1. An Introduction to Designing, Executing and Sharing Workflows with Taverna and myExperiment
Katy Wolstencroft
University of Manchester

2. This tutorial will give you a basic introduction to designing, and reusing workflows in Taverna and some of its main features.
Workflows in this practical use small data-sets and are designed to run in a few minutes. In the real world, you would be using larger data sets and workflows would typically run for longer

3. Exercise 1: Exploring the Workbench
Taverna can be downloaded from
Go to the page and find the latest (2.4)
Follow the instructions on the website to install Taverna for your operating system (this is a simple one-click install for windows and Mac. For Linux, you may also need the GraphViz program. Follow the link on the Taverna download page if so)
The following page shows a screenshot of Taverna and the different panels that make up the workbench

4. Taverna Workbench
Workflow Diagram
Services Panel
Workflow Explorer

5. 1. Workflow Diagram
The workflow diagram is the visual representation of the workflow, it:
Shows inputs, outputs, services and data flows
Allows editing of the workflow by dragging and dropping and connecting services together
Enables saving of workflow diagrams for publishing and sharing

6. 1. Workflow Explorer
The Workflow Explorer shows the detailed view of your workflow. It shows default values and descriptions for service inputs and outputs and it shows where remote services are located. It also shows configuration details, such as iteration and looping
Workflow validation details can also be found here. Before a workflow is run, Taverna checks to see if it is connected correctly and if its services are available.

7. 1. Available Services Panel
Lists services available by default in Taverna
Local java services
WSDL Web Service – secure and public
RESTful Services
R Processor services (for statistical analyses)
Beanshell scripts
Xpath scripts
Spreadsheet import service
The services panel also allows you to add new services or workflows from the web or from file systems – there are loads more available!

8. 1. Taverna exercises – Enrichment Analysis
Today, we will use Taverna to perform enrichment analyses on a list of genes.
Many experiments result in a list of genes (e.g. microarray analysis, Chip-Seq, SNP identification etc).
In this case, the genes are those associated with Chip- Seq peaks
We will enrich our dataset by discovering:
Which pathways our genes are involved in
The functions of the genes
The cellular locations of the gene products
Literature evidence for the phenotype/trait of interest

9. Exercise 2: Building a Simple Workflow
As a simple start, we will find and invoke a single web service
Go to the Services Panel in Taverna and type ‘pathway’ into the search box at the top
You will see several services in the search results
Select ‘get_pathways_by_genes’. This service returns all pathways from KEGG
Drag this service across to the workflow explorer panel

10. 2: Building a Simple Workflow
In a blank space in the workflow diagram panel, right-click and select “Workflow Input Port”
Type in a name for this input (e.g. ID) and click “ok”
Do the same to create a new workflow output. Call this output “pathways” 10

11. 2: Building a Simple Workflow
You now have 3 boxes in the diagram and we need to connect them up
Click on the input box and drag towards “get_pathways_by_genes” and let go. An arrow will connect the two boxes

12. 2: Building a Simple Workflow
Click on the output box, drag towards “get_pathways_by- genes”, and let go.
A pop-up will ask you to select from ‘attachmentList’ and ‘return’. Select ‘return’
An arrow will connect the two boxes
You have now built your first workflow!
It should look something like this

13. 2: Building a Simple Workflow
Run the workflow by selecting “file -> run workflow”, or by clicking on the play button at the top of the workbench

14. 2: Building a Simple Workflow
An input window will appear. As you can see, we have not yet added a description of the workflow or of the input
Click on ‘Set Value’ in the input window and add a KEGG Gene identifier (e.g. mmu:13163) where it says “some input data goes here”

15. 2: Building a Simple Workflow
Click “run workflow”. You will automatically be switched to the ‘Results’ window. Taverna will run the KEGG Web Service in Japan to return pathways that gene is involved in.
In the bottom left of the results window, click on the results. You will see some pathway identifiers. These are good for computers, but not for humans. We need pathway descriptions to properly examine the results
Switch back to the ‘Design’ window using the tab at the top of the workbench

