1. De novo identification of repeat families in large genomes Alkes L. Price, Neil C. Jones and Pavel A. Pevzner June 28, 2005

2. What is a repeat family? A repeat family is a collection of similar sequences which appear many times in a genome. For example, the Alu repeat family has over 1 million approximate occurrences in the human genome: Alu

3. Identifying repeat families: problem formulation Alu INPUT: Genome containing approximate Alu occurrences OUTPUT: 282bp Alu consensus sequence GGCCGGGCGCGGTGGCTCACG………..GCGAGACTCCGTCTC + consensus sequences of all other repeat families in genome

4. Identifying repeat families: an easy problem? Alu

5. Identifying repeat families: an easy problem? Alu

6. Identifying repeat families: an easy problem? Alu Alu consensus

7. Identifying repeat families: an easy problem? Alu Alu consensus Difficulties:

8. Identifying repeat families: an easy problem? Alu Alu consensus Difficulties: Regions containing repeat occurrences are not known a priori

9. Identifying repeat families: an easy problem? Alu Alu consensus Difficulties: Regions containing repeat occurrences are not known a priori Repeat boundaries are not known a priori

10. Identifying repeat families: an easy problem? Alu Alu consensus Difficulties: Regions containing repeat occurrences are not known a priori Repeat boundaries are not known a priori Many repeat occurrences appear as partial copies

11. Identifying repeat families: a difficult problem “The problem of automated repeat sequence family classification is inherently messy and ill-defined and does not appear to be amenable to a clean algorithmic attack.” Bao and Eddy, 2002 In this talk, we present a simple and efficient algorithm for solving this problem.

12. Why is identifying repeat families important? Genome rearrangements (Kazazian, 2004) Drift to new biological function (Kidwell and Lisch, 2001) Increased rate of evolution under stress (Capy et al, 2000) 1. Repeats are biologically meaningful Repeats are drivers of genome evolution (Kazazian, 2004) which can play a beneficial (rather than parasitic) role (Holmes, 2002). In particular, repeats have been implicated in

13. Why is identifying repeat families important? Repeats need to be masked prior to performing most single-species or multi-species analyses. “Every time we compare two species that are closer to each other than either is to humans, we get nearly killed by unmasked repeats.” Webb Miller (personal communication) 2. Repeat masking

14. Why is identifying repeat families important? Repeats need to be masked prior to performing most single-species or multi-species analyses. GENE1 GENE2

15. Why is identifying repeat families important? If repeat families are known, repeats can be masked using RepeatMasker (http://www.repeatmasker.org).http://www.repeatmasker.org GENE1 GENE2

16. Why is identifying repeat families important? If repeat families are known … GENE1 GENE2

17. Identifying repeat families: manual approaches For widely studied genomes such as human and mouse, libraries of repeat families have been manually curated: –Repbase Update library (http://www.girinst.org)http://www.girinst.org –RepeatMasker library (http://www.repeatmasker.org)http://www.repeatmasker.org

18. Identifying repeat families: algorithmic approaches Many, many new genomes are being assembled. How to identify the repeat families present in these genomes? Clearly, algorithmic approaches are needed.

19. Identifying repeat families: algorithmic approaches All existing algorithms for de novo identification of repeat families rely on a set of pairwise similarities: Single-linkage clustering (Agarwal and States, 1994) REPuter (Kurtz et al., 2000) RepeatFinder (Volfovsky et al., 2001) RECON (Bao and Eddy, 2002) RepeatGluer (Pevzner et al., 2004) PILER (Edgar and Myers, 2005)

20. Identifying repeat families: algorithmic approaches Disadvantages of using pairwise similarities: Computational intractability human genome: ~10 6 Alus => ~10 12 pairwise alignments Difficulty defining repeat boundaries “Local sequence alignments do not usually correspond to the biological boundaries … Difficulty in defining element boundaries causes problems in clustering related elements into families.” Bao and Eddy, 2002

21. Identifying repeat families: algorithmic approaches Disadvantages of using pairwise similarities: Computational intractability Difficulty defining repeat boundaries Our RepeatScout algorithm uses an efficient method of similarity search which enables a rigorous definition of repeat boundaries.

