Biological Databases
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Tyrosine Kinase Oncogenes in Normal Hematopoiesis and Hematological Disease
Oncogene (2002) 21, 3314 ± 3333 ã 2002 Nature Publishing Group All rights reserved 0950 ± 9232/02 $25.00 www.nature.com/onc Tyrosine kinase oncogenes in normal hematopoiesis and hematological disease Blanca Scheijen1,2 and James D Grin*,1,2 1Department of Adult Oncology, Dana-Farber Cancer Institute, 44 Binney Street, Boston, Massachusetts, MA 02115, USA; 2Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA Tyrosine kinase oncogenes are formed as a result of Adaptors do not contain intrinsic catalytic activity but mutations that induce constitutive kinase activity. Many consist of independent functioning interaction modules of these tyrosine kinase oncogenes that are derived from like SH2-domain (mediates binding to phosphotyrosine genes, such as c-Abl, c-Fes, Flt3, c-Fms, c-Kit and residues), SH3-domain (interacts with polyproline-rich PDGFRb, that are normally involved in the regulation of PXXP stretch) or pleckstrin homology (PH) domain hematopoiesis or hematopoietic cell function. Despite (binds to inositol lipids). dierences in structure, normal function, and subcellular The ®rst tyrosine kinase oncogene associated with location, many of the tyrosine kinase oncogenes signal human hematologic disease, Bcr ± Abl, was identi®ed through the same pathways, and typically enhance almost twenty years ago, and there is now evidence for proliferation and prolong viability. They represent involvement of multiple tyrosine kinase oncogenes in excellent potential drug targets, and it is likely that acute and chronic leukemias, lymphomas, and myelo- additional mutations will be identi®ed in other kinases, mas. In each case, the tyrosine kinase activity of the their immediate downstream targets, or in proteins oncogene is constitutively activated by mutations that regulating their function. -
Anti-GRAP Antibody (ARG63124)
Product datasheet [email protected] ARG63124 Package: 100 μg anti-GRAP antibody Store at: -20°C Summary Product Description Goat Polyclonal antibody recognizes GRAP Tested Reactivity Hu Predict Reactivity Ms, Rat, Cow Tested Application WB Host Goat Clonality Polyclonal Isotype IgG Target Name GRAP Antigen Species Human Immunogen C-GFFPRSYVQPVHL Conjugation Un-conjugated Alternate Names GRB2-related adapter protein Application Instructions Application table Application Dilution WB 0.3 - 1 µg/ml Application Note WB: Recommend incubate at RT for 1h. * The dilutions indicate recommended starting dilutions and the optimal dilutions or concentrations should be determined by the scientist. Calculated Mw 25 kDa Properties Form Liquid Purification Purified from goat serum by ammonium sulphate precipitation followed by antigen affinity chromatography using the immunizing peptide. Buffer Tris saline (pH 7.3), 0.02% Sodium azide and 0.5% BSA Preservative 0.02% Sodium azide Stabilizer 0.5% BSA Concentration 0.5 mg/ml Storage instruction For continuous use, store undiluted antibody at 2-8°C for up to a week. For long-term storage, aliquot and store at -20°C or below. Storage in frost free freezers is not recommended. Avoid repeated freeze/thaw cycles. Suggest spin the vial prior to opening. The antibody solution should be gently mixed before use. www.arigobio.com 1/2 Note For laboratory research only, not for drug, diagnostic or other use. Bioinformation Database links GeneID: 10750 Human Swiss-port # Q13588 Human Background This gene encodes a member of the GRB2/Sem5/Drk family and functions as a cytoplasmic signaling protein which contains an SH2 domain flanked by two SH3 domains. -
A Semantic Standard for Describing the Location of Nucleotide and Protein Feature Annotation Jerven T
Bolleman et al. Journal of Biomedical Semantics (2016) 7:39 DOI 10.1186/s13326-016-0067-z RESEARCH Open Access FALDO: a semantic standard for describing the location of nucleotide and protein feature annotation Jerven T. Bolleman1*, Christopher J. Mungall2, Francesco Strozzi3, Joachim Baran4, Michel Dumontier5, Raoul J. P. Bonnal6, Robert Buels7, Robert Hoehndorf8, Takatomo Fujisawa9, Toshiaki Katayama10 and Peter J. A. Cock11 Abstract Background: Nucleotide and protein sequence feature annotations are essential to understand biology on the genomic, transcriptomic, and proteomic level. Using Semantic Web technologies to query biological annotations, there was no standard that described this potentially complex location information as subject-predicate-object triples. Description: We have developed an ontology, the Feature Annotation Location Description Ontology (FALDO), to describe the positions of annotated features on linear and circular sequences. FALDO can be used to describe nucleotide features in sequence records, protein annotations, and glycan binding sites, among other features in coordinate systems of the aforementioned “omics” areas. Using the same data format to represent sequence positions that are independent of file formats allows us to integrate sequence data from multiple sources and data types. The genome browser JBrowse is used to demonstrate accessing multiple SPARQL endpoints to display genomic feature annotations, as well as protein annotations from UniProt mapped to genomic locations. Conclusions: Our ontology allows -
Biological Databases What Is a Database ?
