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.
August 31, 2016. New publication on predicting outcomes of hormone and chemotherapy in breast cancer
Rezaeian I, Mucaki EJ, Baranova K et al. Predicting Outcomes of Hormone and Chemotherapy in the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) Study by Biochemically-inspired Machine Learning. F1000Research 2016, 5:2124 (doi:10.12688/f1000research.9417.1) Paper URL: http://f1000research.com/articles/5-2124/v1
Dr. Peter K. Rogan presented “Reversing chromatin accessibility differences that distinguish homologous mitotic meetaphase chromosomes,”at the Gordon Research Conference on DNA Topoisomerases in Biology and Medicine at Sunday River in Newry, ME, United States.
MutationForecaster is catching on. Researchers, clinicians and commercial laboratories are realizing the value of being able to detect and interpret mutations that other platforms miss. Cytognomix has picked up multiple new subscribers from Germany, Switzerland, Australia, China, and Canada this year, and subscription renewals from last year. Cytognomix continues to push the envelope, for the […]
The paper can be accessed at: abstract or full text (web), or downloadable pdf . Please contact us if you would like a copy for non-commercial use.
Our previous preprint in bioRxiv on centromere detection has been published in F1000Research: Subasinghe A, Samarabandu J, Li Y et al. Centromere detection of human metaphase chromosome images using a candidate based method. F1000Research 2016, 5:1565 (doi:10.12688/f1000research.9075.1) URL Link.
Our paper, “Radiation Dose Estimation by Automated Cytogenetic Biodosimetry” by Peter K. Rogan, Yanxin Li, Ruth Wilkins, Farrah N. Flegal, and Joan H. M. Knoll, has been accepted for publication in the journal Radiation Protection Dosimetry. Figure 1. Representative processed metaphase image in ADCI:
Peter K. Rogan, Yanxin Li, Ruth Wilkins, Farrah Flegal, Joan HM Knoll. Radiation Dose Estimation by Automated Cytogenetic Biodosimetry, Great Lakes/Canadian Bioinformatics Conference (CCBC/GLBIO). May 16, 2016. University of Toronto (Platform Presentation). Peter K. Rogan. Radiation Dose Estimation by Automated Cytogenetic Biodosimetry. Platform presentation. Great Lakes Chromosome Conference. May 20, 2016. University of Toronto. Peter K. Rogan. […]
Video interview with Eliseos John Mucaki and Peter K Rogan by Cara Campbell on CTV News-London Ontario. April 29, 2016. (http://london.ctvnews.ca/video?clipId=857860&binId=1.1137789&playlistPageNum=1)