Module 6 Bioinformatics Tools Lecture 38 Analysis of Protein and Nucleic Acid Sequences (Part-I)
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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. -
A SARS-Cov-2 Sequence Submission Tool for the European Nucleotide
Databases and ontologies Downloaded from https://academic.oup.com/bioinformatics/advance-article/doi/10.1093/bioinformatics/btab421/6294398 by guest on 25 June 2021 A SARS-CoV-2 sequence submission tool for the European Nucleotide Archive Miguel Roncoroni 1,2,∗, Bert Droesbeke 1,2, Ignacio Eguinoa 1,2, Kim De Ruyck 1,2, Flora D’Anna 1,2, Dilmurat Yusuf 3, Björn Grüning 3, Rolf Backofen 3 and Frederik Coppens 1,2 1Department of Plant Biotechnology and Bioinformatics, Ghent University, 9052 Ghent, Belgium, 1VIB Center for Plant Systems Biology, 9052 Ghent, Belgium and 2University of Freiburg, Department of Computer Science, Freiburg im Breisgau, Baden-Württemberg, Germany ∗To whom correspondence should be addressed. Associate Editor: XXXXXXX Received on XXXXX; revised on XXXXX; accepted on XXXXX Abstract Summary: Many aspects of the global response to the COVID-19 pandemic are enabled by the fast and open publication of SARS-CoV-2 genetic sequence data. The European Nucleotide Archive (ENA) is the European recommended open repository for genetic sequences. In this work, we present a tool for submitting raw sequencing reads of SARS-CoV-2 to ENA. The tool features a single-step submission process, a graphical user interface, tabular-formatted metadata and the possibility to remove human reads prior to submission. A Galaxy wrap of the tool allows users with little or no bioinformatic knowledge to do bulk sequencing read submissions. The tool is also packed in a Docker container to ease deployment. Availability: CLI ENA upload tool is available at github.com/usegalaxy- eu/ena-upload-cli (DOI 10.5281/zenodo.4537621); Galaxy ENA upload tool at toolshed.g2.bx.psu.edu/view/iuc/ena_upload/382518f24d6d and https://github.com/galaxyproject/tools- iuc/tree/master/tools/ena_upload (development) and; ENA upload Galaxy container at github.com/ELIXIR- Belgium/ena-upload-container (DOI 10.5281/zenodo.4730785) Contact: [email protected] 1 Introduction Nucleotide Archive (ENA). -
Six-Fold Speed-Up of Smith-Waterman Sequence Database Searches Using Parallel Processing on Common Microprocessors
Six-fold speed-up of Smith-Waterman sequence database searches using parallel processing on common microprocessors Running head: Six-fold speed-up of Smith-Waterman searches Torbjørn Rognes* and Erling Seeberg Institute of Medical Microbiology, University of Oslo, The National Hospital, NO-0027 Oslo, Norway Abstract Motivation: Sequence database searching is among the most important and challenging tasks in bioinformatics. The ultimate choice of sequence search algorithm is that of Smith- Waterman. However, because of the computationally demanding nature of this method, heuristic programs or special-purpose hardware alternatives have been developed. Increased speed has been obtained at the cost of reduced sensitivity or very expensive hardware. Results: A fast implementation of the Smith-Waterman sequence alignment algorithm using SIMD (Single-Instruction, Multiple-Data) technology is presented. This implementation is based on the MMX (MultiMedia eXtensions) and SSE (Streaming SIMD Extensions) technology that is embedded in Intel’s latest microprocessors. Similar technology exists also in other modern microprocessors. Six-fold speed-up relative to the fastest previously known Smith-Waterman implementation on the same hardware was achieved by an optimised 8-way parallel processing approach. A speed of more than 150 million cell updates per second was obtained on a single Intel Pentium III 500MHz microprocessor. This is probably the fastest implementation of this algorithm on a single general-purpose microprocessor described to date. Availability: Online searches with the software are available at http://dna.uio.no/search/ Contact: [email protected] Published in Bioinformatics (2000) 16 (8), 699-706. Copyright © (2000) Oxford University Press. *) To whom correspondence should be addressed. -
Impact of the Protein Data Bank Across Scientific Disciplines.Data Science Journal, 19: 25, Pp
