DOCSLIB.ORG

Treball Fi De Grau

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Treball Fi De Grau TREBALL DE FI DE GRAU TFG TITLE: Development of a GUI for InterMineR and Cytoscape to make biological databases FAIR. DEGREE: Biomedical Engineering Degree AUTHOR: Celia Sánchez Laorden ADVISORS: Dr Rachel Lyne Dr Gos Micklem SUPERVISOR: Dr Santiago Marco DATE: June 13, 2021 Títol: Creació d’una interfície gràfica d’usuari entre InterMineR i Cytoscape per fer que les bases de dades siguin FAIR. Autor: Celia Sánchez Laorden Directors: Dr Rachel Lyne Dr Gos Micklem Supervisor: Dr Santiago Marco Data: 13 de juny de 2021 Resum InterMine és un sistema del tipus magatzem de dades que permet crear grans bases de dades biològiques fàcilment des de fonts heterogènies en serveis web RESTful. El sistema proporciona eines estadístiques i d’anàlisis de dades potents. InterMine com a plataforma té una interfície web des d’on l’usuari pot fer cerques i incorpora recursos especialitzats com mètodes d’anàlisis d’enriquiment de conjunts de gens. InterMineR és una de les biblioteques informàtiques que dóna suport a la plataforma des de l’entorn de programació de R Studio. En aquest projecte, s’han implementat noves funcionalitats per permetre que l’usuari treballi amb col·leccions i conjunts de dades creats des de el servei web, anomenats llistes. Les noves funcions han estat incorporades i publicades en la nova versió del paquet a Bioconductor. L’objectiu principal d’aquest projecte ha estat la creació d’una interfície gràfica d’usu- ari (GUI) per fer cerques a les bases de dades d’InterMine i generar visualitzacions de xarxes Cytoscape. S’ha desenvolupat un programari executable en entorns d’es- criptori. Aquest permet als usuaris reduir la complexitat de les dades i extreure’n significat amb fins de recerca. S’espera que permeti analitzar amb més profun- ditat les fonts d’InterMine i augmentar-ne la FAIR (dades trobables, accessibles, interoperables i reutilitzables en anglès). Title : Development of a graphical user interface for InterMineR and Cytoscape to make biological databases FAIR. Author: Celia Sánchez Laorden Advisors: Dr Rachel Lyne Dr Gos Micklem Supervisor: Dr Santiago Marco Date: June 13, 2021 Overview InterMine is a biological data warehousing system for creating large biological databases of heterogenous data sources easily in RESTful web services. It provides powerful statistical and analysis tools. InterMine as a platform has a web interface in which user can run searches and incorporates specialized resources such as enrichment statistics. InterMineR is one of the software ’client’ libraries that interfaces the biological databases built using the InterMine platform with a programming environment, in this case R. In this project, new functionalities have been implemented to enable the user to work with stored collections of data and saved results sets created from a web-service, called lists. The new functions have been merged and published in the new Bioconductor version of the package. The main aim of this project is to create a graphical user interface (GUI) to query the InterMine databases and generating Cytoscape network visualizations. We developed a software that runs on desktop environment. It allows users to reduce the complexity of the data and extract meaning from it. It will allow data in InterMines to be further analysed - increasing its FAIRness. First of all, I would like to show appreciation to Dr Gos Micklem, for giving me the opportunity to conduct this project in his Lab. I would like to express my most sincere thanks of gratitude to Dr Rachel Lyne for her guidance, advice and assistance. This work would not have been possible without her intense commitment and dedication. I cannot fail in my remarks to express appreciation for the coworking with Yo Yehudi, Daniela Butano and Adrián Bazaga. Finally, I would also like to thank to Dr Santiago Marco, for his advice and support. CONTENTS Project Origin and Motivation .......................... 1 CHAPTER 1. Introduction ............................... 2 1.1. Scope ......................................... 2 1.2. Span ......................................... 2 1.2.1. Description of the span of the project .................. 3 1.2.2. Requirements of the project ....................... 3 1.2.3. Limitations of the project ......................... 4 1.2.4. Deliverables of the project ........................ 5 1.2.5. Criterion of acceptation of the results .................. 5 1.2.6. Restrictions of the project ........................ 5 1.2.7. Initial known risks ............................. 6 CHAPTER 2. Background ............................... 7 2.1. State of the art .................................... 7 2.1.1. InterMine as a Data Warehouse ..................... 7 2.1.2. Setting up an InterMine: making data FAIR ............... 