22. RepeatScout: the main idea Consider a repeat family with many occurrences in a genome: Equivalently, we have: TAGCACCTTAGGGCGTCTCGCAACGTCTGCCCACGAACGTTAATCAGTAA GATTATCATGAAGCGCTTCGCAACGTCTGCAGCTGTCCAGACCGCTGTCA TATATCCGGTAATCGCCCCGCAACGTCTGCTAACGGGCGTACGGTCGAAT TGACCTGCTCAGGAGCCTTGCAACGTCTGCTCGCGGATGTGTATGCACGC ATCCATGCTCGGTATGAATCCAACGTCTGCTCATGGACATCTCATGACGT CGATCCTCTGAGGCACCTCACAACGTCTGCTCACTGACGCACGGTTGCTG

23. RepeatScout: the main idea TAGCACCTTAGGGCGTCTCGCAACGTCTGCCCACGAACGTTAATCAGTAA GATTATCATGAAGCGCTTCGCAACGTCTGCAGCTGTCCAGACCGCTGTCA TATATCCGGTAATCGCCCCGCAACGTCTGCTAACGGGCGTACGGTCGAAT TGACCTGCTCAGGAGCCTTGCAACGTCTGCTCGCGGATGTGTATGCACGC ATCCATGCTCGGTATGAATCCAACGTCTGCTCATGGACATCTCATGACGT CGATCCTCTGAGGCACCTCACAACGTCTGCTCACTGACGCACGGTTGCTG Consensus: ?

24. RepeatScout: the main idea TAGCACCTTAGGGCGTCTCGCAACGTCTGCCCACGAACGTTAATCAGTAA GATTATCATGAAGCGCTTCGCAACGTCTGCAGCTGTCCAGACCGCTGTCA TATATCCGGTAATCGCCCCGCAACGTCTGCTAACGGGCGTACGGTCGAAT TGACCTGCTCAGGAGCCTTGCAACGTCTGCTCGCGGATGTGTATGCACGC ATCCATGCTCGGTATGAATCCAACGTCTGCTCATGGACATCTCATGACGT CGATCCTCTGAGGCACCTCACAACGTCTGCTCACTGACGCACGGTTGCTG Consensus: ?

25. RepeatScout: the main idea TAGCACCTTAGGGCGTCTCGCAACGTCTGCCCACGAACGTTAATCAGTAA GATTATCATGAAGCGCTTCGCAACGTCTGCAGCTGTCCAGACCGCTGTCA TATATCCGGTAATCGCCCCGCAACGTCTGCTAACGGGCGTACGGTCGAAT TGACCTGCTCAGGAGCCTTGCAACGTCTGCTCGCGGATGTGTATGCACGC ATCCATGCTCGGTATGAATCCAACGTCTGCTCATGGACATCTCATGACGT CGATCCTCTGAGGCACCTCACAACGTCTGCTCACTGACGCACGGTTGCTG Consensus: CAACGTCTGC Idea: greedily extend 1 bp at a time from short l-mer seed

26. RepeatScout: the main idea TAGCACCTTAGGGCGTCTCGCAACGTCTGCCCACGAACGTTAATCAGTAA GATTATCATGAAGCGCTTCGCAACGTCTGCAGCTGTCCAGACCGCTGTCA TATATCCGGTAATCGCCCCGCAACGTCTGCTAACGGGCGTACGGTCGAAT TGACCTGCTCAGGAGCCTTGCAACGTCTGCTCGCGGATGTGTATGCACGC ATCCATGCTCGGTATGAATCCAACGTCTGCTCATGGACATCTCATGACGT CGATCCTCTGAGGCACCTCACAACGTCTGCTCACTGACGCACGGTTGCTG Consensus: CAACGTCTGCT Idea: greedily extend 1 bp at a time from short l-mer seed

