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The Big Picture: The Industrial Revolutiona talk in berlin, 2008, about industrialising bioinformatics data analysis
- 1. The Big Picture: The Industrial Revolution Robert Stevens Robert.stevens@manchester.ac.uk The University of Manchester, UK
- 2. Industrialisation • Biology has industrialised data production • Beginning to industrialise data analysis • Need to automate experimentation • Need to join them all together
- 3. Data Integration • Data integration is possible • We know how to do it (technically) • We know how to do plumbing • What is left is a social issue
- 4. Classic and Modern Biology Genotype Phenotype Modern biology Classic biology
- 5. Semantic Knowledge Base Experimentation, Data generation Consistency checking Querying Automated reasoning Hypothesis formulation Experimental design Information extraction, Knowledge formalization Semantic Systems Biology Cycle
- 6. What’s in a Lab? • People • Equipment, reagents, etc. • Protocols • Policy, governance • All there to facilitate and manage investigation
- 7. What’s in an e-Lab? People Data Process Investigation
- 8. Data: BioGateway • Uses Virtuoso Open Server – Open Source software that can host a triple store – Can build this from RDF files – Has a DB backend • Supports SPARQL* language which allows querying RDF data (graphs) • Its syntax is similar to that of SQL. *http://www.w3.org/TR/rdf-sparql-query/ http://www.openlinksw.com/virtuoso/
- 9. BioGateWay Resources Uniprot/SWISS-PROT OBO GOA CCONCBI Taxonomy RO RDF
- 10. Data as Input: Asking Questions • Cancer: what candidate genes are involved in cell cycle control, S-phase to G2 transition, DNA damage response and skin cancer? • Gastrin: what genes correlate with cancer and the use of anti-acids, and are involved in the gastrin response, and are associated with cell cycle control? • Inflammation: give me genes that are mentioned in the context of high carbohydrate intake and play a role in (process #1 to be named) and are within x steps from a GO ontology term related to inflammation
- 11. Processes: Genotype to Pathway Created by Paul Fisher
- 12. Pathway to Phenotype Created by Paul Fisher
- 13. 16 Processes: Finding and Curating Services Processes: Finding and Curating Services http://www.biocatalogue.org
- 14. 17 Finding, curating and reusing workflows
- 15. Data & Processes: Hypotheses • Run workflow • Make new data to put in repository • Also generate hypotheses • Generate plan from hypothesis • Execute plan and make more data • Automated?
Hinweis der Redaktion
- Ide
- Slide Title: G 2 P Slide contains two semicircles labelled Genotype and Phenotype Text says: Classic Biology; Modern Biology
- Slide Title: Genotype to Pathway QTL to Pathway workflow This workflow: Identifies all the genes, and their Ensembl ids, in a QTL region using BioMart Cross-references the gene ids to Entrez and Uniprot ids Entrez and Uniprot ids then map onto KEGG gene ids The KEGG gene ids are then used to identify KEGG pathways, including a description and an ID These lists of descriptions and IDs are then returned back to the user
- Slide Title: Pathway to Phenotype Pathways to PubMed abstracts workflow This workflow: Takes in a list of KEGG pathway descriptions Appends a search string to the end of each description Searches through PubMed using the NCBI eUtils Web Services For each article found in PubMed, as a PubMed id, an abstract is returned along with the date of publication These abstracts are then returned to the user as a single file Thos abstracts, coupled with abstracts from the phenotype, provide evidence linking those pathways to the phenotype
- Screenshot of the BioCatalogue homepage
- Screenshot of the myExperiment front page
- Screenshot of the workflows index page on myExperiment
- Screenshot of one of Paul’s workflows on myExperiment