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Efficient Selection of Unique and Popular Oligos for Large EST Databases

  • Jie Zheng
  • Timothy J. Close
  • Tao Jiang
  • Stefano Lonardi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2676)

Abstract

EST databases have grown exponentially in recent years and now represent the largest collection of genetic sequences. An important application of these databases is that they contain information useful for the design of gene-specific oligonucleotides (or simply, oligos) that can be used in PCR primer design, microarray experiments, and genomic library screening. In this paper, we study two complementary problems concerning the selection of short oligos, e.g., 20–50 bases, from a large database of tens of thousands of EST sequences: (i) selection of oligos each of which appears (exactly) in one EST sequence but does not appear (exactly or approximately) in any other EST sequence and (ii) selection of oligos that appear (exactly or approximately) in many ESTs. The first problem is called the unique oligo problem and has applications in PCR primer and microarray probe designs. The second is called the popular oligo problem and is useful in screening genomic libraries (such as BAC libraries) for gene-rich regions. We present an efficient algorithm to identify all unique oligos in the ESTs and an efficient heuristic algorithm to enumerate the most popular oligos. By taking into account the distribution of the frequencies of the words in the EST database, the algorithms have been carefully engineered to achieve remarkable running times on regular PCs. Each of the algorithms takes only a couple of hours (on a 1.2 GHz CPU, 1 GB RAM machine) to run on a dataset 28 Mbases of barley ESTs from the HarvEST database. We present simulation results on synthetic data and a preliminary analysis of the barley EST database.

Keywords

Hash Table Table Entry UPGMA Tree Computational Molecular Biology Unique Oligos 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Jie Zheng
    • 1
  • Timothy J. Close
    • 2
  • Tao Jiang
    • 1
  • Stefano Lonardi
    • 1
  1. 1.Dept. of Computer Science & EngineeringUniversity of CaliforniaRiverside
  2. 2.Department of Botany & Plant SciencesUniversity of CaliforniaRiverside

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