27. RepeatScout: the main idea TAGCACCTTAGGGCGTCTCGCAACGTCTGCCCACGAACGTTAATCAGTAA GATTATCATGAAGCGCTTCGCAACGTCTGCAGCTGTCCAGACCGCTGTCA TATATCCGGTAATCGCCCCGCAACGTCTGCTAACGGGCGTACGGTCGAAT TGACCTGCTCAGGAGCCTTGCAACGTCTGCTCGCGGATGTGTATGCACGC ATCCATGCTCGGTATGAATCCAACGTCTGCTCATGGACATCTCATGACGT CGATCCTCTGAGGCACCTCACAACGTCTGCTCACTGACGCACGGTTGCTG Consensus: CAACGTCTGCTC Idea: greedily extend 1 bp at a time from short l-mer seed

28. RepeatScout: the main idea Consensus: CAACGTCTGCTCA Idea: greedily extend 1 bp at a time from short l-mer seed Discard a sequence after it stops aligning to consensus

29. RepeatScout: the main idea Consensus: CAACGTCTGCTCAC Idea: greedily extend 1 bp at a time from short l-mer seed Discard a sequence after it stops aligning to consensus

30. RepeatScout: the main idea Consensus: CAACGTCTGCTCACG Idea: greedily extend 1 bp at a time from short l-mer seed Discard a sequence after it stops aligning to consensus

31. RepeatScout: the main idea Consensus: CAACGTCTGCTCACGG Idea: greedily extend 1 bp at a time from short l-mer seed Discard a sequence after it stops aligning to consensus

32. RepeatScout: the main idea Consensus: CAACGTCTGCTCACGGA Idea: greedily extend 1 bp at a time from short l-mer seed Discard a sequence after it stops aligning to consensus

33. RepeatScout: the main idea Consensus: CAACGTCTGCTCACGGAC Idea: greedily extend 1 bp at a time from short l-mer seed Discard a sequence after it stops aligning to consensus

34. RepeatScout: the main idea Consensus: CAACGTCTGCTCACGGACG Idea: greedily extend 1 bp at a time from short l-mer seed Discard a sequence after it stops aligning to consensus

35. RepeatScout: the main idea Consensus: CAACGTCTGCTCACGGACGT Idea: greedily extend 1 bp at a time from short l-mer seed Discard a sequence after it stops aligning to consensus

36. RepeatScout: the main idea Consensus: CAACGTCTGCTCACGGACGT Idea: greedily extend 1 bp at a time from short l-mer seed Discard a sequence after it stops aligning to consensus Stop extending when most sequences no longer align

37. RepeatScout: the main idea Consensus: CAACGTCTGCTCACGGACGTACGGT Idea: greedily extend 1 bp at a time from short l-mer seed Discard a sequence after it stops aligning to consensus Stop extending when most sequences no longer align Note: pairwise alignment is a poor boundary criteria.

38. RepeatScout: the main idea Consensus: AGGCGCCTCGCAACGTCTGCTCACGGACGT Idea: greedily extend 1 bp at a time from short l-mer seed Discard a sequence “after it stops aligning to consensus” Stop extending “when most sequences no longer align” First extend right, then extend left in similar manner

39. Repeat boundaries: the objective function Let S 1, …, S n be strings containing occurrences of a repeat family which share a short l-mer seed. We define the consensus sequence Q of the repeat family to be the sequence which maximizes A(Q; S 1, …, S n ) = ∑ k a(Q, S k ) where a(Q, S k ) is a fit-preferred alignment score

40. Repeat boundaries: the objective function Let S 1, …, S n be strings containing occurrences of a repeat family which share a short l-mer seed. We define the consensus sequence Q of the repeat family to be the sequence which maximizes A(Q; S 1, …, S n ) = ∑ k a(Q, S k ) – c |Q| where a(Q, S k ) is a fit-preferred alignment score c is a repeat frequency threshold

41. Repeat boundaries: the objective function A(Q; S 1, …, S n ) = ∑ k a(Q, S k ) – c |Q| Optimizing the objective function: Start with Q = short l-mer seed Greedily extend Q to the right (left) 1 bp at a time. Stop when + many consecutive iterations fail to improve upon the optimal Q. The optimal Q defines the consensus sequence of the repeat family. This provides a rigorous definition of repeat boundaries.

