Transcript Integration of heterogeneous informations sources for

Integration of Heterogeneous
Informations Sources
for Proteomics and
Transcriptomics
Steffen Möller
University of Rostock
Proteome Center
Data Flow and Motivation
Sample A.a
Sample A.1
...
Sample Z.z
Sample Z.1
Sample Selection
Question
Preparation
Interpretation
Analysis
Measurements
• List of genes products with
changed expression level
• Description of variants of genes
Data available online
• Grouping of samples in homogeneous groups Lab-internal
information
• Portioning and preparation of samples
• Data derived from a preparation
–
–
–
–
DNA/RNA sequencing
Affymetrix Microarrays
2DE Gels
(Tandem) mass spectrometry
• External bioinformatics databases
• Internal extensions to the above
– Communication of ideas between researchers
Measurements
Aids for
Interpretation
of Data
Organisation of Samples
Access of MS Spectra
• MASCOT peptide identification
• MS/MS fragment sequencing
Addition to external data sources
• Genes discussed among researchers
Overview on Identified Spots on Gel
• Integration of Protein expression levels
– Spot Volume
– Spot Area
– Spot Peak intensity
• with RNA expression levels
– from Affymetrix chips
Application of Agent Technology
• Automated retrieval and integration of
presumed relevant in-house data
• Assistance in interpretation
– Heuristics to extend/shrink list of genes
presumed relevant
• Integration with external online data
– Pathways
– Known relevance of genes in other diseases
Data Flow
Seed of Genes
Heuristic
Modified
List of Genes
Adapted for Agents:
• Input: List of Gene IDs
• Output: List of
(
Gene ID
Agent ID
Evaluation
Explanation
History
)
Examples for Heuristics
• Towards extension/shrinking of list of genes under
investigation
– Gene lies within chromosomal locus linked to disease
– Chromosomal neighbourhood to other genes of
investigation
– Gene is of presumed low abundance
• Guidance of further wet-lab analysis
– Comparison of ration RNA/protein levels
• Search for pre- or post-transcriptional control
Example: Interaction with EnsEMBL
• Visualisation of QTLs with expression data
(G. Fischer et al. 2002, submitted)
Transfer from Automated
Sequence Annotation
• EDITtoTrEMBL (Möller et al. 1998)
– Introduction of intermediate level for data integration
– Hierarchical organisation of agents
Program
Integration
Program
TrEMBL
EDITtoTrEMBL: Self-introducing Agents
• Sequence-Analysing agents described their input and their output to
dispatching agents
•SWISS-PROT syntax and controlled vocabulary
•Regular expressions as constraints
• Dispatchers provide automated planning of
annotation path of entries
Application in sequence annotation of
transmembrane proteins
• A variety of programs exist to predict
– membrane spanning regions
– direction of insertion into the membrane
Out
In
Conflict resolution
• Implemented with REVISE (C. V. Damasio; 1997)
application described in (S. Möller, M. Schroeder; 2000)
Problems with the transfer of
these techniques to the wet-lab
• Analysers cannot describe themselves or
their results
– No ontology for methods of expression data
analysis has been defined (yet)
– The motivation of an analyser to include a gene
cannot be formally expressed
• No rules for conflict resolution applicable
– Conflicts point the unexpected, not to artefacts
Discussion
• Should I implement the best possible agent system or
rather ASAP hunt for the causing agents of
autoimmune diseases?
• New agents are recruited from Perl scripts that are
implemented to provide a quick answer to requests of
biological researchers.
• Integration on a pragmatical level
• The system is accepted by wet-lab researchers.
• The system has a PHP-based web-frontend,
– communication between agents is implemented via SOAP
– adaptations and extensions to the system are easily
implemented.
Acknowledgements
University of Rostock
Michael Kreutzer, Gertrud Fischer, Bernd Scheidt,
Ines Weber, Angelika Allenberg, Björn Damm,
Michael Glocker, Hans-Jürgen Thiesen
City University, London
Michael Schroeder
EMBL-EBI, Cambridge
Rolf Apweiler
Funded by the
BMBF Leitprojekt „Proteom-Analyse des Menschen“
and the
Landesforschungsschwerpunkt „Genomorientierte
Biotechnologie“