8 2.1.3. Cytoscape: graph analysis software ................... 9 2.2. Project Environment ................................ 10 CHAPTER 3. Scan of the Market ........................... 11 CHAPTER 4. Design Engineering ........................... 13 4.1. Improvements of the core InterMineR R package ............... 13 4.1.1. Preliminary Project Study ........................ 13 4.1.2. Proposed Solution ............................ 13 4.2. Interfacing with Cytoscape ............................ 14 4.2.1. Preliminary Project Study ........................ 14 4.2.2. Proposed Solution ............................ 14 4.3. Tutorials ....................................... 15 4.3.1. Preliminary Project Study ........................ 15 4.3.2. Proposed Solution ............................ 15 4.4. Documentation ................................... 15 4.4.1. Preliminary Project Study ........................ 15 4.4.2. Proposed Solution ............................ 16 CHAPTER 5. Detail Engineering ............................ 17 5.1. Technologies involved ............................... 17 5.2. Design ........................................ 19 5.2.1. Improvements of the core of InterMineR ................ 19 5.2.2. Interfacing InterMineR and Cytoscape with Shiny ........... 23 5.2.3. Set Query Tab ............................... 25 5.2.4. Run your Query Tab ........................... 28 5.2.5. Visualize your Results Tab ........................ 28 5.2.6. Overlay Additional Data Tab ....................... 30 5.2.7. Saved Networks tab ........................... 31 5.2.8. Customer Support ............................ 32 5.2.9. GitHub Repository ............................ 33 5.3. Results ........................................ 33 5.3.1. Improvements of the core of InterMineR ................ 33 5.3.2. Use cases for the InterMineR-Cytoscape Shiny Interface ....... 35 CHAPTER 6. Organization ............................... 41 6.1. Technique pre-feasibility study .......................... 41 6.2. Schedule of Execution ............................... 42 CHAPTER 7. Economic pre-feasibility study .................... 43 7.1. Cost study ...................................... 43 CHAPTER 8. Environmental Impact .......................... 45 CHAPTER 9. License .................................. 46 CHAPTER 10. Future Extensions ........................... 47 Conclusions ...................................... 48 Bibliography ...................................... 49 APPENDIX A. Task 1: Improving InterMineR ............... 2 A.1. Classes ........................................ 2 A.1.1. ListManager-class.R ........................... 2 A.1.2. webservice-class.R ............................ 2 A.1.3. InterMineR-class.R ............................ 2 A.2. Methods and Functions .............................. 3 A.2.1. initInterMine ................................ 3 A.2.2. list_manager ................................ 3 A.2.3. ListManager-methods.R ......................... 3 A.3. Documentation ................................... 7 A.3.1. ListManager-class.Rd .......................... 7 A.3.2. ListManager-class documentation rendered to HTML. ........ 9 A.4. Results ........................................ 10 APPENDIX B. Task 2: Shiny Interface .................... 23 B.1. Welcoming message ................................ 23 B.2. Interface simplified code .............................. 23 B.2.1. Simplified code of the app.R script structure .............. 23 B.2.2. Simplified code of the tab dashboard structure of: ........... 25 B.3. InterMineR fragments of code .......................... 26 B.3.1. Selecting the InterMine and getting the data model: initInterMine, listMines, getModel ............................ 26 B.3.2. Get the information of the templates pre-defined in InterMine and get the query obtained in a template: getTemplates, getTemplatesQuery 26 B.3.3. Constraints: setConstraints ....................... 27 B.3.4. Initialize a new InterMineR query or modify an existing list query: setQuery .................................. 29 B.3.5. Get the summary of constraints: summary ............... 29 B.3.6. Get the results: runQuery ........................ 30 B.4. Cytoscape fragments of code ........................... 31 B.4.1. Visualize tab ................................ 31 B.4.2. Overlay tab ................................ 35 B.4.3. Saved Networks tab ........................... 46 B.5. User guide ...................................... 47 APPENDIX C. Use-cases for the InterMineR-Cytoscape Shiny In- terface ................................ 56 C.1. HumanMine use-case. ............................... 56 C.1.1. Workflow A: ................................ 56 C.1.2. Workflow B: ................................ 57 C.2. CovidMine use-case. ...............................
