Multinational Coordinated Arabidopsis Thaliana Genome Research Project Page 1 of 2
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
Analysis of the Impact of Sequencing Errors on Blast Using Fault Injection
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Illinois Digital Environment for Access to Learning and Scholarship Repository ANALYSIS OF THE IMPACT OF SEQUENCING ERRORS ON BLAST USING FAULT INJECTION BY SO YOUN LEE THESIS Submitted in partial fulfillment of the requirements for the degree of Master of Science in Electrical and Computer Engineering in the Graduate College of the University of Illinois at Urbana-Champaign, 2013 Urbana, Illinois Adviser: Professor Ravishankar K. Iyer ABSTRACT This thesis investigates the impact of sequencing errors in post-sequence computational analyses, including local alignment search and multiple sequence alignment. While the error rates of sequencing technology are commonly reported, the significance of these numbers cannot be fully grasped without putting them in the perspective of their impact on the downstream analyses that are used for biological research, forensics, diagnosis of diseases, etc. I approached the quantification of the impact using fault injection. Faults were injected in the input sequence data, and the analyses were run. Change in the output of the analyses was interpreted as the impact of faults, or errors. Three commonly used algorithms were used: BLAST, SSEARCH, and ProbCons. The main contributions of this work are the application of fault injection to the reliability analysis in bioinformatics and the quantitative demonstration that a small error rate in the sequence data can alter the output of the analysis in a significant way. BLAST and SSEARCH are both local alignment search tools, but BLAST is a heuristic implementation, while SSEARCH is based on the optimal Smith-Waterman algorithm. -
Advancing Solutions to the Carbohydrate Sequencing Challenge † † † ‡ § Christopher J
Perspective Cite This: J. Am. Chem. Soc. 2019, 141, 14463−14479 pubs.acs.org/JACS Advancing Solutions to the Carbohydrate Sequencing Challenge † † † ‡ § Christopher J. Gray, Lukasz G. Migas, Perdita E. Barran, Kevin Pagel, Peter H. Seeberger, ∥ ⊥ # ⊗ ∇ Claire E. Eyers, Geert-Jan Boons, Nicola L. B. Pohl, Isabelle Compagnon, , × † Göran Widmalm, and Sabine L. Flitsch*, † School of Chemistry & Manchester Institute of Biotechnology, The University of Manchester, 131 Princess Street, Manchester M1 7DN, U.K. ‡ Institute for Chemistry and Biochemistry, Freie Universitaẗ Berlin, Takustraße 3, 14195 Berlin, Germany § Biomolecular Systems Department, Max Planck Institute for Colloids and Interfaces, Am Muehlenberg 1, 14476 Potsdam, Germany ∥ Department of Biochemistry, Institute of Integrative Biology, University of Liverpool, Crown Street, Liverpool L69 7ZB, U.K. ⊥ Complex Carbohydrate Research Center, University of Georgia, Athens, Georgia 30602, United States # Department of Chemistry, Indiana University, Bloomington, Indiana 47405, United States ⊗ Institut Lumierè Matiere,̀ UMR5306 UniversitéLyon 1-CNRS, Universitéde Lyon, 69622 Villeurbanne Cedex, France ∇ Institut Universitaire de France IUF, 103 Blvd St Michel, 75005 Paris, France × Department of Organic Chemistry, Arrhenius Laboratory, Stockholm University, S-106 91 Stockholm, Sweden *S Supporting Information established connection between their structure and their ABSTRACT: Carbohydrates possess a variety of distinct function, full characterization of unknown carbohydrates features with -