Feng, Z, et al. 2020. Impact of the Protein Data Bank Across Scientific Disciplines. Data Science Journal, 19: 25, pp. 1–14. DOI: https://doi.org/10.5334/dsj-2020-025 RESEARCH PAPER Impact of the Protein Data Bank Across Scientific Disciplines Zukang Feng1,2, Natalie Verdiguel3, Luigi Di Costanzo1,4, David S. Goodsell1,5, John D. Westbrook1,2, Stephen K. Burley1,2,6,7,8 and Christine Zardecki1,2 1 Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ, US 2 Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ, US 3 University of Central Florida, Orlando, Florida, US 4 Department of Agricultural Sciences, University of Naples Federico II, Portici, IT 5 Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA, US 6 Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer Center, University of California, San Diego, La Jolla, CA, US 7 Rutgers Cancer Institute of New Jersey, Rutgers, The State University of New Jersey, New Brunswick, NJ, US 8 Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA, US Corresponding author: Christine Zardecki ([email protected]) The Protein Data Bank archive (PDB) was established in 1971 as the 1st open access digital data resource for biology and medicine. Today, the PDB contains >160,000 atomic-level, experimentally-determined 3D biomolecular structures. PDB data are freely and publicly available for download, without restrictions. Each entry contains summary information about the structure and experiment, atomic coordinates, and in most cases, a citation to a corresponding scien- tific publication. -
Bioinformatics Courses Lecture 3: (Local) Alignment and Homology
C E N Bioinformatics courses T R E Principles of Bioinformatics (BSc) & F B O I Fundamentals of Bioinformatics R O I I N N (MSc) T F E O G R R M A A T T I I Lecture 3: (local) alignment and V C E S homology searching V U Centre for Integrative Bioinformatics VU (IBIVU) Faculty of Exact Sciences / Faculty of Earth and Life Sciences 1 http://ibi.vu.nl, [email protected], 87649 (Heringa), Room P1.28 Divergent evolution sequence -> structure -> function • Common ancestor (CA) CA • Sequences change over time • Protein structures typically remain the same (robust against Sequence 1 ≠ Sequence 2 multiple mutations) • Therefore, function normally is Structure 1 = Structure 2 preserved within orthologous families Function 1 = Function 2 “Structure more conserved than sequence” 2 Reconstructing divergent evolution Ancestral sequence: ABCD ACCD (B C) ABD (C ø) mutation deletion ACCD or ACCD Pairwise Alignment AB─D A─BD 3 Reconstructing divergent evolution Ancestral sequence: ABCD ACCD (B C) ABD (C ø) mutation deletion ACCD or ACCD Pairwise Alignment AB─D A─BD true alignment 4 Pairwise alignment examples A protein sequence alignment MSTGAVLIY--TSILIKECHAMPAGNE----- ---GGILLFHRTHELIKESHAMANDEGGSNNS * * * **** *** A DNA sequence alignment attcgttggcaaatcgcccctatccggccttaa att---tggcggatcg-cctctacgggcc---- *** **** **** ** ****** 5 Evolution and three-dimensional protein structure information Multiple alignment Protein structure What do we see if we colour code the space-filling (CPK) protein model? • E.g., red for conserved alignment positions to blue for variable (unconserved) positions. 6 Evolution and three-dimensional protein structure information Isocitrate dehydrogenase: The distance from the active site (in yellow) determines the rate of evolution (red = fast evolution, blue = slow evolution) Dean, A. -
Pdbefold Tutorial Tutorial Pdbefold Can May Be Accessed from Multiple Locations on the Pdbe Website
PDBe TUTORIAL PDBeFold (SSM: Secondary Structure Matching) http://pdbe.org/fold/ This PDBe tutorial introduces PDBeFold, an interactive service for comparing protein structures in 3D. This service provides: . Pairwise and multiple comparison and 3D alignment of protein structures . Examination of a protein structure for similarity with the whole Protein Data Bank (PDB) archive or SCOP. Best C -alignment of compared structures . Download and visualisation of best-superposed structures using various graphical packages PDBeFold structure alignment is based on identification of residues occupying “equivalent” geometrical positions. In other words, unlike sequence alignment, residue type is neglected. The PDBeFold service is a very powerful structure alignment tool which can perform both pairwise and multiple three dimensional alignment. In addition to this there are various options by which the results of the structural alignment query can be sorted. The results of the Secondary Structure Matching can be sorted based on the Q score (Cα- alignment), P score (taking into account RMSD, number of aligned residues, number of gaps, number of matched Secondary Structure Elements and the SSE match score), Z score (based on Gaussian Statistics), RMSD and % Sequence Identity. It is hoped that at the end of this tutorial users will be able to use PDbeFold for the analysis of their own uploaded structures or entries already in the PDB archive. Protein Data Bank in Europe http://pdbe.org PDBeFOLD Tutorial Tutorial PDBeFold can may be accessed from multiple locations on the PDBe website. From the PDBe home page (http://pdbe.org/), there are two access points for the program as shown below. -
EMBL-EBI-Overview.Pdf
