Development of Self-Compressing BLSOM for Comprehensive Analysis of Big Sequence Data.
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 so...
| Publicado en: | BioMed Research International Vol. 2015; pp. 1 - 9 |
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| Autores principales: | , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
10/1/2015
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=110311229&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 110311229 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 10/1/2015 vid: 2015 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 110311229 110311229 NLM26495297 110311229 10.1155/2015/506052 NLM26495297 PMC4606171 110311229 ppf: 1 ppct: 8 formats: fmt: @attributes: type: P tig: atl: Development of Self-Compressing BLSOM for Comprehensive Analysis of Big Sequence Data. aug: au: Kikuchi, Akihito Ikemura, Toshimichi Abe, Takashi affil: Graduate School of Science and Technology, Niigata University, Niigata-shi, Niigata-ken 950-2181, Japan sug: subj: Data Analytics Genomics Bacteria ab: 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. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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