 Research article
 Open Access
Seeds for effective oligonucleotide design
 Lucian Ilie^{1}Email author,
 Silvana Ilie^{2},
 Shima Khoshraftar^{1} and
 Anahita Mansouri Bigvand^{1}
https://doi.org/10.1186/1471216412280
© Ilie et al; licensee BioMed Central Ltd. 2011
 Received: 21 January 2011
 Accepted: 1 June 2011
 Published: 1 June 2011
Abstract
Background
DNA oligonucleotides are a very useful tool in biology. The best algorithms for designing good DNA oligonucleotides are filtering out unsuitable regions using a seeding approach. Determining the quality of the seeds is crucial for the performance of these algorithms.
Results
We present a sound framework for evaluating the quality of seeds for oligonucleotide design. The F  score is used to measure the accuracy of each seed. A number of natural candidates are tested: contiguous (BLASTlike), spaced, transitionsconstrained, and multiple spaced seeds. Multiple spaced seeds are the best, with more seeds providing better accuracy. Single spaced and transition seeds are very close whereas, as expected, contiguous seeds come last. Increased accuracy comes at the price of reduced efficiency. An exception is that single spaced and transitionsconstrained seeds are both more accurate and more efficient than contiguous ones.
Conclusions
Our work confirms another application where multiple spaced seeds perform the best. It will be useful in improving the algorithms for oligonucleotide design.
Keywords
 Left Plot
 Single Seed
 Suffix Tree
 Suffix Array
 Maximum Stretch
Background
An oligonucleotide is a short DNA or RNA sequence. It is usually designed to hybridize with a unique position in a target sequence. In this way the target sequence can be uniquely identified using the oligonucleotide as a probe. DNA oligonucleotides have many applications such as gene identification, PCR (polymerase chain reaction) amplification, or DNA microarrays.
Many software programs have been written to construct good DNA oligonucleotides, such as ProbeSelect [1], PROBESEL [2], ProMide [3], OligoArray [4, 5], ArrayOligoSelector [6], OligoWiz [7], ROSO [8], GoArrays [9], and ProDesign [10]. One crucial issue in designing good oligonucleotides is to minimize the chance of crosshybridization. Unsuitable regions are filtered out before checking for crosshybridization. The underlying algorithms of these software programs are based on one or more of the following tools: suffix trees, suffix arrays, sequence alignments, seeds. Those based on seeds are very good, due to the increased accuracy and efficiency. However, their performance depends heavily on the seeds they use and our main goal here is to find the best seeds for oligonucleotide design.
Seeds were made highly popular by the sequence alignment program BLAST [11], the most widely used software program in bioinformatics. Instead of the quadratic dynamic programming exact algorithm of SmithWaterman [12], which is infeasible for long sequences, BLAST searches for 11 contiguous matches between the sequences as an indicator of potential local similarity. It has been noticed first by [13] that considering nonconsecutive matches produces better results. The first similarity search program to use this idea was PatternHunter [14] where the distribution of the match positions is also optimized. If we denote a match by a 1 and a don't care position by a *, then the default seed of BLAST is 11111111111, representing eleven consecutive matches, whereas the matches of the PatternHunter's seed are spaced: 111*1**1*1**11*111. While the distribution of the matches in this latter seed may seem random, it is not. In fact, it is optimal for this particular case, which means that any other distribution of the matches would be less effective in detecting alignments.
It is intuitively clear that several seeds, with different distribution of the matches, may detect more similarities. This idea has been used in PatternHunter II [15] where 16 seeds are used. The increase in sensitivity (that is, probability of detecting alignments) is impressive. Under similar conditions (see the Methods section), the sensitivity increases from 0.3 for the contiguous seed of BLAST to 0.467 for PatternHunter's seed and then to 0.924 for the multiple seed of PatternHunter II. Multiple spaced seeds quickly became the stateoftheart in similarity search in biological applications. They are used not only by similarity search programs such as PatternHunter [14], PatterHunter II [15], Yass [16] but also by many tools for read alignment of the next generation sequencing data, such as SHRiMP [17], PerM [18], SToRM [19]. However, there seems to be no software for oligonucleotide design that uses multiple spaced seeds. Most of them use BLAST and we found that only ProDesign [10] uses a single transitionconstrained seed.
