Efficient discovery of loosely structured motifs in biological data

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Efficient discovery of loosely structured motifs in biological data

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Symposium on Applied Computing archive
Proceedings of the 2006 ACM symposium on Applied computing table of contents

Dijon, France

SESSION: Bioinformatics (BIO) table of contents

Pages: 151 - 155

Year of Publication: 2006

ISBN:1-59593-108-2

Authors

Fabio Fassetti	Università della Calabria, Rende - Italy
Gianluigi Greco	Università della Calabria, Rende - Italy
Giorgio Terracina	Università della Calabria, Rende - Italy

Sponsor

SIGAPP: ACM Special Interest Group on Applied Computing

Publisher

ACM New York, NY, USA

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ABSTRACT

In the last few years, the completion of the human genome sequencing showed up a wide range of new challenging issues involving raw data analysis. In particular, the discovery of information implicitly encoded in biological sequences is assuming a prominent role in identifying genetic diseases and in deciphering biological mechanisms. This information is usually represented by patterns frequently occurring in the sequences, also called motifs. Because of biological observations, the class of structured motifs have received much attention. This paper gives a contribution in this setting by providing an efficient algorithm for the identification of novel classes of structured motifs, where several kinds of "exceptions" (whose biological relevance recently emerged in the literature) may be tolerated in pattern repetitions.

REFERENCES

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