DIANA-EST: a statistical analysis

Motivation: Expressed Sequence Tags (ESTs) are next to cDNA sequences as the most direct way to locate in silico the genes of the genome and determine their structure. Currently ESTs make up more than 60% of all the database entries. The goal of this work is the development of a new program called D...

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Bibliographic Details
Published in:Bioinformatics 2001-10, Vol.17 (10), p.913-919
Main Authors: Hatzigeorgiou, Artemis G., Fiziev, Petko, Reczko, Martin
Format: Article
Language:English
Subjects:
Biological and medical sciences
Computational Biology
Consensus Sequence
DNA, Complementary - genetics
Expressed Sequence Tags
Fundamental and applied biological sciences. Psychology
General aspects
Genetics
Mathematics in biology. Statistical analysis. Models. Metrology. Data processing in biology (general aspects)
Neural Networks (Computer)
Sequence Analysis, DNA - statistics & numerical data
Software
Statistical analysis
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Summary:Motivation: Expressed Sequence Tags (ESTs) are next to cDNA sequences as the most direct way to locate in silico the genes of the genome and determine their structure. Currently ESTs make up more than 60% of all the database entries. The goal of this work is the development of a new program called DNA Intelligent Analysis for ESTs (DIANA-EST) based on a combination of Artificial Neural Networks (ANN) and statistics for the characterization of the coding regions within ESTs and the reconstruction of the encoded protein. Results: 89.7% of the nucleotides from an independent test set with 127 ESTs were predicted correctly as to whether they are coding or non coding. Availability: The program is available upon request from the author. Contact: Present address: Department of Genetics, University of Pennsylvania, School of Medicine, 475 Clinical Research Building, 415 Curie Boulevard, Philadelphia, PA 19104-6145, USA. artemis@pcbi.upenn.edu.
ISSN:1367-4803
1460-2059
1367-4811
DOI:10.1093/bioinformatics/17.10.913