16. 2: Building a Simple Workflow
In the service panel, search for another KEGG service, called ‘btit’.
Drag and drop it into the same workflow
We can now connect the two services together
At the top of the workflow diagram panel, change the view to show all ports by clicking on the icon shown below
Show all ports icon

17. 2: Building a Simple Workflow
Connect the get_pathways_by_gene ‘return’ output to the input (string) of btit
Create a new output called ‘pathway_description’ and connect it the btit ‘return output by dragging an arrow between them
Re-run the workflow and look at the pathway descriptions
The workflow will iterate over each pathway ID to find each description

18. 2: Building a Simple Workflow
A list of pathways and their descriptions is useful, but it would be easier to visualise diagrams of the whole pathways
Additionally, we need to find ALL the pathways for ALL the genes in our lists in order to indentify which pathways are over-represented in our data set
For both these tasks we will find and use workflows from myExperiment

19. Exercise 3: Re-using workflows from myExperiment
Go to and click on ‘find workflows’
You will see a list of the most viewed and downloaded workflow – see what the most popular workflow does by reading the description
Change the rank to ‘Latest’ and see what has been uploaded in the last few weeks

20. 3: Re-using workflows from myExperiment
Find the workflow called “geneID to KEGG Pathways” and look at the workflow entry page (note: if your search returns too many results, you can refine it by adding “Wolstencroft”
Download the workflow by clicking on the link: “Download Workflow File/Package (T2FLOW)”
Open the workflow in Taverna by going to ‘File ->Open Workflow’
Run the workflow using the example values supplied by the workflow creator (Hint: when you run the workflow the examples values will be added by default in the input window)
Look at the workflow output – now you will see pathway diagrams

21. Exercise 4: Combining workflows from myExperiment
To analyse all the genes from our study, we need to extract the gene list from previous analysis results
To make it easier to work through the example, we have provided a Chip-Seq gene list on myExperiment:
Save this file to your local machine
Open the file in Excel
Save the file with a .csv extension
As you can see, the list of genes is in column D
Taverna can process and extract this column automatically

22. 4: Combining workflows from myExperiment
In myExperiment, find and download the workflow called “Import and convert gene list”
This workflow will extract the list of genes in column D using Taverna’s built-in spreadsheet import tool (which can be found in the services panel, for future reference)
The next step in the workflow converts the RefSeq IDs into unigene IDs (required for the pathways workflow – converting between different types of identifiers is a common problem in bioinformatics!)
Run the workflow. This time, in the input window, select “set file location” and set the location to the saved .csv gene list.
Look at the workflow results

23. 4: Combining workflows from myExperiment
We will now combine the two workflows
While you are still in the “import and convert” workflow, go to the top of the workbench and select “insert -> Nested workflow”
In the pop-up window, select “import from file” and find the pathways workflow you downloaded earlier.
Click on “import workflow” and the pathways workflow will appear in the main workflow diagram.

24. 4: Combining workflows from myExperiment
Connect the workflows up by linking the output of the ‘Merge_Gene_List’ with the nested workflow input

25. 4: Combining workflows from myExperiment
Create new output ports for the Nested workflow and connect the Nested workflow outputs to the new outputs
NOTE: you don’t need to connect them all, just pathway descriptions, pathway images and gene descriptions
Save the workflow
Run the workflow

26. 4: Combining workflows from myExperiment
The workflow may take a few minutes to run. Spend the time looking at myExperiment to find other pathway-related workflows
What other pathway workflows are there?
Do they all use KEGG?
What other resources could you use instead?

