Linked Life Data

9847079

Source:http://linkedlifedata.com/resource/pubmed/id/9847079

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
11
pubmed:dateCreated
1999-1-20
pubmed:abstractText
In this report we address the problem of accurate statistical modeling of DNA sequences, either coding or noncoding, for a bacterial species whose genome (or a large portion) was sequenced but not yet characterized experimentally. Availability of these models is critical for successful solution of the genome annotation task by statistical methods of gene finding. We present the method, GeneMark-Genesis, which learns the parameters of Markov models of protein-coding and noncoding regions from anonymous bacterial genomic sequence. These models are subsequently used in the GeneMark and GeneMark.hmm gene-finding programs. Although there is basically one model of a noncoding region for a given genome, several models of protein-coding region are automatically obtained by GeneMark-Genesis. The diversity of protein-coding models reflects the diversity of oligonucleotide compositions, particularly the diversity of codon usage strategies observed in genes from one and the same genome. In the simplest and the most important case, there are just two gene models-typical and atypical ones. We show that the atypical model allows one to predict genes that escape identification by the typical model. Many genes predicted by the atypical model appear to be horizontally transferred genes. The early versions of GeneMark-Genesis were used for annotating the genomes of Methanoccocus jannaschii and Helicobacter pylori. We report the results of accuracy testing of the full-scale version of GeneMark-Genesis on 10 completely sequenced bacterial genomes. Interestingly, the GeneMark.hmm program that employed the typical and atypical models defined by GeneMark-Genesis was able to predict 683 new atypical genes with 176 of them confirmed by similarity search.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
Nov
pubmed:issn
1088-9051
pubmed:author
pubmed:issnType
Print
pubmed:volume
8
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
1154-71
pubmed:dateRevised
2006-11-15
pubmed:meshHeading
pubmed:year
1998
pubmed:articleTitle
How to interpret an anonymous bacterial genome: machine learning approach to gene identification.
pubmed:affiliation
School of Biology, Georgia Institute of Technology, Atlanta, Georgia 30332-0230, USA.
pubmed:publicationType
Journal Article, Research Support, U.S. Gov't, P.H.S.