This document discusses how workflows can help biologists by allowing them to combine various computational tools and databases. It notes that individual biologists have limited time and computational skills, but can use workflows to access various expertises and resources. Workflows allow biologists to design complex computational experiments and analyze large amounts of data by connecting different services and applications in an automated, repeatable process.

1. Feasting on Brains
with Taverna and myExperiment
Marco Roos, Katy Wolstencroft
acknowledging
Carole Goble, Dave de Roure, Alan Williams, Jiten Bhagat, Martijn
Schuemie, Edgar Meij, Sophia Katrenko, Willem van Hage, M. Scott
Marshall, Pieter Adriaans, NBIC, OMII-UK, the myGrid team
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2. Why should a biologist be interested in workflows?
Leiden Students Say…
•…
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3. Hold those thoughts…
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4. A biologist
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5. My prime interest
Structure and function of DNA in the nucleus
Mouse fibroblast (skin) cells
Escherichia coli
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6. Connecting the dots
(example: protein interaction network in yeast)
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7. Thousands of
Databases
Low & High Throughput
Proteomics, Genomics, Transcriptomics,
Protein sequence prediction, Phenotypic
studies, Phylogeny, Sequence analysis,
Protein Structure prediction, Protein-
protein interaction, Metabolomics, Model
organism collections, Systems Biology,
Epidemiology, etcetera …
All with a splendid interface
… all different, of course
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8. Biomedical knowledge repository
PubMed statistics
http://www.ncbi.nlm.nih.gov/entrez
>20 million citations
>400,000 added/year
~70,000 searches/month
…
Does not
compute
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9. My brain is too small !
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10. A typical biologist…
Lots of data
to deal with
Tiny brain
Lots of knowledge
to deal with
Lots of methods No
and algorithms to try computational
and combine superpowers
A needy biologist
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11. Start at the beginning
I have a
computationa
l question…
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12. ‘Old school’ Bioinformatics
A typical bioinformatician
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13. ‘Old school’ Bioinformatics
A biologist behind a computer
who (just) learned perl
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14. /*
* 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 okn");
sprintf(querystring, "SELECT * FROM chromosomes");
printf("%sn", 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("%sn", PQgetvalue(tuples,i,0));
}
return TRUE;
}
int validquery(PGresult *result, char *querystring)
{
printf(" in validqueryn");
if (PQresultStatus(result) != PGRES_TUPLES_OK)
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{ BioAID
printf("Query %s failed.n", querystring);
14

15. The ‘spaghetti’ approach
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16. Computational tools graveyard
rephrasing David Shotton, University of Oxford
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17. Which diseases are
associated with my
protein of interest
‘EZH2’
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18. Combining expertise
Edgar Meij
Information retrieval expert
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19. Combining expertise
Sophia Katrenko
Machine learning expert
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20. Combining expertise
Willem van Hage
Semantic web expert
(and bass guitar player)
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21. “Collaboration through Web Services”
Martijn Schuemie
Bio-text mining expert
BioSemantics group,
Erasmus University Rotterdam
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22. “Collaboration through Web Services”
Hideaki Sugawara
Biological Database expert
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23. e-Science collaboration
AIDA
toolbox
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24. “Collaboration through Web Services”
e-bioscientist
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25. A nice experiment design
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26. A not so nice experiment design
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27. A workflow
Protocol for a computational experiment
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28. Feasting on brilliant brains with Taverna!
Want this…
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29. The enhanced Biologist
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30. What are the benefits of the Workflow approach?
Leiden Students Think…
• …
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