DOCSLIB.ORG

Basic Biogrid

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Basic Biogrid Basic BioGrid Rick Stevens Argonne National Laboratory University of Chicago Outline • Biology: The Science for the 21st Century • Sizing up the opportunities for BioGrid • Biology is a different way of thinking • Systems biology and whole cell modeling • Requirements for the BioGrid • A modest proposal • Some recommended reading • Conclusions Rick Stevens Argonne z Chicago Many BioGrid Projects • EUROGRID BioGRID • Asia Pacific BioGRID • NC BioGrid • Bioinformatics Research Network • Osaka University Biogrid • Indiana University BioArchive BioGrid Rick Stevens Argonne z Chicago The New Biology • Genomics • High-throughput methods • Functional Genomics • Low cost • Robotics • Proteomics • Bioinformatics driven • Structural Biology • Quantitative • Gene Expression • Enables a systems view • Metabolomics • Basis for integrative understanding • Advanced Imaging • Global state • Time dependent Rick Stevens Argonne z Chicago Predicting Life Processes: Reverse Engineering Living Systems DNA (storage) Transcription Gene Expression Translation Proteins Proteomics Biochemical Circuitry Environment Metabolomics Phenotypes (Traits) From Bruno Sobral VBI 24 Orders Magnitude of Spatial and Temporal Range 6 10106 DiscreteDiscrete AutomataAutomata 3 modelsmodels 10103 FiniteFinite elementelement EvolutionaryEvolutionary modelsmodels 00 Processes Organisms Processes Organisms 1010 EcosystemsEcosystems andand EpidemiologyEpidemiology 66 1010 OrganOrgan functionfunction 33 ElectrostaticElectrostatic 10 continuum models 10 continuum models CellCell signalling signalling Cells Cells 0 10100 66 DNADNA 1010 replicationreplication Enzyme 33 Enzyme Mechanisms 1010 AbAb initioinitio Mechanisms Size Scale Size Size Scale Size QuantumQuantum ChemistryChemistry 1000 ProteinProtein Biopolymers 10 Biopolymers FoldingFolding 1066 10 EmpiricalEmpirical forceforce fieldfield MolecularMolecular DynamicsDynamics 33 1010 Homology-basedHomology-based FirstFirst PrinciplesPrinciples Atoms Protein modeling Atoms Protein modeling MolecularMolecular DynamicsDynamics 0 10100 Geologic & -15-15 -12-12 -9-9 -6-6 -3-3 00 33 66 99 Geologic & 1010 1010 1010 1010 1010 1010 1010 1010 1010 EvolutionaryEvolutionary TimescaleTimescale (seconds)(seconds) TimescalesTimescales Genes → Cell Networks → Organisms → Populations → Ecosystems Rick Stevens Argonne z Chicago Computational analysis and simulation have important roles in the study of each step in the hierarchy of biological function DNA Protein sequence Protein Protein/enzyme sequence and regulation structure function T A T Promoter A C A Q Sequence G Homology based Molecular Annotation T A Y protein structure simulations Message C prediction C G R T Expt. data Network integration Organism Pathway analysis simulations simulations Bacterial communities Bacteria and cells Metabolic pathways Multi-protein & multicellular organisms & regulatory networks machines The New Biology in Action Hypothetical example: Fill in component in New data showing that a gene forms DNA repair pathway complex with DNA repair enzyme Predict chemical Update network inhibitors model of microbe Predict fold and assign behavior function ATP ADP Adducted DNA-enzyme DNA complex Add function to + genome database Identify regulatory sequence associated with DNA repair ACTGGCATTCA TATAGCT From Mike Colvin, LLNL An Integrated View of Simulation, Experiment, and Bioinformatics Problem Browsing & Simulation Specification Visualization SIMS* Analysis Tools Database LIMS Experimental Experiment Browsing & Design Visualization *Simulation Information Management System Rick Stevens Argonne z Chicago Genomics is Powering the New Biology, but Computing is in the Drivers Seat Ecological Processes and Populations Computation Tissue and Organismal Physiology Cellular & Developmental Morphogenesis and Development Processes Simulation of Metabolic and Signal Transduction Pathways Biochemical Path ways Predicting & Catalysis, Molecular Processes Dynamics Structures of Multi- molecular Predicting Effects of Experiments complexes Variation Gene s, Prot eins, RNAs, an d other Predicting Three-Dimensional Predicting Functions Biomolecules Structures of Proteins and RNAs Predicting Protein Simulating and Understanding Gene Sequence Expression Networks Reconstructing Phylogeny, Genes and Gene Structures Homology, and Comparitive Genome Large-scale Approaches Sequences Genome Sequencing Assembled Genomes Sequence Variation of Rick Stevens PopulationsArgonne z Chicago Systems Biology • Integrative (synthetic) understanding of a biological system • Cell, organism, community and ecosystem • Counterpoint to reductionism • Requires synthesizing knowledge from multiple levels of the system • Discovery oriented not necessarily hypothesis driven • Data mining vs theorem proving Rick Stevens Argonne z Chicago Biology is BIG!! • 3500 Millions years of evolution • ~10M extant species (distinct genomes) • 200 genomes sequenced so far many to go!! • >100M extinct species • 108 average genome size (coding region) • ~108 bp x ~107 sp = 1015 bp of genetic diversity • 1011-1013 total genes and gene products • ~2,000 protein structures determined • typical bacteria has about 3,000 types of proteins Rick Stevens Argonne z Chicago • Overview of Life on Earth • Basic Microbiology Introduction • Excellent Summary From Five Kingdoms by Lynn Margulis and Karlene Schwartz From John Wooley Biomedical Data: High Complexity and Large Scale billions Protein-Protein Physiology Interactions Cellular biology metabolism Biochemistry pathways Polymorphism Neurobiology millions receptor-ligand Endocrinology 4º structure and Variants genetic variants etc. millions Proteins individual patients sequence epidemiology Hundred 2º structure thousands 3º structure ESTs Genetics and Maps Expression patterns MPMILGYWDIRGLAHAIRLLLEYTDSSYEEKKYT... Linkage Large-scale screens DNA sequences Cytogenetic billions alignments Clone-based ...atcgaattccaggcgtcacattctcaattcca... millions Will Biology Dominate the Grid? • The largest science discipline: • The most scientists (Globally ~500,000-1,000,000) • The most research funding (Globally ~$50 Billion/year) • The most graduate students (>20,000 year) • Strong couplings to: • Medicine and human health • Agriculture and food supplies • Energy and ecology • Future industrial processes (bio-nano) • Consumer of other scientific technologies Rick Stevens Argonne z Chicago A Diverse Bacterial Community • Pocket in the hindgut wall of the Sonoran desert termite Pterotermes occidentis • 10 billion bacteria per milliliter • Anoxic environment • ~30 strains are facultative aerobes From Five Kingdoms • Many/most are unknown Lynn Margulis and Karlene Schwartz Rick Stevens Argonne z Chicago Human Microbial Ecology: Sorting Out Cause, Effect + Cure Helicobacter pylori Stomach cancer Cytomegalovirus Stomach ulcers Porphyromonas gingivalis Coronary artery disease Alzheimer's Chlamydia pneumoniae Cervical cancer Human papilloma virus Liver cancer Hepatitis B, C virus Breast cancer EpsteinBarr Virus Nasopharyngeal carcinoma = suspected linkages Rick Stevens Argonne z Chicago An Initial Focus on Prokaryotic life • ~5,000 known species of prokaryotic life • It is estimated that we have identified only 1%-5% of extant species ⇒ 80,000-400,000 species • More diversity than Eukaryotic life forms • More diverse metabolisms, more diverse environments • A Human contains 1012 cells and 1013 bacterial cells • We have little current understanding of human micro ecology Rick Stevens Argonne z Chicago Biological CAD: Enabling BioDesign • Understanding and manipulating biological systems from an information systems standpoint [e.g. organization, communication, transformation] • Genotype + Environment = Phenotype • Strong analogs to VLSI design tools • Ultimately goal is designing new biological structures and systems • Custom microbe design • Metabolic Engineering • Synthetic model organisms Rick Stevens Argonne z Chicago SynechocystisSynechocystis 33563356 GenesGenes 925925 PathwaysPathways Rick Stevens Argonne z Chicago Degree of whole-genome structure & function assignment varies by organism (and required confidence level) Results for three organisms using Genequiz (EBI) Mycoplasma Genitalium Synechocystis sp. Saccharomyces cer. (468 genes) (3168 genes) (6284 genes) High-confidence structure Function assigned No homologues & function by homology by weak homology Function assigned Homologue exists, by strong homology but function unknown Genome Features Annotated Genome Maps Gene Annotations Genomes Comparisons Annotated Data Sets Visualization Visualization Whole genome Analysis Sequence Analysis And Architecture Module Module Predictions of Regulation Predictions of New pathways Conserved Chromosomal Gene Clusters Functions of Hypotheticals Gene Functions Assignments Networks Comparisons Networks Analysis Module Proteomics Experimentation Operons, regulons networks Metabolic Reconstructions (Annotated stoichiometric Matricies) Conjectures about Gene Functions Metabolic Phenotypes Simulation Module Experimentation Metabolic Engineering Reconstructing and Modeling a Prokaryotic Cell? • Typical bacteria [E. coli] • 1000nm x 300nm x 300nm volume • ~4000 genes and gene products • 1/4 genes ⇒ protein synthesis • 1/4 genes ⇒ glycolysis • 1/4 genes ⇒ citric acid cycle • rest genes involved in regulation, synthesis and degrading tasks • Relatively few genes related to sensing and motility • 1,000’s of small molecular species, not tracked individually • ~3 million total large molecules to track Rick Stevens Argonne
