1. 1 Transcript modeling Brent lab

2. 2 Overview Of Entertainment  Gene prediction Jeltje van Baren  Improving gene prediction with tiling arrays Aaron Tenney  Validating predicted genes Laura Langton

3. 3 How gene finders work in 3 easy steps A computational gene finder annotates a sequence by: 1. Identifying valid gene predictions 2. Assigning a probability to each gene prediction 3. Selecting the gene prediction with the highest probability

4. 4 TGTCCATGACCGGAGTCTACAGTTAAACGGAGTATGATGTCATTACTAGTACCATCAGGATCGTCAATACCTACAGATTACCTAATACC Defining valid gene predictions Start codonStop codon Canonical splice sites No in-frame stops

5. 5 Assigning probabilities to valid gene predictions 0.05 0.20 0.10 0.55 0.10 Probabilities based on “sequence submodels” trained on examples of real genes TGTCCATGACCGGAGTCTACAGTTAAACGGAGTATGATGTCATTACTAGTACCATCAGGATCGTCAATACCTACAGATTACCTAATACC

6. 6 Picking optimal gene prediction 0.05 0.20 0.10 0.55 0.10 Viterbi algorithm TGTCCATGACCGGAGTCTACAGTTAAACGGAGTATGATGTCATTACTAGTACCATCAGGATCGTCAATACCTACAGATTACCTAATACC

7. 7 Alignment.....ATGACTGGGGT-TACAGTTAA.....GTACGATGT-ATTGCT............................GATAACCTAA.... ||||| || || ||||||||| ||| ||||| ||| || ||| |||||| TGTCCATGACCGGAGTCTACAGTTAAACGGAGTATGATGTCATTACTAGTACCATCAGGATCGTCAATACCTACAGATTACCTAATACC Adding external information 0.10 0.60 0.15 0.10 0.05

8. 8 Types of external information DNA sequence Aligned transcripts Evolutionary conservation Tiling array data Gene predictions Conservation: D. erecta and D. pseudoobscura Transcripts: ESTs and mRNA Tiling arrays: Affymetrix and Aaron

9. 9 PASA Assembly 1 Assembly 2 Brian J. Haas et al. Improving the Arabidopsis genome annotation using maximal transcript alignment assemblies. NAR 2003 31: 5654–5666.

10. 10 Adding the cDNA information 0.10 0.05 0.15 0.10 0.60 ||||| |||||| |||| ||||| TGTCCATGACCGGAGTCTACAGTTAAACGGAGTATGATGTCATTACTAGTACCATCAGGATCGTCAATACCTACAGATTACCTAATACC

11. 11 Creating an annotation We can use PASA to “update” gene predictions Assembly 1 Assembly 2 Alternative splice Gene prediction

12. 12 Difficult: dscam

13. 13 Difficult: dscam

14. 14 Quirks  In-frame stop codons (selenocysteines?)  Uncommon splice sites Non GT/AG GC/AG or AT/AC  Genome rearrangements  Dicistronic genes (androcam)  Trans-splicing (mod(mdg4))  More?

15. 15 Storing the data DNA sequence Evolutionary conservation Tiling array data Gene predictions GenomeDB (Brian Koebbe) PASA clusters Manual annotation Genome annotation Aligned transcripts

16. 16 Tiling arrays Aaron Tenney

17. 17 Goal  Combine computational gene finding and tiling array analysis Improve prediction accuracy on protein coding genes Predict different forms of genes in different hybridization conditions

18. 18 Tiling arrays complement other information sources  Tiling arrays vs. DNA sequence No explicit use of sequence, not as biased by genes in training set Easier to find atypical novel genes (odd splice sites or codon usage)  Tiling arrays vs. Evolutionary conservation Much conserved sequence is not transcribed Tiling array will help sift out conserved but non- transcribed sequence  Tiling arrays vs. aligned ESTs Similar to information from aligned ESTs Less biased to high copy number transcripts and 3’ ends More complete view of transcriptome

19. 19 Tiling arrays complement other information sources

20. 20 Challenges  Most literature on analysis of oligonucleotide arrays is about expression arrays Sets of 10-20 probes designed to query specific genes  Analysis of tiling arrays is different Determining which probes are hybridizing instead of estimating expression levels Looser probe design criteria, noiser data

21. 21 Low level analysis questions  Individual probe intensities  Normalization  Probe sequence specific corrections  Cross hybridization

22. 22 Data integration questions  Adding tiling arrays to information we already use  Resolution vs. noise reduction tradeoff  Sequence representation / feature functions  Modeling entities of interest in the genome Protein and non protein coding genes Non-genes, “Dark matter”  Correlations to DNA / conservation / EST signals

23. 23 Validation experiments Laura Langton

24. 24 Prediction Validation  Which predictions to validate? Filter predictions for exon overlap with existing experimental evidence.  Evidence = mRNAs and PASA clustered ESTs Classify predictions into 3 major categories:  Known  Partially verified  Novel Feature of interest = splice sites

25. 25 Known Gene

26. 26 Known Gene

27. 27 Partial

28. 28 Novel

29. 29 Novel

30. 30 Categories not currently tested  Alternative splices  Single exon genes  UTRs  Structural disagreements

31. 31 Structural disagreements

32. 32 Alternate Splice

33. 33  Design primers to span one or more unverified introns.  Reverse transcribe RNA from whole fly or cell lines.  PCR 650 bp amplicons.  Directly sequence.  Align resulting ESTs to genome Experimental Validation - RT PCR

34. 34 Sequence Data

35. 35 RT - PCR Our EST data [RTDB] DNA sequence Aligned ESTs Evolutionary conservation Tiling array data Gene predictions

36. 36 RT Database  Types of data in RTDB (examples) Traces, reads, quality values Primers, amplicons Predictions, genome version Experiment information  Accessible to collaborators  Schema available on request Charles Comstock

37. 37 Preliminary results  Novel 176 predictions tested, 51% hit rate  Partial 442 predictions tested, 74% hit rate