Novel bioinformatics strategies for prediction of directional sequence changes in influenza virus genomes and for surveillance of potentially hazardous strains
BACKGROUND: With the remarkable increase of microbial and viral sequence data obtained from high-throughput DNA sequencers, novel tools are needed for comprehensive analysis of the big sequence data. We have developed “Batch-Learning Self-Organizing Map (BLSOM)” which can characterize very many, eve...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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BioMed Central
2013
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3765179/ https://www.ncbi.nlm.nih.gov/pubmed/23964903 http://dx.doi.org/10.1186/1471-2334-13-386 |
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author | Iwasaki, Yuki Abe, Takashi Wada, Yoshiko Wada, Kennosuke Ikemura, Toshimichi |
author_facet | Iwasaki, Yuki Abe, Takashi Wada, Yoshiko Wada, Kennosuke Ikemura, Toshimichi |
author_sort | Iwasaki, Yuki |
collection | PubMed |
description | BACKGROUND: With the remarkable increase of microbial and viral sequence data obtained from high-throughput DNA sequencers, novel tools are needed for comprehensive analysis of the big sequence data. We have developed “Batch-Learning Self-Organizing Map (BLSOM)” which can characterize very many, even millions of, genomic sequences on one plane. Influenza virus is one of zoonotic viruses and shows clear host tropism. Important issues for bioinformatics studies of influenza viruses are prediction of genomic sequence changes in the near future and surveillance of potentially hazardous strains. METHODS: To characterize sequence changes in influenza virus genomes after invasion into humans from other animal hosts, we applied BLSOMs to analyses of mono-, di-, tri-, and tetranucleotide compositions in all genome sequences of influenza A and B viruses and found clear host-dependent clustering (self-organization) of the sequences. RESULTS: Viruses isolated from humans and birds differed in mononucleotide composition from each other. In addition, host-dependent oligonucleotide compositions that could not be explained with the host-dependent mononucleotide composition were revealed by oligonucleotide BLSOMs. Retrospective time-dependent directional changes of mono- and oligonucleotide compositions, which were visualized for human strains on BLSOMs, could provide predictive information about sequence changes in newly invaded viruses from other animal hosts (e.g. the swine-derived pandemic H1N1/09). CONCLUSIONS: Basing on the host-dependent oligonucleotide composition, we proposed a strategy for prediction of directional changes of virus sequences and for surveillance of potentially hazardous strains when introduced into human populations from non-human sources. Millions of genomic sequences from infectious microbes and viruses have become available because of their medical and social importance, and BLSOM can characterize the big data and support efficient knowledge discovery. |
format | Online Article Text |
id | pubmed-3765179 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2013 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-37651792013-09-07 Novel bioinformatics strategies for prediction of directional sequence changes in influenza virus genomes and for surveillance of potentially hazardous strains Iwasaki, Yuki Abe, Takashi Wada, Yoshiko Wada, Kennosuke Ikemura, Toshimichi BMC Infect Dis Research Article BACKGROUND: With the remarkable increase of microbial and viral sequence data obtained from high-throughput DNA sequencers, novel tools are needed for comprehensive analysis of the big sequence data. We have developed “Batch-Learning Self-Organizing Map (BLSOM)” which can characterize very many, even millions of, genomic sequences on one plane. Influenza virus is one of zoonotic viruses and shows clear host tropism. Important issues for bioinformatics studies of influenza viruses are prediction of genomic sequence changes in the near future and surveillance of potentially hazardous strains. METHODS: To characterize sequence changes in influenza virus genomes after invasion into humans from other animal hosts, we applied BLSOMs to analyses of mono-, di-, tri-, and tetranucleotide compositions in all genome sequences of influenza A and B viruses and found clear host-dependent clustering (self-organization) of the sequences. RESULTS: Viruses isolated from humans and birds differed in mononucleotide composition from each other. In addition, host-dependent oligonucleotide compositions that could not be explained with the host-dependent mononucleotide composition were revealed by oligonucleotide BLSOMs. Retrospective time-dependent directional changes of mono- and oligonucleotide compositions, which were visualized for human strains on BLSOMs, could provide predictive information about sequence changes in newly invaded viruses from other animal hosts (e.g. the swine-derived pandemic H1N1/09). CONCLUSIONS: Basing on the host-dependent oligonucleotide composition, we proposed a strategy for prediction of directional changes of virus sequences and for surveillance of potentially hazardous strains when introduced into human populations from non-human sources. Millions of genomic sequences from infectious microbes and viruses have become available because of their medical and social importance, and BLSOM can characterize the big data and support efficient knowledge discovery. BioMed Central 2013-08-21 /pmc/articles/PMC3765179/ /pubmed/23964903 http://dx.doi.org/10.1186/1471-2334-13-386 Text en Copyright © 2013 Iwasaki et al.; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Iwasaki, Yuki Abe, Takashi Wada, Yoshiko Wada, Kennosuke Ikemura, Toshimichi Novel bioinformatics strategies for prediction of directional sequence changes in influenza virus genomes and for surveillance of potentially hazardous strains |
title | Novel bioinformatics strategies for prediction of directional sequence changes in influenza virus genomes and for surveillance of potentially hazardous strains |
title_full | Novel bioinformatics strategies for prediction of directional sequence changes in influenza virus genomes and for surveillance of potentially hazardous strains |
title_fullStr | Novel bioinformatics strategies for prediction of directional sequence changes in influenza virus genomes and for surveillance of potentially hazardous strains |
title_full_unstemmed | Novel bioinformatics strategies for prediction of directional sequence changes in influenza virus genomes and for surveillance of potentially hazardous strains |
title_short | Novel bioinformatics strategies for prediction of directional sequence changes in influenza virus genomes and for surveillance of potentially hazardous strains |
title_sort | novel bioinformatics strategies for prediction of directional sequence changes in influenza virus genomes and for surveillance of potentially hazardous strains |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3765179/ https://www.ncbi.nlm.nih.gov/pubmed/23964903 http://dx.doi.org/10.1186/1471-2334-13-386 |
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