Unexpected Insertion of Carrier DNA Sequences Into the Fission Yeast Genome During CRISPR–Cas9 Mediated Gene Deletion
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
The ELIXIR Core Data Resources: Fundamental Infrastructure for The
Supplementary Data: The ELIXIR Core Data Resources: fundamental infrastructure for the life sciences The “Supporting Material” referred to within this Supplementary Data can be found in the Supporting.Material.CDR.infrastructure file, DOI: 10.5281/zenodo.2625247 (https://zenodo.org/record/2625247). Figure 1. Scale of the Core Data Resources Table S1. Data from which Figure 1 is derived: Year 2013 2014 2015 2016 2017 Data entries 765881651 997794559 1726529931 1853429002 2715599247 Monthly user/IP addresses 1700660 2109586 2413724 2502617 2867265 FTEs 270 292.65 295.65 289.7 311.2 Figure 1 includes data from the following Core Data Resources: ArrayExpress, BRENDA, CATH, ChEBI, ChEMBL, EGA, ENA, Ensembl, Ensembl Genomes, EuropePMC, HPA, IntAct /MINT , InterPro, PDBe, PRIDE, SILVA, STRING, UniProt ● Note that Ensembl’s compute infrastructure physically relocated in 2016, so “Users/IP address” data are not available for that year. In this case, the 2015 numbers were rolled forward to 2016. ● Note that STRING makes only minor releases in 2014 and 2016, in that the interactions are re-computed, but the number of “Data entries” remains unchanged. The major releases that change the number of “Data entries” happened in 2013 and 2015. So, for “Data entries” , the number for 2013 was rolled forward to 2014, and the number for 2015 was rolled forward to 2016. The ELIXIR Core Data Resources: fundamental infrastructure for the life sciences 1 Figure 2: Usage of Core Data Resources in research The following steps were taken: 1. API calls were run on open access full text articles in Europe PMC to identify articles that mention Core Data Resource by name or include specific data record accession numbers. -
Bioinformatics Study of Lectins: New Classification and Prediction In
Bioinformatics study of lectins : new classification and prediction in genomes François Bonnardel To cite this version: François Bonnardel. Bioinformatics study of lectins : new classification and prediction in genomes. Structural Biology [q-bio.BM]. Université Grenoble Alpes [2020-..]; Université de Genève, 2021. En- glish. NNT : 2021GRALV010. tel-03331649 HAL Id: tel-03331649 https://tel.archives-ouvertes.fr/tel-03331649 Submitted on 2 Sep 2021 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. THÈSE Pour obtenir le grade de DOCTEUR DE L’UNIVERSITE GRENOBLE ALPES préparée dans le cadre d’une cotutelle entre la Communauté Université Grenoble Alpes et l’Université de Genève Spécialités: Chimie Biologie Arrêté ministériel : le 6 janvier 2005 – 25 mai 2016 Présentée par François Bonnardel Thèse dirigée par la Dr. Anne Imberty codirigée par la Dr/Prof. Frédérique Lisacek préparée au sein du laboratoire CERMAV, CNRS et du Computer Science Department, UNIGE et de l’équipe PIG, SIB Dans les Écoles Doctorales EDCSV et UNIGE Etude bioinformatique des lectines: nouvelle classification et prédiction dans les génomes Thèse soutenue publiquement le 8 Février 2021, devant le jury composé de : Dr. Alexandre de Brevern UMR S1134, Inserm, Université Paris Diderot, Paris, France, Rapporteur Dr. -
What Remains to Be Discovered in the Eukaryotic Proteome?
