Literature DB >> 15562521

Design of a data model for developing laboratory information management and analysis systems for protein production.

Anne Pajon1, John Ionides, Jon Diprose, Joël Fillon, Rasmus Fogh, Alun W Ashton, Helen Berman, Wayne Boucher, Miroslaw Cygler, Emeline Deleury, Robert Esnouf, Joël Janin, Rosalind Kim, Isabelle Krimm, Catherine L Lawson, Eric Oeuillet, Anne Poupon, Stéphane Raymond, Tim Stevens, Herman van Tilbeurgh, John Westbrook, Peter Wood, Eldon Ulrich, Wim Vranken, Li Xueli, Ernest Laue, David I Stuart, Kim Henrick.   

Abstract

Data management has emerged as one of the central issues in the high-throughput processes of taking a protein target sequence through to a protein sample. To simplify this task, and following extensive consultation with the international structural genomics community, we describe here a model of the data related to protein production. The model is suitable for both large and small facilities for use in tracking samples, experiments, and results through the many procedures involved. The model is described in Unified Modeling Language (UML). In addition, we present relational database schemas derived from the UML. These relational schemas are already in use in a number of data management projects. (c) 2004 Wiley-Liss, Inc.

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Year:  2005        PMID: 15562521     DOI: 10.1002/prot.20303

Source DB:  PubMed          Journal:  Proteins        ISSN: 0887-3585


  8 in total

Review 1.  Data deposition and annotation at the worldwide protein data bank.

Authors:  Shuchismita Dutta; Kyle Burkhardt; Jasmine Young; Ganesh J Swaminathan; Takanori Matsuura; Kim Henrick; Haruki Nakamura; Helen M Berman
Journal:  Mol Biotechnol       Date:  2008-12-10       Impact factor: 2.695

2.  Data management in the modern structural biology and biomedical research environment.

Authors:  Matthew D Zimmerman; Marek Grabowski; Marcin J Domagalski; Elizabeth M Maclean; Maksymilian Chruszcz; Wladek Minor
Journal:  Methods Mol Biol       Date:  2014

3.  Observation of a low-temperature, dynamically driven structural transition in a polypeptide by solid-state NMR spectroscopy.

Authors:  Vikram S Bajaj; Patrick C A van der Wel; Robert G Griffin
Journal:  J Am Chem Soc       Date:  2009-01-14       Impact factor: 15.419

Review 4.  Benefits of structural genomics for drug discovery research.

Authors:  Marek Grabowski; Maksymilian Chruszcz; Matthew D Zimmerman; Olga Kirillova; Wladek Minor
Journal:  Infect Disord Drug Targets       Date:  2009-11

5.  xtalPiMS: a PiMS-based web application for the management and monitoring of crystallization trials.

Authors:  Ed Daniel; Bill Lin; Jonathan M Diprose; Susanne L Griffiths; Chris Morris; Ian M Berry; Raymond J Owens; Richard Blake; Keith S Wilson; David I Stuart; Robert M Esnouf
Journal:  J Struct Biol       Date:  2011-05-14       Impact factor: 2.867

6.  The Protein Information Management System (PiMS): a generic tool for any structural biology research laboratory.

Authors:  Chris Morris; Anne Pajon; Susanne L Griffiths; Ed Daniel; Marc Savitsky; Bill Lin; Jonathan M Diprose; Alan Wilter da Silva; Katya Pilicheva; Peter Troshin; Johannes van Niekerk; Neil Isaacs; James Naismith; Colin Nave; Richard Blake; Keith S Wilson; David I Stuart; Kim Henrick; Robert M Esnouf
Journal:  Acta Crystallogr D Biol Crystallogr       Date:  2011-03-18

7.  E-MSD: improving data deposition and structure quality.

Authors:  M Tagari; J Tate; G J Swaminathan; R Newman; A Naim; W Vranken; A Kapopoulou; A Hussain; J Fillon; K Henrick; S Velankar
Journal:  Nucleic Acids Res       Date:  2006-01-01       Impact factor: 16.971

8.  Design and implementation of a generalized laboratory data model.

Authors:  Michael C Wendl; Scott Smith; Craig S Pohl; David J Dooling; Asif T Chinwalla; Kevin Crouse; Todd Hepler; Shin Leong; Lynn Carmichael; Mike Nhan; Benjamin J Oberkfell; Elaine R Mardis; LaDeana W Hillier; Richard K Wilson
Journal:  BMC Bioinformatics       Date:  2007-09-26       Impact factor: 3.169

  8 in total

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