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Development of self-compressing BLSOM for comprehensive analysis of big sequence data
Akihito Kikuchi1, Toshimichi Ikemura2, Takashi Abe1
1Graduate School of Science and Technology, Niigata University, Niigata-shi, Niigata-ken 950-2181, Japan.
Biomed Research International
|October 24, 2015
Summary
We developed Self-Compressing Batch-Learning Self-Organizing Maps (SC-BLSOM) to efficiently analyze big genomic sequence data. This novel method reduces computation time and resources, enabling accurate phylotype clustering without supercomputers.
Area of Science:
- Bioinformatics
- Computational Biology
- Genomics
Background:
- Genomic sequence data is rapidly increasing, necessitating advanced analytical tools.
- Batch-Learning Self-Organizing Map (BLSOM) clusters genomic fragments by phylotype using oligonucleotide composition.
- Large-scale BLSOM analysis requires significant computational resources.
Purpose of the Study:
- To develop a computationally efficient method for analyzing big sequence data.
- To reduce the computational burden of BLSOM for genomic analyses.
- To enable comprehensive big data analysis without high-performance supercomputers.
Main Methods:
- Developed Self-Compressing BLSOM (SC-BLSOM) using a hierarchical approach.
- Constructed first-layer BLSOMs on divided data subclasses (e.g., phylotypes) for data compression.
- Built a second-layer BLSOM using weight vectors from the first layer.
- Compared SC-BLSOM with conventional BLSOM using bacterial genome sequences.
Main Results:
- SC-BLSOM demonstrated reduced construction time compared to conventional BLSOM.
- SC-BLSOM accurately clustered bacterial genome sequences according to phylotype.
- The method proved efficient for knowledge discovery from large genomic datasets.
Conclusions:
- SC-BLSOM offers a faster and more resource-efficient alternative for big sequence data analysis.
- This hierarchical approach enables accurate phylotype classification.
- SC-BLSOM facilitates efficient knowledge discovery in genomics and metagenomics.
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