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.
    [Show full text]
  • 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
    [Show full text]
  • 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.
    [Show full text]
  • "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.
    [Show full text]
  • 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
    [Show full text]
  • 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: miguel.roncoroni@psb.vib-ugent.be 1 Introduction Nucleotide Archive (ENA).
    [Show full text]
  • Webnetcoffee
    Hu et al. BMC Bioinformatics (2018) 19:422 https://doi.org/10.1186/s12859-018-2443-4 SOFTWARE Open Access WebNetCoffee: a web-based application to identify functionally conserved proteins from Multiple PPI networks Jialu Hu1,2, Yiqun Gao1, Junhao He1, Yan Zheng1 and Xuequn Shang1* Abstract Background: The discovery of functionally conserved proteins is a tough and important task in system biology. Global network alignment provides a systematic framework to search for these proteins from multiple protein-protein interaction (PPI) networks. Although there exist many web servers for network alignment, no one allows to perform global multiple network alignment tasks on users’ test datasets. Results: Here, we developed a web server WebNetcoffee based on the algorithm of NetCoffee to search for a global network alignment from multiple networks. To build a series of online test datasets, we manually collected 218,339 proteins, 4,009,541 interactions and many other associated protein annotations from several public databases. All these datasets and alignment results are available for download, which can support users to perform algorithm comparison and downstream analyses. Conclusion: WebNetCoffee provides a versatile, interactive and user-friendly interface for easily running alignment tasks on both online datasets and users’ test datasets, managing submitted jobs and visualizing the alignment results through a web browser. Additionally, our web server also facilitates graphical visualization of induced subnetworks for a given protein and its neighborhood. To the best of our knowledge, it is the first web server that facilitates the performing of global alignment for multiple PPI networks. Availability: http://www.nwpu-bioinformatics.com/WebNetCoffee Keywords: Multiple network alignment, Webserver, PPI networks, Protein databases, Gene ontology Background tools [7–10] have been developed to understand molec- Proteins are involved in almost all life processes.
    [Show full text]
  • Genomic Sequencing of SARS-Cov-2: a Guide to Implementation for Maximum Impact on Public Health
    Genomic sequencing of SARS-CoV-2 A guide to implementation for maximum impact on public health 8 January 2021 Genomic sequencing of SARS-CoV-2 A guide to implementation for maximum impact on public health 8 January 2021 Genomic sequencing of SARS-CoV-2: a guide to implementation for maximum impact on public health ISBN 978-92-4-001844-0 (electronic version) ISBN 978-92-4-001845-7 (print version) © World Health Organization 2021 Some rights reserved. This work is available under the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 IGO licence (CC BY-NC-SA 3.0 IGO; https://creativecommons.org/licenses/by-nc-sa/3.0/igo). Under the terms of this licence, you may copy, redistribute and adapt the work for non-commercial purposes, provided the work is appropriately cited, as indicated below. In any use of this work, there should be no suggestion that WHO endorses any specific organization, products or services. The use of the WHO logo is not permitted. If you adapt the work, then you must license your work under the same or equivalent Creative Commons licence. If you create a translation of this work, you should add the following disclaimer along with the suggested citation: “This translation was not created by the World Health Organization (WHO). WHO is not responsible for the content or accuracy of this translation. The original English edition shall be the binding and authentic edition”. Any mediation relating to disputes arising under the licence shall be conducted in accordance with the mediation rules of the World Intellectual Property Organization (http://www.wipo.int/amc/en/mediation/rules/).
