An Empirical Study of Choosing Efficient Discriminative Seeds for Oligonucleotide Design

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An Empirical Study of Choosing Efficient
Discriminative Seeds for Oligonucleotide Design

• Won-Hyong Chung and Seong-Bae Park
• Dept. of Computer Engineering
• Kyungpook National University, South Korea

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Motivation

• Issues for designing oligonucleotides
• To minimize the cross-hybridizations
• To minimize the computing time
• Seeding (or indexing) have been widely used for
  concurring those issues by means of pre-screening
  unreliable sequence regions before calculating
  cross-hybridizations.
• Although many types of seeding methods have been
  proposed, measure of evaluating the seeds
  regarding how adequate and efficient they are in
  the oligonucleotide design is not yet proposed.

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Difference between alignment and oligonucleotide
design

• Alignment
• To find all possible alignments which have enough
  scores.
• Sensitivity is important, while specificity is
  usually guaranteed by seeds own specificity.
• Oligoncleotide design
• To find optimal oligonucleotides to differentiate
  target sequences from the others.
• Specificity should be considered as well as
  sensitivity for checking cross-hybridization.

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Objectives

• We propose novel measures of evaluating the seeds
  based on the discriminability and the efficiency.
• We examine five seeding methods in
  oligonucleotide design.
• continuous, spaced, transition-constrained, BLAT,
  and Vector seed
• We provide a software package SeedChooser which
  enables users to get the adequate seeds under
  their own experimental conditions.

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What is Seed?

• Seeding process
• Filtering step short fixed-length common words
  which are found at both query and target
  sequences are selected.
• Extension step the selected words are extended
  to the size of oligonucleotide and be checked the
  cross-hybridization.
• Seed the filtering template of the fixed-length
  words

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Seeding methods (1/2)

• Continuous seed a seed to find k-length exact
  matches
• BLAST employs 11-bp length seed 11111111111
• Spaced seed allowing dont care letter labeled
  0 in the seed
• 18-bp-length seed containing 11-bp matches
  101101100111001011 is used at PatternHunter.
• Transition-constrained seed adopting transition
  (A lt-gt G, C lt-gt T) letter _at_ in the seed
• YASS used such seed 1110_at_10010_at_1010111, it
  consists of 18-bp length, 10-bp matches and 2
  transitions.

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Seeding methods (2/2)

• Blat seed a continuous seed allowing one or two
  mismatches at any positions of the seed.
• Vector seed a generalized seed by combining the
  idea of BLAT seed and spaced seed.
• BLAT seed and Vector seed allow some mismatches
  in any positions.
• They greatly increase the sensitivity but spends
  much more computing time than the previous seeds.

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The Issues of seeds for oligo design

• An ideal seed should filter all regions as fast
  as possible that have no possibility of being
  chosen as an oligo.

a seed should find as many oligos as possible
a seed should avoid to find non-oligo region
a seed should minimize the cost of indexing to
find oligos
Discriminability
Efficiency
Efficient Discriminability
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Discriminability
The discriminability is a balance between
precision and recall to minimize both false
positives and false negatives.
jump
alpha
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Efficiency

• The efficiency is the proportion of useful
  regions filtered by a seed.
• the duplication ratio of generated indices
• the average number of indices in each oligo

jump
beta, gamma
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Efficient discriminability
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Experiments

• Empirically chosen seeds were evaluated by three
  measures, discriminability, efficiency, and
  efficient discriminability, respectively.
• We tested the seeds for designing the 50mer
  oligos.
• The parameters are set to 1 for evaluation.
• Simulated data set
• A set of random sequences which are generated by
  OligoGenerator in SeedChooser.
• Biological data set
• Ecologically important genes involved in the
  nitrogen and carbon cycles.
• nirS nitrite reductase gene set
• pmoA methane monooxygenase gene set

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Discriminability of the five seeding methods
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Efficiency of the five seeding methods
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Efficient Discriminability the five seeding
methods
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Evaluation results of pmoA data set
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Evaluation results of nirS data set
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SeedChooser Seed Evaluation and Recommendation
Tools

• SeedChooser To recommend best seeds by the
  evaluation parameters. It uses genetic algorithm
  to find best seeds.
• SeedEvaluator To evaluate a set of the seeds by
  the parameters.
• OligoGenerator To generate a set of oligos for
  the desired experimental conditions.
• SeedChooser homepage
• http//ml.knu.ac.kr/whchung/seedchooser.html

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CONCLUSION

• The novel measure for evaluating the seeds in the
  oligo design based on the discriminability and
  the efficiency.
• The spaced seed was generally preferred to the
  other seeding methods.
• Our study can be applied to the oligo design
  programs in order to improve the performance by
  suggesting the experiment-specific seeds.
• We expect that our study will be helpful to the
  other genomic tasks.

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Supplementary materials
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P0
T0
P1
T1
P2
T2
T3

• T1, T2, T3 the target sequences.
• P1 and P2 are the matched oligos for an oligo P0
• S1, S2 and S3 are the seed indices for S0 by a
  seed.

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Relations of precision, recall and
discriminability
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Discriminability according to values of a
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Efficiency according to values of ß and ?
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Efficient Discriminability for 70mer Oligos