QDB (Query Driven Biclustering) is a Bayesian query-driven biclustering framework for microarray data in which the prior distributions allow introducing knowledge from a set of seed genes (query) to guide the pattern search.
QUBIC ( QUalitative BIClustering algorithm) provides a biclustering module for microarray data. For a set of genes and a set of conditions, the program outputs a block-like structure which shows uniform pattern within the block, the block would contain only subsets of all given genes under subsets of all given conditions.
QUBIC2 is a novel biclustering algorithm for analyses of gene expression data from bulk and single-cell RNA-Sequencing.
cMonkey identifies relevant conditions in which the genes within a given bicluster (where “biclustering” is condition- or cell-state-specific clustering) are expected to be co-regulated (importantly, in later stages of analysis we use only these conditions to learn TFs and EFs that influence each bicluster). The methods separates the calculation of the score components associated with each datatype into individual calculations but still effectively sample biclusters that optimally satisfy multiple model components (each representing a separate data-type). The method was designed as a preprocessing step for network inference and performed well in comparison to all other methods tested when the trade-off between sensitivity, specificity, and coverage (fraction of conditions and genes included in one or more biclusters) were considered, particularly in context of the other bulk characteristics (cluster size, residual, etc.).
H. López-Fernández, M. Reboiro-Jato, Sara C. Madeira, Rubén López Cortés, J. D. Nunes-Miranda, H. M. Santos, Florentino Fdez-Riverola, Daniel Glez-Peña A Workflow for the Application of Biclustering to Mass Spectrometry Data
7th International Conference on Practical Applications of Computational Biology & Bioinformatics – Advances in Intelligent Systems and Computing, 222, 2013, pp. 145-153. ISBN: 978-3-319-00577-5 (Print) 978-3-319-00578-2 (Online)
BiGGEsTS (Biclustering Gene Expression Time Series) is a free and open source software tool providing an integrated environment for the biclustering analysis of time series gene expression data. It offers a complete set of operations for retrieving potentially relevant information from the gene expression data, relying either on visualization or additional techniques for manipulating and processing this particular kind of data.
CCS (Condition-dependent Correlation Subgroups) Biclustering is a CUDA C/C++ software for GPU-accelerated algorithm for biclustering analysis and detection of condition-dependent coexpression network modules.
::DEVELOPER
Yan Cui’s Lab at University of Tennessee Health Science Center