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

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.

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 seed’s 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.

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.

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

Seeding methods (1/2) Continuous seed: a seed to find k-length exact matches – BLAST employs 11-bp length seed Spaced seed: allowing don’t care letter labeled ‘0’ in the seed – 18-bp-length seed containing 11-bp matches is used at PatternHunter. Transition-constrained seed: adopting transition (A G, C T) letter in the seed – YASS used such seed it consists of 18-bp length, 10-bp matches and 2 transitions.

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.

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 The discriminability is a balance between precision and recall to minimize both false positives and false negatives. jumpalpha

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

Efficient discriminability The efficient discriminative seed is the seed that has the maximum efficient discriminability value for the given

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

Discriminability of the five seeding methods

Efficiency of the five seeding methods

Efficient Discriminability the five seeding methods

Evaluation results of pmoA data set

Evaluation results of nirS data set

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

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.

Supplementary materials

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. T1 T2 T3 P1 P2 P0 S1 S2 S3 S0 T0 back

Relations of precision, recall and discriminability

Discriminability according to values of α back

Efficiency according to values of β and γ back

Efficient Discriminability for 70mer Oligos