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AlgoAlignementGenomicSequences.ppt
- 1. Algorithms for Alignment of Genomic Sequences Michael Brudno Department of Computer Science Stanford University PGA Workshop 07/16/2004
- 3. Edit Distance Model (1) Weighted sum of insertions, deletions & mutations to transform one string into another AGGCACA--CA AGGCACACA | |||| || or | || || A--CACATTCA ACACATTCA
- 4. Edit Distance Model (2) Given: x, y Define: F(i,j) = Score of best alignment of x1…xi to y1…yj Recurrence: F(i,j) = max (F(i-1,j) – GAP_PENALTY, F(i,j-1) – GAP_PENALTY, F(i-1,j-1) + SCORE(xi, yj))
- 5. Edit Distance Model (3) F(i,j) = Score of best alignment ending at i,j Time O( n2 ) for two seqs, O( nk ) for k seqs F(i,j) F(i,j-1) F(i-1,j-1) F(i-1,j) AGTGCCCTGGAACCCTGACGGTGGGTCACAAAACTTCTGGA AGTGACCTGGGAAGACCCTGACCCTGGGTCACAAAACTC
- 6. Overview • Local Alignment (CHAOS) • Multiple Global Alignment (LAGAN) - Whole Genome Alignment • Glocal Alignment (Shuffle-LAGAN) • Biological Story
- 7. Local Alignment AGTGCCCTGGAACCCTGACGGTGGGTCACAAAACTTCTGGA AGTGACCTGGGAAGACCCTGAACCCTGGGTCACAAAACTC F(i,j) = max (F(i,j), 0) Return all paths with a position i,j where F(i,j) > C Time O( n2 ) for two seqs, O( nk ) for k seqs
- 9. CHAOS: CHAins Of Seeds 1. Find short matching words (seeds) 2. Chain them 3. Rescore chain
- 10. CHAOS: Chaining the Seeds • Find seeds at current location in seq1 location in seq1 seed seq1 seq2
- 11. CHAOS: Chaining the Seeds location in seq1 distance cutoff seed seq1 seq2 • Find seeds at current location in seq1
- 12. CHAOS: Chaining the Seeds location in seq1 distance cutoff gap cutoff seed seq1 seq2 • Find seeds at current location in seq1
- 13. CHAOS: Chaining the Seeds • Find seeds at current location in seq1 • Find the previous seeds that fall into the search box location in seq1 distance cutoff gap cutoff seed Search box seq1 seq2
- 14. CHAOS: Chaining the Seeds • Find seeds at current location in seq1 • Find the previous seeds that fall into the search box • Do a range query: seeds are indexed by their diagonal location in seq1 distance cutoff gap cutoff seed Search box seq1 seq2 Range of search
- 15. CHAOS: Chaining the Seeds • Find seeds at current location in seq1 • Find the previous seeds that fall into the search box • Do a range query: seeds are indexed by their diagonal. • Pick a previous seed that maximizes the score of chain location in seq1 distance cutoff gap cutoff seed Search box seq1 seq2 Range of search
- 16. CHAOS: Chaining the Seeds • Find seeds at current location in seq1 • Find the previous seeds that fall into the search box • Do a range query: seeds are indexed by their diagonal. • Pick a previous seed that maximizes the score of chain location in seq1 distance cutoff gap cutoff seed Search box seq1 seq2 Range of search Time O(n log n), where n is number of seeds.