bioRxiv preprint doi: https://doi.org/10.1101/469569; this version posted November 16, 2018. 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 4.0 International license. Hidden in plain sight: What remains to be discovered in the eukaryotic proteome? Valerie Wood∗1,2, Antonia Lock3, Midori A. Harris1,2, Kim Rutherford1,2, J¨urgB¨ahler3, and Stephen G. Oliver1,2 1Cambridge Systems Biology Centre, University of Cambridge, Cambridge, UK 2Department of Biochemistry, University of Cambridge, Cambridge, UK 3Department of Genetics, Evolution and Environment, University College London, London, UK November 12, 2018 Abstract The first decade of genome sequencing stimulated an explosion in the charac- terization of unknown proteins. More recently, the pace of functional discovery has slowed, leaving around 20% of the proteins even in well-studied model or- ganisms without informative descriptions of their biological roles. Remarkably, many uncharacterized proteins are conserved from yeasts to human, suggesting that they contribute to fundamental biological processes. To fully understand biological systems in health and disease, we need to account for every part of the system. Unstudied proteins thus represent a collective blind spot that limits the progress of both basic and applied biosciences. We use a simple yet powerful metric based on Gene Ontology (GO) bio- logical process terms to define characterized and uncharacterized proteins for human, budding yeast, and fission yeast. We then identify a set of conserved but unstudied proteins in S. -
NCBI Databases
Jon K. Lærdahl, Structural Bioinforma�cs NCBI databases Read this ar�cle! Nucleic Acids 43 Res. , D6 (2015) Jon K. Lærdahl, EMBL-‐EBI databases Structural Bioinforma�cs European Nucleo�de Archive (ENA) nucleo�de sequence database Ensembl -‐ automa�c and manually curated annota�on on selected eukaryo�c (vertebrate) genomes Ensembl Genomes – Ensembl for “all other organisms” UniProt – protein sequence and func�onal informa�on ChEMBL – database of bioac�ve compounds IntAct -‐ repository of molecular interac�ons, including protein-‐protein, protein-‐small molecule and protein-‐nucleic acid interac�ons CiteXplore – 25 million literature abstracts including PubMed, Agricola & patents Gene Ontology (GO) -‐ controlled vocabulary to describe gene and gene product a�ributes in any organism Gene Ontology Annota�on (GOA) – GO annota�ons for proteins in UniProt All data is publicly available Jon K. Lærdahl, Structural Bioinforma�cs GenBank a comprehensive public database of nucleo�de sequences and suppor�ng bibliographic and biological annota�on all publicly available DNA sequences submissions from authors – web-‐based BankIt – standalone program Sequin submissions from EST and other high-‐throughput sequencing projects daily exchange of data with ENA and DNA Data Bank of Japan (DDBJ) – all sequences submi�ed to DDBJ, ENA, or GenBank will end up in all 3 databases within few days Jon K. Lærdahl, Structural Bioinforma�cs INSDC Jon K. Lærdahl, Structural Bioinforma�cs Sequin – for submi�ng to GenBank BankIt is web-‐ based alterna�ve Link to “Create a submission” Jon K. Lærdahl, Structural Bioinforma�cs Entry in GenBank format Oct 2015: 202,237,081,559 bases in 188,372,017 sequence records in the tradi�onal GenBank divisions 1,222,635,267,498 bases in 309,198,943 sequence records in the WGS Jon K. -
The Biogrid Interaction Database
D470–D478 Nucleic Acids Research, 2015, Vol. 43, Database issue Published online 26 November 2014 doi: 10.1093/nar/gku1204 The BioGRID interaction database: 2015 update Andrew Chatr-aryamontri1, Bobby-Joe Breitkreutz2, Rose Oughtred3, Lorrie Boucher2, Sven Heinicke3, Daici Chen1, Chris Stark2, Ashton Breitkreutz2, Nadine Kolas2, Lara O’Donnell2, Teresa Reguly2, Julie Nixon4, Lindsay Ramage4, Andrew Winter4, Adnane Sellam5, Christie Chang3, Jodi Hirschman3, Chandra Theesfeld3, Jennifer Rust3, Michael S. Livstone3, Kara Dolinski3 and Mike Tyers1,2,4,* 1Institute for Research in Immunology and Cancer, Universite´ de Montreal,´ Montreal,´ Quebec H3C 