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

Presentation. 2015 Canadian Cancer Research Conference

Peter Rogan will be presenting: Seeking the “Missing Heritability” in High-Risk Hereditary Breast and Ovarian Cancer (HBOC) Patients By Prioritizing Coding and Non-Coding Variants in 21 Genes.  Natasha Caminsky G, Eliseos Mucaki J,  Amelia Perri M, Ruipeng Lu, Matthew Halvorsen, Alain Laederach, Joan Knoll HM, Peter Rogan K on Tuesday, November 10 from 12-2 PM in the poster session: Genomics, Proteomics, and […]

October 22, 2015. Presentation at the 2015 Toronto NGS Symposium

Ben Shirley, Chief software architect at Cytognomix, will be presenting: Interpreting variants in complete gene and genome sequences with MutationForecaster® at 11:50 AM at the Toronto NGS Symposium (Ben Sadowski Auditorium, 18th Floor, Mt Sinai Hospital, University Ave.). Presentation schedule

October 6, 2015. Presentations at the 2015 International EPR Biodose meeting

Drs. Joan Knoll and Peter Rogan gave platform presentations about the underlying algorithms and application of the Automated Dicentric Chromosome Identifier and Radiation Dose Estimator: “Radiation dose estimation by automated chromosome biodosimetry”  and “Automated Discrimination of Dicentric and Monocentric Chromosomes by Machine Learning-based Image Processing” at the EPRBiodose meeting at Dartmouth College, organized by the International Association of […]

Sept. 18, 2015. Press release about chemotherapy resistance paper

Western University hopes to use artificial intelligence to improve breast cancer patient outcomes. (, other links at end of post) Western University researchers are working on a way to use artificial intelligence to predict a patient’s response to two common chemotherapy medications used to treat breast cancer – paclitaxel and gemcitabine. Peter Rogan, PhD, and […]

September 11, 2015. Final version of paclitaxel and gemcitabine chemotherapy signature paper now published

Genomic signatures for paclitaxel and gemcitabine resistance in breast cancer derived by machine learning Stephanie N. Dorman, Katherina Baranova, Joan H.M. Knoll, Brad L. Urquhart, Gabriella Mariani, Maria Luisa Carcangiu, Peter K. Rogan DOI: Download paper Received: July 20, 2015; Accepted: July 31, 2015; Published Online: August 21, 2015 Publication stage: In Press, Corrected Proof

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