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

December 21, 2015. New capability in Cytognomix User Variation Database (CUVD)

Every gene variant imported into CUVD from our other genome interpretation modules can be searched in several external databases seamlessly. Currently, all LOVD locus specific databases, dbSNP, ClinVar, and the Exome Variant Server are searched together and  variants found in any of these resources  are added to CUVD and hyperlinked when the search is completed. Until […]

December 14, 2015. Try MutationForecaster® for two weeks: Free of Charge!

We are excited to be able to offer our customers and registrants this opportunity to experience our integrated suite of genome interpretation products. For the first time, Cytognomix is offering a free trial of our MutationForecaster® genome interpretation suite to all registrants of the product. No subscription is required to analyze data with any of our software tools. […]

November 26, 2015. Splicing Mutation Calculator software

The Splicing Mutation Calculator web software described in: Caminsky NG, Mucaki EJ and Rogan PK. Interpretation of mRNA splicing mutations in genetic disease: review of the literature and guidelines for information-theoretical analysis [version 2; referees: 2 approved] F1000Research 2015, 3:282 (doi: 10.12688/f1000research.5654.2) has been migrated to the MutationForecaster system (   Subscribers to MutationForecaster have unlimited access to this […]

November 21, 2015. Literature based filtering in the MutationForecaster system

In next generation sequencing, exomes in particular, the challenge is to find relevant pathogenic gene variants among a sea of superfluous sequence changes. But the track record for filtering the most likely causative changes is dismal (20-25%). Most filtering methods remove common variants but do little else. Cytognomix has developed CytoVA, software that relates variants […]

Upcoming Presentation at University of Windsor, Ontario, Canada.

Peter Rogan will present: “Genomic analysis of metastasis and tumor chemotherapy response based on information theory and machine learning” Department of Computer Science University of Windsor Date:  Friday, November 13th, 2015 Time: 11:00 am Location: Chrysler Hall – G100  Abstract: The integrated analyses of cancer phenotypes with complex genomic datasets has resulted in many new insights into diagnosis and […]

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