BicPAMS - Software for Biological Data Analysis with Pattern-based Biclustering
As such, BicPAMS is able to efficiently discover exhaustive and flexible structures of biclusters, with parameterizable coherency and robustness to noise and missings. BicPAMS has been successfully tested on biomedical data (including expression data, clinical data and biological networks) and social data.
Biclustering with Plaid ModelsBiP (http://web.ist.utl.pt/~rmch/software/bip/) is an algorithm that learns flexible plaid models for an effective discovery of overlapping biclusters. BiP addresses the limitations of existing plaid models, namely overcomes existing restrictions on the allowed types and structures of biclusters. BiP makes available different functions to compose contributions from overlapping biclusters, such as weighted and multiplicative functions. BiP allows the use of different relaxation for noise-tolerant and biologically-meaningful validation of plaid effects.
BicPAMBicPAM (http://web.ist.utl.pt/rmch/software/bicpam/) makes available a set of pattern-based approaches for biclustering. BicPAM integrates existing disperse efforts towards pattern-based biclustering and introduces novel methods to discover biclusters with multiple patterns of expression, varying quality and alternative underlying structures. Additionally, BicPAM allows for parameterizable definition of mining, mapping, and closing options (including search, pattern representation, normalization, discretization, extension, merging and filtering strategies) and alternative ways to deal with missing values and noise.
BicSPAMBicSPAM (http://web.ist.utl.pt/~rmch/software/bicspam/) makes available a set of pattern-based approaches for order-preserving biclustering.
BicSPAM approaches are proposed to perform flexible and exhaustive biclustering based on sequential patterns.
BicSPAM approaches are easily parameterizable, allowing the selection of different strategies with varying levels of noise and missing values, underlying SPM methods, pattern representations (simple, condensed and approximate), and discretization options. Flexible structures and quality criteria can be easily defined through extension-merging-filtering steps without the need to adapt the core task.
Finally, BicSPAM allows for order-preserving biclusters with symmetries, useful for instance to capture activation and regulatory mechanisms in biological processes.
LateBiclusteringLateBiclustering is an efficient algorithm for time-lagged bicluster identification, recently published in IEEE/ACM Transactions on Computational Biology and Bioinformatics. The next release of the BiGGEsTS software will include LateBiclustering. In the meantime, please feel free to contact me if you are interested in applying LateBiclustering to your data.
CCC-Biclustering is a linear time biclustering algorithm for ﬁnding perfect expression patterns in gene expression time series. The implementation coded in Java and made available in http://kdbio.inesc-id.pt/software/ccc-biclustering provides a command line interface enabling the use of the algorithm in real data sets. This implementation is also integrated in BiGGEsTS and Babelomics.
e-CCC-Biclustering is a polynomial time biclustering algorithm for ﬁnding approximate expression patterns in gene expression time series.
The e-CCC-Biclustering algorithm is coded in Java. The implementation coded in Java and made available in http://kdbio.inesc-id.pt/software/e-ccc-biclustering/ provides a command line interface enabling the use of the algorithm in real data sets. This implementation is also integrated in BiGGEsTS.
BiGGEsTS (http://kdbio.inesc-id.pt/software/biggests/) is a software for biclustering analysis of time series data. Although it has been originally introduced in the context of gene expression time series, most of the software’s functionalities can be applied to time series in any domain of knowledge. BiGGEsTS includes data preprocessing techniques, state of the art temporal biclustering algorithms (CCC-Biclustering, e-CCC-Biclustering), bicluster post-processing methods, and different kinds of visualisation. In its next release, BiGGEsTS will also integrate our latest LateBiclustering for efficient time-lagged biclustering.
BiClassifier - Biclustering-based Classifiers
Regulatory Snapshots & TFRank
Regulatory Snapshots (http://kdbio.inesc-id.pt/software/regulatorsnapshots) combines temporal biclustering and ranking of transcription factors to identify regulatory modules composed of genes exhibiting a coherent expression profile in a given time frame, together with potential key regulators at each time point.
TFRank (http://kdbio.inesc-id.pt/software/tfrank) is a framework for ranking and highlighting key transcription factors putatively involved in the regulation of a set of target genes of interest. It uses a graph diffusion approach to propagate an initial signal on the targets through the network of regulatory interactions, traversing the edges in the reverse direction and accumulating a relevance score in every node.