Our goal is to show that multiple spaced seeds perform the best for the task of oligonucleotide design. We shall describe a sound framework to evaluate the quality of various types of seeds for oligonucleotide search. Two aspects are to be considered: accuracy and efficiency. Accuracy is the ability of a seed to distinguish between regions that are similar with a given one and those that are not. Efficiency concerns the speed of this process.
To the best of our knowledge, there is only one study on this problem, due to Chung and Park [20]. Their conclusion is that "multiple seed selection method is not good at oligo design." This is not only incorrect but also misleading as it argues against what we believe to be the best tool for oligo design. As explained in details in the Methods section, their approach has several problems which invalidate their conclusions. Essentially, their statistical tests are incorrectly defined. In addition, they tested only some weaker variants of the multiple spaced seeds.
We introduce a different approach here and show that the multiple spaced seeds actually provide the best accuracy. The accuracy increases with the number of seeds but this comes at the price of reduced efficiency. It is interesting to notice that spaced seeds are both more accurate and more efficient than contiguous seeds.
Methods
In this section we describe our framework for comparing various types of seeds for oligonucleotide design. We first introduce seeds and describe their working mechanism. We also introduce seed sensitivity and explain the intuitive advantages of multiple seeds.
Seeds
A DNA sequences is seen as a string over the alphabet Σ = {A, C, G, T } whereas a seed is a string over {1, *}; a 1 stands for a match and a * for a don't care position. The number of 1's in a seed is called the weight of that seed and the total number of characters is its length. The i th letter of a string s is denoted by s[i]. A hash of a seed s is obtained by replacing all 1's in s by letters from Σ. If the weight of s is w, then 4 ^{ w } different hashes can be obtained from s. A given hash of s, say h, occurs at position i in a DNA sequence D if aligning h with D starting at position i causes all letters of h to match the corresponding letters of D.
A hit means there is a chance for an actual similarity. The ability of a seed to detect similarities is called sensitivity. A Bernoulli model of sequence alignments has been introduced in [15] in order to formally define sensitivity. An alignment is represented as a binary sequence, R, where a 1 represents a match and a 0 a mismatch between the two sequences. The probability p of a 1 is called similarity. A seed s hits R at a positions i if aligning s with R starting at i causes all 1's in s to match 1's in R. The sensitivity of s is formally defined as the probability that it hits R. It depends on both the length N of the random region R and similarity level p. The sensitivity of the contiguous BLAST seed for N = 64 and p = .7 is 0.3 whereas the sensitivity of the spaced PatternHunter seed, under the same conditions, is considerably higher: 0.467.
Multiple spaced seeds
Multiple spaced seeds are sets of seeds. A multiple spaced seed containing k ≥ 1 seeds will be called a kseed. The definition of a hit is naturally extended to multiple seeds: a multiple seed hits when one of its seeds does so. The sensitivity is therefore defined similarly. A dynamic programming algorithm for computing sensitivity for multiple seeds is given in [15]. Under the same conditions N = 64, p = 0.7, the multiple spaced seed of PatternHunter II, consisting of 16 seeds of weight 11, has sensitivity 0.924, much higher than a single spaced seed. It is therefore natural to consider multiple spaced seeds as the best candidate to oligonucleotide design.
The sensitivity alone is not sufficient to assess the quality of a seed. That is because we can increase the sensitivity as much as we like simply by decreasing the weight. However, that would cause an increase in the number of random hits. We have therefore a trade off: decreasing the weight increases the sensitivity but also the number of random hits whereas increasing the weight decreases both. Weight 11 achieves a good balance and this is why it is used in the above mentioned programs.
