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Nucleic Acids Res. 2014 May;42(9):e78. doi: 10.1093/nar/gku201. Epub 2014 Mar 20.

Bi-Force: large-scale bicluster editing and its application to gene expression data biclustering.

Author information

1
Max Planck Institute for Informatics, Campus E1 4, Saarland University, 66123 Saarbrücken, Germany Cluster of Excellence for Multimodel Computing and Interaction, Campus E1 7, Saarland University, 66123 Saarbrücken, Germany psun@mpi-inf.mpg.de.
2
Max Planck Institute for Informatics, Campus E1 4, Saarland University, 66123 Saarbrücken, Germany Cluster of Excellence for Multimodel Computing and Interaction, Campus E1 7, Saarland University, 66123 Saarbrücken, Germany.
3
Cluster of Excellence for Multimodel Computing and Interaction, Campus E1 7, Saarland University, 66123 Saarbrücken, Germany.
4
Max Planck Institute for Informatics, Campus E1 4, Saarland University, 66123 Saarbrücken, Germany Institute for Mathematics and Computer Science, University of Southern Denmark, Campusvej 55, 5230 Odense M, Denmark.

Abstract

The explosion of the biological data has dramatically reformed today's biological research. The need to integrate and analyze high-dimensional biological data on a large scale is driving the development of novel bioinformatics approaches. Biclustering, also known as 'simultaneous clustering' or 'co-clustering', has been successfully utilized to discover local patterns in gene expression data and similar biomedical data types. Here, we contribute a new heuristic: 'Bi-Force'. It is based on the weighted bicluster editing model, to perform biclustering on arbitrary sets of biological entities, given any kind of pairwise similarities. We first evaluated the power of Bi-Force to solve dedicated bicluster editing problems by comparing Bi-Force with two existing algorithms in the BiCluE software package. We then followed a biclustering evaluation protocol in a recent review paper from Eren et al. (2013) (A comparative analysis of biclustering algorithms for gene expressiondata. Brief. Bioinform., 14:279-292.) and compared Bi-Force against eight existing tools: FABIA, QUBIC, Cheng and Church, Plaid, BiMax, Spectral, xMOTIFs and ISA. To this end, a suite of synthetic datasets as well as nine large gene expression datasets from Gene Expression Omnibus were analyzed. All resulting biclusters were subsequently investigated by Gene Ontology enrichment analysis to evaluate their biological relevance. The distinct theoretical foundation of Bi-Force (bicluster editing) is more powerful than strict biclustering. We thus outperformed existing tools with Bi-Force at least when following the evaluation protocols from Eren et al. Bi-Force is implemented in Java and integrated into the open source software package of BiCluE. The software as well as all used datasets are publicly available at http://biclue.mpi-inf.mpg.de.

PMID:
24682815
PMCID:
PMC5769343
DOI:
10.1093/nar/gku201
[Indexed for MEDLINE]
Free PMC Article

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