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Open Access Highly Accessed Methodology article

A parallel and incremental algorithm for efficient unique signature discovery on DNA databases

Hsiao Ping Lee123, Tzu-Fang Sheu4* and Chuan Yi Tang3

Author Affiliations

1 Department of Applied Information Sciences, Chung Shan Medical University, Taichung, 40201 Taiwan, ROC

2 Department of Medical Research, Chung Shan Medical University Hospital, Taichung, 40201 Taiwan, ROC

3 Department of Computer Science, National Tsing Hua University, Hsinchu, Taiwan, ROC

4 Department of Computer Science and Communication Engineering, Providence University, Taichung, 43301 Taiwan, ROC

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BMC Bioinformatics 2010, 11:132  doi:10.1186/1471-2105-11-132

Published: 16 March 2010



DNA signatures are distinct short nucleotide sequences that provide valuable information that is used for various purposes, such as the design of Polymerase Chain Reaction primers and microarray experiments. Biologists usually use a discovery algorithm to find unique signatures from DNA databases, and then apply the signatures to microarray experiments. Such discovery algorithms require to set some input factors, such as signature length l and mismatch tolerance d, which affect the discovery results. However, suggestions about how to select proper factor values are rare, especially when an unfamiliar DNA database is used. In most cases, biologists typically select factor values based on experience, or even by guessing. If the discovered result is unsatisfactory, biologists change the input factors of the algorithm to obtain a new result. This process is repeated until a proper result is obtained. Implicit signatures under the discovery condition (l, d) are defined as the signatures of length ≤ l with mismatch tolerance ≥ d. A discovery algorithm that could discover all implicit signatures, such that those that meet the requirements concerning the results, would be more helpful than one that depends on trial and error. However, existing discovery algorithms do not address the need to discover all implicit signatures.


This work proposes two discovery algorithms - the consecutive multiple discovery (CMD) algorithm and the parallel and incremental signature discovery (PISD) algorithm. The PISD algorithm is designed for efficiently discovering signatures under a certain discovery condition. The algorithm finds new results by using previously discovered results as candidates, rather than by using the whole database. The PISD algorithm further increases discovery efficiency by applying parallel computing. The CMD algorithm is designed to discover implicit signatures efficiently. It uses the PISD algorithm as a kernel routine to discover implicit signatures efficiently under every feasible discovery condition.


The proposed algorithms discover implicit signatures efficiently. The presented CMD algorithm has up to 97% less execution time than typical sequential discovery algorithms in the discovery of implicit signatures in experiments, when eight processing cores are used.