More precisely, consider a single seed s of length ℓ and weight w. Consider also a random region R of length N and similarity level p, as done in the definition of sensitivity. The expected number of hits s has in R is (N  ℓ + 1)p^{ w } , since there are N  ℓ + 1 places where s can hit and each has probability p^{ w } . If we increase the weight of the seed by 1, then the expected number of hits becomes essentially a fraction p of the old one. Assuming that the four bases A, C, G, T appear with equal probability, that means that the number of expected hits is reduced to one quarter of the previous one. Less hits means less wasted ones, that is, less false positives and therefore increased specificity. However, increasing the weight of a seed also decreases the true positives, and therefore the sensitivity. In order to increase both, we can increase not only the weight but also the number of seeds. It turns out, as noticed by [15], that simultaneously increasing the weight by one and doubling the number of seeds provides slightly better sensitivity. But doubling the number of seeds only increases the expected number of hits by a factor of two whereas increasing the weight by one reduces it to a quarter. Essentially, this is the main reason why multiple spaced seeds are so good.
One should be aware however, that more memory is required for a higher number of seeds in order to store more hash tables and this enforces an upper bound on the number of seeds that can be used.
Accuracy and Efficiency
The oligo design problem requires the ability to construct oligos that will hybridize only at unique positions in a given sequence. That is, for a given sequence (a potential oligo), we need to be able to accurately distinguish sequences that are similar with it from those that are not. Our setup will therefore include precisely constructed sequences of both types which need to be distinguished.

TP is the number of oligos that are hit,

FP is the number of nonoligos that are hit,

TN is the number of nonoligos that are not hit,

FN is the number of oligos that are not hit.
The precision P and the recall R are defined by , respectively, and the
We shall define the accuracy of a seed as its F score.
Note that, in binary classification, "recall" is called also "sensitivity." To avoid any confusion, we use the term "sensitivity" only with the meaning of "seed sensitivity" as defined in the "Seeds" subsection above.
In our example in Figure 3 for the seed s = 11*1**111, it is easy to verify that the oligos 1, 2, and 3 and the nonoligos 5 and 6 are hit. The oligo 4 and the nonoligos 7 to 10 are not hit. We have TP = 3, FP = 2, FN = 1, and TN = 4. Therefore, P = 0.6, R = 0.75 and F = 0.667.
The approach in [20] uses also the F score as measure of accuracy (called discriminability), however, different definitions for true positives are used for precision and recall, making the F score irrelevant as it involves the precision and recall of different tests.
In our example in Figure 3 the oligo 1 is hit twice, whereas the other hit sequences are hit only once each. The total number of hits is 6, TP = 3 and therefore the efficiency is E = 0.5.
The efficiency of [20] is defined by an arbitrary normalization of the average number of hits per oligo, where the average considers, incorrectly, all oligos, instead of those that are hit (TP). Efficient discriminability, the most significant measure of seed quality in [20], is obtained by multiplying discriminability with efficiency. This measure depends completely on the way normalization of efficiency is done and therefore impossible to interpret. In fact, by changing the normalization, most any seed can be made to appear the best. We shall not mix accuracy and efficiency.
Results and Discussion
We compare in this section various types of seeds using the framework constructed above and then discuss the obtained results.
Data sets

identity level with target sequence: 85%

maximum stretch of continuous matches:15bp

hybridization free energy: 30 kcal/mol
Seeds
Computing optimal spaced seeds is a hard problem; see [15, 22]. For a single seed it is feasible to try all seeds and compute the sensitivity of each to determine the optimal. Therefore, the single seeds we consider, contiguous, transitionconstrained, and spaced, are the same as those of [20]. Transitionconstrained seeds were introduced by [16] and used in their YASS software program. Such a seed contains, in addition to matches and don't cares, a new character, @, which stands for either a match or a transition, that is, a substitution A↔G or C↔T. The biological motivation for this is that transitions are more common than transversions, that is, A/G↔C/T. The seed used in YASS is 1@1**11**1*11@1.
Computing optimal multiple spaced seeds is significantly harder than single seeds. Even computing an optimal 2seed of usable weight and length is infeasible. Therefore, many heuristic algorithms have been designed to compute multiple spaced seeds but they are all exponential, with the exception of SpEED [23](http://www.csd.uwo.ca/~ilie/SpEED), which is based on the notion of overlap complexity of [24]. Chung and Park used two weaker versions of multiple spaced seeds, namely BLAT and vector seeds.