- 17. CHAOS Scoring • Initial score = # matching bp - gaps • Rapid rescoring: extend all seeds to find optimal location for gaps
- 18. Overview • Local Alignment (CHAOS) • Multiple Global Alignment (LAGAN) - Whole Genome Alignment • Glocal Alignment (Shuffle-LAGAN) • Biological Story
- 20. LAGAN: 1. FIND Local Alignments 1. Find Local Alignments 2. Chain Local Alignments 3. Restricted DP
- 21. LAGAN: 2. CHAIN Local Alignments 1. Find Local Alignments 2. Chain Local Alignments 3. Restricted DP
- 22. LAGAN: 3. Restricted DP 1. Find Local Alignments 2. Chain Local Alignments 3. Restricted DP
- 23. MLAGAN: 1. Progressive Alignment Given N sequences, phylogenetic tree Align pairwise, in order of the tree (LAGAN) Human Baboon Mouse Rat
- 24. MLAGAN: 2. Multi-anchoring X Z Y Z X/Y Z To anchor the (X/Y), and (Z) alignments:
- 25. Cystic Fibrosis (CFTR), 12 species • Human sequence length: 1.8 Mb • Total genomic sequence: 13 Mb Human Baboon Cat Dog Cow Pig Mouse Rat Chimp Chicken Fugufish Zebrafish
- 26. CFTR (cont’d ) 90 550 99.7% Mammals LAGAN 90 862 96% Chicken & Fishes Chicken & Fishes Mammals 670 4547 99.8% MLAGAN 98% MAX MEMORY (Mb) TIME (sec) % Exons Aligned
- 27. Automatic computational system for comparative analysis of pairs of genomes http://pipeline.lbl.gov Alignments (all pair combinations): Human Genome (Golden Path Assembly) Mouse assemblies: Arachne, Phusion (2001) MGSC v3 (2002) Rat assemblies: January 2003, February 2003 ---------------------------------------------------------- D. Melanogaster vs D. Pseudoobscura February 2003
- 28. Tandem Local/Global Approach •Finding a likely mapping for a contig (BLAT)
- 29. Progressive Alignment Scheme yes no yes no Human, Mouse and Rat genomes Pairwise M/R mapping Aligned M&R fragments Unaligned M&R sequences Map to Human Genome Mapping aligned fragments by union of M&R local BLAT hits on the human genome H/M/R MLAGAN alignment M/R pairwise alignment M/H and R/H pairwise alignment Unassigned M&R DNA fragments yes no
- 30. Computational Time 23 dual 2.2GHz Intel Xeon node PC cluster. Pair-wise rat/mouse – 4 hours Pair-wise rat/human and mouse/human – 2 hours Multiple human/mouse/rat – 9 hours Total wall time: ~ 15 hours
- 31. Distribution of Large Indels 0 20 40 60 80 100 120 140 160 180 200 100 150 200 250 300 350 400 450 500 550 Indel length Count
- 32. Evolution Over a Chromosome
- 33. Overview • Local Alignment (CHAOS) • Multiple Global Alignment (LAGAN) - Whole Genome Alignment • Glocal Alignment (Shuffle-LAGAN) • Biological Story
- 34. Evolution at the DNA level …ACGGTGCAGTTACCA… …AC----CAGTCCACCA… Mutation SEQUENCE EDITS REARRANGEMENTS Deletion Inversion Translocation Duplication
- 35. Local & Global Alignment AGTGCCCTGGAACCCTGACGGTGGGTCACAAAACTTCTGGA AGTGACCTGGGAAGACCCTGAACCCTGGGTCACAAAACTC AGTGCCCTGGAACCCTGACGGTGGGTCACAAAACTTCTGGA AGTGACCTGGGAAGACCCTGAACCCTGGGTCACAAAACTC Local Global
- 36. Glocal Alignment Problem Find least cost transformation of one sequence into another using new operations •Sequence edits •Inversions •Translocations •Duplications •Combinations of above AGTGCCCTGGAACCCTGACGGTGGGTCACAAAACTTCTGGA AGTGACCTGGGAAGACCCTGAACCCTGGGTCACAAAACTC
- 37. Shuffle-LAGAN A glocal aligner for long DNA sequences
- 38. S-LAGAN: Find Local Alignments 1. Find Local Alignments 2. Build Rough Homology Map 3. Globally Align Consistent Parts
- 39. S-LAGAN: Build Homology Map 1. Find Local Alignments 2. Build Rough Homology Map 3. Globally Align Consistent Parts
- 40. Building the Homology Map d a b c Chain (using Eppstein Galil); each alignment gets a score which is MAX over 4 possible chains. Penalties are affine (event and distance components) Penalties: a) regular b) translocation c) inversion d) inverted translocation
- 41. S-LAGAN: Build Homology Map 1. Find Local Alignments 2. Build Rough Homology Map 3. Globally Align Consistent Parts
- 42. S-LAGAN: Global Alignment 1. Find Local Alignments 2. Build Rough Homology Map 3. Globally Align Consistent Parts
- 45. S-LAGAN Results (IGF cluster)
- 46. S-LAGAN results (HOX) • 12 paralogous genes • Conserved order in mammals
- 47. S-LAGAN results (HOX) • 12 paralogous genes • Conserved order in mammals
- 48. S-LAGAN Results (Chr 20) • Human Chr 20 v. homologous Mouse Chr 2. • 270 Segments of conserved synteny • 70 Inversions
- 49. S-LAGAN Results (Whole Genome) LAGAN S-LAGAN Total 37% 38% Exon 93% 96% Ups200 78% 81% CPU Time 350 Hrs 450 Hrs • Used Berkeley Genome Pipeline • % Human genome aligned with mouse sequence • Evaluation criteria from Waterston, et al (Nature 2002)
- 50. Rearrangements in Human v. Mouse Preliminary conclusions: • Rearrangements come in all sizes • Duplications worse conserved than other rearranged regions • Simple inversions tend to be most common and most conserved
- 51. What is next? (Shuffle) • Better algorithm and scoring • Whole genome synteny mapping • Multiple Glocal Alignment(!?)