3J7, Canada, 2The Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, Ontario M5G 1X5, Canada, 3Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544, USA, 4School of Biological Sciences, University of Edinburgh, Edinburgh EH9 3JR, UK and 5Centre Hospitalier de l’UniversiteLaval´ (CHUL), Quebec,´ Quebec´ G1V 4G2, Canada Received September 26, 2014; Revised November 4, 2014; Accepted November 5, 2014 ABSTRACT semi-automated text-mining approaches, and to en- hance curation quality control. The Biological General Repository for Interaction Datasets (BioGRID: http://thebiogrid.org) is an open access database that houses genetic and protein in- INTRODUCTION teractions curated from the primary biomedical lit- Massive increases in high-throughput DNA sequencing erature for all major model organism species and technologies (1) have enabled an unprecedented level of humans. As of September 2014, the BioGRID con- genome annotation for many hundreds of species (2–6), tains 749 912 interactions as drawn from 43 149 pub- which has led to tremendous progress in the understand- lications that represent 30 model organisms. -
Biocuration 2016 - Posters
Biocuration 2016 - Posters Source: http://www.sib.swiss/events/biocuration2016/posters 1 RAM: A standards-based database for extracting and analyzing disease-specified concepts from the multitude of biomedical resources Jinmeng Jia and Tieliu Shi Each year, millions of people around world suffer from the consequence of the misdiagnosis and ineffective treatment of various disease, especially those intractable diseases and rare diseases. Integration of various data related to human diseases help us not only for identifying drug targets, connecting genetic variations of phenotypes and understanding molecular pathways relevant to novel treatment, but also for coupling clinical care and biomedical researches. To this end, we built the Rare disease Annotation & Medicine (RAM) standards-based database which can provide reference to map and extract disease-specified information from multitude of biomedical resources such as free text articles in MEDLINE and Electronic Medical Records (EMRs). RAM integrates disease-specified concepts from ICD-9, ICD-10, SNOMED-CT and MeSH (http://www.nlm.nih.gov/mesh/MBrowser.html) extracted from the Unified Medical Language System (UMLS) based on the UMLS Concept Unique Identifiers for each Disease Term. We also integrated phenotypes from OMIM for each disease term, which link underlying mechanisms and clinical observation. Moreover, we used disease-manifestation (D-M) pairs from existing biomedical ontologies as prior knowledge to automatically recognize D-M-specific syntactic patterns from full text articles in MEDLINE. Considering that most of the record-based disease information in public databases are textual format, we extracted disease terms and their related biomedical descriptive phrases from Online Mendelian Inheritance in Man (OMIM), National Organization for Rare Disorders (NORD) and Orphanet using UMLS Thesaurus. -
Genbank Is a Reliable Resource for 21St Century Biodiversity Research
GenBank is a reliable resource for 21st century biodiversity research Matthieu Leraya, Nancy Knowltonb,1, Shian-Lei Hoc, Bryan N. Nguyenb,d,e, and Ryuji J. Machidac,1 aSmithsonian Tropical Research Institute, Smithsonian Institution, Panama City, 0843-03092, Republic of Panama; bNational Museum of Natural History, Smithsonian Institution, Washington, DC 20560; cBiodiversity Research Centre, Academia Sinica, 115-29 Taipei, Taiwan; dDepartment of Biological Sciences, The George Washington University, Washington, DC 20052; and eComputational Biology Institute, Milken Institute School of Public Health, The George Washington University, Washington, DC 20052 Contributed by Nancy Knowlton, September 15, 2019 (sent for review July 10, 2019; reviewed by Ann Bucklin and Simon Creer) Traditional methods of characterizing biodiversity are increasingly (13), the largest repository of genetic data for biodiversity (14, 15). being supplemented and replaced by approaches based on DNA In many cases, no vouchers are available to independently con- sequencing alone. These approaches commonly involve extraction firm identification, because the organisms are tiny, very difficult and high-throughput sequencing of bulk samples from biologically or impossible to identify, or lacking entirely (in the case of eDNA). complex communities or samples of environmental DNA (eDNA). In While concerns have been raised about biases and inaccuracies in such cases, vouchers for individual organisms are rarely obtained, often laboratory and analytical methods used in metabarcoding -
