Alignment of large genomic sequences

1. Alignment of large genomic sequences Fragment-based alignment approach (DIALIGN) useful for alignment of genomic sequences. Possible applications: • Detection of regulatory elements • Identification of pathogenic microorganisms • Gene prediction

2. The DIALIGN approach atctaatagttaaactcccccgtgcttag cagtgcgtgtattactaacggttcaatcgcg caaagagtatcacccctgaattgaataa

3. The DIALIGN approach atctaatagttaaactcccccgtgcttag cagtgcgtgtattactaacggttcaatcgcg caaagagtatcacccctgaattgaataa

4. The DIALIGN approach atctaatagttaaactcccccgtgcttag cagtgcgtgtattactaacggttcaatcgcg caaagagtatcacccctgaattgaataa

5. The DIALIGN approach atctaatagttaaactcccccgtgcttag cagtgcgtgtattactaacggttcaatcgcg caaagagtatcacccctgaattgaataa

6. The DIALIGN approach atctaatagttaaactcccccgtgcttag cagtgcgtgtattactaacggttcaatcgcg caaagagtatcacccctgaattgaataa

7. The DIALIGN approach atctaatagttaaactcccccgtgcttag cagtgcgtgtattactaacggttcaatcgcg caaagagtatcacccctgaattgaataa

8. The DIALIGN approach atc------taatagttaaactcccccgtgcttag cagtgcgtgtattactaacggttcaatcgcg caaagagtatcacccctgaattgaataa

9. The DIALIGN approach atc------taatagttaaactcccccgtgcttag cagtgcgtgtattactaacggttcaatcgcg caaa--gagtatcacccctgaattgaataa

10. The DIALIGN approach atc------taatagttaaactcccccgtgcttag cagtgcgtgtattactaacggttcaatcgcg caaa--gagtatcacc----------cctgaattgaataa

11. The DIALIGN approach atc------taatagttaaactcccccgtgc-ttag cagtgcgtgtattactaac----------gg-ttcaatcgcg caaa--gagtatcacc----------cctgaattgaataa

12. The DIALIGN approach atc------taatagttaaactcccccgtgc-ttag cagtgcgtgtattactaac----------gg-ttcaatcgcg caaa--gagtatcacc----------cctgaattgaataa Consistency!

13. The DIALIGN approach atc------TAATAGTTAaactccccCGTGC-TTag cagtgcGTGTATTACTAAc----------GG-TTCAATcgcg caaa--GAGTATCAcc----------CCTGaaTTGAATaa

14. First step in sequence comparison: alignment S3 S1 S2

15. For genomic sequences: Neither local nor global methods appropriate First step in sequence comparison: alignment S3 S1 S2 S1’ S2’ S3’

16. Local method finds single best local similarity First step in sequence comparison: alignment S3 S1 S2 S1’ S2’ S3’

17. Multiple application of local methods possible First step in sequence comparison: alignment S3 S1 S2 S1’ S2’ S3’

18. First step in sequence comparison: alignment S3 S1 S2 S1’ S2’ S3’ • Multiple application of local methods possible

19. Multiple application of local methods possible First step in sequence comparison: alignment S3 S1 S2 S1’ S2’ S3’

20. Multiple application of local methods possible First step in sequence comparison: alignment S3 S1 S2 S1’ S2’ S3’

21. Threshold has to be applied to filter alignments: reduced sensitivity! First step in sequence comparison: alignment S3 S1 S2 S1’ S2’ S3’

22. Alternative approach: During evolution few large-scale re-arrangements -> relative order homologies conserved Search for chain of local homologies First step in sequence comparison: alignment

23. Genomic alignment: chain of homologies First step in sequence comparison: alignment S3 S1 S2 S1’ S2’ S3’

24. Genomic alignment: chain of homologies First step in sequence comparison: alignment S3 S1 S2 S1’ S2’ S3’

25. Genomic alignment: chain of homologies First step in sequence comparison: alignment S3 S1 S2 S1’ S2’ S3’

26. Genomic alignment: chain of homologies First step in sequence comparison: alignment S3 S1 S2 S1’ S2’ S3’

27. Novel approaches for genomic alignment: WABA PipMaker MGA TBA Lagan Avid DIALIGN First step in sequence comparison: alignment

