PMID- 26495297
OWN - NLM
STAT- MEDLINE
DCOM- 20160802
LR  - 20191210
IS  - 2314-6141 (Electronic)
IS  - 2314-6133 (Print)
VI  - 2015
DP  - 2015
TI  - Development of self-compressing BLSOM for comprehensive analysis of big sequence 
      data.
PG  - 506052
LID - 10.1155/2015/506052 [doi]
LID - 506052
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.
FAU - Kikuchi, Akihito
AU  - Kikuchi A
AD  - Graduate School of Science and Technology, Niigata University, Niigata-shi, 
      Niigata-ken 950-2181, Japan.
FAU - Ikemura, Toshimichi
AU  - Ikemura T
AD  - Nagahama Institute of Bio-Science and Technology, Nagahama-shi, Shiga-ken 
      526-0829, Japan.
FAU - Abe, Takashi
AU  - Abe T
AD  - Graduate School of Science and Technology, Niigata University, Niigata-shi, 
      Niigata-ken 950-2181, Japan.
LA  - eng
PT  - Evaluation Study
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
DEP - 20151001
PL  - United States
TA  - Biomed Res Int
JT  - BioMed research international
JID - 101600173
SB  - IM
MH  - *Algorithms
MH  - Chromosome Mapping/*methods
MH  - Data Compression/*methods
MH  - Genome, Bacterial/genetics
MH  - High-Throughput Nucleotide Sequencing/*methods
MH  - Pattern Recognition, Automated/*methods
MH  - Sequence Analysis, DNA/*methods
MH  - Software
PMC - PMC4606171
EDAT- 2015/10/27 06:00
MHDA- 2016/08/03 06:00
PMCR- 2015/10/01
CRDT- 2015/10/24 06:00
PHST- 2015/03/27 00:00 [received]
PHST- 2015/06/25 00:00 [revised]
PHST- 2015/07/12 00:00 [accepted]
PHST- 2015/10/24 06:00 [entrez]
PHST- 2015/10/27 06:00 [pubmed]
PHST- 2016/08/03 06:00 [medline]
PHST- 2015/10/01 00:00 [pmc-release]
AID - 10.1155/2015/506052 [doi]
PST - ppublish
SO  - Biomed Res Int. 2015;2015:506052. doi: 10.1155/2015/506052. Epub 2015 Oct 1.