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...

Descripción completa

Detalles Bibliográficos
Autores principales: Iwasaki, Yuki, Abe, Takashi, Wada, Yoshiko, Wada, Kennosuke, Ikemura, Toshimichi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2013
Materias:
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
_version_ 1782283244926926848
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
work_keys_str_mv AT iwasakiyuki novelbioinformaticsstrategiesforpredictionofdirectionalsequencechangesininfluenzavirusgenomesandforsurveillanceofpotentiallyhazardousstrains
AT abetakashi novelbioinformaticsstrategiesforpredictionofdirectionalsequencechangesininfluenzavirusgenomesandforsurveillanceofpotentiallyhazardousstrains
AT wadayoshiko novelbioinformaticsstrategiesforpredictionofdirectionalsequencechangesininfluenzavirusgenomesandforsurveillanceofpotentiallyhazardousstrains
AT wadakennosuke novelbioinformaticsstrategiesforpredictionofdirectionalsequencechangesininfluenzavirusgenomesandforsurveillanceofpotentiallyhazardousstrains
AT ikemuratoshimichi novelbioinformaticsstrategiesforpredictionofdirectionalsequencechangesininfluenzavirusgenomesandforsurveillanceofpotentiallyhazardousstrains