42. Repeat boundaries: the objective function Consensus: AGGCGCCTCGCAACGTCTGCTCACGGACGT Greedily extend right/left to optimize A(Q, S 1, …, S n )

43. RepeatScout: finding all repeat families To find all repeat families in a genome, we could apply this procedure to extend all frequent l-mers.

44. RepeatScout: finding all repeat families To find all repeat families in a genome, we could apply this procedure to extend all frequent l-mers. However, each repeat family spawns a large number of frequent l-mers and could be repeatedly rediscovered.

45. RepeatScout: finding all repeat families To find all repeat families in a genome, we could apply this procedure to extend all frequent l-mers. However, each repeat family spawns a large number of frequent l-mers and could be repeatedly rediscovered. To address this, we dynamically adjust l-mer frequencies to exclude contributions from repeat families we have already identified.

46. RepeatScout: postprocessing We discard very short “repeat families” arising from spurious frequent l-mers. We discard repeat families with less than 10 copies. We may further wish to distinguish between Low-complexity repeat families Tandem repeat families Multicopy exon families Segmental duplication units Transposon families

47. Results: the human Alu family Alu Input: Genome containing approximate Alu occurrences Desired Output: 282bp Alu consensus sequence GGCCGGGCGCGGTGGCTCACG………..GCGAGACTCCGTCTC

48. Results: the human Alu family Alu Input: Genome containing approximate Alu occurrences Desired Output: 282bp Alu consensus sequence GGCCGGGCGCGGTGGCTCACG………..GCGAGACTCCGTCTC RepeatScout Output (on human X chr): 282bp sequence GGCCGGGCGCGGTGGCTCACG………..GCGAGACTCCGTCTC

49. Results: C. briggsae We benchmarked RepeatScout using the 108Mb C. briggsae genome (Stein et al., 2003), which Stein et al. analyzed using the RECON algorithm (Bao and Eddy, 2002). We ran RepeatMasker (http://www.repeatmasker.org) using either the RECON repeat library or the RepeatScout library as input, and compared the results:http://www.repeatmasker.org

50. Results: C. briggsae RECON RepeatScout library library 2.0 Mb 23.1 Mb 4.8 Mb

51. Results: human, mouse, rat We ran RepeatScout on human, mouse and rat X chromosomes. We filtered out Low-complexity repeat families Tandem repeat families Multicopy exon families Known segmental duplication units We ran RepeatMasker using either the RepeatMasker library or the RepeatScout library as input, and compared the results:

52. Results: human X chromosome RepeatMasker RepeatScout library library 8.3 Mb 53.5 Mb 2.4 Mb

53. Results: mouse X chromosome RepeatMasker RepeatScout library library 5.3 Mb 47.6 Mb 3.3 Mb

54. Results: mouse X chromosome RepeatMasker RepeatScout library library 5.3 Mb 47.6 Mb 3.3 Mb

55. Results: mouse X chromosome Repbase Update RepeatScout library library 2.7 Mb 43.2 Mb 6.4 Mb results presented in our paper

56. Results: mouse X chromosome RepeatMasker RepeatScout library library 5.3 Mb 47.6 Mb 3.3 Mb latest results

57. Running times 3.0 Mb (human) 9.0 Mb (human) X chr (human) RECON 4 hours * 39 hours * -- RepeatScout 6 min † 21 min † 8 hours † * on a single 1.7 GHz Intel Xeon processor † on a single 0.5 GHz DEC Alpha processor

58. Future Directions Distinguish segmental duplications from transposons Unify fragmented repeat families Improve sensitivity via inexact or noncontiguous l-mer seeds Run RepeatScout on entire mammalian genomes

59. RepeatScout web site Google search on RepeatScout RepeatScout source code and documentation RepeatScout repeat libraries Slides of this talk Google search on RepeatScout

60. Acknowledgements We are grateful to Lincoln Stein for providing RECON C. briggsae output. Evan Eichler for providing segmental duplication annotations for human, mouse and rat X chromosomes. Arian Smit, Robert Hubley and Brian Haas for testing RepeatScout and offering numerous helpful comments and suggestions.