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Algorithms for Alignment of Genomic Sequences Michael Brudno Department of Computer Science Stanford University PGA Workshop 07/16/2004.

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Presentation on theme: "Algorithms for Alignment of Genomic Sequences Michael Brudno Department of Computer Science Stanford University PGA Workshop 07/16/2004."— Presentation transcript:

1 Algorithms for Alignment of Genomic Sequences Michael Brudno Department of Computer Science Stanford University PGA Workshop 07/16/2004

2 Conservation Implies Function Exon Gene CNS: Other Conserved

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 x 1 …x i to y 1 …y j Recurrence:F(i,j) = max (F(i-1,j) – GAP_PENALTY, F(i,j-1) – GAP_PENALTY, F(i-1,j-1) + SCORE(x i, y j ))

5 Edit Distance Model (3) F(i,j) = Score of best alignment ending at i,j Time O( n 2 ) for two seqs, O( n k ) 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( n 2 ) for two seqs, O( n k ) for k seqs

8 Heuristic Local Alignment AGTGCCCTGGAACCCTGACGGTGGGTCACAAAACTTCTGGA AGTGACCTGGGAAGACCCTGAACCCTGGGTCACAAAACTC AGTGCCCTGGAACCCTGACGGTGGGTCACAAAACTTCTGGA AGTGACCTGGGAAGACCCTGAACCCTGGGTCACAAAACTC BLAST FASTA

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

19 Global Alignment AGTGCCCTGGAACCCTGACGGTGGGTCACAAAACTTCTGGA AGTGACCTGGGAAGACCCTGACCCTGGGTCACAAAACTC x y z

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 ) % Mammals LAGAN % Chicken & Fishes Mammals % MLAGAN 98% MAX MEMORY (Mb) TIME (sec) % Exons Aligned

27 Automatic computational system for comparative analysis of pairs of genomes Alignments (all pair combinations): Human Genome (Golden Path Assembly) Mouse assemblies: Arachne, Phusion (2001) MGSC v3 (2002) Rat assemblies: January 2003, February 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

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

43 S-LAGAN Results (CFTR) LocalLocal GlocalGlocal

44 Hum/MusHum/Mus Hum/RatHum/Rat

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 Segments of conserved synteny 70 Inversions

49 S-LAGAN Results (Whole Genome) LAGANS-LAGAN Total37%38% Exon93%96% Ups20078%81% CPU Time350 Hrs450 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 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 : 57336/ mus_a : 78565/ rat_a : / fug_a : 36013/68174 hum_a : 57386/ mus_a : 78615/ rat_a : / fug_a : 36063/68174

56 Can we align human & fly??? CGCGGTGC-GGAGCGTCTGGAGCGGAGCACGCGCTGTCAGCTGGTGAGCGCACTCTCCTTTCAGGCAGCTCCCCGGGGAG CCCGGTGC-GGAGCGTCTGGAGCGGAGCACGCGCTGTCAGCTGGTGAGCGCACTCG-CTTTCAGGCAGCTCCCCGGGGAG GAGGTGTTGGATGGCCTGAGTGA-AGCACGCGCTGTCAGCTGGCGAGCGCTCGCG-AGTCCCTGCCGTGTCCCCG Melan GCTACTCCAGCT-ACCACCTGCATGCAGCTGCACAGC Pseudo GCCACTGAGACT-GCCACCTGCATGCAGCTGCACAGA

57 Putting it all together CGCGGTGC-GGAGCGTCTGGAGCGGAGCACGCGCTGTCAGCTGGTGAGCGCACTCTCCTTTCAGGCAGCTCCCCGGGGAG 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


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