Genome Sequence of the Progenitor of the Wheat D Genome Aegilops Tauschii Ming-Cheng Luo1*, Yong Q
OPEN LETTER doi:10.1038/nature24486 Genome sequence of the progenitor of the wheat D genome Aegilops tauschii Ming-Cheng Luo1*, Yong Q. Gu2*, Daniela Puiu3*, Hao Wang4,5,6*, Sven O. Twardziok7*, Karin R. Deal1, Naxin Huo1,2, Tingting Zhu1, Le Wang1, Yi Wang1,2, Patrick E. McGuire1, Shuyang Liu1, Hai Long1, Ramesh K. Ramasamy1, Juan C. Rodriguez1, Sonny L. Van1, Luxia Yuan1, Zhenzhong Wang1,8, Zhiqiang Xia1, Lichan Xiao1, Olin D. Anderson2, Shuhong Ouyang2,8, Yong Liang2,8, Aleksey V. Zimin3, Geo Pertea3, Peng Qi4,5, Jeffrey L. Bennetzen6, Xiongtao Dai9, Matthew W. Dawson9, Hans-Georg Müller9, Karl Kugler7, Lorena Rivarola-Duarte7, Manuel Spannagl7, Klaus F. X. Mayer7,10, Fu-Hao Lu11, Michael W. Bevan11, Philippe Leroy12, Pingchuan Li13, Frank M. You13, Qixin Sun8, Zhiyong Liu8, Eric Lyons14, Thomas Wicker15, Steven L. Salzberg3,16, Katrien M. Devos4,5 & Jan Dvořák1 Aegilops tauschii is the diploid progenitor of the D genome of We conclude therefore that the size of the Ae. tauschii genome is about hexaploid wheat1 (Triticum aestivum, genomes AABBDD) and 4.3 Gb. an important genetic resource for wheat2–4. The large size and To assess the accuracy of our assembly, sequences of 195 inde- highly repetitive nature of the Ae. tauschii genome has until now pendently sequenced and assembled AL8/78 BAC clones8, which precluded the development of a reference-quality genome sequence5. contained 25,540,177 bp in 2,405 unordered contigs, were aligned to Here we use an array of advanced technologies, including ordered- Aet v3.0. Five contigs failed to align and six extended partly into gaps, clone genome sequencing, whole-genome shotgun sequencing, accounting for 0.25% of the total length of the contigs. -
Arabidopsis Thaliana
Downloaded from genome.cshlp.org on September 28, 2021 - Published by Cold Spring Harbor Laboratory Press RESEARCH A Physical Map of Chromosome 2 of Arabidopsis thaliana Eve Ann Zachgo, 2,4 Ming Li Wang, 1'2'4 Julia Dewdney, 1'2 David Bouchez, 3 Christine Carnilleri, 3 Stephen Belmonte, 2 Lu Huang, 2 Maureen Dolan, 2 and Howard M. Goodman 1'2'5 1Department of Genetics, Harvard Medical School and 2Department of Molecular Biology, Massachusetts General Hospital, Boston, Massachusetts 02114; 3Laboratoire de Biologie Cellulaire, Institut National de la Recherche Agronomique (INRA), 78026 Versailles CEDEX, France A yeast artificial chromosome (YAC] physical map of chromosome 2 of Arabidopsis thaliana has been constructed by hybridization of 69 DNA markers and 61 YAC end probes to gridded arrays of YAC clones. Thirty-four YACs in four contigs define the chromosome. Complete closure of the map was not attained because some regions of the chromosome were repetitive or were not represented in the YAC library. Based on the sizes of the YACs and their coverage of the chromosome, the length of chromosome 2 is estimated to be at least 18 Mb. These data provide the means for immediately identifying the YACs containing a genetic locus mapped on Arabidopsis chromosome 2. The small flowering plant Arabidopsis thaliana is ters (Maluszynska and Heslop-Harrison 1991; A1- an excellent model system for metabolic, genetic, bini 1994; Copenhaver et al. 1995). We present and developmental studies in plants. Its haploid here a YAC contig physical map of chromosome nuclear genome is small (-100 Mb), consisting of 2 of A. -
Genomes:Genomes: Whatwhat Wewe Knowknow …… Andand Whatwhat Wewe Don’Tdon’T Knowknow