Genbank Dennis A
Published online 28 November 2016 Nucleic Acids Research, 2017, Vol. 45, Database issue D37–D42 doi: 10.1093/nar/gkw1070 GenBank Dennis A. Benson, Mark Cavanaugh, Karen Clark, Ilene Karsch-Mizrachi, David J. Lipman, James Ostell and Eric W. Sayers* National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Building 38A, 8600 Rockville Pike, Bethesda, MD 20894, USA Received September 15, 2016; Revised October 19, 2016; Editorial Decision October 24, 2016; Accepted November 07, 2016 ABSTRACT data from sequencing centers. The U.S. Patent and Trade- ® mark Office also contributes sequences from issued patents. GenBank (www.ncbi.nlm.nih.gov/genbank/)isa GenBank participates with the EMBL-EBI European Nu- comprehensive database that contains publicly avail- cleotide Archive (ENA) (2) and the DNA Data Bank of able nucleotide sequences for 370 000 formally de- Japan (DDBJ) (3) as a partner in the International Nu- Downloaded from scribed species. These sequences are obtained pri- cleotide Sequence Database Collaboration (INSDC) (4). marily through submissions from individual labora- The INSDC partners exchange data daily to ensure that tories and batch submissions from large-scale se- a uniform and comprehensive collection of sequence infor- quencing projects, including whole genome shotgun mation is available worldwide. NCBI makes GenBank data (WGS) and environmental sampling projects. Most available at no cost over the Internet, through FTP and a submissions are made using the web-based BankIt wide range of Web-based retrieval and analysis services (5). http://nar.oxfordjournals.org/ or the NCBI Submission Portal. GenBank staff assign accession numbers upon data receipt. -
Biogrid Australia Facilitates Collaborative Medical And
SPECIAL ARTICLE Human Mutation OFFICIAL JOURNAL BioGrid Australia Facilitates Collaborative Medical and Bioinformatics Research Across Hospitals and Medical www.hgvs.org Research Institutes by Linking Data from Diverse Disease and Data Types Robert B. Merriel,1,2Ã Peter Gibbs,1–3 Terence J. O’Brien,1,4 and Marienne Hibbert4,5 1Melbourne Health, Melbourne, Australia; 2BioGrid Australia Ltd, Melbourne, Australia; 3Ludwig Institute for Cancer Research, Melbourne, Australia; 4Department of Medicine, The Royal Melbourne Hospital, The University of Melbourne, Melbourne, Australia; 5Victorian Partnership for Advanced Computing, Melbourne, Australia For the HVP Bioinformatics Special Issue Received 17 September 2010; accepted revised manuscript 14 December 2010. Published online in Wiley Online Library (www.wiley.com/humanmutation). DOI 10.1002/humu.21437 between institutions. Although there was a positive collective ABSTRACT: BioGrid Australia is a federated data linkage will—there were limited resources, a lack of standards and an and integration infrastructure that uses the Internet to inconsistent approach to data collection, storage and utilization enable patient specific information to be utilized for within and across Australian hospitals. research in a privacy protected manner, from multiple Market analysis plus consultations and workshops with research- databases of various data types (e.g. clinical, treatment, ers, clinicians and key government stakeholders defined the genomic, image, histopathology and outcome), from a ‘‘preferred future state’’ and a pilot system, the Molecular Medicine range of diseases (oncological, neurological, endocrine Informatics Model (MMIM) was proposed. The vision was a virtual and respiratory) and across more than 20 health services, platform, where information is accessible to authorized users, yet the universities and medical research institutes. -
