1. 1 Highly Scalable Algorithms for Robust String Barcoding Bhaskar DasGupta * Kishori M. Konwar Ion Mandoiu Alex Shavartsman Computer Science & Engineering Department University of Connecticut Storrs, CT * Department of Computer Science University of Illinois at Chicago Chicago, IL

2. 2 Motivation There are many critical situations when one needs to rapidly identify an unknown genomic sequence from among a given set of known sequences  Rapid identification of pathogens in epidemic outbreaks  Monitoring of microbial communities, e.g., in environmental studies  Fast database search

3. 3 Possible Approaches Sequencing based: sequence the unknown DNA sequence, then use similarity search programs such as BLAST to identify the unknown virus sequence for pathogens in database  Sequencing is prohibitively expensive and time consuming Hybridization Based: identify the unknown sequence by testing for the presence of certain subsequences  Subsequence tests can be performed quickly and at low cost using a variety of hybridization based methods

4. 4 Sequence Fingerprints For each sequence, find a subsequence that appears in that sequence and only in that sequence GTTGCGTTCCAT CAGTTGC100 CAGTTC010 CATGGA001 Sequence barcodes: 0/1 vectors When using fingerprints, barcode length = #sequences

5. 5 String Barcoding (Borneman et al.’01, Rash & Gusfield’02): Unique occurrence of tested subsequences not needed, as long as 0/1 barcodes are unique TGCAGT CAGTTGC11 CAGTTC01 CATGGA10 When using non-unique subsequences, barcode length can be much smaller than #sequences

6. 6 Overview Problem Formulation and Previous Work Greedy Setcover Algorithm Experimental Results Conclusions

7. 7 Given: Genomic sequences g 1,…, g n Find: Minimum number of distinguisher strings t 1,…,t k Such that: For every g i  g j there exists a distinguisher t l which is a substring of g i or g j but not of both - At least log 2 n distinguishers needed - n distinguishers are always sufficient Problem Definition

8. 8 Computational Complexity [Berman et al.’04] Cannot be approximated within a factor of (1-  )ln(n) unless NP=DTIME(n loglog(n) )

9. 9 A non-redundant set of candidate distinguishers is generated using a suffix tree One variable v i for each candidate distinguisher x i  v i = 1  x i is selected  v i = 0  x i is not selected Rash & Gusfield Integer Program

10. 10 Integer Program Example Minimize V TG + V ATGGA + V CAGT + V +V V TG + V ATGGA + V CAGT + V TTC +V GTGC #objective function Such that V TG VV V TG + V TTC + V GTGC >= 1 #constraint to cover pair 1,2 V ATGGA V CAGT V V ATGGA + V CAGT + V GTGC >= 1 #constraint to cover pair 1,3 V TG V ATGGA V CAGT V V TG + V ATGGA + V CAGT + V TTC >= 1 #constraint to cover pair 2,3 Binaries #all variables are 0/1 V TG V ATGGA V CAGT VV V TG V ATGGA V CAGT V TTC V GTGC End TGATGGA 1. CAGTGC10 2. CAGTTC00 3. CCATGGA11

11. 11 Limitations of Integer Program Method Works only for moderately sized datasets  50-150 sequences  Average sequence length ~1000 nucleotides  Up to 4 hours needed to come within 20% of optimum

12. 12 Information Content Heuristic [Berman et al. 2004]  Keep track of the partition defined by distinguishers selected so far  In every step, choose candidate that reduces partition entropy by largest amount Theorem: Information Content Heuristic is always finding a #distinguishers within 1+ln(n) of optimum

13. 13 CATCAGA TTCAGT TAT AATAG AATCAG D = { } Entropy = log 2 5! Information Content Heuristic

14. 14 CATCAGA TTCAGT TAT AATAG AATCAG c=TCAG D = { } New Entropy = log 2 (3!2!) Change = log 2 5! - log 2 (3!2!) Information Content Heuristic

15. 15 CATCAGA TTCAGT TAT AATAG AATCAG D = {TCAG} c=AAT New Entropy = log 2 (2!1!1!1!) Change = log 2 (3!2!) - log 2 (2!) Information Content Heuristic

16. 16 CATCAGA TTCAGT TAT AATAG AATCAG D = {TCAG,AAT} Information Content Heuristic

17. 17 Limitations of ICH Real genomic sequences contain degenerate nucleotides (e.g., N for any of {A,T,C,G} ) due to sequencing errors and known single nucleotide polymorphisms Distinguisher-to-sequence matches:  Perfect matches  Perfect mismatches  Uncertain matches Information Content cannot be defined in the presence of uncertain matches ATCNAT ATC1 CCC0 CCA?

