Dictionary-driven prokaryotic gene finding.
Publication/Presentation Date
6-15-2002
Abstract
Gene identification, also known as gene finding or gene recognition, is among the important problems of molecular biology that have been receiving increasing attention with the advent of large scale sequencing projects. Previous strategies for solving this problem can be categorized into essentially two schools of thought: one school employs sequence composition statistics, whereas the other relies on database similarity searches. In this paper, we propose a new gene identification scheme that combines the best characteristics from each of these two schools. In particular, our method determines gene candidates among the ORFs that can be identified in a given DNA strand through the use of the Bio-Dictionary, a database of patterns that covers essentially all of the currently available sample of the natural protein sequence space. Our approach relies entirely on the use of redundant patterns as the agents on which the presence or absence of genes is predicated and does not employ any additional evidence, e.g. ribosome-binding site signals. The Bio-Dictionary Gene Finder (BDGF), the algorithm's implementation, is a single computational engine able to handle the gene identification task across distinct archaeal and bacterial genomes. The engine exhibits performance that is characterized by simultaneous very high values of sensitivity and specificity, and a high percentage of correctly predicted start sites. Using a collection of patterns derived from an old (June 2000) release of the Swiss-Prot/TrEMBL database that contained 451 602 proteins and fragments, we demonstrate our method's generality and capabilities through an extensive analysis of 17 complete archaeal and bacterial genomes. Examples of previously unreported genes are also shown and discussed in detail.
Volume
30
Issue
12
First Page
2710
Last Page
2725
ISSN
1362-4962
Published In/Presented At
Shibuya, T., & Rigoutsos, I. (2002). Dictionary-driven prokaryotic gene finding. Nucleic acids research, 30(12), 2710–2725. https://doi.org/10.1093/nar/gkf338
Disciplines
Business Administration, Management, and Operations | Health and Medical Administration | Management Sciences and Quantitative Methods
PubMedID
12060689
Department(s)
Administration and Leadership
Document Type
Article