Combining microarray and genomic data to predict DNA binding motifs

• DOI: 10.1099/mic.0.28167-0
• Publication Date: 2005-10-05T18:08:00Z
• Authors: Linyong Mao; Chris Mackenzie; Jung H. Roh; Jesus M. Eraso; Samuel Kaplan; Haluk Resat
• Keywords: 0303 health sciences; Base Sequence; Gene Expression Profiling; Molecular Sequence Data; Computational Biology; Gene Expression Regulation, Bacterial; Rhodobacter sphaeroides; Sequence Analysis, DNA; DNA-Binding Proteins; Repressor Proteins; 03 medical and health sciences; Enhancer Elements, Genetic; Bacterial Proteins; Consensus Sequence; Trans-Activators; Photosynthesis; Genome, Bacterial; Oligonucleotide Array Sequence Analysis

The ability to detect regulatory elements within genome sequences is important in understanding how gene expression controlled biological systems. In this work, microarray data analysis combined with sequence predict DNA the photosynthetic bacterium Rhodobacter sphaeroides that bind regulators PrrA, PpsR and FnrL. These predictions were made by using hierarchical clustering genes share similar patterns. upstream of these then searched for possible transcription factor recognition motifs may be involved their co-regulation. approach used promises widely applicable prediction cis -acting binding elements. Using method authors independently able extend previously described consensus have been suggested FnrL PpsR. addition, recognized global regulator PrrA predicted. results support earlier suggestions a variable-sized gap between its conserved block predicted sequences, whole-genome-scale was performed determine relative importance interplay three PpsR, PrrA. Results showed that, compared regulation FnrL, much larger number are candidates regulated study demonstrates example integration multiple types can powerful inferring transcriptional patterns microbial systems, it allowed detection photosynthesis-related R. .