- 52. Overview • Local Alignment (CHAOS) • Multiple Global Alignment (LAGAN) - Whole Genome Alignment • Glocal Alignment (Shuffle-LAGAN) • Biological Story
- 53. Biological Story • Math1 (Mouse Atonal Homologue 1, also ATOH) is a gene that is responsible for nervous system development
- 54. Align Human, Mouse, Rat & Fugu
- 55. Detailed Alignment hum_a : CAATAGAGGGTCTGGCAGAGGCTC---------------------CTGGC @ 57336/400001 mus_a : CAATAGAGGGGCTGGCAGAGGCTC---------------------CTGGC @ 78565/400001 rat_a : CAATAGAGGGGCTGGCAGAGACTC---------------------CTGGC @ 112663/369938 fug_a : TGATGGGGAGCGTGCATTAATTTCAGGCTATTGTTAACAGGCTCGTGGGC @ 36013/68174 hum_a : CGCGGTGCGGAGCGTCTGGAGCGGAGCACGCGCTGTCAGCTGGTGAGCGC @ 57386/400001 mus_a : CCCGGTGCGGAGCGTCTGGAGCGGAGCACGCGCTGTCAGCTGGTGAGCGC @ 78615/400001 rat_a : CCCGGTGCGGAGCGTCTGGAGCGGAGCACGCGCTGTCAGCTGGTGAGCGC @ 112713/369938 fug_a : CGAGGTGTTGGATGGCCTGAGTGAAGCACGCGCTGTCAGCTGGCGAGCGC @ 36063/68174
- 56. Can we align human & fly??? CGCGGTGC-GGAGCGTCTGGAGCGGAGCACGCGCTGTCAGCTGGTGAGCGCACTCTCCTTTCAGGCAGCTCCCCGGGGAG CCCGGTGC-GGAGCGTCTGGAGCGGAGCACGCGCTGTCAGCTGGTGAGCGCACTCG-CTTTCAGGCAGCTCCCCGGGGAG CCCGGTGC-GGAGCGTCTGGAGCGGAGCACGCGCTGTCAGCTGGTGAGCGCACTCG-CTTTCAGGCAGCTCCCCGGGGAG GAGGTGTTGGATGGCCTGAGTGA-AGCACGCGCTGTCAGCTGGCGAGCGCTCGCG-AGTCCCTGCCGTGTCCCCG Melan GCTACTCCAGCT-ACCACCTGCATGCAGCTGCACAGC Pseudo GCCACTGAGACT-GCCACCTGCATGCAGCTGCACAGA
- 57. Putting it all together CGCGGTGC-GGAGCGTCTGGAGCGGAGCACGCGCTGTCAGCTGGTGAGCGCACTCTCCTTTCAGGCAGCTCCCCGGGGAG CCCGGTGC-GGAGCGTCTGGAGCGGAGCACGCGCTGTCAGCTGGTGAGCGCACTCG-CTTTCAGGCAGCTCCCCGGGGAG CCCGGTGC-GGAGCGTCTGGAGCGGAGCACGCGCTGTCAGCTGGTGAGCGCACTCG-CTTTCAGGCAGCTCCCCGGGGAG GAGGTGTTGGATGGCCTGAGTGA-AGCACGCGCTGTCAGCTGGCGAGCGCTCGCG-AGTCCCTGCCGTGTCCCCG Melan GCTACTCCAGCT-ACCACCTGCATGCAGCTGCACAGC Pseudo GCCACTGAGACT-GCCACCTGCATGCAGCTGCACAGA
- 58. Overview • Local Alignment (CHAOS) • Multiple Global Alignment (LAGAN) - Whole Genome Alignment • Glocal Alignment (Shuffle-LAGAN) • Biological Story
- 59. Acknowledgments Stanford: Serafim Batzoglou Arend Sidow Matt Scott Gregory Cooper Chuong (Tom) Do Sanket Malde Kerrin Small Mukund Sundararajan Berkeley: Inna Dubchak Alexander Poliakov Göttingen: Burkhard Morgenstern Rat Genome Sequencing Consortium http://lagan.stanford.edu/