    [Show full text]
  • 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 (christine.zardecki@rcsb.org) 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.
    [Show full text]
  • 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.
    [Show full text]
  • PINOT: an Intuitive Resource for Integrating Protein-Protein Interactions James E
    Tomkins et al. Cell Communication and Signaling (2020) 18:92 https://doi.org/10.1186/s12964-020-00554-5 METHODOLOGY Open Access PINOT: an intuitive resource for integrating protein-protein interactions James E. Tomkins1, Raffaele Ferrari2, Nikoleta Vavouraki1, John Hardy2,3,4,5,6, Ruth C. Lovering7, Patrick A. Lewis1,2,8, Liam J. McGuffin9* and Claudia Manzoni1,10* Abstract Background: The past decade has seen the rise of omics data for the understanding of biological systems in health and disease. This wealth of information includes protein-protein interaction (PPI) data derived from both low- and high-throughput assays, which are curated into multiple databases that capture the extent of available information from the peer-reviewed literature. Although these curation efforts are extremely useful, reliably downloading and integrating PPI data from the variety of available repositories is challenging and time consuming. Methods: We here present a novel user-friendly web-resource called PINOT (Protein Interaction Network Online Tool; available at http://www.reading.ac.uk/bioinf/PINOT/PINOT_form.html) to optimise the collection and processing of PPI data from IMEx consortium associated repositories (members and observers) and WormBase, for constructing, respectively, human and Caenorhabditis elegans PPI networks. Results: Users submit a query containing a list of proteins of interest for which PINOT extracts data describing PPIs. At every query submission PPI data are downloaded, merged and quality assessed. Then each PPI is confidence scored based on the number of distinct methods used for interaction detection and the number of publications that report the specific interaction. Examples of how PINOT can be applied are provided to highlight the performance, ease of use and potential utility of this tool.
    [Show full text]
  • Biocuration Experts on the Impact of Duplication and Other Data Quality Issues in Biological Databases
    Genomics Proteomics Bioinformatics 18 (2020) 91–103 Genomics Proteomics Bioinformatics www.elsevier.com/locate/gpb www.sciencedirect.com PERSPECTIVE Quality Matters: Biocuration Experts on the Impact of Duplication and Other Data Quality Issues in Biological Databases Qingyu Chen 1,*, Ramona Britto 2, Ivan Erill 3, Constance J. Jeffery 4, Arthur Liberzon 5, Michele Magrane 2, Jun-ichi Onami 6,7, Marc Robinson-Rechavi 8,9, Jana Sponarova 10, Justin Zobel 1,*, Karin Verspoor 1,* 1 School of Computing and Information Systems, University of Melbourne, Melbourne, VIC 3010, Australia 2 European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Cambridge CB10 1SD, UK 3 Department of Biological Sciences, University of Maryland, Baltimore, MD 21250, USA 4 Department of Biological Sciences, University of Illinois at Chicago, Chicago, IL 60607, USA 5 Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA 6 Japan Science and Technology Agency, National Bioscience Database Center, Tokyo 102-8666, Japan 7 National Institute of Health Sciences, Tokyo 158-8501, Japan 8 Swiss Institute of Bioinformatics, CH-1015 Lausanne, Switzerland 9 Department of Ecology and Evolution, University of Lausanne, CH-1015 Lausanne, Switzerland 10 Nebion AG, 8048 Zurich, Switzerland Received 8 December 2017; revised 24 October 2018; accepted 14 December 2018 Available online 9 July 2020 Handled by Zhang Zhang Introduction assembled, annotated, and ultimately submitted to primary nucleotide databases such as GenBank [2], European Nucleo- tide Archive (ENA) [3], and DNA Data Bank of Japan Biological databases represent an extraordinary collective vol- (DDBJ) [4] (collectively known as the International Nucleotide ume of work.
    [Show full text]