Abstract
Expressed Sequence Tags (EST) are widely used for the discovery of new genes, particularly those involved in human disease processes. A subsequence in an EST dataset is unique if it appears only in one EST sequence of the dataset but does not appear in any other EST sequence. The unique subsequences can be regarded as signatures that distinguish an EST from all the others, and provide valuable information for many applications, such as PCR primer designs and microarray experiments. The discoveries of unique signatures on large-scale EST datasets are previously computational challenges. In this paper, we propose two efficient algorithms to extract the unique signatures from EST databases. The algorithms perform impressive discovery efficiencies in the experiments on real human ESTs.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
