The document summarizes the experience of a biologist in adopting an e-science approach to their work. It describes how before e-science, the biologist took an uncoordinated "spaghetti" approach using various tools without a unified strategy. The biologist then explains how adopting e-science principles like collaboration, reusable workflows, and web services helped enhance their work by allowing experts from different domains to combine their expertise. The biologist also reflects on outreach efforts to promote e-science to other researchers.

1. A PAL’s life About a biologist in e-science Presentation for the OMII-UK Board, Southampton, May 16, 2008

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4. Biological motivation: F unction and architecture of DNA in the cell Escherichia coli Mouse fibroblast (skin) cells

5. Many components... 10/06/09 BioAID

6. Example: bioinformatics before e-science Human Transcriptome Map (HTM) (Versteeg et al. , Genome Research, 2003) Sage tag count (TU, Sage library) TU identifier position Transcriptional Unit (TU)

7. Before e-science HTM construction and RIDGE detection /* * determines ridges in htm expression table */ #include &quot;ridge.h&quot; int selecthtm(PGconn *conn, char *htmtablename, char *chromname, PGresult *htmtable) { char querystring[256]; sprintf(&quot;SELECT * FROM %s WHERE chrom = %s ORDER BY genstart&quot;, 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, &quot;movmed39expr&quot;)) < 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(&quot;dbname=htm port=6400 user=mroos password=geheim&quot;); if (PQstatus(conn)==CONNECTION_BAD) { fprintf(stderr, &quot;connection to database failed.&quot;); fprintf(stderr, &quot;%s&quot;, PQerrorMessage(conn)); exit(1); } else printf(&quot;Connection ok&quot;); sprintf(querystring, &quot;SELECT * FROM chromosomes&quot;); printf(&quot;%s&quot;, 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(&quot;%d, &quot;, i); printf(&quot;%s&quot;, PQgetvalue(tuples,i,0)); } return TRUE; } int validquery(PGresult *result, char *querystring) { printf(&quot; in validquery&quot;); if (PQresultStatus(result) != PGRES_TUPLES_OK) { printf(&quot;Query %s failed.&quot;, querystring); fprintf(stderr, &quot;Query %s failed.&quot;, querystring); return FALSE; } return TRUE; } IT used Perl PostgresSQL C MS Excel + VBA SPSS No predefined development strategy No design phase Data Data Data Data Data Data

8. Bioinformatics A typical bioinformatician

9. Bioinformatics A biologist behind a computer who (just) learned perl

10. The ‘spaghetti’ approach

13. Example An e-science approach to text mining

14. Biological knowledge extraction 10/06/09 BioAID Biological question/model Computational experiment Extracted knowledge I want to do it my way Carole Goble’s me -scientist >17 million citations +400,000/yr

15. 10/06/09 BioAID Which diseases may be associated with my protein of interest EZH2

16. Combining expertise Edgar Meij Information retrieval expert

17. Combining expertise Sophia Katrenko Machine learning expert

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

19. Combining expertise Towards a knowledge framework Computer scientist and bioinformatician Scott Marshall

20. The AIDA toolbox for knowledge extraction and knowledge management in a virtual laboratory for e -Science

21. Combining web services

22. “ Collaboration through web services” Bio-text mining expert Martijn Schuemie

23. “ Collaboration through web services” Biological Database expert Hideaki Sugawara

24. “ Collaboration through web services” e -bioscientist

25. A nice tool

26. A not so nice tool

27. 10/06/09 BioAID

28. Sharing

29. Bio AID Disease Discovery workflow 10/06/09 BioAID AIDA AIDA OMIM service (Japan) AIDA ‘ Taverna shim’ Taverna ‘shim’

30. Bio AID Disease discovery workflow 10/06/09 BioAID

31. Bio AID Disease discovery workflow from 100 abstracts: 29 proteins associated with 1280 diseases 10/06/09 BioAID

34. e -Science is about people 10/06/09 BioAID Want this…

35. e -Science is about people 10/06/09 BioAID … need this

40. Full circle a biological question… 10/06/09 BioAID Could be running on a Grid or cluster

41. 10/06/09 BioAID Thank you for sending me this e -Experiment from myExperiment.org!

45. A hopefully mutually felt: warm and fuzzy feeling! Thank you for your attention

46. Why should I adopt e-Science? I do not believe in e -Science I only believe in Me -Science

47. Why adopt e-science? For determined sinners: ‘ The seven deadly sins of bioinformatics’ by Carole Goble http://www.slideshare.net/dullhunk/the-seven-deadly-sins-of-bioinformatics/