- PAIDB v2.0: exploration and analysis of pathogenicity and resistance islands. [PMID: 25336619]
Sung Ho Yoon, Young-Kyu Park, Jihyun F Kim
Nucleic acids research 2015:43(Database issue)
2 Citations (Google Scholar as of 2016-03-24)
Abstract: Pathogenicity is a complex multifactorial process confounded by the concerted activity of genetic regions associated with virulence and/or resistance determinants. Pathogenicity islands (PAIs) and resistance islands (REIs) are key to the evolution of pathogens and appear to play complimentary roles in the process of bacterial infection. While PAIs promote disease development, REIs give a fitness advantage to the host against multiple antimicrobial agents. The Pathogenicity Island Database (PAIDB, http://www.paidb.re.kr) has been the only database dedicated to providing comprehensive information on all reported PAIs and candidate PAIs in prokaryotic genomes. In this study, we present PAIDB v2.0, whose functionality is extended to incorporate REIs. PAIDB v2.0 contains 223 types of PAIs with 1331 accessions, and 88 types of REIs with 108 accessions. With an improved detection scheme, 2673 prokaryotic genomes were analyzed to locate candidate PAIs and REIs. With additional quantitative and qualitative advancements in database content and detection accuracy, PAIDB will continue to facilitate pathogenomic studies of both pathogenic and non-pathogenic organisms. © The Author(s) 2014. Published by Oxford University Press on behalf of Nucleic Acids Research.
- Towards pathogenomics: a web-based resource for pathogenicity islands. [PMID: 17090594]
Sung Ho Yoon, Young-Kyu Park, Soohyun Lee, Doil Choi, Tae Kwang Oh, Cheol-Goo Hur, Jihyun F Kim
Nucleic acids research 2007:35(Database issue)
55 Citations (Google Scholar as of 2016-01-28)
Abstract: Pathogenicity islands (PAIs) are genetic elements whose products are essential to the process of disease development. They have been horizontally (laterally) transferred from other microbes and are important in evolution of pathogenesis. In this study, a comprehensive database and search engines specialized for PAIs were established. The pathogenicity island database (PAIDB) is a comprehensive relational database of all the reported PAIs and potential PAI regions which were predicted by a method that combines feature-based analysis and similarity-based analysis. Also, using the PAI Finder search application, a multi-sequence query can be analyzed onsite for the presence of potential PAIs. As of April 2006, PAIDB contains 112 types of PAIs and 889 GenBank accessions containing either partial or all PAI loci previously reported in the literature, which are present in 497 strains of pathogenic bacteria. The database also offers 310 candidate PAIs predicted from 118 sequenced prokaryotic genomes. With the increasing number of prokaryotic genomes without functional inference and sequenced genetic regions of suspected involvement in diseases, this web-based, user-friendly resource has the potential to be of significant use in pathogenomics. PAIDB is freely accessible at http://www.gem.re.kr/paidb.
- A computational approach for identifying pathogenicity islands in prokaryotic genomes. [PMID: 16033657]
Sung Ho Yoon, Cheol-Goo Hur, Ho-Young Kang, Yeoun Hee Kim, Tae Kwang Oh, Jihyun F Kim
BMC bioinformatics 2005:6
53 Citations (Google Scholar as of 2016-01-13)
Abstract: 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. 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. 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.