BIOLOGICAL DATABASES WHAT IS A DATABASE ? A structured collection of data held in computer storage; esp. one that incorporates software to make it accessible in a variety of ways; any large collection of information. A collection of data structured searchable (index) -> table of contents updated periodically (release) -> new edition cross-referenced (hyperlinks ) -> links with other db Includes also associated tools (software) necessary for access, updating, information insertion, information deletion…. A database consists of basic units called records or entries. Each record consists of fields, which hold pre-defined data related to the record. For example, a protein database would have protein sequences as records and protein properties as fields (e.g., name of protein, length, amino-acid sequence, …) 2 DATABASES ON THE INTERNET Biological databases often have web interfaces, which allow users to send queries to the databases. Some databases can be accessed by different web servers, each offering a different interface. request query web page result User Web server Database server 3 WHY BIOLOGICAL DATABASES ? Exponential growth in biological data. Data (genomic sequences, 3D structures, 2D gel analysis, MS analysis, Microarrays….) are no longer published in a conventional manner, but directly submitted to databases. Essential tools for biological research. The only way to publish massive amounts of data without using all the paper in the world. 4 NUCLEOTIDES 5 COMPLETE GENOMES Until 2018: Eukaryotes 5262 Prokaryotes 131446 Viruses 14027 6 SOME STATISTICS More than 1000 different ‘biological’ databases Variable size: <100Kb to >20Gb DNA: > 20 Gb Protein: 1 Gb 3D structure: 5 Gb Other: smaller Update frequency: daily to annually to seldom to forget about it . -
In Silico Analysis of Single Nucleotide Polymorphisms
Central JSM Bioinformatics, Genomics and Proteomics Original Research *Corresponding author Anfal Ibrahim Bahereldeen, Department of medical laboratory sciences, Sudan University of Science and In silico analysis of Single Technology, Khartoum, Sudan; Tel: 249916060120; Email: [email protected] Submitted: 14 October 2019 Nucleotide Polymorphisms Accepted: 23 January 2020 Published: 27 January 2020 (SNPs) in Human HFE Gene ISSN: 2576-1102 Copyright coding region © 2020 Bahereldeen AI, et ail. OPEN ACCESS Anfal Ibrahim Bahereldeen1*, Reel Gamal Alfadil2, Hala Mohammed Ali2, Randa Musa Nasser2, Nada Tagelsir Eisa2, Keywords Wesal Hassan Mahmoud2, and Shaymaa Mohammedazeem • In silico • HFE gene, Polyphen-2 2 Osman • I mutant 1Department of Medical laboratory sciences, Sudan University of science and • Project hope Technology, Sudan 2Department of Medical laboratory sciences, Al-Neelain University, Sudan Abstract Background: HFE gene is a HLA class1-like molecule, expressed in the different cells and tissues, mutations on this gene reported to cause about 80-90% of Hereditary haemochromatosis (HHC) and it is increasing the risk of different diseases. In this study; we aimed to analysis the SNPs in HFE gene by using different computational methods. Methodology: We obtained HFE gene nsSNPs from dbSNP/NCBI database, Deleterious nsSNPs predicted by different bioinformatics servers including; SIFT, polyphen-2, I-mutant and SNPs & GO servers. Protein structure analysis done by using Project hope and RaptorX tools then visualized by Chimera software and function, interaction and network of HFE gene analysis done by Gene MANIA program. Results: SIFT and Polyphen-2 servers predicted 75 deleterious nsSNPs and nine polymorphisms from them predicted as highly damaging and disease associated. -
HTG Data Input: Annotation File Output in DDBJ Flat File