18. 18 Other Heuristics (Cazalis et al 2004): greedy setcover, simulated annealing, and genetic algorithms for distinguisher selection To achieve practical running time, only a small random subset (2000 candidates) of all candidate distinguishers is considered  No data provided on the loss of solution quality due to this restriction

19. 19 Overview Problem Formulation and Previous work Greedy Setcover Algorithm Experimental Results Conclusions

20. 20 Setcover Greedy Heuristic Phase I: Candidate Generation  Generate a representative set of candidate distinguishers from the source sequences Phase II: Greedy Distinguisher Selection  In every step, choose candidate that distinguishes the largest number of not yet distinguished pairs

21. 21 Candidate Generation A set of candidate distinguishers guaranteed to contain an optimum solution is generated from the sequences We do not generate certain redundant candidates  A candidate is redundant if there is another candidate that appears exactly in the same set of sequences  For every sequence we generate only one of the substrings that appear exclusively in that sequence

22. 22 Efficient Candidate Generation Our implementation uses simple array datastructures  We generate candidates in increasing order of length  Exact match positions for candidates of length l-1 used to generate the exact matches for candidates of length l Candidates that do not satisfy individual given biochemical constraints, such as minimum/maximum length, GC content, melting temperature, are discarded

23. 23 Setcover Greedy Heuristic Phase I: Candidate Generation  Generate a set of candidate distinguishers from the source sequences Phase II: Greedy Distinguisher Selection  In every step, choose candidate that distinguishes the largest number of not yet distinguished pairs

24. 24 Distinguisher Selection as Set Cover Set Cover Problem: given a universal set U and a family of subsets, find a minimum number of subsets covering U Distinguisher selection is a special case of set cover:  Elements to be covered are the pairs of sequences  Each candidate distinguisher defines a set of pairs that it separates By a classical result, the greedy algorithm has an approximation factor of 1+ln(|U|)  Setcover greedy has approximation factor of 2*ln(n) for distinguisher selection with n sequences

25. 25 Distinguisher Selection Start with an empty set D of distinguishers While there are pairs of sequences not yet distinguished, do:  Compute for each remaining candidate c its coverage gain  (c, D) – the number of not yet distinguished pairs of sequences that are distinguished by c  Add the candidate with maximum coverage gain to D Return D

26. 26 Computation of  (c, D): CATCAGA TTCAGT TAT AATAG AATCAG D = { }

27. 27 Computation of  (c, D): CATCAGA TTCAGT TAT AATAG AATCAG c=TCAG D = { }

28. 28 Computation of  (c, D): CATCAGA TTCAGT TAT AATAG AATCAG c=TCAG D = { }  (c, D)= 3 x (5 –3) = 6

29. 29 Computation of  (c, D): CATCAGA TTCAGT TAT AATAG AATCAG D = {TCAG}

30. 30 Computation of  (c, D): CATCAGA TTCAGT TAT AATAG AATCAG D = {TCAG} c=AAT

31. 31 Computation of  (c, D): CATCAGA TTCAGT TAT AATAG AATCAG D = {TCAG} c=AAT  (c,D)= 1 x (2-1) + 1 x (3-1) = 3

32. 32 Computation of  (c, D): CATCAGA TTCAGT TAT AATAG AATCAG D = {TCAG,AAT}

33. 33 Computation of  (c, D) S 1, S 2, …, S k are the subsets in the partition defined by D M c is the set of matches of candidate c Using simple datastructures, computation can be done in linear time (in the number of sequences)

34. 34 Lazy Update of Gains Coverage gains are monotonically non-increasing during the algorithm Re-compute coverage gain for a candidate only if last saved gain is higher than the gain of current best candidate In practice this speeds-up the selection algorithm by a factor of ~2

35. 35 Degenerate bases  A pair of sequences is separated by candidate c if  c has at least one perfect match with one of the sequences, and  c has perfect mismatches at all positions of the other sequence  Gain computation done in O(n 2 ) time using a simple coverage matrix data-structure Redundancy r  A pair of sequences is counted in the gain function until r distinguishers separate it Distinguisher cross-hybridization  Minimum edit distance, or maximum common substring weight, bound for every pair of selected distinguishers  Candidates incompatible with a selected distinguisher removed from candidate list Algorithm Extensions

36. 36 Overview Problem Formulation and Previous work Greedy Setcover Algorithm Experimental Results Conclusions