Using SpEED, we have computed highly sensitive multiple spaced seeds with 2, 4, 8, and 16 seeds. The parameters used by SpEED for computing the seeds are derived from those of the oligos. That is, N = 50 for 50mer oligos and N = 70 for 70mer oligos. In both cases, p = 0.85, which is the identity level. (All seeds that we have used are given in the additional file 1.)
Comparison
Highest accuracy values
50mer data sets  70mer data sets  

seed type  max. accuracy  weight  seed type  max. accuracy  weight  
mean  stdev.  mean  stdev.  
contiguous  0.8760  0.0011  10  contiguous  0.8822  0.0003  11 
transition  0.8856  0.0013  12  transition  0.8985  0.0002  13 
1seed  0.8888  0.0017  12  1seed  0.9009  0.0001  13 
2seed  0.9018  0.0019  13  2seed  0.9082  0.0006  14 
4seed  0.9051  0.0014  15  4seed  0.9138  0.0003  16 
8seed  0.9080  0.0018  16  8seed  0.9176  0.0008  17 
16seed  0.9117  0.0013  17  16seed  0.9191  0.0006  19 
A last comment concerns the transition seeds. A single transition seed is slightly less accurate than a single spaced seed. However, this reason is not sufficient to rule out multiple transition seeds. Our analysis focuses on multiple spaced seeds since we were in position to compute very good ones. Multiple transition seeds should be investigated further.
Discussion
As explained earlier, accuracy and efficiency cannot be mixed. Taken separately, they show clearly the ranking. Together, they give the trade off: better accuracy comes with a price in efficiency (except when contiguous seeds are replaced by transitionconstrained or single spaced seeds).
Conclusions
We have presented a sound framework to compare seeds for oligonucleotide design. It is known that multiple spaced seeds perform better than the other seeds in many applications but the requirements of oligo design are different. We have proved that, also in this application, multiple spaced seeds have the highest accuracy. This corrects the conclusion of Chung and Park [20]. We hope that our study will determine researchers in this area to use multiple spaced seeds in software programs for oligonucleotide design. The seeds can be created using the SpEED program that we mentioned before. The assessment of the seeds can be done using a framework as above.
Declarations
Acknowledgements
LI and SI were each supported by a grant from the Natural Sciences and Engineering Research Council of Canada (NSERC).
Authors’ Affiliations
References
 Li F, Stormo G: Selection of optimal DNA oligos for gene expression arrays. Bioinformatics. 2001, 17: 10671076. 10.1093/bioinformatics/17.11.1067.View ArticlePubMedGoogle Scholar
 Kaderali L, Schliep A: Selecting signature oligonucleotides to identify organisms using DNA arrays. Bioinformatics. 2002, 18 (10): 13401349. 10.1093/bioinformatics/18.10.1340.View ArticlePubMedGoogle Scholar
 Rahman S: Fast large scale oligonucleotide selection using the longest common factor approach. Journal of Bioinformatics and Computational Biology. 2003, 1 (2): 343361. 10.1142/S0219720003000125.View ArticleGoogle Scholar
 Rouillard J, Herbert C, Zuker M: OligoArray: genomescale oligonucleotide design for microarrays. Bioinformatics. 2002, 18 (3): 486487. 10.1093/bioinformatics/18.3.486.View ArticlePubMedGoogle Scholar
 Rouillard J, Zuker M, Gulari E: OligoArray 2.0: design of oligonucleotide probes for DNA microarrays using a thermodynamic approach. Nucleic Acids Res. 2003, 31: 30573062. 10.1093/nar/gkg426.View ArticlePubMedPubMed CentralGoogle Scholar
 Bozdech Z, Zhu J, Joachimiak M, Cohen F, Pulliam B, DeRisi J: Expression profiling of the schizont and trophozoite stages of Plasmodium falciparum with a longoligonucleotide microarray. Genome Biol. 2003, 4: R910.1186/gb200342r9.View ArticlePubMedPubMed CentralGoogle Scholar