Bioinformatics: a Practical Guide to the Analysis of Genes and Proteins, Second Edition Andreas D
BIOINFORMATICS A Practical Guide to the Analysis of Genes and Proteins SECOND EDITION Andreas D. Baxevanis Genome Technology Branch National Human Genome Research Institute National Institutes of Health Bethesda, Maryland USA B. F. Francis Ouellette Centre for Molecular Medicine and Therapeutics Children’s and Women’s Health Centre of British Columbia University of British Columbia Vancouver, British Columbia Canada A JOHN WILEY & SONS, INC., PUBLICATION New York • Chichester • Weinheim • Brisbane • Singapore • Toronto BIOINFORMATICS SECOND EDITION METHODS OF BIOCHEMICAL ANALYSIS Volume 43 BIOINFORMATICS A Practical Guide to the Analysis of Genes and Proteins SECOND EDITION Andreas D. Baxevanis Genome Technology Branch National Human Genome Research Institute National Institutes of Health Bethesda, Maryland USA B. F. Francis Ouellette Centre for Molecular Medicine and Therapeutics Children’s and Women’s Health Centre of British Columbia University of British Columbia Vancouver, British Columbia Canada A JOHN WILEY & SONS, INC., PUBLICATION New York • Chichester • Weinheim • Brisbane • Singapore • Toronto Designations used by companies to distinguish their products are often claimed as trademarks. In all instances where John Wiley & Sons, Inc., is aware of a claim, the product names appear in initial capital or ALL CAPITAL LETTERS. Readers, however, should contact the appropriate companies for more complete information regarding trademarks and registration. Copyright ᭧ 2001 by John Wiley & Sons, Inc. All rights reserved. No part of this publication may be reproduced, stored in a retrieval system or transmitted in any form or by any means, electronic or mechanical, including uploading, downloading, printing, decompiling, recording or otherwise, except as permitted under Sections 107 or 108 of the 1976 United States Copyright Act, without the prior written permission of the Publisher. -
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: -
Uniprot.Ws: R Interface to Uniprot Web Services
Package ‘UniProt.ws’ September 26, 2021 Type Package Title R Interface to UniProt Web Services Version 2.33.0 Depends methods, utils, RSQLite, RCurl, BiocGenerics (>= 0.13.8) Imports AnnotationDbi, BiocFileCache, rappdirs Suggests RUnit, BiocStyle, knitr Description A collection of functions for retrieving, processing and repackaging the UniProt web services. Collate AllGenerics.R AllClasses.R getFunctions.R methods-select.R utilities.R License Artistic License 2.0 biocViews Annotation, Infrastructure, GO, KEGG, BioCarta VignetteBuilder knitr LazyLoad yes git_url https://git.bioconductor.org/packages/UniProt.ws git_branch master git_last_commit 5062003 git_last_commit_date 2021-05-19 Date/Publication 2021-09-26 Author Marc Carlson [aut], Csaba Ortutay [ctb], Bioconductor Package Maintainer [aut, cre] Maintainer Bioconductor Package Maintainer <[email protected]> R topics documented: UniProt.ws-objects . .2 UNIPROTKB . .4 utilities . .8 Index 11 1 2 UniProt.ws-objects UniProt.ws-objects UniProt.ws objects and their related methods and functions Description UniProt.ws is the base class for interacting with the Uniprot web services from Bioconductor. In much the same way as an AnnotationDb object allows acces to select for many other annotation packages, UniProt.ws is meant to allow usage of select methods and other supporting methods to enable the easy extraction of data from the Uniprot web services. select, columns and keys are used together to extract data via an UniProt.ws object. columns shows which kinds of data can be returned for the UniProt.ws object. keytypes allows the user to discover which keytypes can be passed in to select or keys via the keytype argument. keys returns keys for the database contained in the UniProt.ws object .