Genomes:Genomes: WhatWhat wewe knowknow …… andand whatwhat wewe don’tdon’t knowknow Complete draft sequence 2001 OctoberOctober 15,15, 20072007 Dr.Dr. StefanStefan Maas,Maas, BioSBioS Lehigh Lehigh U.U. © SMaas 2007 What we know Raw genome data © SMaas 2007 The range of genome sizes in the animal & plant kingdoms !! NoNo correlationcorrelation betweenbetween genomegenome sizesize andand complexitycomplexity © SMaas 2007 What accounts for the often massive and seemingly arbitrary differences in genome size observed among eukaryotic organisms? The fruit fly The mountain grasshopper Drosophila melanogaster Podisma pedestris 180 Mb 18,000 Mb The difference in genome size of a factor of 100 is difficult to explain in view of the apparently similar levels of evolutionary, developmental and behavioral complexity of these organisms. © SMaas 2007 ComplexityComplexity doesdoes notnot correlatecorrelate withwith genomegenome sizesize 3.4 × 10 9 bp 6.7 × 1011 bp Homo sapiens Amoeba dubia © SMaas 2007 ComplexityComplexity doesdoes notnot correlatecorrelate withwith genegene numbernumber ~31,000~31,000 genesgenes ~26,000~26,000 genesgenes ~50,000~50,000 genesgenes © SMaas 2007 IsIs anan ExpansionExpansion inin GeneGene NumberNumber drivingdriving EvolutionEvolution ofof HigherHigher Organisms?Organisms? Vertebrata 30,000 Urochordata 16,000 Arthropoda 14,000 Nematoda 21,000 Fungi 2,000 – 13,000 Vascular plants 25,000 – 60,000 Unicellular sps. 5,000 – 10,000 Prokaryotes 500 - 7,000 © SMaas 2007 Structure of DNA Watson and Crick in 1953 proposed that DNA is a double helix in which the 4 bases are base paired, Adenine (A) with Thymine (T) and Guanine (G) with Cytosine (C). © SMaas 2007 © SMaas 2007 © SMaas 2007 Steps in the folding of DNA to create an eukaryotic chromosome 30 nm fiber (6 nucleosomes per turn) FactorFactor ofof condensation:condensation: Ca.Ca. -
Le Monde Végétal S'ouvre Aux Biotechnologies
LLLeee mmmooonnndddeee vvvééégggééétttaaalll sss’’’ooouuuvvvrrreee aaauuuxxx bbbiiioottteeeccchhhnnnooolllooogggiiieeesss NNNeeewww tttrrreeennndddsss iiinnn ppplllaaannnttt bbbiiiooolllooogggyyy aaannnddd bbbiiiooottteeeccchhhnnnooolllooogggyyy Colloque international de l’Académie des sciences Institut de France, 23 quai de Conti – 75006 Paris 15-16 septembre 2008 Grande salle des Résumés / Abstracts Comité scientifique Jean-François Bach Michel Caboche Henri Décamps Roland Douce Michel Delseny Christian Dumas Jules Hoffmann Jean-Dominique Lebreton Nicole Le Douarin Georges Pelletier Alain Prochiantz de l’Académie des sciences Marc Van Montagu Université de Gand Organisation Service des Colloques, Académie des sciences, Fabienne Bonfils Site Internet : www.academie-sciences.fr Société française de biologie végétale LLLeee mmmooonnndddeee vvvééégggééétttaaalll sss’’’ooouuuvvvrrreee aaauuuxxx bbbiiiooottteeccchhhnnnooolllooogggiiieeesss NNNeeewww tttrrreeennndddsss iiinnn ppplllaaannnttt bbbiiiooolllooogggyyy aaannnddd bbbiiiooottteeeccchhhnnnooolllooogggyyy PPRROOGGRRAAMMMMEE ___________________________________________________________________ _________________________________________________________________________________________ Colllloque iinternatiionall de ll’’Académiie des sciiences, Pariis 15-16 septembre 2008 Le monde végétal s’ouvre aux biotechnologies / New trends in plant biology and biotechnology Lundi 15 septembre / Monday, September 15th Session 1 Les plantes dévoilent leurs secrets / Plants Release their Secrets 8h45- 9h00 -