 Nielsen H, Wernersson R, Knudsen S: Design of oligonucleotides for microarrays and perspectives for design of multitranscriptome arrays. Nucleic Acids Res. 2003, 31: 34913496. 10.1093/nar/gkg622.View ArticlePubMedPubMed CentralGoogle Scholar
 Reymond N, Charles H, Duret L, Calevro F, Beslon G, Fayard J: ROSO: optimizing oligonucleotide probes for microarrays. Bioinformatics. 2004, 20: 271273. 10.1093/bioinformatics/btg401.View ArticlePubMedGoogle Scholar
 Rimour S, Hill D, Militon C, Peyret P: GoArrays: highly dynamic and efficient microarray probe design. Bioinformatics. 2004, 21: 10941103.View ArticlePubMedGoogle Scholar
 Feng S, Tillier E: A fast and flexible approach to oligonucleotide probe design for genomes and gene families. Bioinformatics. 2007, 23 (10): 11951202. 10.1093/bioinformatics/btm114.View ArticlePubMedGoogle Scholar
 Altschul S, Gish W, Miller W, Myers E, Lipman D: Basic local alignment search tool. Journal of Molecular Biology. 1990, 215: 403410.View ArticlePubMedGoogle Scholar
 Smith T, Waterman M: Identification of common molecular subsequences. Nucleic Acids Res. 1981, 147: 3195197.Google Scholar
 Califano A, Rigoutsos I: Flash: a fast lookup algorithm for string homology. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. 1993, IEEE Computer Society, 353359.View ArticleGoogle Scholar
 Ma B, Tromp J, Li M: PatternHunter: faster and more sensitive homology search. Bioinformatics. 2002, 18: 440445. 10.1093/bioinformatics/18.3.440.View ArticlePubMedGoogle Scholar
 Li M, Ma B, Kisman D, Tromp J: PatternHunter II: Highly sensitive and fast homology search. Journal of Bioinformatics and Computational Biology. 2004, 2 (3): 417440. 10.1142/S0219720004000661.View ArticlePubMedGoogle Scholar
 Noe L, Kucherov G: Yass: enhancing the sensitivity of DNA similarity search. Nucleic Acids Research. 2005, 33: 540543. 10.1093/nar/gki478.View ArticleGoogle Scholar
 David M, Dzamba M, Lister D, Ilie L, Brudno M: SHRiMP2: Sensitive yet Practical Short Read Mapping. Bioinformatics. 2011, 27 (7): 10111012. 10.1093/bioinformatics/btr046.View ArticlePubMedGoogle Scholar
 Chen Y, Souaiaia T, Chen T: PerM: efficient mapping of short sequencing reads with periodic full sensitive spaced seeds. Bioinformatics. 2009, 25: 25142521. 10.1093/bioinformatics/btp486.View ArticlePubMedPubMed CentralGoogle Scholar
 Girdea M, Noe L, Kucherov G: Read mapping tool for AB SOLiD data. Proceedings of the 9th International Workshopon Algorithms in Bioinformatics (WABI'09), Philadelphia, Pa, USA. 2009Google Scholar
 Chung WH, Park SB: An empirical study of choosing efficient discriminative seeds for oligonucleotide design. BMC Genomics. 2009, 10 (Suppl 3): S3.View ArticlePubMedPubMed CentralGoogle Scholar
 He Z, Wu L, Li X, Fields M, Zhou J: Empirical establishment of oligonucleotide probe design criteria. Appl Environ Microbiol. 2005, 71 (7): 37533760. 10.1128/AEM.71.7.37533760.2005.View ArticlePubMedPubMed CentralGoogle Scholar
 Ma B, Yao H: Seed optimization for i.i.d. similarities is no easier than optimal Golomb ruler design. Inf Proc Lett. 2009, 109: 11201124. 10.1016/j.ipl.2009.07.008.View ArticleGoogle Scholar
 Ilie L, Ilie S, Mansouri Bigvand A: SpEED: fast computation of sensitive spaced seeds. Bioinformatics. 2011,Google Scholar
 Ilie L, Ilie S: Multiple spaced seeds for homology search. Bioinformatics. 2007, 23 (22): 29692977. 10.1093/bioinformatics/btm422.View ArticlePubMedGoogle Scholar
Copyright
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.