QUBIC: a bioconductor package for qualitative biclustering analysis of gene co-expression data.
Biclustering is widely used to identify co-expressed genes under subsets of all the conditions in a large-scale transcriptomic dataset. The program, QUBIC, is recognized as one of the most efficient and effective biclustering methods for biological data interpretation. However, its availability is limited to a C implementation and to a low-throughput web interface.



An R implementation of QUBIC is presented here with two unique features: (i) a 82% average improved efficiency by refactoring and optimizing the source C code of QUBIC; and (ii) a set of comprehensive functions to facilitate biclustering-based biological studies, including the qualitative representation (discretization) of expression data, query-based biclustering, bicluster expanding, biclusters comparison, heatmap visualization of any identified biclusters and co-expression networks elucidation.



The package is implemented in R (as of version 3.3) and is available from Bioconductor at the URL: http://bioconductor.org/packages/QUBIC, where installation and usage instructions can be found.



qin.ma@sdstate.edu



Supplementary data are available at Bioinformatics online.
College of Computer Science and Technology, Jilin University, Changchun, China.|Key Laboratory of Symbolic Computation and Knowledge Engineering (Jilin University), Ministry of Education, Changchun, China.
Yu
Y
Zhang
Department of Mathematics and Statistics, South Dakota State University, Brookings, SD, USA.|Department of Agronomy, Horticulture and Plant Science, South Dakota State University, Brookings, SD, USA.
Juan
J
Xie
Department of Mathematics and Statistics, South Dakota State University, Brookings, SD, USA.|Department of Agronomy, Horticulture and Plant Science, South Dakota State University, Brookings, SD, USA.
Jinyu
J
Yang
Department of Agronomy, Horticulture and Plant Science, South Dakota State University, Brookings, SD, USA.|BioSNTR, Brookings, SD, USA.
Anne
A
Fennell
Center for Computational Biology and Bioinformatics and Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA.
Chi
C
Zhang
Department of Mathematics and Statistics, South Dakota State University, Brookings, SD, USA.|Department of Agronomy, Horticulture and Plant Science, South Dakota State University, Brookings, SD, USA.|BioSNTR, Brookings, SD, USA.
Qin
Q
Ma
D000465:Algorithms; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods; D004926:Escherichia coli / Q000235:genetics; D020869:Gene Expression Profiling / Q000379:methods*; D012329:RNA, Bacterial; D017423:Sequence Analysis, RNA / Q000379:methods*; D012984:Software
D012329:RNA, Bacterial