EMBL-EBI Overview EMBL-EBI Overview Welcome Welcome to the European Bioinformatics Institute (EMBL-EBI), a global hub for big data in biology. We promote scientific progress by providing freely available data to the life-science research community, and by conducting exceptional research in computational biology. At EMBL-EBI, we manage public life-science data on a very large scale, offering a rich resource of carefully curated information. We make our data, tools and infrastructure openly available to an increasingly data-driven scientific community, adjusting to the changing needs of our users, researchers, trainees and industry partners. This proactive approach allows us to deliver relevant, up-to-date data and tools to the millions of scientists who depend on our services. We are a founding member of ELIXIR, the European infrastructure for biological information, and are central to global efforts to exchange information, set standards, develop new methods and curate complex information. Our core databases are produced in collaboration with other world leaders including the National Center for Biotechnology Information in the US, the National Institute of Genetics in Japan, SIB Swiss Institute of Bioinformatics and the Wellcome Trust Sanger Institute in the UK. We are also a world leader in computational biology research, and are well integrated with experimental and computational groups on all EMBL sites. Our research programme is highly collaborative and interdisciplinary, regularly producing high-impact works on sequence and structural alignment, genome analysis, basic biological breakthroughs, algorithms and methods of widespread importance. EMBL-EBI is an international treaty organisation, and we serve the global scientific community. -
Human Genetics 1990–2009
Portfolio Review Human Genetics 1990–2009 June 2010 Acknowledgements The Wellcome Trust would like to thank the many people who generously gave up their time to participate in this review. The project was led by Liz Allen, Michael Dunn and Claire Vaughan. Key input and support was provided by Dave Carr, Kevin Dolby, Audrey Duncanson, Katherine Littler, Suzi Morris, Annie Sanderson and Jo Scott (landscaping analysis), and Lois Reynolds and Tilli Tansey (Wellcome Trust Expert Group). We also would like to thank David Lynn for his ongoing support to the review. The views expressed in this report are those of the Wellcome Trust project team – drawing on the evidence compiled during the review. We are indebted to the independent Expert Group, who were pivotal in providing the assessments of the Wellcome Trust’s role in supporting human genetics and have informed ‘our’ speculations for the future. Finally, we would like to thank Professor Francis Collins, who provided valuable input to the development of the timelines. The Wellcome Trust is a charity registered in England and Wales, no. 210183. Contents Acknowledgements 2 Overview and key findings 4 Landmarks in human genetics 6 1. Introduction and background 8 2. Human genetics research: the global research landscape 9 2.1 Human genetics publication output: 1989–2008 10 3. Looking back: the Wellcome Trust and human genetics 14 3.1 Building research capacity and infrastructure 14 3.1.1 Wellcome Trust Sanger Institute (WTSI) 15 3.1.2 Wellcome Trust Centre for Human Genetics 15 3.1.3 Collaborations, consortia and partnerships 16 3.1.4 Research resources and data 16 3.2 Advancing knowledge and making discoveries 17 3.3 Advancing knowledge and making discoveries: within the field of human genetics 18 3.4 Advancing knowledge and making discoveries: beyond the field of human genetics – ‘ripple’ effects 19 Case studies 22 4. -
EC-PSI: Associating Enzyme Commission Numbers with Pfam Domains
bioRxiv preprint doi: https://doi.org/10.1101/022343; this version posted July 10, 2015. 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. EC-PSI: Associating Enzyme Commission Numbers with Pfam Domains Seyed Ziaeddin ALBORZI1;2, Marie-Dominique DEVIGNES3 and David W. RITCHIE2 1 Universite´ de Lorraine, LORIA, UMR 7503, Vandœuvre-les-Nancy,` F-54506, France 2 INRIA, Villers-les-Nancy,` F-54600, France 3 CNRS, LORIA, UMR 7503, Vandœuvre-les-Nancy,` F-54506, France Corresponding Author: [email protected] Abstract With the growing number of protein structures in the protein data bank (PDB), there is a need to annotate these structures at the domain level in order to relate protein structure to protein function. Thanks to the SIFTS database, many PDB chains are now cross-referenced with Pfam domains and enzyme commission (EC) numbers. However, these annotations do not include any explicit relationship between individual Pfam domains and EC numbers. This article presents a novel statistical training-based method called EC-PSI that can automatically infer high confi- dence associations between EC numbers and Pfam domains directly from EC-chain associations from SIFTS and from EC-sequence associations from the SwissProt, and TrEMBL databases. By collecting and integrating these existing EC-chain/sequence annotations, our approach is able to infer a total of 8,329 direct EC-Pfam associations with an overall F-measure of 0.819 with respect to the manually curated InterPro database, which we treat here as a “gold standard” reference dataset. -
RCSB Protein Data Bank: Overview
