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Motivation: Pattern identification in biological sequence data is one of the main objectives of bioinformatics research. However, few methods are available for detecting patterns (substructures) in unordered datasets. Data mining algorithms mainly developed outside the realm of bioinformatics have been adapted for that purpose, but typically do not determine the statistical significance of the identified patterns. Moreover, these algorithms do not exploit the often modular structure of biological data. Results: We present the algorithm DASS (Discovery of All Significant Substructures) that first identifies all substructures in unordered data (DASSSub) in a manner that is especially efficient for modular data. In addition, DASS calculates the statistical significance of the identified substructures, for sets with at most one element of each type (DASSPset), or for sets with multiple occurrence of elements (DASSPmset). The power and versatility of DASS is demonstrated by four examples: combinations of protein domains in multi-domain proteins, combinations of proteins in protein complexes (protein subcomplexes), combinations of transcription factor target sites in promoter regions, and evolutionary conserved protein interaction subnetworks. |
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SupplementaryInformation.pdf |
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TranscriptionFactorModules.pdf |
List of
transcription factor modules |
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ConservedSubgraphs.pdf |
List of conserved
subgraphs in protein-protein interaction networks |
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ProteinSubcomplexes.pdf |
List of
protein subcomplexes (of the recently published MALDI-TOF MS data |
| DASS algoritm in C++ (gcc) (Windows / Linux Suse9.3 | DASS algorithm |
* wilhelm at fli-leibniz.de
© FLI, Jena 2004-2007
