A computational approach for identifying pathogenicity islands in prokaryotic genomes

Cited 54 time in scopus
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Title
A computational approach for identifying pathogenicity islands in prokaryotic genomes
Author(s)
Sung Ho Yoon; Cheol-Goo Hur; Hoyoung Ghang; Yeoun Hee Kim; Tae Kwang Oh; Jihyun Kim
Bibliographic Citation
BMC Bioinformatics, vol. 6, pp. 184-184
Publication Year
2005
Abstract
Background: Pathogenicity islands (PAIs), distinct genomic segments of pathogens encoding virulence factors, represent a subgroup of genomic islands (GIs) that have been acquired by horizontal gene transfer event. Up to now, computational approaches for identifying PAIs have been focused on the detection of genomic regions which only differ from the rest of the genome in their base composition and codon usage. These approaches often lead to the identification of genomic islands, rather than PAIs. Results: We present a computational method for detecting potential PAIs in complete prokaryotic genomes by combining sequence similarities and abnormalities in genomic composition. We first collected 207 GenBank accessions containing either part or all of the reported PAI loci. In sequenced genomes, strips of PAI-homologs were defined based on the proximity of the homologs of genes in the same PAI accession. An algorithm reminiscent of sequence-assembly procedure was then devised to merge overlapping or adjacent genomic strips into a large genomic region. Among the defined genomic regions, PAI-like regions were identified by the presence of homolog(s) of virulence genes. Also, GIs were postulated by calculating G+C content anomalies and codon usage bias. of 148 prokaryotic genomes examined, 23 pathogenic and 6 non-pathogenic bacteria contained 77 candidate PAIs that partly or entirely overlap GIs. Conclusion: Supporting the validity of our method, included in the list of candidate PAIs were thirty four PAIs previously identified from genome sequencing papers. Furthermore, in some instances, our method was able to detect entire PAIs for those only partial sequences are available. Our method was proven to be an efficient method for demarcating the potential PAIs in our study. Also, the function(s) and origin(s) of a candidate PAI can be inferred by investigating the PAI queries comprising it. Identification and analysis of potential PAIs in prokaryotic genomes will broaden our knowledge on the structure and properties of PAIs and the evolution of bacterial pathogenesis.
ISSN
1471-2105
Publisher
Springer-BMC
DOI
http://dx.doi.org/10.1186/1471-2105-6-184
Type
Article
Appears in Collections:
Ochang Branch Institute > Division of National Bio-Infrastructure > Laboratory Animal Resource & Research Center > 1. Journal Articles
Division of Biomedical Research > Metabolic Regulation Research Center > 1. Journal Articles
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