1. Using Spotfire for Proteomic Analysis
Steve Marshall, Bioinformatics Scientist
Caprion Pharmaceuticals
October 2003

2. Caprion Overview Montreal-based private company founded in 1998
80 employees (27 PhD, 30 MSc)
Collaborations in prion disease diagnostics, tumor immunotherapy and biomarkers of disease:

3. CellCarta PROTEOMICS
Fractionate and enrich biological samples containing disease-related proteins
Comprehensively and quantitatively compare proteomes from normal and diseased samples

4. CellCarta PROGRAMS Target Oncology: tumor antigen targets discovery
Biomarker discovery
Mechanism of action studies
Oncology: tumor antigen targets
Metabolic diseases: therapeutic proteins
Pre-clinical and clinical markers of therapeutic efficacy
Diagnostics development
Protein expression profiling
Sub-cellular protein location and translocation
Phosphorylation and other post-translational modifications

5. Proteomics Pipeline Purified Sample Protein Separation Raw data
LC-MS Injections
Custom Tools
Expression Profiling
Mascot &
Custom Tools
Protein Identification &
Peptide Sequencing
On a per sample basis go from about 2Gb of data down to a couple of Mbs
Validation of Expression
& Identification
Custom Tools
Protein Annotation & Analysis (function, location, IP, novelty, expression, etc) ??
protein reports

6. Proteomics Pipeline (con’t)
Annotation
Computationally derived
(Interpro, SignalP,TMHMM)
Automatic
Manual
Curation
(Tuva)
Develop a web based prototype called Tuva that enabled our scientist to provide additional scientific information (ie. protein location, disease mechanism, etc…)
The prototype served it’s purpose by giving our scientist a means of accessing the data, asking different questions about the data.
However it didn’t scale well and wasn’t flexible. We needed a more viable long term solution

7. Information Overload? External Data Internal Data
How do you cope with all this data?
External data: genome, literature
Internal data: peptide expression, lab info, sample info
Scientists also need to play with it, get a feel for the data
How do you manage it?

8. Data Analysis Scientists require a tool that:
Allows them to navigate the data
Interact dynamically
Link various data sources
Automate certain tasks
Short learning curve
Presentation of results
Bioinformatics / IT requirements:
Scalable
Customizable
Extensible
With this in mind, we felt that it would be better to bring in a third party tool that could help us in this process
With the increasing amounts of data available to a scientist, visualization and integration of data sets is necessary
Visualization allows you:
Find patterns, clusters, relationships etc..
It allows one to identify outliers and anomalous data
Integration
Link information together
Interactive
Needed a tool that:
Easy to use
Scalable
Customizable
Extensible
In the next slide, I’ll show you where Spotfire fits in the proteomics platform at Caprion

9. Proteomics Pipeline LC-MS Injections raw Expression Profiling
Custom Tools
Peptide Sequencing & Protein Identification
Mascot &
Custom Tools
Validation of Expression
& Identification
Custom Tools
On a per sample basis go from about 2Gb of data down to a couple of Mbs
Protein Analysis
(function, location, IP, novelty, expression, etc)
DecisionSite
protein reports

10. Proteomics Pipeline (con’t)
We see Spotfire as a platform to build upon
By using an Information Link, SQL query or join data by column allows the scientist to view different types of information
In the annotation db (which is based on protein accession from ncbi) have information such as TM domains, Interpro domains, Signal peptide etc….
In sample info, have information like band, patient etc.
In expression file

11. Workflow Example
Once proteins are identified, it is necessary to collapse the data into a more manageable form
Accomplish this by clustering the data either by the peptides or by protein homology within a given sample
This way biologists can chose how to view the data
Demonstration with some Caco data
This demo will show how we annotate clusters of proteins for a given sample in this case some old Caco data

12. Workflow Example (con’t)

13. Workflow Example (con’t)

14. Queries??
Once the annotation process is complete some typical queries include:
return all PM localized peptides over-expressed in disease in 40% of patients
Find the intersection of all peptides or proteins that are upregulated in condition 1 vs 2 and condition 3 vs 4
Find all proteins that span across X number of gel bands or identify discrepancies between a protein’s theoretical vs actual MW
DecisionSite gives the bench scientist the power to analyze data easily

15. Summary
DecisionSite allows our scientists to access and analyze data quickly and easily
Important to communicate with your end users
Future work:
Possiblility of integrating additional in house tools for protein expression
pathway viewer
DecisionSite gives the bench scientist the power to analyze data easily

16. Acknowlegments Bioinformatics Group: Paul Kearney Jason Yen IT:
Marcelo Filgueira
Scientists:
Nathan Currier
Joachim Ostermann
Michel Dominguez