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

Conservation Implies Function Exon Gene CNS: Other Conserved

Edit Distance Model (1) Weighted sum of insertions, deletions & mutations to transform one string into another AGGCACA--CA AGGCACACA | |||| || or | || || A--CACATTCA ACACATTCA

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 ))

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

Overview Local Alignment (CHAOS) Multiple Global Alignment (LAGAN) -Whole Genome Alignment Glocal Alignment (Shuffle-LAGAN) Biological Story

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

Heuristic Local Alignment AGTGCCCTGGAACCCTGACGGTGGGTCACAAAACTTCTGGA AGTGACCTGGGAAGACCCTGAACCCTGGGTCACAAAACTC AGTGCCCTGGAACCCTGACGGTGGGTCACAAAACTTCTGGA AGTGACCTGGGAAGACCCTGAACCCTGGGTCACAAAACTC BLAST FASTA

CHAOS: CHAins Of Seeds 1.Find short matching words (seeds) 2.Chain them 3.Rescore chain

CHAOS: Chaining the Seeds Find seeds at current location in seq1 location in seq1 seed seq1 seq2

CHAOS: Chaining the Seeds location in seq1 distance cutoff seed seq1 seq2 Find seeds at current location in seq1

CHAOS: Chaining the Seeds location in seq1 distance cutoff gap cutoff seed seq1 seq2 Find seeds at current location in seq1

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

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

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

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.

CHAOS Scoring Initial score = # matching bp - gaps Rapid rescoring: extend all seeds to find optimal location for gaps

Overview Local Alignment (CHAOS) Multiple Global Alignment (LAGAN) -Whole Genome Alignment Glocal Alignment (Shuffle-LAGAN) Biological Story

Global Alignment AGTGCCCTGGAACCCTGACGGTGGGTCACAAAACTTCTGGA AGTGACCTGGGAAGACCCTGACCCTGGGTCACAAAACTC x y z

LAGAN: 1. FIND Local Alignments 1.Find Local Alignments 2.Chain Local Alignments 3.Restricted DP

LAGAN: 2. CHAIN Local Alignments 1.Find Local Alignments 2.Chain Local Alignments 3.Restricted DP

LAGAN: 3. Restricted DP 1.Find Local Alignments 2.Chain Local Alignments 3.Restricted DP

MLAGAN: 1. Progressive Alignment Given N sequences, phylogenetic tree Align pairwise, in order of the tree (LAGAN) Human Baboon Mouse Rat

MLAGAN: 2. Multi-anchoring X Z Y Z X/Y Z To anchor the (X/Y), and (Z) alignments:

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

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

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

Tandem Local/Global Approach Finding a likely mapping for a contig (BLAT)

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

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

Distribution of Large Indels

Evolution Over a Chromosome

Overview Local Alignment (CHAOS) Multiple Global Alignment (LAGAN) -Whole Genome Alignment Glocal Alignment (Shuffle-LAGAN) Biological Story

Evolution at the DNA level …ACGGTGCAGTTACCA… …AC----CAGTCCACCA… Mutation SEQUENCE EDITS REARRANGEMENTS Deletion Inversion Translocation Duplication

Local & Global Alignment AGTGCCCTGGAACCCTGACGGTGGGTCACAAAACTTCTGGA AGTGACCTGGGAAGACCCTGAACCCTGGGTCACAAAACTC AGTGCCCTGGAACCCTGACGGTGGGTCACAAAACTTCTGGA AGTGACCTGGGAAGACCCTGAACCCTGGGTCACAAAACTC Local Global

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

Shuffle-LAGAN A glocal aligner for long DNA sequences

S-LAGAN: Find Local Alignments 1.Find Local Alignments 2.Build Rough Homology Map 3.Globally Align Consistent Parts

S-LAGAN: Build Homology Map 1.Find Local Alignments 2.Build Rough Homology Map 3.Globally Align Consistent Parts

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

S-LAGAN: Build Homology Map 1.Find Local Alignments 2.Build Rough Homology Map 3.Globally Align Consistent Parts

S-LAGAN: Global Alignment 1.Find Local Alignments 2.Build Rough Homology Map 3.Globally Align Consistent Parts

S-LAGAN Results (CFTR) LocalLocal GlocalGlocal

Hum/MusHum/Mus Hum/RatHum/Rat

S-LAGAN Results (IGF cluster)

S-LAGAN results (HOX) 12 paralogous genes Conserved order in mammals

S-LAGAN results (HOX) 12 paralogous genes Conserved order in mammals

S-LAGAN Results (Chr 20) Human Chr 20 v. homologous Mouse Chr Segments of conserved synteny 70 Inversions

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)

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

What is next? (Shuffle) Better algorithm and scoring Whole genome synteny mapping Multiple Glocal Alignment(!?)

Overview Local Alignment (CHAOS) Multiple Global Alignment (LAGAN) -Whole Genome Alignment Glocal Alignment (Shuffle-LAGAN) Biological Story

Math1 (Mouse Atonal Homologue 1, also ATOH) is a gene that is responsible for nervous system development

Align Human, Mouse, Rat & Fugu

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

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

Putting it all together CGCGGTGC-GGAGCGTCTGGAGCGGAGCACGCGCTGTCAGCTGGTGAGCGCACTCTCCTTTCAGGCAGCTCCCCGGGGAG CCCGGTGC-GGAGCGTCTGGAGCGGAGCACGCGCTGTCAGCTGGTGAGCGCACTCG-CTTTCAGGCAGCTCCCCGGGGAG GAGGTGTTGGATGGCCTGAGTGA-AGCACGCGCTGTCAGCTGGCGAGCGCTCGCG-AGTCCCTGCCGTGTCCCCG Melan GCTACTCCAGCT-ACCACCTGCATGCAGCTGCACAGC Pseudo GCCACTGAGACT-GCCACCTGCATGCAGCTGCACAGA

Overview Local Alignment (CHAOS) Multiple Global Alignment (LAGAN) -Whole Genome Alignment Glocal Alignment (Shuffle-LAGAN) Biological Story

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