28. Alignment of large genomic sequences Gene-regulatory sites identified by mulitple sequence alignment (phylogenetic footprinting)

29. Alignment of large genomic sequences

30. Objective function for DIALIGN: • Weight score for every possible fragment f based on P-value: P(f) = probability of finding a fragment “like f” by chance in random sequences with same length as input sequences w(f) = -log P(f) (“weight score” of f) ”like f” means: at least same # matches (DNA, RNA) or sum of similarity values (proteins)

31. Objective function for DIALIGN: • Score of alignment: sum of weight scores of fragments – no gap penalty!

32. Optimization problem for DIALIGN: • Find consistent collection of fragments with maximum total weight score!

33. Alternative fragment weight scores for genomic sequences: • Calculate fragment scores at nucleotide level and at peptide level.

34. catcatatcttatcttacgttaactcccccgt cagtgcgtgatagcccatatccgg

35. catcatatcttatcttacgttaactcccccgt cagtgcgtgatagcccatatccgg

36. catcatatcttatcttacgttaactcccccgt cagtgcgtgatagcccatatccgg Standard score: Consider length, # matches, compute probability of random occurrence

37. Translation option: catcatatcttatcttacgttaactcccccgt cagtgcgtgatagcccatatccgg

38. Translation option: L S Y V catcatatc ttatct tac gtt aactcccccgt cagtgcgtg atagcc cat atc cgg I A H I DNA segments translated to peptide segments; fragment score based on peptide similarity: Calculate probability of finding a fragment of the same length with (at least) the same sum of BLOSUM values

39. P-fragment (in both orientations) L S Y V catcatatc ttatct tac gtt aactcccccgt cagtgcgtg atagcc cat atc cgg I A H I N-fragment catcatatc ttatcttacgttaactcccccgtgct || | | | cagtgcgtg atagcccatatccg For each fragment fthree probability values calculated; Score of f based on smallest P value.

40. Alternative fragment weight scores for genomic sequences: • Calculate fragment scores at nucleotide level and at peptide level.

41. DIALIGN alignment of human and murine genomic sequences

42. DIALIGN alignment of tomato and Thalianagenomic sequences

43. Alignment of large genomic sequences Evaluation of signal detection methods: Apply method to data with known signals (correct answer is known!). E.g. experimentally verified genes for gene finding • TP = true positves = # signals correctly predicted (i.e. signal present) • FP = false positives = # signals predicted but wrong (i.e no signal present) • TN = true negative = # no signal predicted, no signal present • FN = false negative = # no signal predicted, signal present!

44. Alignment of large genomic sequences Sn = Sensitivity = correctly predicted signals / present signals = TP / (TP + FN) Sp = Specificity = correctly predicted signals / predicted signals • = TP / (TP + FP)

45. Alignment of large genomic sequences Comprehensive evaluation of signal prediction method: Method assigns score to predictions Apply threshold parameter High threshold -> high specificity (Sp), low sensitivity (Sn) Low threshold -> high sensitivity , low specificity ROC curve („receiver-operator curve“) Vary threshold parameter, plot Sn against Sp

46. Performance of long-range alignment programs for exon discovery (human - mouse comparison)

47. DIALIGN alignment of tomato and Thalianagenomic sequences

48. AGenDA:Alignment-based Gene Detection Algorithm • Bridge small gaps between DIALIGN fragments -> cluster of fragments

49. AGenDA:Alignment-based Gene Detection Algorithm • Bridge small gaps between DIALIGN fragments -> cluster of fragments • Search conserved splice sites and start/stop codons at cluster boundaries to Identify candidate exons

50. AGenDA:Alignment-based Gene Detection Algorithm • Bridge small gaps between DIALIGN fragments -> cluster of fragments • Search conserved splice sites and start/stop codons at cluster boundaries to Identify candidate exons • Recursive algorithm finds biologically consistent chain of potential exons