HTG data Input: Annotation file Output in DDBJ flat file LOCUS ############ 123456 bp DNA linear HTG 30-SEP-2017 Entry Feature Location Qualifier Value DEFINITION Arabidopsis thaliana DNA, BAC clone: CIC5D1, chromosome 1, *** COMMON DATE hold_date 20191130 SEQUENCING IN PROGRESS *** SUBMITTER contact Hanako Mishima ACCESSION ############ ab_name Mishima,H. VERSION ############.1 ab_name Yamada,T. DBLINK ab_name Park,C.S. KEYWORDS HTG; HTGS_PHASE1. ab_name Liu,G.Q. SOURCE Arabidopsis thaliana (thale cress) email [email protected] ORGANISM Arabidopsis thaliana phone 81-55-981-6853 Eukaryota; Viridiplantae; Streptophyta; Embryophyta; Tracheophyta; fax 81-55-981-6849 Spermatophyta; Magnoliophyta; eudicotyledons; core eudicotyledons; institute National Institute of Genetics rosids; malvids; Brassicales; Brassicaceae; Camelineae; department DNA Data Bank of Japan Arabidopsis. country Japan REFERENCE 1 (bases 1 to 123456) state Shizuoka AUTHORS Mishima,H., ada,T., Park,C.S. and Liu,G.Q. city Mishima TITLE Direct Submission street Yata 1111 JOURNAL Submitted (dd-mmm-yyyy) to the DDBJ/EMBL/GenBank databases. zip 411-8540 Contact:Hanako Mishima REFERENCE ab_name Mishima,H. National Institute of Genetics, DNA Data Bank of Japan; Yata 1111, ab_name Yamada,T. Mishima, Shizuoka 411-8540, Japan ab_name Park,C.S. REFERENCE 2 ab_name Liu,G.Q. AUTHORS Mishima,H., ada,T., Park,C.S. and Liu,G.Q. title Arabidopsis thaliana DNA TITLE Arabidopsis thaliana Sequencing year 2017 JOURNAL Unpublished (2017) status Unpublished COMMENT Please visit our web site -
Lie Therory and Hermite Polynomials
International Journal For Research In Advanced Computer Science And Engineering ISSN: 2208-2107 A Computational Method for Sequence analyzing Rekardo Fosalli, Teriso Joloobi School of Computer Science, University of De Cyprus, Cyprus Abstract: To study how normal cellular activities are altered in different disease states, the biological data must be combined to form a comprehensive picture of these activities. Therefore, the field of bioinformatics has evolved such that the most pressing task now involves the analysis and interpretation of various types of data. This includes nucleotide and amino acid sequences, protein domains, and protein structures. Common uses of bioinformatics include the identification of candidate genes and nucleotides (SNPs). Often, such identification is made with the aim of better understanding the genetic basis of disease, unique adaptations, desirable properties (esp. in agricultural species), or differences between populations. In a less formal way, bioinformatics also tries to understand the organisational principles within nucleic acid and protein sequences. Keywords: Clustering, RNA sequences, RNA-Seq, Data Mining, Bioinformatics, Graph Mining. 1. INTRODUCTION Bioinformatics has become an important part of many areas of biology. In experimental molecular biology, bioinformatics techniques such as image and signal processing allow extraction of useful results from large amounts of raw data. In the field of genetics and genomics, it aids in sequencing and annotating genomes and their observed mutations. It plays a role in the text mining of biological literature and the development of biological and gene ontologies to organize and query biological data. It also plays a role in the analysis of gene and protein expression and regulation. -
Nucleic Acid Databases on the Web Richard Peters and Robert S
© 1996 Nature Publishing Group http://www.nature.com/naturebiotechnology RESOURCES • WWWGUIDE Nucleic acid databases on the Web Richard Peters and Robert S. Sikorski For many researchers, searchable sequence data is The National Center for Biotechnology the Washington University (St. Louis, MO) bases on the Internet are becoming as indis Information (NCBI) site. Some of the things sequencing project. dbSTS is a database of pensable as pipettes. Especially in the fields of you can now find at their site include BLAST, genomic sequence tagged sites that serve as molecular and structural biology, many excel Genbank, Entrez, dbEST, dbSTS, OMIM, mapping landmarks in the human genome. lent online databases have cropped up in just and UniGene. A brief description of each of OMIM (Online Mendelian Inheritance in the past year. Despite this wealth of informa