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DASS-GUI: a user interface for identification and analysis of significant patterns in non-sequential data

Jens Hollunder UGent, Maik Friedel, Martin Kuiper and Thomas Wilhelm (2010) BIOINFORMATICS. 26(7). p.987-989
abstract
Many large 'omics' datasets have been published and many more are expected in the near future. New analysis methods are needed for best exploitation. We have developed a graphical user interface (GUI) for easy data analysis. Our discovery of all significant substructures (DASS) approach elucidates the underlying modularity, a typical feature of complex biological data. It is related to biclustering and other data mining approaches. Importantly, DASS-GUI also allows handling of multi-sets and calculation of statistical significances. DASS-GUI contains tools for further analysis of the identified patterns: analysis of the pattern hierarchy, enrichment analysis, module validation, analysis of additional numerical data, easy handling of synonymous names, clustering, filtering and merging. Different export options allow easy usage of additional tools such as Cytoscape.
Please use this url to cite or link to this publication:
author
organization
year
type
journalArticle (original)
publication status
published
subject
keyword
PROTEIN SUBCOMPLEXES, ALIGNMENT
journal title
BIOINFORMATICS
Bioinformatics
volume
26
issue
7
pages
3 pages
Web of Science type
Article
Web of Science id
000276045800027
JCR category
MATHEMATICAL & COMPUTATIONAL BIOLOGY
JCR impact factor
4.877 (2010)
JCR rank
2/35 (2010)
JCR quartile
1 (2010)
ISSN
1367-4803
DOI
10.1093/bioinformatics/btq071
language
English
UGent publication?
yes
classification
A1
copyright statement
I have retained and own the full copyright for this publication
id
947388
handle
http://hdl.handle.net/1854/LU-947388
date created
2010-05-17 18:24:59
date last changed
2012-09-19 14:03:49
@article{947388,
  abstract     = {Many large 'omics' datasets have been published and many more are expected in the near future. New analysis methods are needed for best exploitation. We have developed a graphical user interface (GUI) for easy data analysis. Our discovery of all significant substructures (DASS) approach elucidates the underlying modularity, a typical feature of complex biological data. It is related to biclustering and other data mining approaches. Importantly, DASS-GUI also allows handling of multi-sets and calculation of statistical significances. DASS-GUI contains tools for further analysis of the identified patterns: analysis of the pattern hierarchy, enrichment analysis, module validation, analysis of additional numerical data, easy handling of synonymous names, clustering, filtering and merging. Different export options allow easy usage of additional tools such as Cytoscape.},
  author       = {Hollunder, Jens and Friedel, Maik and Kuiper, Martin and Wilhelm, Thomas},
  issn         = {1367-4803},
  journal      = {BIOINFORMATICS},
  keyword      = {PROTEIN SUBCOMPLEXES,ALIGNMENT},
  language     = {eng},
  number       = {7},
  pages        = {987--989},
  title        = {DASS-GUI: a user interface for identification and analysis of significant patterns in non-sequential data},
  url          = {http://dx.doi.org/10.1093/bioinformatics/btq071},
  volume       = {26},
  year         = {2010},
}

Chicago
Hollunder, Jens, Maik Friedel, Martin Kuiper, and Thomas Wilhelm. 2010. “DASS-GUI: a User Interface for Identification and Analysis of Significant Patterns in Non-sequential Data.” Bioinformatics 26 (7): 987–989.
APA
Hollunder, J., Friedel, M., Kuiper, M., & Wilhelm, T. (2010). DASS-GUI: a user interface for identification and analysis of significant patterns in non-sequential data. BIOINFORMATICS, 26(7), 987–989.
Vancouver
1.
Hollunder J, Friedel M, Kuiper M, Wilhelm T. DASS-GUI: a user interface for identification and analysis of significant patterns in non-sequential data. BIOINFORMATICS. 2010;26(7):987–9.
MLA
Hollunder, Jens, Maik Friedel, Martin Kuiper, et al. “DASS-GUI: a User Interface for Identification and Analysis of Significant Patterns in Non-sequential Data.” BIOINFORMATICS 26.7 (2010): 987–989. Print.