Load more

Recommended publications
  • Description of the Biomart Package
    Description of the biomaRt package Steffen Durinck‡∗, Wolfgang Huber¶†, Yves Moreau‡, Bart De Moor‡ July 25, 2006 ‡Department of Electronical Engineering, ESAT-SCD, K.U.Leuven, Kasteelpark Arenberg 10, 3001 Leuven-Heverlee, Belgium, http://www.esat.kuleuven.ac.be/~dna/BioI and ¶European Bioinformatics Institute, Hinxton, UK Contents 1 Introduction 2 2 objects 2 2.1 Mart-class..................................... 2 3 Simple biomaRt functions for frequently used queries to Ensembl 3 3.1 Selecting a BioMart database to use . 3 3.1.1 useMart .................................. 3 3.1.2 martDisconnect ............................. 3 3.2 Annotating identifiers with gene information . 4 3.2.1 GOannotation .............................. 6 3.2.2 OMIMannotation ............................ 7 3.2.3 INTERPRO protein domains . 7 3.3 Homologymapping................................ 8 3.4 Identify subsets of genes for further analysis with the getFeature function . 8 3.5 Sequenceinformation............................... 9 3.6 Single Nucleotide Polymorphisms . 9 3.7 Moreexoticfunctions............................... 10 3.7.1 getPossibleXrefs ............................ 10 3.7.2 getXref .................................. 10 ∗Steff[email protected] †[email protected] 1 4 Advanced data retrieval with BioMart API functions 10 4.1 listMarts...................................... 11 4.2 useMart ...................................... 11 4.3 listDatasets .................................... 11 4.4 useDataset..................................... 12 4.5 Filter, Values and Attributes . 12 5 Local BioMart databases 13 1 Introduction The BioConductor biomaRt package provides and API in R to query BioMart databases such as Ensembl (http://www.ensembl.org), a software system which produces and maintains au- tomatic annotation on metazoan genomes. Two sets of functions are currently implemented. A first set of functions is tailored towards Ensembl and are a set of commonly used queries in microarray data analysis.
    [Show full text]
  • The Biomart User's Guide
    The biomaRt user's guide Steffen Durinck,∗ Wolfgang Hubery September 15, 2015 Contents 1 Introduction 2 2 Selecting a BioMart database and dataset 3 3 How to build a biomaRt query 7 4 Examples of biomaRt queries 9 4.1 Task 1: Annotate a set of Affymetrix identifiers with HUGO symbol and chromosomal locations of corresponding genes . 9 4.2 Task 2: Annotate a set of EntrezGene identifiers with GO annotation . 9 4.3 Task 3: Retrieve all HUGO gene symbols of genes that are located on chromosomes 17,20 or Y , and are associated with one the following GO terms: "GO:0051330","GO:0000080","GO:0000114","GO:0000082" (here we'll use more than one filter) . 10 4.4 Task 4: Annotate set of idenfiers with INTERPRO protein domain identifiers . 10 4.5 Task 5: Select all Affymetrix identifiers on the hgu133plus2 chip and Ensembl gene identifiers for genes located on chro- mosome 16 between basepair 1100000 and 1250000. 11 4.6 Task 6: Retrieve all entrezgene identifiers and HUGO gene symbols of genes which have a "MAP kinase activity" GO term associated with it. 11 ∗[email protected] [email protected] 1 4.7 Task 7: Given a set of EntrezGene identifiers, retrieve 100bp upstream promoter sequences . 12 4.8 Task 8: Retrieve all 5' UTR sequences of all genes that are located on chromosome 3 between the positions 185514033 and 185535839 . 13 4.9 Task 9: Retrieve protein sequences for a given list of Entrez- Gene identifiers . 13 4.10 Task 10: Retrieve known SNPs located on the human chro- mosome 8 between positions 148350 and 148612 .
