SUMMARY: 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. AVAILABILITY: Source code, pre-compiled binaries for different systems, a comprehensive tutorial, case studies and many additional datasets are freely available at http://www.ifr.ac.uk/dass/gui/. DASS-GUI is implemented in Qt.

MOTIVATION: DiProGB is an easy to use new genome browser that encodes the primary nucleotide sequence by thermodynamical and geometrical dinucleotide properties. The nucleotide sequence is thus converted into a sequence graph. This visualization, supported by different graph manipulation options, facilitates genome analyses, because the human brain can process visual information better than textual information. Also, DiProGB can identify genomic regions where certain physical properties are more conserved than the nucleotide sequence itself. Most of the DiProGB tools can be applied to both, the primary nucleotide sequence and the sequence graph. They include motif and repeat searches as well as statistical analyses. DiProGB adds a new dimension to the common genome analysis approaches by taking into account the physical properties of DNA and RNA. AVAILABILITY AND IMPLEMENTATION: Source code and binaries are freely available for download at http://diprogb.fli-leibniz.de, implemented in C++ and supported on MS Windows and Linux (using e.g. WineHQ).

We present a new data structure, called a Decomposition Tree (DT), for analysing Boolean functions, and demonstrate a variety of applications. In each node of the DT, appropriate bit-string decomposition fragments are combined by a logical operator. The DT has 2 k nodes in the worst case, which implies exponential complexity for problems where the whole tree has to be considered. However, it is important to note that many problems are simpler. We show that these can be handled in an efficient way using the DT. Nevertheless, many problems are of exponential complexity and cannot be made any simpler: for example, the calculation of prime implicants. Using our general DT structure, we present a new worst case algorithm to compute all prime implicants. This algorithm has a lower time complexity than the well-known Quine–McCluskey algorithm and is the fastest corresponding worst case algorithm so far.

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