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Dynamic visual data mining: biological sequence analysis and annotation using SeqVISTA.

Tianhua Niu1, Zhenjun Hu

  • 1Department of Meidcine, Brigham and Women's Hospital, Boston, MA 02215, USA. tniu@rics.bwh.harvard.edu

International Journal of Bioinformatics Research and Applications
|December 1, 2007
PubMed
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Dynamic Visual Data Mining (DVDM) integrates biological modeling, display, and analysis for large sequence databases. The SeqVISTA software, built on DVDM, offers versatile tools for biological data mining and analysis.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Exponential growth of public sequence databases necessitates advanced analysis techniques.
  • Visualization-centric methods are crucial for biological sequence analysis and annotation.

Purpose of the Study:

  • To present Dynamic Visual Data Mining (DVDM), an integrated platform for biological data analysis.
  • To introduce SeqVISTA, an object-oriented software developed using DVDM principles.

Main Methods:

  • Developed SeqVISTA using Java Development Kit v1.4 based on the DVDM methodology.
  • Integrated biological object modeling, interactive display, and data analysis tools.
  • Applied DVDM to various biological sequence analysis tasks.

Related Experiment Videos

Main Results:

  • SeqVISTA demonstrates applications in pattern matching, alternative exon splicing analysis, Fourier analyses, and exon prediction (MZEF, GENSCAN).
  • The DVDM methodology effectively unveils information within large genomic and proteomic databases.

Conclusions:

  • Dynamic Visual Data Mining (DVDM) is a key technique for exploring vast biological sequence data.
  • SeqVISTA serves as a versatile tool, integrating computational algorithms for biological data mining needs.