    [Show full text]
  • Variation Data in Ensembl Plants
    Overview of the next two hours... Before tea After tea Session 1, Browser: Session 2, Tools: ● Introduction ● Variant Effect ○ Ensembl Plants and Predictor plant variation data ○ Introduction and ● Hands-on hands-on ○ Variation in the ● Mining variation Ensembl browser data with BioMart ● Displaying your ○ Introduction and data in Ensembl hands-on Plants Resources These slides: http://tinyurl.com/transplant2015 Course booklet: http://tinyurl.com/transplant2015b ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● → → Currently 39 genomes in Ensembl Plants http://plants.ensembl.org Brassicales Fabales Malpighiales Rosales Solanales Malvales Vitales Zingiberales Poales ... ● ● ● Or all the above! ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● Genome Browsers Genome Browser Databases • • Baseline Annotation • • • Transposon • discovery • • Gene prediction • • EST Alignment • PAG 2015 Recent updates (Release 27) http://plants.ensembl.org ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● On to variation... ● ● ● ● ● ● ● ● ● ● Species Samps Pops* ~Variations Arabidopsis 1,610 7 14,000,000 thaliana SV: 14,000 Brachypodium 3 3 300,000 ● distachyon Hordeum vulgare 188 16 24,000,000 Oryza glaberrima 21 1 800,000 Oryza indica 19 19 5,000,000 ● Oryza sativa 416 21 6,000,000 ● Solanum 84 8 71,000,000 lycopersicum ● Sorghum bicolor 3 1 SV: 64,000 Triticum aestivum 248 1 700,000 ✱ Vitis vinifera 17 3 500,000 Zea mays 103 1 51,000,000 Variation Data • • PAG 2015 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● Viewing Variation Data PAG 2015 Looking at some tomato variation data... EXERCISES Variation in the Ensembl Browser Exercise 1 – Exploring a SNP in Arabidopsis The arabidopsis ATCDSP32 gene is a chloroplastic, 32 kDa, drought-induced stress protein that is proposed to participate in cell redox homeostasis (GO: 0045454). A. How many variants have been identified in the gene that can cause a change in the protein sequence? B.
    [Show full text]
  • Original Article the Reactome Biomart
    Database, Vol. 2011, Article ID bar031, doi:10.1093/database/bar031 ............................................................................................................................................................................................................................................................................................. Original article The Reactome BioMart Robin A. Haw1, David Croft2, Christina K. Yung1, Nelson Ndegwa2,3, Peter D’Eustachio4, Henning Hermjakob2 and Lincoln D. Stein1,5,6,* Downloaded from 1Ontario Institute for Cancer Research, Toronto, ON, M5G0A3, Canada, 2European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, 3Faculty of Life Science, University of Manchester, Michael Smith Building, Oxford Road, Manchester M13 9PT, UK, 4NYU School of Medicine, Department of Biochemistry, New York, NY 10016, 5Cold Spring Harbor Laboratory, Bioinformatics and Genomics, Cold Spring Harbor, NY 11724, USA and 6Department of Molecular Genetics, University of Toronto, Toronto, ON, M5S 1A1, Canada *Corresponding author: Tel: +1 416 673 8514; Fax: +1 416 977 1118; Email: [email protected] http://database.oxfordjournals.org/ Submitted 24 March 2011; Revised 20 June 2011; Accepted 21 June 2011 ............................................................................................................................................................................................................................................................................................
    [Show full text]
  • The Biomart User's Guide
    The biomaRt user's guide Steffen Durinck,∗ Wolfgang Hubery October 18, 2010 Contents 1 Introduction 2 2 Selecting a BioMart database and dataset 3 3 How to build a biomaRt query 5 4 Examples of biomaRt queries 7 4.1 Task 1: Annotate a set of Affymetrix identifiers with HUGO symbol and chromosomal locations of corresponding genes . 7 4.2 Task 2: Annotate a set of EntrezGene identifiers with GO annotation . 8 4.3 Task 3: Retrieve all HUGO gene symbols of genes that are located on chromosomes 1,2 or Y , and are associated with one the following GO terms: "GO:0051330","GO:0000080","GO:0000114","GO:0000082" (here we'll use more than one filter) . 9 4.4 Task 4: Annotate set of idenfiers with INTERPRO protein domain identifiers . 9 4.5 Task 5: Select all Affymetrix identifiers on the hgu133plus2 chip and Ensembl gene identifiers for genes located on chro- mosome 16 between basepair 1100000 and 1250000. 10 4.6 Task 6: Retrieve all entrezgene identifiers and HUGO gene symbols of genes which have a "MAP kinase activity" GO term associated with it. 10 ∗steff[email protected] [email protected] 1 4.7 Task 7: Given a set of EntrezGene identifiers, retrieve 100bp upstream promoter sequences . 11 4.8 Task 8: Retrieve all 5' UTR sequences of all genes that are located on chromosome 3 between the positions 185514033 and 185535839 . 12 4.9 Task 9: Retrieve protein sequences for a given list of Entrez- Gene identifiers . 12 4.10 Task 10: Retrieve known SNPs located on the human chro- mosome 8 between positions 148350 and 148612 .