To Find Information About Arabidopsis Genes Leonore Reiser1, Shabari
UNIT 1.11 Using The Arabidopsis Information Resource (TAIR) to Find Information About Arabidopsis Genes Leonore Reiser1, Shabari Subramaniam1, Donghui Li1, and Eva Huala1 1Phoenix Bioinformatics, Redwood City, CA USA ABSTRACT The Arabidopsis Information Resource (TAIR; http://arabidopsis.org) is a comprehensive Web resource of Arabidopsis biology for plant scientists. TAIR curates and integrates information about genes, proteins, gene function, orthologs gene expression, mutant phenotypes, biological materials such as clones and seed stocks, genetic markers, genetic and physical maps, genome organization, images of mutant plants, protein sub-cellular localizations, publications, and the research community. The various data types are extensively interconnected and can be accessed through a variety of Web-based search and display tools. This unit primarily focuses on some basic methods for searching, browsing, visualizing, and analyzing information about Arabidopsis genes and genome, Additionally we describe how members of the community can share data using TAIR’s Online Annotation Submission Tool (TOAST), in order to make their published research more accessible and visible. Keywords: Arabidopsis ● databases ● bioinformatics ● data mining ● genomics INTRODUCTION The Arabidopsis Information Resource (TAIR; http://arabidopsis.org) is a comprehensive Web resource for the biology of Arabidopsis thaliana (Huala et al., 2001; Garcia-Hernandez et al., 2002; Rhee et al., 2003; Weems et al., 2004; Swarbreck et al., 2008, Lamesch, et al., 2010, Berardini et al., 2016). The TAIR database contains information about genes, proteins, gene expression, mutant phenotypes, germplasms, clones, genetic markers, genetic and physical maps, genome organization, publications, and the research community. In addition, seed and DNA stocks from the Arabidopsis Biological Resource Center (ABRC; Scholl et al., 2003) are integrated with genomic data, and can be ordered through TAIR. -
Saccharomyces Cerevisiae
letters to nature 19Department of Yeast Genetics, Institute of Molecular Medecine, John Radcliffe Hospital, Headington, Oxford OX3 9DU, UK The nucleotide sequence of 20L.N.C.I.B., Area Science Park, Padriciano 99, I-34012 Trieste, Italy 21GATC GmbH, Fitz-Arnold-Strasse 23, 78467 Konstanz, Germany Saccharomyces cerevisiae 22Carlsberg Laboratory, Gamle Carlsberg Vej 10, DK-2500 Copenhagen Valby, Denmark chromosome IV 23AGON GmbH, Glienicker Weg 185, D-12489 Berlin, Germany 24Katholieke Universiteit Leuven, Laboratory of Gene Technology, Willem de C. Jacq1, J. Alt-Mörbe2, B. Andre3, W. Arnold4, A. Bahr5, Croylaan, 42, B-3001 Leuven, Belgium J. P. G. Ballesta6, M. Bargues7, L. Baron8, A. Becker4, N. Biteau8, 25The Sanger Centre, Wellcome Trust Genome Campus, Hinxton, Cambridge H. Blöcker9, C. Blugeon1, J. Boskovic6, P. Brandt9, M. Brückner10 , CB10 1SA, UK M. J. Buitrago11 , F. Coster12, T. Delaveau1, F. del Rey11 , B. Dujon13, 26Department of Biochemistry, Stanford University, Beckman Center, Stanford L. G. Eide14, J. M. Garcia-Cantalejo6, A. Goffeau12, A. Gomez-Peris15, CA 94305-5307, USA C. Granotier8, V. Hanemann16, T. Hankeln5, J. D. Hoheisel17, W. Jäger9, 27The Genome Sequencing Center, Department of Genetics, Washington University, A. Jimenez6, J.-L. Jonniaux12, C. Krämer5, H. Küster4, P. Laamanen18, School of Medicine, 630 S. Euclid Avenue, St Louis, Missouri 63110, USA Y. Legros8, E. Louis19, S. Möller-Rieker5, A. Monnet8, M. Moro20, 28Martinsrieder Institut für Protein Sequenzen, Max-Planck-Institut für S. Müller-Auer10 , B. Nußbaumer4, N. Paricio7, L. Paulin18, J. Perea1, Biochemie, D-82152 Martinsried bei München, Germany. M. Perez-Alonso7, J. E. Perez-Ortin15, T. M. Pohl21, H. Prydz14, B. -
