1. Bioinformatics. Strategies for proteomics: which database? Dr Richard J Edwards 27 August 2009; CALMARO workshop. R.Edwards@Southampton.ac.uk

2. Bioinformatic Strategies for proteomics: which database?  Sequence databases  The importance of sequence databases in proteomics  Database options  Proteomics search strategies  Completeness vs. Redundancy  Local vs. online databases  Species-specific vs. multi-species  EST libraries  Which database?  Decoy databases  Open Discussion

3. Sequence databases.

4. The importance of the database Database Protein List

5.  Idealised assumptions for matching spectra  Protein sample is pure  All peaks correspond to protein fragments  Protein fragmentation is complete and perfect  Can predict peaks from sequence  Every peak is present & precise  Peaks can be matched exactly  Exact protein will be present in database  Every peak should find in silico match  Reality  Impurities  Incomplete digestion  Measurement error  Post-translational modifications The importance of the database NOISE Incomplete

6. Quality vs. Quantity  Trade-off in any analysis Quantity (Completeness) Quality (Accuracy) Ideal Trade-off Assumptions Database Reality

7.  Idealised database for matching spectra  Every protein in sample is present in database  100% coverage  No unnecessary duplication of data  Non-redundant  Sequence present in database matches sequence in sample  Every peak should find exact in silico match  Reality  Incomplete proteome annotation  Proteins missing  Duplicate entries (redundancy)  Incomplete protein annotation  Truncated sequences  Sequencing errors  Missing sequence variants The importance of the database NOISE Incomplete

8. The importance of the database Database Protein List NOISE Incomplete NOISE Incomplete

9. The importance of the database Database NOISE Incomplete NOISE Incomplete NOISE Incomplete Protein List

10. Database options.

11. Common proteomics databases DatabaseDescription; source databasesOrgan- isms Update frequency UniProt/ Swiss-Prot [EBI] Expertly curated; high level of annotation; minimum level of redundancy; high level of integration with other databases. ManyRelease every 4 months; updates every 2 weeks UniProt/ TrEMBL [EBI] Computer-annotated supplement to Uni-Prot/Swiss-Prot. Contains translated coding sequences from GenBankTM nucleotide database, protein sequences extracted from the literature or submitted to Uni-Prot/Swiss-Prot but not yet manually curated. ManyRelease every 4 months; updates every 2 weeks RefSeq [NCBI] Ongoing curation by NCBI staff; non-redundant; explicitly linked nucleotide and protein sequences; stable reference; high level of integration with other databases. ManyRelease every 3 months Ensembl [EBI] Created using automated genome annotation pipeline; eukaryotic genomes only; explicitly linked nucleotide and protein sequences; stable reference; high level of integration with other databases. Peptides identified by MS/MS can be mapped to the genome via Ensembl Protein database and visualized using Ensembl Genome Browser. SeveralEvery 1–2 months IPI [EBI] Good balance between degree of redundancy and completeness; references to the primary data sources; attempts to maintain stable identifiers (with incremental versioning), but still in flux. Assembled from Uni-Prot (Swiss-Prot + TrEMBL), RefSeq, Emsembl, H-Invitational database. A fewMonthly Entrez Protein (NCBInr) [NCBI] More complete with regard to sequence polymorphisms and splice forms; annotations extracted from curated databases; high degree of sequence redundancy makes interpretation difficult. Assembled from GenBankTM and RefSeq coding sequence translations, Protein Information Resource (PIR), Protein Data Bank (PDB), Uni-Prot/Swiss-Prot, Protein Research Foundation (PRF). ManyFrequent updates

12. Which database?  What resources are available?  What is most important for your analysis? Quantity (Completeness) Quality (Accuracy) Database Trade-off Protein List NOISE Incomplete Protein List

13. Generic Protein Databases  UniProtKB / NCBI  Advantages  Ease of access  Completeness  Updated  Disadvantages:  Redundancy  High noise levels  Inappropriate species etc.  In silico annotation  Not made with proteomics in mind  May not have relevant variants Patent Data UniParc WormBaseFlyBase Sub/ Peptide Data PDBVEGAEnsemblRefSeq INSDC (incl. WGS, Env.) UniProtKBUniMes UniRef 100 UniRef 90 UniRef 50 UniSave Database sources Proteome Sets IPI UniProt data sources and data flow

14. Genome databases  EnsEMBL/FlyBase/Wormbase etc.  Advantages:  Organism-specific information  Potentially high (~100%) coverage  Potentially very low redundancy/noise  Disadvantages:  Very dependent on annotation level/quality  Poor annotation = low completeness  May need other databases to interpret results  Best database to use if well-annotated genome available

15. EST libraries  NCBI dbEST / Organism-specific EST Projects  Generate your own!  Advantages:  Reasonable coverage of high expression proteins  Matches proteomics bias  Species-specific = more accurate matches  Enables identification of species-specific proteins  Not so reliant on annotation  Sequence variants  Transcripts without known homology/function  Disadvantages:  Often very poor annotation = Extra work  High levels of redundancy = Extra work  Sequence fragments = missed indentifications  Search as DNA in six reading frames or annotate proteins first

16. Proteomics search strategies.

17. Proteomics search strategies  No universal “best” strategy  Trade-offs: best depends on focus  Common trade-offs:  Completeness vs. Redundancy  Local vs. Online  Translated vs. Untranslated ESTs  Species-specific vs. Generic database  Why are you doing the experiment?  What do you want to identify?