    [Show full text]
  • The Biomart User's Guide
    The biomaRt user's guide Steffen Durinck,∗ Wolfgang Hubery May 4, 2016 Contents 1 Introduction 2 2 Selecting a BioMart database and dataset 3 3 How to build a biomaRt query 5 4 Examples of biomaRt queries 7 4.1 Task 1: Annotate a set of Affymetrix identifiers with HUGO symbol and chromosomal locations of corresponding genes . 7 4.2 Task 2: Annotate a set of EntrezGene identifiers with GO annotation . 8 4.3 Task 3: Retrieve all HUGO gene symbols of genes that are located on chromosomes 17,20 or Y , and are associated with one the following GO terms: "GO:0051330","GO:0000080","GO:0000114","GO:0000082" (here we'll use more than one filter) . 8 4.4 Task 4: Annotate set of idenfiers with INTERPRO protein domain identifiers . 8 4.5 Task 5: Select all Affymetrix identifiers on the hgu133plus2 chip and Ensembl gene identifiers for genes located on chro- mosome 16 between basepair 1100000 and 1250000. 9 4.6 Task 6: Retrieve all entrezgene identifiers and HUGO gene symbols of genes which have a "MAP kinase activity" GO term associated with it. 10 ∗[email protected] [email protected] 1 4.7 Task 7: Given a set of EntrezGene identifiers, retrieve 100bp upstream promoter sequences . 10 4.8 Task 8: Retrieve all 5' UTR sequences of all genes that are located on chromosome 3 between the positions 185514033 and 185535839 . 11 4.9 Task 9: Retrieve protein sequences for a given list of Entrez- Gene identifiers . 11 4.10 Task 10: Retrieve known SNPs located on the human chro- mosome 8 between positions 148350 and 148612 .
    [Show full text]
  • Biomart–Biological Queries Made Easy Damian Smedley, Syed Haider, Benoit Ballester, Richard Holland, Darin London, Gudmundur Thorisson, Arek Kasprzyk
    BioMart–biological queries made easy Damian Smedley, Syed Haider, Benoit Ballester, Richard Holland, Darin London, Gudmundur Thorisson, Arek Kasprzyk To cite this version: Damian Smedley, Syed Haider, Benoit Ballester, Richard Holland, Darin London, et al.. BioMart– biological queries made easy. BMC Genomics, BioMed Central, 2009, 10 (1), pp.22. 10.1186/1471- 2164-10-22. hal-01615162 HAL Id: hal-01615162 https://hal-amu.archives-ouvertes.fr/hal-01615162 Submitted on 19 Dec 2018 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. Distributed under a Creative Commons Attribution| 4.0 International License BMC Genomics BioMed Central Software Open Access BioMart – biological queries made easy Damian Smedley1, Syed Haider1, Benoit Ballester1, Richard Holland1, Darin London2, Gudmundur Thorisson3 and Arek Kasprzyk*4 Address: 1European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK, 2Institute for Genome Sciences & Policy (IGSP), Duke University CIEMAS, 101 Science Drive, DUMC Box 3382, Durham, NC 27708, USA, 3Department
    [Show full text]
  • The Biomart Community Portal
    The BioMart community portal: an innovative alternative to large, centralized data repositories Damian Smedley, Syed Haider, Steffen Durinck, Luca Pandini, Paolo Provero, James Allen, Olivier Arnaiz, Mohammad Hamza Awedh, Richard Baldock, Giulia Barbiera, et al. To cite this version: Damian Smedley, Syed Haider, Steffen Durinck, Luca Pandini, Paolo Provero, et al.. The BioMart community portal: an innovative alternative to large, centralized data repositories. Nucleic Acids Research, Oxford University Press, 2015, 43 (W1), pp.W589-W598. 