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xseq specifications


Unique identifier OMICS_30138
Name xseq
Software type Package/Module
Interface Command line interface
Restrictions to use None
Input data A patient-gene gene expression matrix, a patient-gene mutation matrix and graph containing known interactions between genes.
Output data The probability that a recurrently mutated gene g influences gene expression across the population of patients; and the probability that an individual mutation in gene g in an individual patient m influences expression within that patient.
Operating system Unix/Linux, Mac OS, Windows
Programming languages R
License GNU General Public License version 3.0, GNU General Public License version 2.0
Computer skills Advanced
Version 0.2.1
Stability Stable
Source code URL https://cran.r-project.org/src/contrib/xseq_0.2.1.tar.gz
Maintained Yes




No version available


  • person_outline Sohrab P. Shah

Additional information

https://cran.r-project.org/web/packages/xseq/ https://github.com/shahcompbio/xseq

Publication for xseq

xseq citations


Cancer driver mutation prediction through Bayesian integration of multi omic data

PLoS One
PMCID: 5940219
PMID: 29738578
DOI: 10.1371/journal.pone.0196939

[…] ntext free prediction of rDriver lends itself to recover the driver that has regulatory effects unaccounted for by the known interaction network. For example, a known network-based approaches such as xSeq did not report PIK3CA as a top candidate driver gene in breast cancer []. In contrast, rDriver, which examines the expression levels of all the genes (including those in distant pathways), indica […]


Utilizing somatic mutation data from numerous studies for cancer research: proof of concept and applications

PMCID: 5485176
PMID: 28092680
DOI: 10.1038/onc.2016.489

[…] hat harbor many mutated genes., Hofree et al. stratified patients by quantifying the impact of their mutations on how information propagates in a protein–protein interaction network. ResponseNet and xseq modeled the impact of mutations on the gene expression profiles., Liu et al. developed an ensemble method for detecting driver genes by integrating predictions from several approaches. Most of t […]


A new molecular signature method for prediction of driver cancer pathways from transcriptional data

Nucleic Acids Res
PMCID: 4914110
PMID: 27098033
DOI: 10.1093/nar/gkw269

[…] (,) by specifics tasks, computational methods and data used, and by applicability for interpretation of individual tumor profiles. All reviewed approaches (MOCA (), CONEXIC (), EPoc (), DriverNet (), xseq ()) are aimed on prediction driver genes or driver pathways (PARADIGM ()). All approaches except of MOCA (), use gene interaction networks or pathway information. However, neither of the approach […]


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xseq institution(s)
Department of Molecular Oncology, BC Cancer Agency, Vancouver, BC, Canada; Department of Computer Science, University of British Columbia, Vancouver, BC, Canada; Centre for the Translational and Applied Genomics, BC Cancer Agency, Vancouver, BC, Canada; Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, BC, Canada; The Donnelly Centre, University of Toronto, Toronto, ON, Canada; Canada’s Michael Smith Genome Sciences Centre, Vancouver, BC, Canada
xseq funding source(s)
Supported by the BC Cancer Foundation and the Canadian Cancer Society Research Institute (grant no. 2012-701125); by the Michael Smith Foundation for Health Research and the Canada Research Chair program; by Genome BC/Genome Canada B/CB (grant no. 5125) and a The Terry Fox New Frontiers Program Project grant.

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