Genome-Scale Variant Interpretation

Automated Radiation Dose Estimation

Mission Statement

MutationForecaster® (mutationforecaster.com) 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:  https://chemotherapy.cytognomix.com. 

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: https://f1000research.com/articles/7-233/v2

Single copy genomic technologies

Latest Posts

April 22, 2014. New paper describing Automated Biodosimetry Software published

We have published an article in Radiation Protection Biodosimetry describing our patented Automated Dicentric Chromosome Identifier Software for both Desktop and Supercomputer systems. The citation is: Peter K. Rogan,  Yanxin Li,  Asanka Wickramasinghe,  Akila Subasinghe, Natasha Caminsky,  Wahab Khan,  Jagath Samarabandu,  Ruth Wilkins, Farrah Flegal, and Joan H. Knoll.  AUTOMATING DICENTRIC CHROMOSOME DETECTION FROM CYTOGENETIC BIODOSIMETRY DATA. Radiat Prot […]

April 9, 2014. Presentation at the American Association for Cancer Research meeting

Stephanie Dorman presented our paper, “Non-coding mutation analysis reveals previously unrecognized pathways in lymph node-invasive breast cancer,” (Abstract)  at the Annual AACR meeting.  The poster presentation was very well attended with more than 30 visitors, including representatives from several personalized medicine and genomics companies.  Many of the attendees expressed interest in the Shannon human mRNA splicing mutation and Veridical […]

March 3, 2014. Oral presentation at the The Fifth International Symposium on Hereditary Breast and Ovarian Cancer Conference

Our abstract, “Identification, Prediction and Prioritization of Non-Coding Variants of Uncertain Significance in Heritable Breast/Ovarian Cancer,” has been accepted for oral presentation at the BRCA: Twenty Years of Advances – The Fifth International Symposium on Hereditary Breast and Ovarian Cancer Conference in Montreal, Quebec (Apr 23-25). The authors are: E.J. Mucaki(1), N. Caminsky(1), A. Stuart(1), C. Viner(1), B. Shirley(2), […]

February 6, 2014. Biodosimetry manuscript accepted for publication

The paper that we presented at the 2013 EPR BioDose meeting is now in press in the journal:  Radiation Protection Biodosimetry: Automating dicentric chromosome detection from cytogenetic biodosimetry data Peter K. Rogan*1, Yanxin Li1, Asanka Wickramasinghe1, Akila Subasinghe1, Natasha Caminsky1, Wahab Khan1, Jagath Samarabandu1, Joan H. Knoll1, Ruth Wilkins2,  and Farrah Flegal3 1University of Western […]