10.1093/nar/gkv350. hal- 01146849 HAL Id: hal-01146849 https://hal-univ-rennes1.archives-ouvertes.fr/hal-01146849 Submitted on 28 May 2020 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. Nucleic Acids Research Advance Access published April 20, 2015 Nucleic Acids Research, 2015 1 doi: 10.1093/nar/gkv350 The BioMart community portal: an innovative alternative to large, centralized data repositories Damian Smedley1, Syed Haider2, Steffen Durinck3, Luca Pandini4, Paolo Provero4,5, James Allen6, Olivier Arnaiz7, Mohammad Hamza Awedh8, Richard Baldock9, Giulia Barbiera4, Philippe Bardou10,TimBeck11, Andrew Blake12, Merideth Bonierbale13, Anthony J. Brookes11, Gabriele Bucci4, Iwan Buetti4, Sarah Burge6,Cedric´ Cabau10, Joseph W. Carlson14, Claude Chelala15, Charalambos Chrysostomou11, Davide Cittaro4, Olivier Collin16, Raul Cordova13,RosalindJ.Cutts15, Erik Dassi17, Alex Di Genova18, Anis Djari19, Anthony Esposito20, Heather Estrella20, Eduardo Eyras21,22, Julio Fernandez-Banet20, Simon Forbes1, Robert C.
    [Show full text]
  • The Biomart User's Guide
    The biomaRt user's guide Steffen Durinck,∗ Wolfgang Hubery October 13, 2014 Contents 1 Introduction 2 2 Selecting a BioMart database and dataset 3 3 How to build a biomaRt query 7 4 Examples of biomaRt queries 9 4.1 Task 1: Annotate a set of Affymetrix identifiers with HUGO symbol and chromosomal locations of corresponding genes . 9 4.2 Task 2: Annotate a set of EntrezGene identifiers with GO annotation . 10 4.3 Task 3: Retrieve all HUGO gene symbols of genes that are located on chromosomes 17,20 or Y , and are associated with one the following GO terms: "GO:0051330","GO:0000080","GO:0000114","GO:0000082" (here we'll use more than one filter) . 10 4.4 Task 4: Annotate set of idenfiers with INTERPRO protein domain identifiers . 10 4.5 Task 5: Select all Affymetrix identifiers on the hgu133plus2 chip and Ensembl gene identifiers for genes located on chro- mosome 16 between basepair 1100000 and 1250000. 11 4.6 Task 6: Retrieve all entrezgene identifiers and HUGO gene symbols of genes which have a "MAP kinase activity" GO term associated with it. 11 ∗[email protected] [email protected] 1 4.7 Task 7: Given a set of EntrezGene identifiers, retrieve 100bp upstream promoter sequences . 12 4.8 Task 8: Retrieve all 5' UTR sequences of all genes that are located on chromosome 3 between the positions 185514033 and 185535839 . 13 4.9 Task 9: Retrieve protein sequences for a given list of Entrez- Gene identifiers . 13 4.10 Task 10: Retrieve known SNPs located on the human chro- mosome 8 between positions 148350 and 148612 .
    [Show full text]
  • The Biomart User's Guide
    The biomaRt user's guide Steffen Durinck,∗ Wolfgang Hubery April 3, 2013 Contents 1 Introduction 2 2 Selecting a BioMart database and dataset 3 3 How to build a biomaRt query 7 4 Examples of biomaRt queries 9 4.1 Task 1: Annotate a set of Affymetrix identifiers with HUGO symbol and chromosomal locations of corresponding genes . 9 4.2 Task 2: Annotate a set of EntrezGene identifiers with GO annotation . 10 4.3 Task 3: Retrieve all HUGO gene symbols of genes that are located on chromosomes 1,2 or Y , and are associated with one the following GO terms: "GO:0051330","GO:0000080","GO:0000114","GO:0000082" (here we'll use more than one filter) . 10 4.4 Task 4: Annotate set of idenfiers with INTERPRO protein domain identifiers . 11 4.5 Task 5: Select all Affymetrix identifiers on the hgu133plus2 chip and Ensembl gene identifiers for genes located on chro- mosome 16 between basepair 1100000 and 1250000. 11 4.6 Task 6: Retrieve all entrezgene identifiers and HUGO gene symbols of genes which have a "MAP kinase activity" GO term associated with it. 12 ∗steff[email protected] [email protected] 1 4.7 Task 7: Given a set of EntrezGene identifiers, retrieve 100bp upstream promoter sequences . 12 4.8 Task 8: Retrieve all 5' UTR sequences of all genes that are located on chromosome 3 between the positions 185514033 and 185535839 . 13 4.9 Task 9: Retrieve protein sequences for a given list of Entrez- Gene identifiers . 13 4.10 Task 10: Retrieve known SNPs located on the human chro- mosome 8 between positions 148350 and 148612 .
