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 (BLAST-like), spaced, transitions-constrained, 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 transitions-constrained 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.

Highlights

  • DNA oligonucleotides are a very useful tool in biology

  • In this way the target sequence can be uniquely identified using the oligonucleotide as a probe

  • Our goal is to show that multiple spaced seeds perform the best for the task of oligonucleotide design

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Summary

Introduction

The best algorithms for designing good DNA oligonucleotides are filtering out unsuitable regions using a seeding approach. One crucial issue in designing good oligonucleotides is to minimize the chance of cross-hybridization. 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. Their performance depends heavily on the seeds they use and our main goal here is to find the best seeds for oligonucleotide design

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