Linked Life Data

12603015

Source:http://linkedlifedata.com/resource/pubmed/id/12603015

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:dateCreated
2003-2-26
pubmed:abstractText
Identifying prokaryotic promoter sequences is notoriously difficult and for most sequenced bacterial genomes the promoter sequences are still unknown. Since experimental analysis trails behind sequencing, genome-wide computational promoter discovery is often the only realistic way to discover these sequences in newly sequenced bacterial genomes. However, genome-wide samples for promoter discovery may be very large and corrupted complicating promoter discovery. We discuss three aspects of genome-wide promoter discovery: sample generation, signal finding algorithms, and scoring signals. We applied our new MITRA algorithm to analyze samples of divergent and convergent genes in 20 bacterial genomes and found strong putative dyad signals in 17 out of the 20 genomes. Moreover, in 12 out of 20 genomes the found signals are identical or similar to the known regulatory patterns (Pribnow-Gilbert boxes and CRP binding sites). Since many of putative signals correspond to previously known elements of bacterial transcriptional regulation, the remaining discovered signals are good candidates for unknown regulatory elements.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:issn
1793-5091
pubmed:author
pubmed:issnType
Print
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
29-40
pubmed:dateRevised
2008-11-21
pubmed:meshHeading
pubmed:year
2003
pubmed:articleTitle
Genome-wide analysis of bacterial promoter regions.
pubmed:affiliation
Department of Computer Science, Columbia University, New York, NY 10027, USA. eeskin@cs.columbia.edu
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't