Development of Self-Compressing BLSOM for Comprehensive Analysis of Big Sequence Data
Joint Authors
Ikemura, Toshimichi
Abe, Takashi
Kikuchi, Akihito
Source
Issue
Vol. 2015, Issue 2015 (31 Dec. 2015), pp.1-8, 8 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2015-10-01
Country of Publication
Egypt
No. of Pages
8
Main Subjects
Abstract EN
With the remarkable increase in genomic sequence data from various organisms, novel tools are needed for comprehensive analyses of available big sequence data.
We previously developed a Batch-Learning Self-Organizing Map (BLSOM), which can cluster genomic fragment sequences according to phylotype solely dependent on oligonucleotide composition and applied to genome and metagenomic studies.
BLSOM is suitable for high-performance parallel-computing and can analyze big data simultaneously, but a large-scale BLSOM needs a large computational resource.
We have developed Self-Compressing BLSOM (SC-BLSOM) for reduction of computation time, which allows us to carry out comprehensive analysis of big sequence data without the use of high-performance supercomputers.
The strategy of SC-BLSOM is to hierarchically construct BLSOMs according to data class, such as phylotype.
The first-layer BLSOM was constructed with each of the divided input data pieces that represents the data subclass, such as phylotype division, resulting in compression of the number of data pieces.
The second BLSOM was constructed with a total of weight vectors obtained in the first-layer BLSOMs.
We compared SC-BLSOM with the conventional BLSOM by analyzing bacterial genome sequences.
SC-BLSOM could be constructed faster than BLSOM and cluster the sequences according to phylotype with high accuracy, showing the method’s suitability for efficient knowledge discovery from big sequence data.
American Psychological Association (APA)
Kikuchi, Akihito& Ikemura, Toshimichi& Abe, Takashi. 2015. Development of Self-Compressing BLSOM for Comprehensive Analysis of Big Sequence Data. BioMed Research International،Vol. 2015, no. 2015, pp.1-8.
https://search.emarefa.net/detail/BIM-1055736
Modern Language Association (MLA)
Kikuchi, Akihito…[et al.]. Development of Self-Compressing BLSOM for Comprehensive Analysis of Big Sequence Data. BioMed Research International No. 2015 (2015), pp.1-8.
https://search.emarefa.net/detail/BIM-1055736
American Medical Association (AMA)
Kikuchi, Akihito& Ikemura, Toshimichi& Abe, Takashi. Development of Self-Compressing BLSOM for Comprehensive Analysis of Big Sequence Data. BioMed Research International. 2015. Vol. 2015, no. 2015, pp.1-8.
https://search.emarefa.net/detail/BIM-1055736
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1055736