Linked Life Data

16207904

Source:http://linkedlifedata.com/resource/pubmed/id/16207904

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
Pt 10
pubmed:dateCreated
2005-10-6
pubmed:abstractText
The ability to detect regulatory elements within genome sequences is important in understanding how gene expression is controlled in biological systems. In this work, microarray data analysis is combined with genome sequence analysis to predict DNA sequences in the photosynthetic bacterium Rhodobacter sphaeroides that bind the regulators PrrA, PpsR and FnrL. These predictions were made by using hierarchical clustering to detect genes that share similar expression patterns. The DNA sequences upstream of these genes were then searched for possible transcription factor recognition motifs that may be involved in their co-regulation. The approach used promises to be widely applicable for the prediction of cis-acting DNA binding elements. Using this method the authors were independently able to detect and extend the previously described consensus sequences that have been suggested to bind FnrL and PpsR. In addition, sequences that may be recognized by the global regulator PrrA were predicted. The results support the earlier suggestions that the DNA binding sequence of PrrA may have a variable-sized gap between its conserved block elements. Using the predicted DNA binding sequences, a whole-genome-scale analysis was performed to determine the relative importance of the interplay between the three regulators PpsR, FnrL and PrrA. Results of this analysis showed that, compared to the regulation by PpsR and FnrL, a much larger number of genes are candidates to be regulated by PrrA. The study demonstrates by example that integration of multiple data types can be a powerful approach for inferring transcriptional regulatory patterns in microbial systems, and it allowed the detection of photosynthesis-related regulatory patterns in R. sphaeroides.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
Oct
pubmed:issn
1350-0872
pubmed:author
pubmed:issnType
Print
pubmed:volume
151
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
3197-213
pubmed:dateRevised
2009-7-17
pubmed:meshHeading
pubmed-meshheading:16207904-Bacterial Proteins, pubmed-meshheading:16207904-Base Sequence, pubmed-meshheading:16207904-Computational Biology, pubmed-meshheading:16207904-Consensus Sequence, pubmed-meshheading:16207904-DNA-Binding Proteins, pubmed-meshheading:16207904-Enhancer Elements, Genetic, pubmed-meshheading:16207904-Gene Expression Profiling, pubmed-meshheading:16207904-Gene Expression Regulation, Bacterial, pubmed-meshheading:16207904-Genome, Bacterial, pubmed-meshheading:16207904-Molecular Sequence Data, pubmed-meshheading:16207904-Oligonucleotide Array Sequence Analysis, pubmed-meshheading:16207904-Photosynthesis, pubmed-meshheading:16207904-Repressor Proteins, pubmed-meshheading:16207904-Rhodobacter sphaeroides, pubmed-meshheading:16207904-Sequence Analysis, DNA, pubmed-meshheading:16207904-Trans-Activators
pubmed:year
2005
pubmed:articleTitle
Combining microarray and genomic data to predict DNA binding motifs.
pubmed:affiliation
Pacific Northwest National Laboratory, Computational Biology and Bioinformatics Group, PO Box 999, MS: K7-90, Richland, WA 99352, USA.
pubmed:publicationType
Journal Article, Research Support, U.S. Gov't, Non-P.H.S., Evaluation Studies