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QUBIC specifications


Unique identifier OMICS_13142
Alternative name QUery BICluster
Software type Package/Module
Interface Command line interface
Restrictions to use None
Operating system Unix/Linux, Mac OS, Windows
Programming languages C++, R
License Other
Computer skills Advanced
Version 1.8.0
Stability Stable
methods, Matrix, rmarkdown, knitr, fields, Rcpp(>=0.11.0), biclust, R(>=3.1), QUBICdata, qgraph
Maintained Yes




No version available



  • person_outline Qin Ma

Publications for QUery BICluster

QUBIC citations


A new method of finding groups of coexpressed genes and conditions of coexpression

BMC Bioinformatics
PMCID: 5124285
PMID: 27887568
DOI: 10.1186/s12859-016-1356-3

[…] h extract groups of co-expressed genes and associated conditions of co-expression from a microarray data. Few of them are CC algorithm [], ISA algorithm [], BiMax algorithm [], SAMBA algorithm [] and QUBIC algorithm []. These algorithms require different input parameters for generating biclusters from high throughput microarray data. This is the first limitation of these algorithms. On a real data […]


Characterizing Aeroallergens by Infrared Spectroscopy of Fungal Spores and Pollen

PLoS One
PMCID: 4395086
PMID: 25867755
DOI: 10.1371/journal.pone.0124240

[…] he effective optical path length (multiplicative parameter b in ). In the employed EMSC pre-processing, the “baseline” was estimated by the following EMSC parameters: constant, linear, quadratic, and qubic (parameters a, d, e, and f, respectively, in ). These parameters are used to estimate non-chemical absorption, reflection and scattering of light that is usually caused by irregularities in the […]


eMBI: Boosting Gene Expression based Clustering for Cancer Subtypes

Cancer Inform
PMCID: 4213194
PMID: 25374455
DOI: 10.4137/CIN.S13777

[…] me conditions, corresponding to the sub-matrices of the raw matrix (called biclusters), are possible to be identified. Later, many biclustering methods have been developed, such as BIMAX, FABIA, ISA, QUBIC, and SAMBA, just to name a few.For the current molecular study of different cancers with large amount of datasets from different platforms, one critical computational challenge is to conduct uns […]


Clostridium thermocellum transcriptomic profiles after exposure to furfural or heat stress

Biotechnol Biofuels
PMCID: 3848806
PMID: 24028713
DOI: 10.1186/1754-6834-6-131
call_split See protocol

[…] pair of motifs (use 0.45 as the similarity score cutoff). For each one out of the 54 motif patterns, the co-expression property of downstream operons was assessed by a popular biclustering algorithm, QUBIC []. The motif consensus sequences were mapped to known prokaryotic cis regulatory elements databases [] to determine whether the predicted motif patterns were real cis regulatory elements in oth […]


A comparison and evaluation of five biclustering algorithms by quantifying goodness of biclusters for gene expression data

BioData Min
PMCID: 3447720
PMID: 22824157
DOI: 10.1186/1756-0381-5-8

[…] ion is available; (3) whether the algorithm is considered to be novel. The selected algorithms are BIMAX [], FABIA(Factor Analysis for Bicluster Acquisition) [], ISA (Iterate Signature Algorithm) [], QUBIC (Qualitative Biclustering algorithm) [] and SAMBA (Statistical-Algorithmic Method for Bicluster Analysis) [].For real transcriptome data sets, the most meaningful verification of biclusters is b […]


Quantitative proteomics combined with BAC TransgeneOmics reveals in vivo protein interactions

PMCID: 2872919
PMID: 20479470
DOI: 10.1083/jcb.200911091
call_split See protocol

[…] methods because of their small size. One of the proteins, C10orf104/ANAPC16, was identified in parallel studies as a genuine member of the APC core complex (; ). C11orf51 was also identified by SILAC-QUBIC when using double labeling with arginine and lysine combined with tryptic digestion (Fig. S3). Furthermore, when we GFP tagged C11orf51 at both the N and C terminus, it showed a similar localiza […]


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QUBIC institution(s)
Bioinformatics and Mathematical Biosciences Lab, Department of Agronomy, Horticulture and Plant Science, South Dakota State University, BioSNTR, Brookings, SD, USA; Department of Mathematics and Statistics, South Dakota State University, Brookings, SD, USA; Colleges of Computer Science and Technology, Jilin University, Changchun, China; School of Mathematics, Shandong University, Jinan, China; Center for Computational Biology and Bioinformatics, Department of Medical and Molecular Genetics, Indiana University, School of Medicine, Indianapolis, IN, USA
QUBIC funding source(s)
This work was supported by the State of South Dakota Research Innovation Center, the Agriculture Experiment Station of South Dakota State University, and the National Natural Science Foundation of China (61402194).

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