    [Show full text]
  • The Biomart Community Portal
    View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Digital Repository @ Iowa State University Animal Science Publications Animal Science 7-2015 The ioMB art Community Portal: An Innovative Alternative to Large, Centralized Data Repositories Damian Smedley Wellcome Trust Sanger Institute Syed Haider University of Oxford Steffen Durinck Genentech, Inc. Luca Pandini Center for Translational Genomics and Bioinformatics San Raffaele Scientific nI stitut Paolo Provero FCenoltloerw fo rthi Trsa nasndlatio anddal Genitionomalic sw aorknd Bsio aitnf:ohrmttap://ltics Saibn. drRaff.iasaeleta Sciente.edu/atific nI nsts_pubsitut See nePxat pratge of for the addiAtiongraicl aulturuthors e Commons, Animal Sciences Commons, Genetics and Genomics Commons, Health Information Technology Commons, Laboratory and Basic Science Research Commons, and the Medical Sciences Commons The ompc lete bibliographic information for this item can be found at http://lib.dr.iastate.edu/ ans_pubs/169. For information on how to cite this item, please visit http://lib.dr.iastate.edu/ howtocite.html. This Article is brought to you for free and open access by the Animal Science at Digital Repository @ Iowa State University. It has been accepted for inclusion in Animal Science Publications by an authorized administrator of Digital Repository @ Iowa State University. For more information, please contact [email protected]. Authors Damian Smedley, Syed Haider, Steffen Durinck, Luca Pandini, Paolo Provero, James Allen, Olivier Arnaiz, Mohammad Hamza Awedh, Richard Baldock, Giulia Barbiera, Philippe Bardou, Tim Beck, Andrew Blake, Meredith Bonierbale, Anthony J. Brookes, Gabrielle Bucci, Iwan Bueti, Sarah Burge, Cédric Cabua, Joseph W. Carlson, Claude Chelala, Charalambos Chrysostomou, Davide Citaro, Olivier Collin, Raul Cordova, Rosalind J.
    [Show full text]
  • Advanced Genomic Data Mining
    Education Advanced Genomic Data Mining Xose´ M. Ferna´ndez-Sua´rez*, Ewan Birney European Bioinformatics Institute, Ensembl Group, Wellcome Trust Genome Campus, Hinxton, Cambridge, United Kingdom Introduction trieve information through different plat- pathways in the Reactome database). forms such as Galaxy [5] and the biomaRt Click on the ‘count’ button at the top to As data banks increase their size, one of package of BioConductor [11,12]. Many obtain this number. the current challenges in bioinformatics is of these tools also interact with the UCSC Next, we can enrich our search for to be able to query them in a sensible way. Table Browser and have similar approach- enzymes in the UniProt database. This Information is contained in different es using the UCSC system. We also will require the ‘linked’ or secondary databases, with various data representa- address programmatic access using Bio- dataset. Follow this description, or view tions or formats, making it very difficult to Mart’s own implementation of Web ser- the tutorials for use of the linked database use a single query tool to search more than vices (MartService). For local deployment at http://www.ensembl.org/common/ a single data source. of BioMart, see Table 1. Workshops_Online?id = 117. Data mining is vital to bioinformatics as Click on the second ‘Dataset’ option at it allows users to go beyond simple BioMart Web Interface the left of the page. Select ‘UniProt browsing of genome browsers, such as proteomes’ as the database. In this Ensembl [1,2] or the UCSC Genome Browser
    [Show full text]