tetano
Editor, Senior Moderator
Mol Cell Probes. 2010 Aug 23. [Epub ahead of print]
An integrated bioinformatics approach to the characterization of influenza A/H5N1 viral sequences by microarray data: Implication for monitoring H5N1 emerging strains and designing appropriate influenza vaccines.
Kaewpongsri S, Sukasem C, Srichunrusami C, Pasomsub E, Zwang J, Piroj W, Chantratita W.
Virology and Molecular Microbiology Unit, Department of Pathology, Faculty of Medicine, Ramathibodi Hospital, Mahidol University, Bangkok 10400, Thailand; Shoklo Malaria Research Unit, Maesot, Tak, Thailand.
Abstract
In order to characterize A/H5N1 viral sequences, a bioinformatics approach accurately identified viral sequences from discovery of a sequence signature, which provided enough distinctive information for sequence identification. Eight highly pathogenic H5N1 viral isolations were collected from different areas of Thailand between 2003 and 2006, and were used for analysis of H5N1 genotypic testing with a semiconductor-based oligonucleotide microarray. All H5N1 samples and H1N1, H4N8 negative controls were correctly subtyped. Sensitivity of the eight oligonucleotide probes, with optimized cut-offs, ranged from 70% (95% CI 65-75) to 87% (95% CI 84-91), and the corresponding Kappa values ranged from 0.76 (95% CI 0.72-0.80) to 0.86 (95% CI 0.83-0.89). Semi-conductor-based oligonucleotide array and oligonucleotide probes corresponded well when detecting H5N1. After fully correcting the subtype from the result of microarray signal intensity, the microarray output method combined with bioinformatics tools, identified and monitored genetic variations of H5N1. Capability of distinguishing different strains of H5N1 from Thailand was the outstanding feature of this assay. Ninety percent of HA and NA (4/5) genes were sequenced correctly, in accordance with previous examinations performed by classical diagnostic methods. The low-medium-high bioinformatics resolutions were able to predict an epidemic strain of H5N1. This study also showed the advantage of using a large genotypic database to predict the epidemic strain of H5N1. However, the monitoring protocol of this new strain has been recommended for further study with a large-scale sample.
PMID: 20797431 [PubMed - as supplied by publisher]
http://www.ncbi.nlm.nih.gov/pubmed/20797431
An integrated bioinformatics approach to the characterization of influenza A/H5N1 viral sequences by microarray data: Implication for monitoring H5N1 emerging strains and designing appropriate influenza vaccines.
Kaewpongsri S, Sukasem C, Srichunrusami C, Pasomsub E, Zwang J, Piroj W, Chantratita W.
Virology and Molecular Microbiology Unit, Department of Pathology, Faculty of Medicine, Ramathibodi Hospital, Mahidol University, Bangkok 10400, Thailand; Shoklo Malaria Research Unit, Maesot, Tak, Thailand.
Abstract
In order to characterize A/H5N1 viral sequences, a bioinformatics approach accurately identified viral sequences from discovery of a sequence signature, which provided enough distinctive information for sequence identification. Eight highly pathogenic H5N1 viral isolations were collected from different areas of Thailand between 2003 and 2006, and were used for analysis of H5N1 genotypic testing with a semiconductor-based oligonucleotide microarray. All H5N1 samples and H1N1, H4N8 negative controls were correctly subtyped. Sensitivity of the eight oligonucleotide probes, with optimized cut-offs, ranged from 70% (95% CI 65-75) to 87% (95% CI 84-91), and the corresponding Kappa values ranged from 0.76 (95% CI 0.72-0.80) to 0.86 (95% CI 0.83-0.89). Semi-conductor-based oligonucleotide array and oligonucleotide probes corresponded well when detecting H5N1. After fully correcting the subtype from the result of microarray signal intensity, the microarray output method combined with bioinformatics tools, identified and monitored genetic variations of H5N1. Capability of distinguishing different strains of H5N1 from Thailand was the outstanding feature of this assay. Ninety percent of HA and NA (4/5) genes were sequenced correctly, in accordance with previous examinations performed by classical diagnostic methods. The low-medium-high bioinformatics resolutions were able to predict an epidemic strain of H5N1. This study also showed the advantage of using a large genotypic database to predict the epidemic strain of H5N1. However, the monitoring protocol of this new strain has been recommended for further study with a large-scale sample.
PMID: 20797431 [PubMed - as supplied by publisher]
http://www.ncbi.nlm.nih.gov/pubmed/20797431