P.-O. 2 Catalan
P.-O. VALLESPIR Primaire de la droite et 12 chiens maltraités du centre : comment voter et 10 cadavres découverts PAGE 3 PAGE 2 CATALAN Jeudi 17 novembre 2016 • N°321 • Espagne 1,50€ • France 1,10€ lindependant.fr FIONA CANDIDAT À LA PRÉSIDENTIELLE Le témoignage tourne court Macron dans le grand bain PAGE ACTU ET ÉDITO Train jaune à la casse Le témoin-surprise appelé hier devant les assises du Puy-de- Dôme, où sont jugés la mère de la petite Fiona et son ancien compagnon, s’est révélé être une médium qui a dû être évacuée après avoir fait un malaise. PAGE SOCIÉTÉ EUROPE Trump divise les « 28 » Les dirigeants européens ne sont pas unanimes sur la conduite à tenir face au nouveau président des Etats-Unis dont l’isolationnisme inquiète l’Union. PAGE INTERNATIONAL CATALOGNE Dali avait-il une fille cachée ? Pilar Abel, âgée de 60 ans, affirme être la fille cachée du peintre surréaliste. Elle a saisi la justice espagnole pour obtenir la reconnaissance de sa paternité. PAGE EURORÉGION ◗ Les défenseurs du Canari jaune sont vent debout après avoir découvert que deux anciens wagons étraves, du matériel roulant déclassé, avaient été vendus à un ferrailleur. Ils souhaitent la création d’un musée du Train jaune pour préserver ce patrimoine. PAGE 6 Photo Fred Berlic DANS LES VILLAGES Argelès-sur-Mer Des experts au chevet du lycée Bourquin PAGE 14 ä Prades £]£ä Un médecin urgentiste £££Ç se penche sur le malaise hospitalier äÓ{{ PAGE 19 9 & ,&F 8 372 2, B O U L E V A R D D E S P Y R É N É E S, C S 4 0 0 6 6, 6 6 0 0 7 P E R P I G N A N C E D E X - T É L. -
"Phylogenetic Analysis of Protein Sequence Data Using The
Phylogenetic Analysis of Protein Sequence UNIT 19.11 Data Using the Randomized Axelerated Maximum Likelihood (RAXML) Program Antonis Rokas1 1Department of Biological Sciences, Vanderbilt University, Nashville, Tennessee ABSTRACT Phylogenetic analysis is the study of evolutionary relationships among molecules, phenotypes, and organisms. In the context of protein sequence data, phylogenetic analysis is one of the cornerstones of comparative sequence analysis and has many applications in the study of protein evolution and function. This unit provides a brief review of the principles of phylogenetic analysis and describes several different standard phylogenetic analyses of protein sequence data using the RAXML (Randomized Axelerated Maximum Likelihood) Program. Curr. Protoc. Mol. Biol. 96:19.11.1-19.11.14. C 2011 by John Wiley & Sons, Inc. Keywords: molecular evolution r bootstrap r multiple sequence alignment r amino acid substitution matrix r evolutionary relationship r systematics INTRODUCTION the baboon-colobus monkey lineage almost Phylogenetic analysis is a standard and es- 25 million years ago, whereas baboons and sential tool in any molecular biologist’s bioin- colobus monkeys diverged less than 15 mil- formatics toolkit that, in the context of pro- lion years ago (Sterner et al., 2006). Clearly, tein sequence analysis, enables us to study degree of sequence similarity does not equate the evolutionary history and change of pro- with degree of evolutionary relationship. teins and their function. Such analysis is es- A typical phylogenetic analysis of protein sential to understanding major evolutionary sequence data involves five distinct steps: (a) questions, such as the origins and history of data collection, (b) inference of homology, (c) macromolecules, developmental mechanisms, sequence alignment, (d) alignment trimming, phenotypes, and life itself. -
