alva Nihat Ay Josep Abril Francisco Alvarez-Leefmans Francisco Azuaje Luigi Acerbi Rommie Amaro Marc Baaden Orlando Acevedo Ettore Ambrosini M. Madan Babu Christoph Adami Bagrat Amirikian Mohan Babu Frederick Adler Uri Amit Marco Bacci Boris Adryan Alexander Anderson Stephen Baccus Tinri Aegerter-Wilmsen Noemi Andor Omar Bagasra Vera Afreixo Isabelle Andre Marc Baguelin Ashutosh Agarwal R. David Andrew Timothy Bailey Ira Agrawal Steven Andrews Wyeth Bair Jacobo Aguirre Ioan Andricioaei Chris Bakal Alaa Ahmed Ioannis Androulakis Joseph Bak-Coleman Hasan Ahmed Iris Antes Adam Baker Natalie Ahn Maciek Antoniewicz Douglas Bakkum Thomas Akam Haroon Anwar Gabor Balazsi Ilya Akberdin Stefano Anzellotti Nilesh Banavali Eyal Akiva Miguel Aon Rahul Banerjee Sahar Akram Lucy Aplin Edward Banigan Tomas Alarcon Kevin Aquino Martin Banks Larissa Albantakis Leonardo Arbiza Mukul Bansal Reka Albert Murat Arcak Shweta Bansal Martí Aldea Gil Ariel Wolfgang Banzhaf Bree Aldridge Nimalan Arinaminpathy Lei Bao Helen Alexander Jeffrey Arle Gyorgy Barabas Alexander Alexeev Alain Arneodo Omri Barak Leonidas Alexopoulos Markus Arnoldini Matteo Barberis Emil Alexov -
Motif Selection Using Simulated Annealing Algorithm with Application to Identify Regulatory Elements
Motif Selection Using Simulated Annealing Algorithm with Application to Identify Regulatory Elements A thesis presented to the faculty of the Russ College of Engineering and Technology of Ohio University In partial fulfillment of the requirements for the degree Master of Science Liang Chen August 2018 © 2018 Liang Chen. All Rights Reserved. 2 This thesis titled Motif Selection Using Simulated Annealing Algorithm with Application to Identify Regulatory Elements by LIANG CHEN has been approved for the Department of Electrical Engineering and Computer Science and the Russ College of Engineering and Technology by Lonnie Welch Professor of Electrical Engineering and Computer Science Dennis Irwin Dean, Russ College of Engineering and Technology 3 Abstract CHEN, LIANG, M.S., August 2018, Computer Science Master Program Motif Selection Using Simulated Annealing Algorithm with Application to Identify Regulatory Elements (106 pp.) Director of Thesis: Lonnie Welch Modern research on gene regulation and disorder-related pathways utilize the tools such as microarray and RNA-Seq to analyze the changes in the expression levels of large sets of genes. In silico motif discovery was performed based on the gene expression profile data, which generated a large set of candidate motifs (usually hundreds or thousands of motifs). How to pick a set of biologically meaningful motifs from the candidate motif set is a challenging biological and computational problem. As a computational problem it can be modeled as motif selection problem (MSP). Building solutions for motif selection problem will give biologists direct help in finding transcription factors (TF) that are strongly related to specific pathways and gaining insights of the relationships between genes. -
Annual Scientific Report 2011 Annual Scientific Report 2011 Designed and Produced by Pickeringhutchins Ltd
European Bioinformatics Institute EMBL-EBI Annual Scientific Report 2011 Annual Scientific Report 2011 Designed and Produced by PickeringHutchins Ltd www.pickeringhutchins.com EMBL member states: Austria, Croatia, Denmark, Finland, France, Germany, Greece, Iceland, Ireland, Israel, Italy, Luxembourg, the Netherlands, Norway, Portugal, Spain, Sweden, Switzerland, United Kingdom. Associate member state: Australia EMBL-EBI is a part of the European Molecular Biology Laboratory (EMBL) EMBL-EBI EMBL-EBI EMBL-EBI EMBL-European Bioinformatics Institute Wellcome Trust Genome Campus, Hinxton Cambridge CB10 1SD United Kingdom Tel. +44 (0)1223 494 444, Fax +44 (0)1223 494 468 www.ebi.ac.uk EMBL Heidelberg Meyerhofstraße 1 69117 Heidelberg Germany Tel. +49 (0)6221 3870, Fax +49 (0)6221 387 8306 www.embl.org [email protected] EMBL Grenoble 6, rue Jules Horowitz, BP181 38042 Grenoble, Cedex 9 France Tel. +33 (0)476 20 7269, Fax +33 (0)476 20 2199 EMBL Hamburg c/o DESY Notkestraße 85 22603 Hamburg Germany Tel. +49 (0)4089 902 110, Fax +49 (0)4089 902 149 EMBL Monterotondo Adriano Buzzati-Traverso Campus Via Ramarini, 32 00015 Monterotondo (Rome) Italy Tel. +39 (0)6900 91402, Fax +39 (0)6900 91406 © 2012 EMBL-European Bioinformatics Institute All texts written by EBI-EMBL Group and Team Leaders. This publication was produced by the EBI’s Outreach and Training Programme. Contents Introduction Foreword 2 Major Achievements 2011 4 Services Rolf Apweiler and Ewan Birney: Protein and nucleotide data 10 Guy Cochrane: The European Nucleotide Archive 14 Paul Flicek: -
Signals Through Two Different Pathways Immunoglobulin Receptor Able to Transduce of Cd300b, a New Activating Molecular and Funct
Molecular and Functional Characterization of CD300b, a New Activating Immunoglobulin Receptor Able to Transduce Signals through Two Different Pathways This information is current as of September 26, 2021. Águeda Martínez-Barriocanal and Joan Sayós J Immunol 2006; 177:2819-2830; ; doi: 10.4049/jimmunol.177.5.2819 http://www.jimmunol.org/content/177/5/2819 Downloaded from References This article cites 47 articles, 20 of which you can access for free at: http://www.jimmunol.org/content/177/5/2819.full#ref-list-1 http://www.jimmunol.org/ Why The JI? Submit online. • Rapid Reviews! 30 days* from submission to initial decision • No Triage! Every submission reviewed by practicing scientists • Fast Publication! 4 weeks from acceptance to publication by guest on September 26, 2021 *average Subscription Information about subscribing to The Journal of Immunology is online at: http://jimmunol.org/subscription Permissions Submit copyright permission requests at: http://www.aai.org/About/Publications/JI/copyright.html Email Alerts Receive free email-alerts when new articles cite this article. Sign up at: http://jimmunol.org/alerts The Journal of Immunology is published twice each month by The American Association of Immunologists, Inc., 1451 Rockville Pike, Suite 650, Rockville, MD 20852 Copyright © 2006 by The American Association of Immunologists All rights reserved. Print ISSN: 0022-1767 Online ISSN: 1550-6606. The Journal of Immunology Molecular and Functional Characterization of CD300b, a New Activating Immunoglobulin Receptor Able to Transduce Signals through Two Different Pathways1 A´ gueda Martı´nez-Barriocanal and Joan Sayo´s2 In this study, we describe the characterization of human CD300b, a novel member of the CMRF-35/immune receptor expressed by myeloid cell (IREM) multigene family of immune receptors. -
Gene Expression Profiling of Nfatc1-Knockdown In
cells Article Gene Expression Profiling of NFATc1-Knockdown in RAW 264.7 Cells: An Alternative Pathway for Macrophage Differentiation Roberta Russo , Selene Mallia, Francesca Zito and Nadia Lampiasi * Institute of Biomedicine and Molecular Immunology “Alberto Monroy”, National Research Council, Via Ugo La Malfa 153, 90146 Palermo, Italy; [email protected] (R.R.); [email protected] (S.M.); [email protected] (F.Z.) * Correspondence: [email protected]; Tel.: +39-091-680-9513 Received: 13 December 2018; Accepted: 5 February 2019; Published: 7 February 2019 Abstract: NFATc1, which is ubiquitous in many cell types, is the master regulator of osteoclastogenesis. However, the molecular mechanisms by which NFATc1 drives its transcriptional program to produce osteoclasts from macrophages (M) remains poorly understood. We performed quantitative PCR (QPCR) arrays and bioinformatic analyses to discover new direct and indirect NFATc1 targets. The results revealed that NFATc1 significantly modified the expression of 55 genes in untransfected cells and 31 genes after NFATc1-knockdown (≥2). Among