Feature recognition on expressed sequence tags of human DNA

Author

Hatzigeorgiou, Artemis G. ; Reczko, Martin

Author_Institution

Synaptic Ltd., Heraklion, Greece

Volume

5

fYear

1999

fDate

1999

Firstpage

3490

Abstract

Expressed sequence tags (EST) are small parts of DNA, which are used to clone new genes. One main characteristic of EST is that they contain more than 1% sequencing errors. What we need to know is which parts of the EST contain information about proteins, the so called coding regions. In this paper we describe an error-tolerant program for the prediction of such coding regions in EST. The program is based on a combination of statistical methods and several artificial neural networks (ANN). 89.7% of the nucleotides of a independent test set with 127 EST´s are predicted correctly as to whether they are coding or noncoding. These results are independent of the existence of homologous gene or protein sequences and representative for the application to the largest part of all EST

Keywords

DNA; biology computing; feature extraction; neural nets; pattern recognition; statistical analysis; ANN; EST; artificial neural networks; coding regions; error-tolerant program; expressed sequence tags; feature recognition; homologous gene sequences; human DNA; independent test set; nucleotides; protein sequences; proteins; sequencing errors; statistical methods; Bioinformatics; Cloning; DNA; Error correction; Genomics; Humans; Polymers; Proteins; Sequences; Technical Activities Guide -TAG;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 1999. IJCNN '99. International Joint Conference on

Conference_Location

Washington, DC

ISSN

1098-7576

Print_ISBN

0-7803-5529-6

Type

conf

DOI

10.1109/IJCNN.1999.836228

Filename

836228