Your Brains in My e-Laboratory

1. Amsterdam, May 28, 2009 Your Brains in My e-Laboratory Feasting on brains with Taverna and Semantic Web tools Marco Roos acknowledging the AID team (Scott Marshall, Sophia Katrenko, Willem van Hage, Edgar Meij, KonstantinosKrommydas, Pieter Adriaans), Andrew Gibson, MartijnSchuemie, Piter de Boer, the myGrid team (in particular Katy Wolstencroft, Carole Goble, and Dave de Roure), OMII-UK and NBIC

2. A biologist in e-Science • Marco Roos • Biologist and bioinformatician • Post-doc e-(bio)science, University of Amsterdam (BioRange/VL-e) • Project or Area Liaison (PAL) OMII-UK • Member UK e-Science All Hands Foundation • Member BioAssist programme committee NBIC

3. Mouse fibroblast (skin) cells My primary motivationStructure and function of DNA in the nucleus Escherichia coli

4. /* * determines ridges in htm expression table */ #include "ridge.h" int selecthtm(PGconn *conn, char *htmtablename, char *chromname, PGresult *htmtable) { char querystring[256]; sprintf("SELECT * FROM %s WHERE chrom = %s ORDER BY genstart", htmtablename, chromname); htmtable = PQexec(conn, querystring); return(validquery(htmtable, querystring)); } int is_ridge(PGresult *htmtable, int row, double exprthreshold, int mincount) /* determines if mincount genes in a row are (part of) a ridge */ /* pre: htmtable is valid and sorted on genStart (ascending) /* post: { if (mincount<=0) return TRUE; if (row>=PQntuples(htmtable)) return FALSE; if(PQgetvalue(htmtable, 0, PQfnumber(htmtable, "movmed39expr")) < exprthreshold) { return FALSE; } return(is_ridge(htmtable, ++row, exprthreshold, --mincount)); } int main() { PGconn *conn; /* holds database connection */ char querystring[256]; /* query string */ PGresult *result; int i; conn = PQconnectdb("dbname=htm port=6400 user=mroos password=geheim"); if (PQstatus(conn)==CONNECTION_BAD) { fprintf(stderr, "connection to database failed.\n"); fprintf(stderr, "%s", PQerrorMessage(conn)); exit(1); } else printf("Connection ok\n"); sprintf(querystring, "SELECT * FROM chromosomes"); printf("%s\n", querystring); result = PQexec(conn, querystring); if (validquery(result, querystring)) { printresults(result); } else { PQclear(result); PQfinish(conn); return FALSE; } PQclear(result); PQfinish(conn); return TRUE; } int printresults(PGresult *tuples) { int i; for (i=0; i< PQntuples(tuples) && i < 10; i++) { printf("%d, ", i); printf("%s\n", PQgetvalue(tuples,i,0)); } return TRUE; } int validquery(PGresult *result, char *querystring) { printf(" in validquery\n"); if (PQresultStatus(result) != PGRES_TUPLES_OK) { printf("Query %s failed.\n", querystring); fprintf(stderr, "Query %s failed.\n", querystring); return FALSE; } return TRUE; }

5. ‘Old school’ bioinformatics approach Local Database Local Database

6. My tiny brain

7. * My ws Your ws My ws My ws Your ws Virtual professor * From P.J. Verschure, Journal of Cellular Biochemistry 2006, vol. 99(1), pg 23-34

8. Combining expertise Edgar Meij Information retrieval expert

9. Combining expertise Sophia Katrenko Machine learning expert

10. Combining expertise Willem van Hage Semantic web expert (and bass guitar player)

11. Combining expertiseTowards a knowledge framework Scott Marshall Computer scientist and bioinformatician

12. The AIDA toolbox, Web Services for knowledge extraction and knowledge management

13. e-Science collaboration AIDA toolbox

14. “Collaboration through Web Services” Martijn Schuemie Bio-text mining expert BioSemantics group, Erasmus University Rotterdam

15. “Collaboration through Web Services” Hideaki Sugawara Biological Database expert

16. “Collaboration through Web Services” e-bioscientist

17. An insightful computational experiment

18. Workflow paradigm for biologists

19. e-Science leveraging the use of more brains Want this…

20. e-Science leveraging the use of more brains …need this

21. Workflow and Semantic Web Alpha versionof Concept Web

22. Separation of models and instances in OWL

23. Biological model (representing cartoon elements) <myModel:HDAC1><rdfs:type><myModel:Protein> <myModel:Protein><rdfs:type><owl:Class>

24. Model for text mining observations

25. Experiment log model

26. Mappings between Biological and other models

27. Preliminary results

28. Pseudo RDF query and results SELECT label(comment), label(query1), label(query2) FROM {protein_instance} rdf:type {bio:Protein} rdf:type {owl:Class}, {protein_instance} rdfs:comment {comment}; bioModel:isModelComponentOf {model1}; bioModel:isModelComponentOf {model2}, {representation1} mappingModel:partially_represents {model1}; methodModel:has_query {query1}, {representation2} mappingModel:partially_represents {model2}; methodModel:has_query {query2} WHERE model1 != model2

29. Protein references Proteinname component of discovered by Discoveryprocess run implemented by Servicerun has input run at Document Run date & time creator Creator

30. UniProt:P19838 references NF-KappaB component of discovered by Conditional Random FieldsProtein Name Recognition implemented by AIDA:applyCRF has input run at PMID:17540846 2008-11-1803:29:30 creator Sophia Katrenko(UvA)

31. Access to triples in Taverna via AIDA plugin

32. Knowledge mining Knowledge mining:my knowledge is mine, your knowledge is mine

33. * My ws Your ws My ws My ws Your ws Demonstrate Exploiting Brains (2x) Computationalbrains Biologicalbrains * From P.J. Verschure, Journal of Cellular Biochemistry 2006, vol. 99(1), pg 23-34

34. A typical biologist… Lots of data to deal with Tiny brain Lots of knowledge to deal with No computationalsuperpowers Lots of methodsand algorithms to try and combine Aneedy biologist

35. An enhanced biologist… Lots of data to support me Many brains Knowledge basesto query Other people’scomputationalsuperpowers Web Services, Workflows, and their creatorsavailable Anenhanced biologist

36. Publish and share on myExperiment.org Publish & share research objects

37. e-Laboratory factories

38. http://www.epigenius.org/(mock-up)

39. End of presentation... Thank you http://adaptivedisclosure.org Are you willing to share your brain?