A Novel Bioinformatics Strategy to Analyze Microbial Big Sequence Data for Efficient Knowledge Discovery: Batch-Learning Self-Organizing Map (BLSOM).
With the remarkable increase of genomic sequence data of microorganisms, novel tools are needed for comprehensive analyses of the big sequence data available. The self-organizing map (SOM) is an effective tool for clustering and visualizing high-dimensional data, such as oligonucleotide composition on one map. By modifying the conventional SOM, we developed batch-learning SOM (BLSOM), which allowed classification of sequence fragments (e.g., 1 kb) according to phylotypes, solely depending on oligonucleotide composition. Metagenomics studies of uncultivable microorganisms in clinical and environmental samples should allow extensive surveys of genes important in life sciences. BLSOM is most suitable for phylogenetic assignment of metagenomic sequences, because fragmental sequences can be clustered according to phylotypes, solely depending on oligonucleotide composition. We first constructed oligonucleotide BLSOMs for all available sequences from genomes of known species, and by mapping metagenomic sequences on these large-scale BLSOMs, we can predict phylotypes of individual metagenomic sequences, revealing a microbial community structure of uncultured microorganisms, including viruses. BLSOM has shown that influenza viruses isolated from humans and birds clearly differ in oligonucleotide composition. Based on this host-dependent oligonucleotide composition, we have proposed strategies for predicting directional changes of virus sequences and for surveilling potentially hazardous strains when introduced into humans from non-human sources.
Department of Bioscience, Nagahama Institute of Bio-Science and Technology, Nagahama-shi, Shiga-ken 526-0829, Japan. b105023@nagahama-i-bio.ac.jp.|Japan Society for the Promotion of Science, Chiyoda-ku, Tokyo 102-0083, Japan. b105023@nagahama-i-bio.ac.jp.
Yuki
Y
Iwasaki
Department of Bioscience, Nagahama Institute of Bio-Science and Technology, Nagahama-shi, Shiga-ken 526-0829, Japan. takaabe@ie.niigata-u.ac.jp.|Department of Information Engineering, Faculty of Engineering, Niigata University, Niigata-ken 950-2181, Japan. takaabe@ie.niigata-u.ac.jp.
Takashi
T
Abe
Department of Bioscience, Nagahama Institute of Bio-Science and Technology, Nagahama-shi, Shiga-ken 526-0829, Japan. k_wada@nagahama-i-bio.ac.jp.
Kennosuke
K
Wada
Department of Bioscience, Nagahama Institute of Bio-Science and Technology, Nagahama-shi, Shiga-ken 526-0829, Japan. y_wada@nagahama-i-bio.ac.jp.|Faculty of Medicine, Shiga University of Medical Science, Shiga-ken 520-2121, Japan. y_wada@nagahama-i-bio.ac.jp.
Yoshiko
Y
Wada
Department of Bioscience, Nagahama Institute of Bio-Science and Technology, Nagahama-shi, Shiga-ken 526-0829, Japan. t_ikemura@nagahama-i-bio.ac.jp.
Toshimichi
T
Ikemura
SOM; big data; bioinformatics; genome signature; influenza virus; metagenome; microbial community; oligonucleotide composition; peptide composition
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