Genome-Scale Variant Interpretation

Automated Radiation Dose Estimation

Mission Statement

MutationForecaster® ( is Cytognomix’s patented web-portal for analysis of all types of mutations – coding and non-coding- including interpretation, comparison and management of genetic variant data. It’s a fully automated genome interpretation solution for research, translational and clinical labs.

Run our world-leading genome interpretation software on your exome, gene panel, or complete genome (Shannon transcription factor and splicing pipelines, ASSEDA, Veridical) with the Cytognomix User Variation Database and  Variant Effect Predictor.  With our integrated suite of software products, analyze coding, non-coding, and copy number variants, and compare new results with existing or your own database.  Select predicted mutations  by phenotype using articles with CytoVisualization Analytics.  With Workflows,  automatically perform end-to-end analysis with all of our software products.  Download an 1 page overview of MutationForecaster® (link)

Subscribe and analyze your own data via the cloud or… Don’t want to run your own analyses on MutationForecaster®? Let us do it for you with our Bespoke Analysis Service.

Experience our suite of genome interpretation products through a free trial of MutationForecaster®. Once you register, we provide datasets from our peer-reviewed publications to evaluate these software tools.

Automated radiation biodosimetry

Ionizing radiation produces characteristic chromosome changes. The altered chromosomes  are known as dicentric chromosomes [DCs]). DC biodosimetry is approved by the IAEA for occupational radiation exposure, radiation emergencies, or monitoring long term exposures.  The DC assay can also monitor effects of interventional radiation therapies.

Cytognomix has developed  a novel approach to find DCs (TBME).  The Automated Dicentric Chromosome Identifier and Dose Estimator (ADCI) software  works on multiple platforms and uses images produced by any of the existing automated metaphase capture systems found in most cytogenetic laboratories. ADCI is now available for for trial or  purchase (link).  Or contact us for details (pricing).

ADCI* uses machine learning to distinguish monocentric and dicentric chromosomes (Try the Dicentric Chromosome Identifier web app). With novel image segmentation, ADCI has become a fully functional cytogenetic biodosimetry system. ADCI takes images from metaphase scanning systems,  selects high quality cells, identifies dicentric chromosomes, builds biodosimetry calibration curves, and estimates exposures.  ADCI fulfills the criteria established by the IAEA for accurate triage biodosimetry of a sample in less than an hour. The accuracy is comparable to an experienced cytogeneticist. Check out our online user manual: wiki.

We find and validate mutations and gene signatures that others cannot with advanced,  patented genomic bioinformatic technologies. Cytognomix continues our  long track record of creating technologies for genomic medicine. We anticipate and implement the needs of the molecular medicine and genomics communities.

Predict chemotherapy outcomes

Pharmacogenomic responses to chemotherapy drugs can be predicted by supervised machine learning of expression and copy number of relevant gene combinations. Since 2015,  CytoGnomix has used biochemical evidence to derive gene signatures from changes in gene expression in cell lines, which can subsequently be examined in patients that have been treated with the same drugs. We have derived signatures for 30 different commonly used drugs.  Try out out our online predictor: 

Quantifying responses to ionizing radiation with gene expression signatures.

Gene signatures derived by machine learning have low error rates in externally validated, independent radiation exposed data. They exhibit high specificity and granularity for dose estimation in humans and mice.  These signatures can be designed to avoid the effects of confounding, comorbidities which can reduce specificity for detecting radiation exposures. See:

Single copy genomic technologies

Latest Posts

April 28, 2016. Press release

The University of Western Ontario has issued a press release about our studies describing a unified framework for prioritization of mutations in breast and ovarian cancer. Other press outlets: EurekAlert Medical Xpress Science Newsline Science Codex NewsUnited MyInforms My-News-Site BioPortfolio Ovarian cancer and us HealthyTips SistersNetwork Sci24h Spjnews  

April 11, 2016. New paper on analysis of variants of uncertain significance in hereditary breast & ovarian cancer

  Our paper, which describes a generalized information theory-based approach for mutation analysis of protein-nucleic binding sites, has been published: Mucaki, E, Caminsky N, Perri A, Lu R, Laederach A, Halvorsen, M, Knoll, JHM, Rogan PK. A unified analytic framework for prioritization of non-coding variants of uncertain significance in heritable breast and ovarian cancer, BMC […]

March 29, 2016. New publication on cost effectiveness of gene expression microarray testing in cancer diagnosis

Through a pan-Canadian collaboration led by Greg Zaric, we have published: Cost-effectiveness of using a gene expression profiling test to aid in identifying the primary tumour in patients with cancer of unknown primary. M B Hannouf, E Winquist, S M Mahmud, M Brackstone, S Sarma, G Rodrigues, P Rogan, J S Hoch and G S Zaric. The Pharmacogenomics Journal advance online publication 29 March 2016;  doi: 10.1038/tpj.2015.94  (Link)

March 15, 2015. Funding from the Build-in-Canada Innovation Program

Cytognomix pre-qualifies for funding from Build in Canada Innovation Program. Our proposal to test the Automated Dicentric Chromosome Identifier with Health Canada and Canadian Nuclear Laboratories has been approved by Public Works and Government Services Canada (link).

March 10, 2016. New paper accepted on prioritization strategy for gene variants of uncertain significance in breast/ovarian cancer

Our paper: “A unified analytic framework for prioritization of non-coding variants of uncertain significance in heritable breast and ovarian cancer,” by Eliseos J. Mucaki; Natasha G. Caminsky; Ami M. Perri; Ruipeng Lu; Alain Laederach; Matthew Halvorsen; Joan H.M. Knoll; and Peter K. Rogan has been accepted for publication in the journal, BMC Medical Genomics. A […]

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