Mise En Page 1
Le public scientifique Trajectoires de la génétique 150 ans après Mendel 11, 12 et 13 septembre 2016 Grande salle des séances de l’Institut de France Les travaux pionniers de Gregor Mendel, publiés il y a 150 ans, ouvraient une voie intellectuelle originale dans l’étude des processus de l’hérédité qui, quelques dizaines d’années plus tard, devait donner naissance à une nouvelle discipline scientifique, la génétique, dont les découvertes fondamentales n’allaient cesser de bou- leverser notre compréhension du monde vivant et de nous-même jusqu’à aujourd’hui. Si l’élucidation de la structure de l’ADN, au milieu du siècle dernier, ouvrait la voie de la connaissance moléculaire des gènes et offrait les outils de leur étude, bien d’autres travaux devaient révéler ensuite la complexité intrinsèque de la nature, de l’origine et du fonctionnement des génomes. Ceux-ci nous apportent un éclairage nouveau sur l’évolution des organismes vivants et leurs fonctionne- ments normaux ou pathologiques. Ce colloque, en choisissant quelques thèmes parmi le foisonnement des recherches actuelles illustrées par leurs acteurs, tentera d’anticiper les « trajectoires de la génétique » dans les années futures, avec les questionnements qu’elles suscitent et les espoirs qu’elles portent. Les organisateurs du colloque Bernard Dujon Membre de l’Académie des sciences, Université Pierre et Marie Curie, Institut Pasteur, Paris Bernard Dujon est membre de l’Académie des sciences, professeur émérite à l’université Pierre et Marie Curie et à l’Institut Pasteur. Ses principaux travaux de recherche portent sur les génomes et les mécanismes moléculaires de leur dyna- mique et de leur évolution. -
EMBL-EBI Powerpoint Presentation
Processing data from high-throughput sequencing experiments Simon Anders Use-cases for HTS · de-novo sequencing and assembly of small genomes · transcriptome analysis (RNA-Seq, sRNA-Seq, ...) • identifying transcripted regions • expression profiling · Resequencing to find genetic polymorphisms: • SNPs, micro-indels • CNVs · ChIP-Seq, nucleosome positions, etc. · DNA methylation studies (after bisulfite treatment) · environmental sampling (metagenomics) · reading bar codes Use cases for HTS: Bioinformatics challenges Established procedures may not be suitable. New algorithms are required for · assembly · alignment · statistical tests (counting statistics) · visualization · segmentation · ... Where does Bioconductor come in? Several steps: · Processing of the images and determining of the read sequencest • typically done by core facility with software from the manufacturer of the sequencing machine · Aligning the reads to a reference genome (or assembling the reads into a new genome) • Done with community-developed stand-alone tools. · Downstream statistical analyis. • Write your own scripts with the help of Bioconductor infrastructure. Solexa standard workflow SolexaPipeline · "Firecrest": Identifying clusters ⇨ typically 15..20 mio good clusters per lane · "Bustard": Base calling ⇨ sequence for each cluster, with Phred-like scores · "Eland": Aligning to reference Firecrest output Large tab-separated text files with one row per identified cluster, specifying · lane index and tile index · x and y coordinates of cluster on tile · for each