37. 37 Randomly generated instances  Equal probabilities assigned to each of the four nucleotides Microbial genomes extracted from NCBI databases  Sequence lengths between 490 Kbases to 4.75 Mbases  Small number of degenerate bases Testcases

38. 38 Selection time, L=10k, r=1 basic – O(n 2 ) computation of gains using matrix datastructure partition – O(n) computation of gains using partition-based datastructure

39. 39 Candidate Sampling, n=1000, L=10k, r=1

40. 40 Comparison to ICH, L=10k, r=1 Algo n 10 20 50 100 200 500 1000  log 2 n  ICH SGA 4 5 6 7 8 9 10 4.0 5.0 7.0 8.0 10.0 12.2 14.1 4.0 5.0 7.0 8.0 10.0 12.3 14.1

41. 41 Varying Redundancy, L=10k

42. 42 20 NCBI microbial genomic sequences Distinguisher melting temperature range of 55- 60 o C GC content range of 40-60% Max common subsequence weight bound of 5  weight(A)=weight(T)=1, weight(C)=weight(G)=2 NCBI testcase

43. 43 AACTGTCTCACGACGTTCTGAA GATTCGAACCCCCGA GTGGATGCCTTGGCA GGACTACCAGGGTATCTAATCCTG AAAGAAGATAGAGCAGCAGCT AAGCGCGTCGCAAA CACAAGGAGTGAGTGTTGC CGGTTTTGTGCTTCATGG CCATTGACAATTTCAACACC Organism Mb Barcode Nanoarchaeum equitans Kin4-M 0.49 0 0 0 0 0 0 0 0 1 Mycobacterium tuberculosis CDC1551 4.40 0 0 0 0 0 0 1 0 0 Brucella suis 1330 chromosome 1 2.11 0 0 0 0 1 1 0 1 0 Leifsonia xyli subsp. xyli str. CTCB07 2.58 0 0 0 0 0 0 1 0 1 Mannheimia succiniciproducens MBEL55E 2.31 0 0 0 0 1 1 1 0 0 Geobacter sulfurreducens PCA 3.81 0 0 0 1 0 0 0 0 0 Rickettsia typhi str. Wilmington 1.11 0 0 0 0 0 1 1 0 1 Picrophilus torridus DSM 9790 1.55 0 1 0 0 0 0 0 0 1 Mesoplasma florum L1 0.79 0 0 0 0 0 0 0 1 1 Methylococcus capsulatus str. Bath 3.30 0 0 0 0 0 1 0 0 1 Propionibacterium acnes KPA171202 2.56 0 0 0 0 0 0 1 1 0 Mycoplasma mobile 163K 0.78 0 0 0 0 0 1 0 1 1 Mycoplasma hyopneumoniae 232 0.89 1 0 0 0 0 1 0 1 1 Bacillus licheniformis DSM 13 4.22 0 0 0 0 0 1 1 1 0 Legionella pneumophila subsp. pneumophila str. Philadelphia 1 3.40 0 0 0 0 0 1 1 0 0 Onion yellows phytoplasma OY-M DNA 0.86 0 0 0 0 1 1 1 1 0 Staphylococcus aureus subsp. Aureus strain MRSA252 2.90 0 0 1 0 0 1 1 1 1 Staphylococcus aureus strain MSSA476 2.80 0 0 0 0 0 1 1 1 1 Burkholderia pseudomallei strain K96243 chromosome 1 4.07 0 0 0 0 0 1 0 0 0 Bartonella henselae strain Houston-1 1.93 0 0 0 0 0 1 0 1 0 GC (%)60.0 45.5 60.0 50.0 57.1 50.0 52.6 42.9 40.0 Tm ( o C)55.6 59.6 55.4 59.3 56.9 58.6 55.1 55.4 56.3 NCBI testcase, r=1

44. 44 Results on 29 Microbial Sequences (76 Mb) Redunl min l max MinEdit Select Time #Distinguishers 1111 0  0 14.2 6.0 15 40 6 2.6 8.0 5555 0  0 20.3 21.0 15 40 6 8.7 31.0 10 0  0 22.9 41.0 15 40 6 16.4 60.0 20 0  0 26.8 76.0 15 40 6 33.4 123.0

45. 45 Overview Problem Formulation and Previous work Greedy Setcover Algorithm Experimental Results Conclusions

46. 46 We provided highly scalable algorithms for the robust string barcoding problem, capable of handling whole genomic sequences of up to bacterial size Distinguisher selection based whole genomic sequences results in a number of distinguishers nearly matching the information theoretic lower bounds for the problem The software can be used online at http://dna.engr.uconn.edu/~software/DNA-BAR/ Conclusions