them, we focused on 19 common genes that showed changes in the PCR arrays between the two groups of cells. Gene Ontology (GO) demonstrated that genes related to cell differentiation and the development process were significantly (p > 0.05) affected by NFATc1-knockdown. Among all the genes analyzed, we focused on GATA2, which was up-regulated in NFATc1-knockdown cells, while its expression was reduced after NFATc1 rescue. Thus, we suggest GATA2 as a new target of NFATc1. Ingenuity Pathway Analysis (IPA) identified up-regulated GATA2 and the STAT family members as principal nodes involved in cell differentiation. -
Genome Informatics
Joint Cold Spring Harbor Laboratory/Wellcome Trust Conference GENOME INFORMATICS September 15–September 19, 2010 View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Cold Spring Harbor Laboratory Institutional Repository Joint Cold Spring Harbor Laboratory/Wellcome Trust Conference GENOME INFORMATICS September 15–September 19, 2010 Arranged by Inanc Birol, BC Cancer Agency, Canada Michele Clamp, BioTeam, Inc. James Kent, University of California, Santa Cruz, USA SCHEDULE AT A GLANCE Wednesday 15th September 2010 17.00-17.30 Registration – finger buffet dinner served from 17.30-19.30 19.30-20:50 Session 1: Epigenomics and Gene Regulation 20.50-21.10 Break 21.10-22.30 Session 1, continued Thursday 16th September 2010 07.30-09.00 Breakfast 09.00-10.20 Session 2: Population and Statistical Genomics 10.20-10:40 Morning Coffee 10:40-12:00 Session 2, continued 12.00-14.00 Lunch 14.00-15.20 Session 3: Environmental and Medical Genomics 15.20-15.40 Break 15.40-17.00 Session 3, continued 17.00-19.00 Poster Session I and Drinks Reception 19.00-21.00 Dinner Friday 17th September 2010 07.30-09.00 Breakfast 09.00-10.20 Session 4: Databases, Data Mining, Visualization and Curation 10.20-10.40 Morning Coffee 10.40-12.00 Session 4, continued 12.00-14.00 Lunch 14.00-16.00 Free afternoon 16.00-17.00 Keynote Speaker: Alex Bateman 17.00-19.00 Poster Session II and Drinks Reception 19.00-21.00 Dinner Saturday 18th September 2010 07.30-09.00 Breakfast 09.00-10.20 Session 5: Sequencing Pipelines and Assembly 10.20-10.40 -
(Title of the Thesis)*
Discovery of Flexible Gap Patterns from Sequences by En Hui Zhuang A thesis presented to the University of Waterloo in fulfillment of the thesis requirement for the degree of Doctor of Philosophy in Systems Design Engineering Waterloo, Ontario, Canada, 2014 ©En Hui Zhuang 2014 AUTHOR'S DECLARATION I hereby declare that I am the sole author of this thesis. This is a true copy of the thesis, including any required final revisions, as accepted by my examiners. I understand that my thesis may be made electronically available to the public. ii Abstract Human genome contains abundant motifs bound by particular biomolecules. These motifs are involved in the complex regulatory mechanisms of gene expressions. The dominant mechanism behind the intriguing gene expression patterns is known as combinatorial regulation, achieved by multiple cooperating biomolecules binding in a nearby genomic region to provide a specific regulatory behavior. To decipher the complicated combinatorial regulation mechanism at work in the cellular processes, there is a pressing need to identify co-binding motifs for these cooperating biomolecules in genomic sequences. The great flexibility of the interaction distance between nearby cooperating biomolecules leads to the presence of flexible gaps in between component motifs of a co- binding motif. Many existing motif discovery methods cannot handle co-binding motifs with flexible gaps. Existing co-binding motif discovery methods are ineffective in dealing with the following problems: (1) co-binding motifs may not appear in a large fraction of the input sequences, (2) the lengths of component motifs are unknown and (3) the maximum range of the flexible gap can be large.