Bioinformatics & experimental practice in proteomics.

1. Bioinformatics & experimental practice in proteomics.

2. Perfection (in design) is achieved not when there is nothing more to add, but rather when there is nothing more to take away.1 1. The Cathedral and the Bazaar, Eric Steven Raymond

3. parameters & ontologies MIAME/Plant experiment sample hybridization array normalization data MIAME

4. C. Taylor, et al. Nature Biotechnology 21, 247 - 254 (2003)

5. Chemical Modification Chromatography Affinity Depletion/Enrichment IEX (SCX, AEC) SEC RP Chemical Enzymatic Chemoenzymatic Antibody Lectin Chemistry Peptides Proteins Sample Preparation Technologies Chemical Labeling cICAT iTRAQ 18O

6. SCX RP HPLC RP HPLC SEC IEX cICAT iTRAQ Strepavidin Column Immuno depletion SCX Global Sample Preparation Workflow MS bound Enzymatic Digestion unbound MS

7. ESI/MS/MS QSTAR, QTRAP, Q-TOF, LCQ-Deca QSTAR, QTRAP, Q-TOF, LCQ-Deca 1D, 2D HPLC MS/MS MS survey split flow Bioinformatics DE STR, TOF/TOF. vMALDI-LTQ TOF/TOF. vMALDI-LTQ Spotting Robot MS/MS MS survey MALDI/MS/MS Global Mass Spectrometry Workflow

8. SCX RP HPLC Enzymatic Digestion 4000 QTRAP MS/MS IEX 4000 QTRAP Bioinformatics MS survey, precursor ion Lectin Affinity Immuno depletion Targeted MS-based Platforms for Glycoproteins

9. SCX 4000 QTRAP anti-pS,T,Y anti-pS,T,Y RP HPLC RP HPLC IEX 4000 QTRAP Bioinformatics Bioinformatics MS/MS MS survey, precursor ion/ neutral loss pS,T to Aec conversion Immuno depletion TiO2 IMAC vMALDI-LTQ MS survey vMALDI-LTQ MS/MS Spotting Robot Targeted MS-based Platforms for Phosphoproteins Enzymatic Digestion Enzymatic Digestion

10. Bioinformatics Storage archive (LBNL) Platform-independent analysis (UBC) Data (mzXML) Quality Quantity Identity Statistical analysis (LBNL) Views (HTML) Experiment details (FuGE) Data generation (UCSF/Buck/LBNL) Pattern & trends (HTML)

11. What proteins are present? - IDENTITY 2. How much of each protein? - QUANTITY 3. How reliable are the results? - QUALTITY What is the desired output?

12. Study design and sample generation • Separations and sample handling • Column chromatography • Capillary electrophoresis • Mass spectrometry • Informatics for mass spectrometry • Gel electrophoresis • Gel image informatics • Molecular Interaction Experiments • Statistical Analysis of Data The Minimum Information About a Proteomics Experiment (MIAPE)

13. “The problem of legacy data sets will be significant in scale and difficult to address. Clearly, a lack of annotation does not mean that a data set is without worth …, so the following principles should be applied when re-annotating such legacy data: 1. The data set should be re-annotated as fully as possible, with reference to the appropriate MIAPE modules; the data set should then be flagged as legacy, and an indication given of where the reporting requirements have not been met (e.g. a summary of missing items). 2. Data and metadata should never be created to supplement the real data in a file. The only allowable additions are those that serve to indicate the absence of real data ….” http://psidev.sourceforge.net/miape/MIAPE_Parent_3.1.pdf

14. Protein Sequence Collections (2001) Collection Annotations PIR Good (public) SWISS-PROT Good (private) GenPept Some (public) TREMBL Some (public) NR Good (public) OWL n/a (public) dbEST n/a (public) HGP progressing (both) YGP Good (both)

15. Genomes/Unigene collections

16. "Biologists would rather share their toothbrush than share a gene name," says Michael Ashburner, ... "Gene nomenclature is beyond redemption." “Without the umbrella of HUPO, hopes for standardization in proteomics would have been bleak, with researchers being more inclined to use their rivals' toothbrushes than their protocols.” Quotes from Nature editorials

17. GPMDB design GPMDB design

18. Public information • query interfaces (keyword, sequence, mass, accession) search ENSEMBL • publicly available sequence assignment servers (search engine sites) • multiple locations • specialized sequence collections • accept MS/MS data in multiple formats (MGF, mzXML, mzData, DTA) • user interface and data analysis software • all software available as open source code repository public • central repository • daily import from servers • not publicly accessible • responsible for routine data processing tasks search UNIGENE repository master research • information for bioinformatics analysis • multiple sites search Boutique research • public site used directly for on-line analysis • user interface simplifies query process Database servers Search servers Practical systems lead to complications XIAPE repository deployment

19. Minimum user interface?

20. What does homologue mean if you only have a bunch of peptides? 2. How do you resolve privacy issues? 3. What data formats should be allowed, both for input and output? 4. Which computer operating systems should be supported? Which computer languages should be used? 5. How much detail about each experiment has to be recorded to make the data useful? Decisions needed to create a repository

21. Best protein sequence { } Peptides Dependent homologue Independent homologue What does homologue mean if you only have a bunch of peptides?

22. Current proteomics respositories

23. mzXML • mzData • analysisXML • PRIDE XML • protXML • pepXML • bioML • GAML • MI XML • Mascot Search Results XML XML to the rescue?

24. The Semantic Web to the Rescue?

25. FUnctional Genomics (FUGE) object model

27. FUGE protocol model

28. FUGE sequence model

29. FUGE ontology model

30. Google to the rescue?