27. Exercise 5: GO Associations
There are many different tools we could use to find Gene Ontology associations for your gene list
For example, we could simply modify the BioMart/Ensembl service in the ‘Import and convert gene list’ workflow we have already used
Reload the ‘Import and Convert gene list’ workflow
Right-click on the ‘mmusculus_gene_ensembl’ service and select ‘Copy’
Paste an extra copy of this service into the same workflow diagram 27

28. 5: GO Associations
This is a BioMart service. It allows you to retrieve omics data from ENSEMBL and other genomics resources. If you are familiar with BioMart, you will see the interface in Taverna is very similar to the web interface
We will modify the BioMart query to find all GO associations for each gene associated with a Chip-Seq peak
Right-click on the new copy of the service and select ‘Configure BioMart Query’ 28

29. 5: GO Associations
The inputs (or filters) already accept RefSeq Ids from our input file, but we need to modify the outputs (or attributes)
Select ‘Attributes’ and expand the ‘External’ section.
Unselect ‘UniGeneID’ and select ‘RefSeq mRNA’
Additionally, select ‘Go Term Accession’, ‘GO Name’ and ‘Go Domain’
At the top of the page, change the output format from multiple to single (TSV format)
(See screenshot on the next slide for an example) 29

30. 5: GO Associations 30

31. 5: GO Associations
Click ‘apply’ to save your changes, and ‘close’, to go back to Taverna
At the top of the workflow diagram, change the workflow view to show all ports by clicking on the table icon 31

32. 5: GO Associations
Connect your new service to the workflow by linking the ‘D’ output port of the spreadsheet service to the input of your new service
Make a new output port called ‘GO_Report’ and connect it to your new service 32

33. 5: GO Associations
Save the workflow by going to ‘File -> Save Workflow’
Run the workflow
Download and view the GO report 33

34. Exercise 6: Adding New Services to Taverna
In Taverna, new tools can be ‘added’ very easily because we are often actually calling external tools
Go to and look around. Biocatalogue is a registry of available Web Services for the Life Sciences. You can use any of these tools in Taverna
Search for the ‘ontology lookup service’
Look at the entry for that service
find and copy the WSDL location URL
HINT: it will be a URL ending in .wsdl (

35. 6. Adding New Services
Go to the services panel in Taverna and click “import new services”. For each type of service, you are given the option to add a new service
Select ‘WSDL service…’ A window will pop-up asking for a URL

36. 6. Adding New Services
Enter the Ontology Lookup service URL you just copied
Scroll down to the bottom of the Services list in Taverna and you will see the new service you added
It is now ready to be used in your workflows 36

37. 6: Adding New Services to Taverna
Now we have Gene Ontology descriptions for our genes, we might want to find out what other ontology descriptions we can find
From the service set you have just imported, add the service ‘getontologyname’ to a new workflow
This service does not require any inputs, so just create an output port called ‘ontologyNames’ and connect it to the service
Run the workflow
You will see a list of all ontologies you can search using these services
Sometimes, documentation about services is embedded in the service set like this

38. Exercise 7: Text Mining
So far we have looked at enriching the genomic information, but we could also use workflows for running data analyses (e.g. aligning mouse genes with human homologues) or performing literature searches
Think about the ways you could extend this analysis with literature searches (e.g. Correlations between pathways, genes, GO terms, phenotypes etc)
Search myExperiment for workflows involving text mining, using the search terms “text mining” and “Pubmed” 38

39. 7: Text Mining Find and open the workflow “Phenotype to pubmed”
One of the services is no longer available in the nested workflow (the faded-out service). Taverna checks the availability of each service when you load the workflow and when you run it
In this case, the workflow will still run without the final nested workflow (clean text)
Delete the ‘clean text’ nested workflow (by selecting it and right-clicking), and reconnect the workflow output
Run the workflow with the search term ‘erythropoiesis’ (or a phenotype term to describe the disease you are studying) 39

40. 8: Sharing Workflows
If you want to save and share any workflows on myExperiment, you can create an account and upload them
If you wish to share them with each other, we can set up a workshop group with restricted membership 40

41. 8: Outcomes
These exercises have given you a brief introduction to Taverna, but we have just scratched the surface.
The examples are taken from a real investigation, but the data has been reduced to a level that will run in a few minutes.
These exercises demonstrated that Taverna can use many different types of services. In this case, we have been working on data integration, but Taverna can also connect analysis services together to automate data processing 41