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
- Customized genomic microarrays
- Ultrahigh resolution FISH probes (article):
- Microarray-based comparative genomic hybridization (aCGH) can use SC technology to increase reproducibility and reduce cost per sample.
Latest Posts
April 18, 2017. US Patent 9,624,549 issued about chemotherapy target identification
US Patent 9,624,549 has been issued! “Stable gene targets in breast cancer and use thereof for optimizing therapy. ” Peter K Rogan and Joan Knoll. (link) This technology is the basis of biochemically inspired chemotherapy prediction by machine learning (http://chemotherapy.cytognomix.com).
April 8, 2017. Presentation about chemotherapy outcome prediction
Rogan PK, Mucaki EJ, Baranova K, Dorman S, Knoll JHM. Predicting responses to chemotherapies by biochemically-inspired machine learning. Innovative Approaches to Optimal Cancer Care in Canada, Canadian Partnership against Cancer, Toronto, Apr. 6-8, 2017. (Link to abstract)
March 31, 2017. New preprint on increased accuracy in radiation biodosimetry
Accurate Cytogenetic Biodosimetry Through Automation Of Dicentric Chromosome Curation And Metaphase Cell Selection Jin Liu, Yaking Li, Ruth Wilkins, Farrah Flegal, Joan H. M. Knoll, Peter K. Rogan. bioRxiv, doi: https://doi.org/10.1101/120410 Abstract: Software to automate digital pathology relies on image quality and the rates of false positive and negative objects in these images. Cytogenetic biodosimetry […]
February 27, 2017. CytoGnomix finalizes contract with Government of Canada
CytoGnomix has finalized our contract with Public Works Government Services Canada under the Build in Canada Innovation Program. This agreement licenses the Automated Dicentric Chromosome Identifier (ADCI) to the Consumer and Clinical Radiation Protection Bureau at Health Canada and Canadian Nuclear Laboratories and provides on-site training to these labs. These biodosimetry reference labs will test […]
Jan. 28, 2017. New version of F1000Research paper on chemotherapy response in breast cancer
We have published a new version of: Predicting Outcomes of Hormone and Chemotherapy in the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) Study by Biochemically-inspired Machine Learning. F1000Research 2017, 5:2124 (doi:10.12688/f1000research.9417.2) The revision addresses the comments of the reviewers and adds several new analyses and results. Among our findings was the discovery of significant batch effects that, respectively, differentiate gene expression […]
January 25, 2017. Comment from the Transforming Genetic Medicine Initiative Blog
From: http://www.thetgmi.org/genetics/missed-diagnoses-and-misdiagnoses/
Jan. 23, 2017. Automated interpretation of digital pathology images is currently at an embryonic stage of development
Counting pixel area and pixel intensities (stained antibodies, DNA or RNA) does not determine the identities of the cellular objects that are labeled. The challenge is that every microscope field exhibits different morphology, so traditional image segmentation algorithms aimed at identifying specific subcellular components may not be reliable. We need to be clever to ferret […]
December 13, 2016. Postdoctoral Position available for high performance computing application in radiation biodosimetry
A postdoctoral position is available to work on a newly funded high-performance computing project: “Automated Cytogenetic Dosimetry as a Public Health Emergency Medical Countermeasure.” This 2 year project is supported by the SOSCIP-TalentEdge program. Candidates should be qualified in C++ development, preferably with experience in parallel computing. The position is at Western University in combination with the […]