RCSB Protein Data Bank www.pdb.org RCSB Protein Data Bank: Overview Helen M. Berman July 24, 2009 Vision To provide a global resource for the advancement of research and education in biology and medicine by curating, integrating, and disseminating biological macromolecular structural information in the context of function, biological processes, evolution, pathways and disease states. We will implement standards, and anticipate and develop appropriate technologies to support evolving science. Structural Views of Biology and Medicine Mission Support a resource that is by, for, and of the community by providing . leadership in the representation of biological structures derived via experimental methods . data in an accurate and timely manner . comprehensive, integrated view and unique views of the data so as to enable scientific innovation and education What is the PDB? . Single international repository for all information about the structure of large biological molecules . Archival database with hundreds of thousands of users who depend on the data Archive Contents . Public archive . Internal archive – More than 400,000 files (as – Depositor correspondence of June, 2009) – Depositor contact information – Requires over 93 GBbytes of storage – Paper records – Data dictionaries – Documentation – Derived data files – Historical records from Day One . For each entry – Atomic coordinates – Sequence information – Description of structure – Experimental data – Release status information History of the PDB 1970s . Community discusses how to establish a protein structure archive . Cold Spring Harbor meeting in protein crystallography . PDB established at Brookhaven (Oct 1971; 7 structures) 1980s . Number of structures increases as technology improves . Community discussions about requiring depositions . IUCr guidelines established . Number of structures deposited increases 1990s . -
Uniprot Knowledgebase: a Hub of Integrated Protein Data
Database, Vol. 2011, Article ID bar009, doi:10.1093/database/bar009 ............................................................................................................................................................................................................................................................................................. Original article UniProt Knowledgebase: a hub of integrated protein data Michele Magrane1,* and UniProt Consortium1,2,3 1European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK, 2Swiss Institute of Bioinformatics, Centre Medical Universitaire, 1 rue Michel Servet, 1211 Geneva 4, Switzerland, 3Protein Information Resource, Georgetown University Medical Center, 3300 Whitehaven St. NW, Suite 1200, Washington, DC 20007; University of Delaware, 15 Innovation Way, Suite 205, Newark, DE 19711, USA *Corresponding author: Tel: +44 (0)1223 494 656; Fax: +44 (0)1223 494 468; Email: [email protected] Submitted 24 November 2010; Accepted 10 March 2011 ............................................................................................................................................................................................................................................................................................. The UniProt Knowledgebase (UniProtKB) acts as a central hub of protein knowledge by providing a unified view of protein sequence and functional information. Manual and automatic annotation procedures are used to add data directly to -
Lab Manual.Indd 1 17/01/2019 4:34:55 PM Title : Bioinformatics for Beginners Laboratory Manual
BIOINFORMATICS For Beginners LABORATORY MANUAL Prepared Under DBT STAR COLLEGE SCHEME Department of Computer Science PSGR KRISHNAMMAL COLLEGE FOR WOMEN College of Excellence An Autonomous Institution - Affiliated to Bharathiar University Reaccredited with ‘A’ Grade by NAAC An ISO 9001:2015 Certified Institution Peelamedu, Coimbatore – 641 004 Published by BLUE HILL PUBLISHERS Coimbatore - 641 113, Tamil Nadu, India. Web: www.bluehillpublishers.com Project 3_Lab Manual.indd 1 17/01/2019 4:34:55 PM Title : Bioinformatics for Beginners Laboratory Manual Language : English Year : 2018 Author : Department of Computer Science PSGR Krishnammal College for Women, Coimbatore – 641 004, Tamil Nadu, India. ISBN Number : 9788193708828 Copyright Warning : No part of this book may be reproduced or transmitted in any form or by any means electronic or mechanical including photocopying or recording or by any information storage and retrieval system without permission in writing from PSGR Krishnammal College for Women, Coimbatore, Tamil Nadu, India. Published By : Blue Hill Publishers, Coimbatore. Project 3_Lab Manual.indd 2 17/01/2019 4:34:55 PM EDITORS Dr.M.S.Vijaya Dr.J.Viji Gripsy Dr.S.C.Punitha Mrs.N.Deepa Dr.S.Karpagavalli Mrs.J.Shalini Dr.C.Arunpriya Mrs.N.A.Sheela Selvakumari Mrs.R.Kavitha Dr.R.Vishnupriya Mrs.A.S.Kavitha Project 3_Lab Manual.indd 3 17/01/2019 4:34:55 PM Project 3_Lab Manual.indd 4 17/01/2019 4:34:55 PM PREFACE Bioinformatics is the application of computational techniques and tools to analyzeand manage biological data. The purpose of this laboratory manual is to pre- sent a collection of laboratory techniques for the benefit of beginners in bioinfor- matics.