18. Completeness vs. Redundancy  Redundancy  Multiple identifications of essentially the same protein  Sequence variants within a species  Same protein (family) in different species  More redundancy = extra work  Which hits are unique? (Different peptides)

19. Redundancy: identification issues Copyright ©2005 American Society for Biochemistry and Molecular Biology Nesvizhskii, A. I. (2005) Mol. Cell. Proteomics 4: 1419-1440 Sequences of identified peptides often do not allow discrimination between different protein isoforms

20. Completeness vs. Redundancy  Redundancy  Multiple identifications of essentially the same protein  Sequence variants within a species  Same protein (family) in different species  More redundancy = extra work  Which hits are unique? (Different peptides)  Poor annotation  Splice variant vs. Protein family?  More redundancy = lower sensitivity  Larger databases = more random hits = stricter score thresholds

21. Completeness vs. Redundancy Copyright ©2005 American Society for Biochemistry and Molecular Biology Nesvizhskii, A. I. (2005) Mol. Cell. Proteomics 4: 1419-1440 Protein sequence databases differ in terms of their completeness and the degree of sequence redundancy Quantity (Completeness) Quality (Non Redundancy) Trade-off Database

22. NCBI Redundancy Copyright ©2005 American Society for Biochemistry and Molecular Biology Nesvizhskii, A. I. (2005) Mol. Cell. Proteomics 4: 1419-1440

23. Completeness vs. Redundancy Large, Redundant DatabaseSmall, Non-Redundant Database Every major protein and specific variant is important Happy to group similar hits to a single protein/family (e.g. HSP90) Willing and able to perform extensive post-identification analysis Want to minimise need for additional cleanup/data analysis  Try to maximise both quality and quantity  Quality genome, IPI or custom-built search database Quantity (Completeness) Quality (Non- Redundancy) dbEST Genome X IPI

24. Copyright ©2005 American Society for Biochemistry and Molecular Biology Nesvizhskii, A. I. (2005) Mol. Cell. Proteomics 4: 1419-1440 An example of a protein family: alpha tubulins Inconclusive identification

25. Local vs. Online  Standard databases plug in to search engines  Can also search “Local” databases, stored on own machine Standard Online DatabaseLocal Database Regularly updated: latest sequence data & annotation More control of content – customise species & sequences Easy to describe & referenceStable database for multiple searches Don’t have to worry about sequence formats/naming etc. Ease comparisons & redundancy removal across multiple experiments Eases generation of decoy database

26. EST libraries  Can search untranslated in six reading frames (RFs)  Or Assemble, annotate & search proteins UntranslatedAssembled & Annotated Quick and easy preparationTime-consuming and difficult (unless already done!) Suffers from short fragmentsLonger sequences = more chance of multiple peptides Potential to detect SNPs/isoformsAssembly may incorrectly assimilate/remove variants Low coverage: more robust to sequencing error High coverage: more robust to sequencing error Large quantity of random translations (UTRs + 5 incorrect RFs) Smaller, higher quality dataset = less False Positives Detect novel proteins (no homology to known proteins) ORFs without homology to known proteins may be removed Need to annotate hits

27. Search species  Best to use species-specific data where possible  More chance of identical peptide sequences  More chance of family member/isoform discrimination  Can identify taxa-specific proteins  Can search in other species and infer  Only works for conserved proteins  Increases noise and False Positive rate  Compromise  Subset of well-annotated & closely-related species  Maximise completeness, minimise noise

28. Poor genome vs. Wrong species  Annotation generally based on homology to known proteins  Proteins similar enough to be found by proteomics will be easy to find & annotate in genome  What is the genome coverage?  High coverage = little to be gained by additional search  Low coverage = may be many conserved proteins missing  Multi-species search may find extra proteins  Compromise:  Search available species data (genome/EST)  Second search against selected taxa (UniProtKB)  Bacteria: can search genome in 6RF! (No introns)

29. Which database? How to choose.

30. How to choose?  What do you want to do?  Priority  Reproducibility/Comparability/Hypothesis testing  Fewer, high quality identifications  Smaller, more focused database  Hypothesis generation  More identifications, more potential false positives  Larger/multiple search databases  How much post-identification analysis?  Not much  Higher quality, lower numbers  Detailed manual analysis  More identifications, more potential false positives  Always sensible to look for probable identifications  Are sequences missing from your search database?

31. Experimental design Copyright ©2005 American Society for Biochemistry and Molecular Biology Nesvizhskii, A. I. (2005) Mol. Cell. Proteomics 4: 1419-1440  Protein separation  Better discrimination of variants  Cope with more redundancy  Shotgun (no separation)  Less discrimination

32. Decoy databases.

33. False positives: NOISE Database NOISE Incomplete NOISE Incomplete NOISE Incomplete Protein List

34. False positives: NOISE  How much noise?  How many False Positives due to noise? NOISE Incomplete Database Decoy Database Protein List NOISE RANDOM Protein List

35. Conclusions.

36. Summary  Selection of database is very important for quality of results  Primarily a trade-off between completeness & noise  Choice of database depends on:  Availability of data  Experimental design  Aims/objectives of study  Priorities of analysis  Well annotated genomes (proteomes!) best  Poorly annotated genomes & ESTs can be supplemented with searching related taxa  Local databases give more control/repeatability  Decoy databases can help estimate false positive rates

37. Open Discussion R.Edwards@Southampton.ac.uk