DASS: efficient discovery and p-value calculation of substructures in unordered data.
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DASS: efficient discovery and p-value calculation of substructures in unordered data. / Hollunder, Jens; Friedel, Maik; Beyer, Andreas; Workman, Christopher T; Wilhelm, Thomas.
In: BIOINFORMATICS, Vol. 23, No. 1, 1, 2007, p. 77-83.Research output: SCORING: Contribution to journal › SCORING: Journal article › Research › peer-review
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TY - JOUR
T1 - DASS: efficient discovery and p-value calculation of substructures in unordered data.
AU - Hollunder, Jens
AU - Friedel, Maik
AU - Beyer, Andreas
AU - Workman, Christopher T
AU - Wilhelm, Thomas
PY - 2007
Y1 - 2007
N2 - 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 (DASS(Sub)) 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 (DASS(P(set))), or for sets with multiple occurrence of elements (DASS(P(mset))). 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 evolutionarily conserved protein interaction subnetworks. AVAILABILITY: The program code and additional data are available at http://www.fli-leibniz.de/tsb/DASS
AB - 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 (DASS(Sub)) 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 (DASS(P(set))), or for sets with multiple occurrence of elements (DASS(P(mset))). 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 evolutionarily conserved protein interaction subnetworks. AVAILABILITY: The program code and additional data are available at http://www.fli-leibniz.de/tsb/DASS
M3 - SCORING: Zeitschriftenaufsatz
VL - 23
SP - 77
EP - 83
JO - BIOINFORMATICS
JF - BIOINFORMATICS
SN - 1367-4803
IS - 1
M1 - 1
ER -
