1. Part I: Identifying sequences with … Speaker : S. Gaj Date 11-01-2005

2. Annotation Best possible description available for a given sequence at the current time. How to annotate? Combining Alignment Tools Databases Datamining (scripts) Background

3. Microarrays

6. Introduction Global alignment Optimal alignment between two sequences containing as much characters of the query as possible. Ex: predicting evolutionary relationship between genes, … Local alignment Optimal alignment between two sequences identifying identical area(s) Ex: Identifying key molecular structures (S-bonds, - helices, …) Background

7. Introduction Basic Local Alignment Search Tool Aligning an unknown sequence (query) against all sequences present in a chosen database based on a score-value. Aim : Obtaining structural or functional information on the unknown sequence. BLAST

8. Programs Different BLAST programs available Usable criteria: E-Value, Gap Opening Penalty (GOP), Gap Extension Penalty (GEP), … Terms Query Sequence which will be aligned Subject Sequence present in database Hit Alignment result. BLAST NucleicProtein NucleicBlastNBlastX Protein-BlastP

9. Common BLAST problems BlastN BLAST CGATAGCCCGCCAGGAT AT ACGATAGCCC -CCAGGAT AT A Sequencing Error Clone seq mRNA Solution: Low penalty for GOP and GEP = 1 |||||||||||||||||||

10. Translation Problems 6-Frame translation BLAST >embl|J03801|HSLSZ Human lysozyme mRNA, complete cds with an Alu repeat in the 3' flank. ctagcactctgacctagcagtcaacatgaaggctctcattgttctggggct... +1 L A L * P S S Q H E G S H C S G A

11. Translation Problems 6-Frame translation BLAST >embl|J03801|HSLSZ Human lysozyme mRNA, complete cds with an Alu repeat in the 3' flank. ctagcactctgacctagcagtcaacatgaaggctctcattgttctggggct... +1 +2 +3 -3 -2 L A L * P S S Q H E G S H C S G A * H S D L A V N M K A L I V L G

12. Common BLAST problems BLAST Gene X full mRNA mRNA intron exon Translation Splicing

13. Common BLAST problems BLAST mRNA Clones derived from mRNA Coding region Non-coding region BlastX against protein sequence 3 possible hit-situations

14. Common BLAST problems BLAST  Yields no protein hit  Aligns with protein in 1 of the 6 frames.  Part perfect alignment Coding region Non-coding region or

15. Part II: Databases and annotation

16. Introduction Primary database: – DNA Sequence (EMBL, GenBank, … ) – AminoAcid Sequence (SwissProt, PIR, …) – Protein Structure (PDB, …) Secondary database: – Derived from primary DB – DNA Sequence (UniGene, RefSeq, …) – Combination of all (LocusLink, ENSEMBL, …) Structure: – Flat file databases Databases

17. Primary Databases EMBL: – DNA Sequence – Human: 4.126.190.851 nucleotides in 292.205 entries – Clones, mRNA, (Riken) cDNA, … – New sequences can be admitted by everyone. – No curative check before admittance. Databases

18. Primary Databases SwissProt: – Amino Acid sequence – Human: – Contains protein information – SwissProt (EU)  PIR (USA) – Crosslinks to most informative DB (PDB, OMIM) – Part of UniProt consortium. – Each addition needs validation by appointed curators. – Highly curated Databases

19. Secondary Databases TrEMBL: – Translated EMBL – Hypothetical proteins – After careful assessment  SpTrEMBL  SwissProt Databases

20. Secondary Databases UniGene: – Automated clustering of sequences with high similarity – Derived from GenBank / EMBL – 1 consensus-sequence – Species-specific Databases

21. Secondary Databases LocusLink: – Curated sequences – Descriptive information about genetic loci RefSeq: – Non-redundant set of sequences. – Genomic DNA, mRNA, Protein – Stable reference for gene identification and characterization. – High curation Databases

22. Database Quality? Databases mRNAProtein EMBLSwissProt Submitter Database Manager Submitter Database Manager Curators DNA

23. How to Annotate? BlastN against random nucleotide DB – EST’s BlastN against structured nucleotide DB (UniGene, RefSeq) – mRNA hits – Sometimes not annotated at all – Best information Databases

24. Microarrays

27. Part III: Annotation Techniques

28. What do we have? Probe sequence Alignment Tools (e.g. BLAST) Databases !?! What to choose ?!? Annotation

29. Possibilities? 1.Do it like everyone else does. 2.Make use of curative properties of certain databases Goal: Annotate as many genes with as much information as possible (e.g. SwissProt ID) Annotation

30. 1 st Approach - General “Done by most array manufacturers” Step-by-step approach: – BLAST sequences against nucleic database (preferably UniGene) – Extract high quality (HQ) hits (>95%) – For each HQ hit search crosslinks. – Find a well-described (SwissProt) ID for each sequence. Annotation Techniques

31. 1 st Approach - Concept Annotation Techniques

32. 2 nd Approach - General “Make use of present database curation” Other way around: – Use SwissProt to clean out EMBL – Result: “Cleaned” EMBL database with direct SP crosslinks – BLAST against cEMBL – Extract high quality alignment hits (>95%) – Convert EMBL ID to SP ID. Annotation Techniques

33. 2 nd Approach - Concept Annotation Techniques

34. Annotating Incyte Reporters Total: 13.497 cEMBL-approach: 2.898 (21,47%) SP-IDs DM approach: 10.013 (74,18%) UG-IDs in which M = 4.723 (34,9%) SP-IDs ; MR = 5.147 (38,1%) SP-IDs; MRH = 6.641 (49,2%) SP-IDs Results

35. Annotating Incyte Reporters All reporters present on “Incyte Mouse UniGene 1” converted Total: 9.596 reporters Old annotation : 9.370 (97,6%) UG-IDs in which Non-existing UG-IDs = 5.713 (59,5%); M = 1.939 (20,2%) SP-IDs; MR = 2.096 (21,8%) SP-IDs; MRH = 2.582 (26,9%) SP-IDs Datamining approach : 8.532 (88,9%) UG-IDs in which M = 4.145 (43,2%) SP-IDs ; MR = 4.499 (38,1%) SP-IDs; MRH = 5.576 (60,1%) SP-IDs Custom EMBL-approach : 2.898 (30,2%) SP-IDs Results

36. Annotating Incyte Reporters Combined methods “Incyte Mouse UniGene 1” reporters Total: 9.596 reporters No annotation : 1.062 (11%) reporters Annotated with SP-ID : 5.895 (61,3%) reporters of which 2.184 (22,7%) identical SP-IDs; 532 (5%) reporters with improved SP-IDs by EMBL-method; 174 (1,8%) reporters with different mouse SP-IDs; 5 reporters found only by EMBL-method Results

37. Conclusions Annotation is much needed  Array sequences can point to different genes Direct translation into protein not best option:  Sequencing errors  Addition or deletion of nucleotides  6-Frame window Public nucleotide databases are redundant.  Sequencing errors  Differences in sequence-length  Attachment of vector-sequence Conclusions

38. Questions? End