these tools demonstrates what we mean. Man) is a unique database that catalogs cur tion, it still requires considerable effort to sort BLAST is the standard program used for rent research and clinical information about through these myriad databases to find ones querying your DNA or protein sequence individual human genes. UniGene is a subset that are useful. Keeping this in mind, we started against all known sequences housed in data of GenBank that defines a collection of DNA by using our Net search tools to see if we could bases. You can even have the output sent to sequences likely to represent the transcrip winnow out the best of the Net. This month, we you by e-mail. Genbank is an annotated col tion products of distinct genes. -
Biological Databases
Biological Databases Biology Is A Data Science ● Hundreds of thousand of species ● Million of articles in scientific literature ● Genetic Information ○ Gene names ○ Phenotype of mutants ○ Location of genes/mutations on chromosomes ○ Linkage (relationships between genes) 2 Data and Metadata ● Data are “concrete” objects ○ e.g. number, tweet, nucleotide sequence ● Metadata describes properties of data ○ e.g. object is a number, each tweet has an author ● Database structure may contain metadata ○ Type of object (integer, float, string, etc) ○ Size of object (strings at most 4 characters long) ○ Relationships between data (chromosomes have zero or more genes) 3 What is a Database? ● A data collection that needs to be : ○ Organized ○ Searchable ○ Up-to-date ● Challenge: ○ change “meaningless” data into useful, accessible information 4 A spreadsheet can be a Database ● Rectangular data ● Structured ● No metadata ● Search tools: ○ Excel ○ grep ○ python/R 5 A filesystem can be a Database ● Hierarchical data ● Some metadata ○ File, symlink, etc ● Unstructured ● Search tools: ○ ls ○ find ○ locate 6 Organization and Types of Databases ● Every database has tools that: ○ Store ○ Extract ○ Modify ● Flat file databases (flat DBMS) ○ Simple, restrictive, table ● Hierarchical databases ○ Simple, restrictive, tables ● Relational databases (RDBMS) ○ Complex, versatile, tables ● Object-oriented databases (ODBMS) ● Data warehouses and distributed databases ● Unstructured databases (object store DBs) 7 Where do the data come from ? 8 Types of Biological Data -
Sophie Dumont
Sophie Dumont University of California, San Francisco 513 Parnassus Ave, HSW-613, San Francisco, CA 94143-0512, USA Office: (415) 502-1229 / Cell: (510) 229-9846 [email protected] ; http://dumontlab.ucsf.edu/ EDUCATION 9/2000-12/2005 Ph.D., Biophysics, University of California, Berkeley, CA 10/1999-8/2000 Candidate for D.Phil., Theoretical Physics, University of Oxford, UK 9/1995-6/1999 B.A., Physics, magna cum laude, Princeton University, Princeton, NJ RESEARCH POSITIONS & TRAINING 7/2012-present Assistant Professor, University of California, San Francisco Dept of Cell & Tissue Biology and Dept of Cellular & Molecular Pharmacology Member, QB3 California Institute for Quantitative Biosciences Member, NSF Center for Cellular Construction Affiliate Member, UCSF Cancer Center Graduate program member: Bioengineering, Biomedical Sciences, Biophysics, Tetrad 7/2006-5/2012 Postdoctoral Fellow, Harvard Medical School Mentor: Prof. Timothy Mitchison (Systems Biology) 7/2006-6/2009 Junior Fellow, Harvard Society of Fellows 9/2000-12/2005 Graduate Student, University of California, Berkeley Advisor: Prof. Carlos Bustamante (Physics and Molecular & Cell Biology) 10/1999-8/2000 Graduate Student, University of Oxford Advisor: Prof. Douglas Abraham (Theoretical Physics) 6/1997-5/1999 Undergraduate Student, Princeton University Advisor: Prof. Stanislas Leibler (Physics) AWARDS 2016-2021 NSF CAREER Award 2016 Margaret Oakley Dayhoff Award of the Biophysical Society 2015-2020 NIH New Innovator Award (DP2) 2013-2018 Rita Allen Foundation and Milton -
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. -
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.