Motivation: Biclustering is an unsupervised technique of simultaneous clustering of rows and columns of input matrix. With multiple biclustering algorithms proposed, UniBic remains one of the most accurate methods developed so far. Results: In this paper we introduce a Bioconductor package called runibic with parallel implementation of UniBic. For the convenience the algorithm was reimplemented, parallelized and wrapped within an R package called runibic. The package includes: (i) a couple of times faster parallel version of the original sequential algorithm, (ii) much more efficient memory management, (iii) modularity which allows to build new methods on top of the provided one and (iv) integration with the modern Bioconductor packages such as SummarizedExperiment, ExpressionSet and biclust. Availability and implementation: The package is implemented in R and is available from Bioconductor (starting from version 3.6) at the following URL http://bioconductor.org/packages/runibic with installation instructions and tutorial. Supplementary information: Supplementary data are available at Bioinformatics online.

In this article we introduce QtBiVis -a novel software intended for the comparative analysis of biclustering results.This modular tool has been efficiently implemented in C++ with Qt framework GUI.It may be successfully used for coverage analysis of the results of biclustering as well filtering or sorting biclusters by Gene Ontology (GO) identifiers or bicluster enrichment values.It may also be useful for parameter studies of biclustering algorithms.In future releases we plan to add different modules for visualizing and comparing different GO terms and biclusters.