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Designing Multiple Simultaneous Seeds for DNA Similarity Search. Yanni Sun , Jeremy Buhler Washington University in Saint Louis. Outline. Problem of multi-seed design Methods Greedy covering algorithm Compute conditional match probabilities Experiments and results

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designing multiple simultaneous seeds for dna similarity search

Designing Multiple Simultaneous Seeds for DNA Similarity Search

Yanni Sun, Jeremy Buhler Washington University in Saint Louis

outline
Outline
  • Problem of multi-seed design
  • Methods
    • Greedy covering algorithm
      • Compute conditional match probabilities
  • Experiments and results
  • Conclusion and future work

WashU. Laboratory for Computational Genomics

sequence alignment
Sequence Alignment
  • Functional regions conserved despite DNA mutations over time
  • Conserved region can be aligned with high score
  • Exact solution: DP; time complexity: O(MN)
  • Fast but heuristic solution: seeded alignment algorithm

WashU. Laboratory for Computational Genomics

seeded alignment algorithm

TAGGACCTAACC

GACCACCTTTT

Seeded Alignment Algorithm
  • BLAST is the most popular tool.

Step 1: word matchstep 2: extend the match to find the high similarity pair

TAGGACCTAACC

GACCACCTTTT

WashU. Laboratory for Computational Genomics

seed and similarity
Seed and Similarity
  • Example of a similarity and a single seed

tgcagaaatgcagaggca

| || | | ||||

tacacaggcaccgaggag

Similarity: 101101000010111100

Seed: 11*1, weight = 3, span = 4

The seed detects/matchesthis similarity.

WashU. Laboratory for Computational Genomics

seed choice is important
Seed Choice is Important

1 1 1 1 1 1 1 1 1 1 1

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

Significant alignment

Seed match

WashU. Laboratory for Computational Genomics

seed design previous work
Seed Design: Previous Work
  • Traditional seed: word (e.g. 11111111111)
  • Discontiguous patterns of matching bases: [CR1993]; [MTL’02] {111010010100110111}
  • Our work on single discontiguous seed: [BKS’03]

WashU. Laboratory for Computational Genomics

multiple simultaneous seeds
Multiple Simultaneous Seeds
  • Multiple simultaneous seeds are defined as a set of seeds.
    • ∏= {seed1, seed2,…seed i,…, seedn}
    • ∏ detects a similarity if at least one of the component seeds detects the similarity
    • Example
      • Simultaneous seeds {11*1, 1*11} detect similarities 100110100001, 1000010110001, 1101001011001

WashU. Laboratory for Computational Genomics

multi seed design balance sensitivity with specificity
Multi-seed Design – Balance Sensitivity with Specificity
  • Sensitivity=A / Biologically

meaningful alignments

  • Specificity=A / seed matches
  • Increase sensitivity:
    • Decrease weight of single seed
    • Use multiple seeds
    • Both methods hurt specificity
  • Hypothesis: a set of multiple seeds

has a better tradeoff of sensitivity vs. specificity comparing to single seed

biologically meaningful alignments

A

seed matches

our work design multiple simultaneous seeds efficiently
Our Work – Design Multiple Simultaneous Seeds Efficiently
  • Use a new local search method to optimize seed set
  • Design an efficient algorithm to calculate conditional match probability
  • Empirical verification that multiple simultaneous seeds have better tradeoff of sensitivity vs. specificity

WashU. Laboratory for Computational Genomics

multi seed design problem
Multi-seed Design Problem
  • Input:
    • Ungapped alignments sampled from two genomic DNA sequences
    • Resource constraints of seeds: weight, span, number
  • Goal: find a set of seeds ∏ to maximize the detection probability Pr[∏ detects S].
    • Pr(∏ detects S) = Pr( (seed1 detects S) or (seed2 detects S)…or (seedn detects S))
outline12
Outline
  • Problem of multi-seed Design
  • Methods
    • Greedy covering algorithm
      • Compute conditional match probabilities
  • Experiments and results
  • Conclusion and future work

WashU. Laboratory for Computational Genomics

computing match probability for specified seeds bks 03
Computing Match Probability for Specified Seeds [BKS ’03]
  • Learn a kth-order Markov model from similarities.
  • Build a DFA that only accepts strings containing the given seeds
  • Compute the probability that the DFA accepts a string chosen randomly from model M by DP.

WashU. Laboratory for Computational Genomics

seek the locally optimal set of seeds
Seek the Locally Optimal Set of Seeds
  • Original local search
  • Greedy covering algorithm – a faster local search strategy
    • Efficient computation of conditional match probability

WashU. Laboratory for Computational Genomics

find optimal set of seeds by original local search

1***1*1,

1*****11

Pr=0.75

1**1**1,

1*****11

Pr=0.67

1****11, 1*****11

Pr=0.71

Find Optimal Set of Seeds by Original Local Search

Seed space with span<=8,weight=3

1*1***1,

1*****11

Pr=0.70

WashU. Laboratory for Computational Genomics

greedy covering algorithm

Similarities detected by S1

Similarities detected by S2

Similarities detected by S3

Greedy Covering Algorithm

Similarity space

Design 3 simultaneous seeds:{s1,s2,s3}

s1= argmaxxPr(x)

s2=argmaxx Pr(x|~s1)

s3=argmaxx Pr(x|~{s1,s2})

WashU. Laboratory for Computational Genomics

calculate conditional match probabilities
Calculate Conditional Match Probabilities
  • Challenge: how to calculate the conditional probability efficiently ?
    • Seeds with small span: exact computation via DFAs
    • Seeds with large span: Monte Carlo

WashU. Laboratory for Computational Genomics

calculate conditional match probability via dfa
Calculate Conditional Match Probability via DFA
  • Pr( x| ) = Pr(x )/ Pr( )
  • Build DFA corresponding to x by using cross product and complementation of DFA
  • Efficiency: in the process of local search to find optimal single seed x, Pr( ) can be precomputed

WashU. Laboratory for Computational Genomics

outline19
Outline
  • Problem of multi-seed design
  • Methods
    • Greedy covering algorithm
      • Compute conditional match probabilities
  • Experiments and results
  • Conclusion and future work

WashU. Laboratory for Computational Genomics

greedy covering is much faster
Greedy Covering is Much Faster
  • When n=5, on the same hardware platform(P4)
    • Greedy covering needs 20 minutes
    • The original local search needs 2.4 hours

WashU. Laboratory for Computational Genomics

experimental setup
Experimental Setup
  • The ungapped alignments are sampled uniformly from human and mouse syntenies
  • For a specified seed set
    • sensitivity : the number of significant gapped alignments found by our BLAST-like alignment tool
    • False positive rate : approximated by the number of seed matches

WashU. Laboratory for Computational Genomics

results verify the hypothesis on noncoding sequences
Results: Verify the Hypothesis on Noncoding Sequences

WashU. Laboratory for Computational Genomics

summary of contributions
Summary of Contributions
  • Efficient algorithms to design multiple simultaneous seeds at reasonable cost
  • Empirical verification: multiple simultaneous seeds have a better tradeoff between sensitivity and specificity

WashU. Laboratory for Computational Genomics

future work
Future Work
  • Design a better evaluation platform for different seeds
  • Investigate utility of seeds in multiple sequence alignment

WashU. Laboratory for Computational Genomics

acknowledgements
Acknowledgements
  • Dr. Jeremy Buhler (advisor), Ben Westover, Rachel Nordgren, Joseph Lancaster and Christopher Swope
  • Laboratory for computational genomics in Washington University in Saint Louis

http://www.cse.wustl.edu/~jbuhler/mandala

WashU. Laboratory for Computational Genomics