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
December 6, 2016. Finding mutations in transcription factor binding sites with MutationForecaster
Genome-scale transcription factor binding site analysis now available on MutationForecaster: Cytognomix‘s goal is to enable complete gene or genome bioinformatic mutation interpretation for our customers and partners. We will be introducing multiple new types of mutation analyses to our MutationForecaster ® product over the coming year. We are excited to announce the first of these […]
December 1, 2016. Faux controversies in variant genomic analysis
Recently, we published 2 papers describing our unifying framework for non-coding mutation analysis (Mucaki et al. BMC Medical Genomic, 2016; http://bmcmedgenomics.biomedcentral.com/articles/10.1186/s12920-016-0178-5, and Caminsky et al. Human Mutation, 2016; http://onlinelibrary.wiley.com/doi/10.1002/humu.22972/full). Among the results were SNP analyses of transcription factor binding site mutations. These gene regions are very rich in variation, but only a small percentage of […]
November 30, 2016. Contract award to Cytognomix by the Government of Canada.
Cytognomix receives contract from the Build in Canada Innovation Program from the Government of Canada to test our novel ADCI software to estimate effects of exposure to ionizing radation. The project will be a collaboration with Health Canada and Canadian Nuclear Laboratories. ADCI determines the biological dose received without manual review and is suitable for evaluation […]
November 27, 2016. New patent issued in Germany
US Patent No. 8,605,981 on CytoGnomix’s centromere finding algorithm, which is a key component of the Automated Dicentric Chromosome Identifier and Dose Estimator (ADCI) software, was awarded in 2013. On November 8th 2016, German patent application No. 11 2011 103 687.6 on the same invention was granted as Patent No. 11 2011103687. We note that both […]
November 12, 2016. MutationForecaster detects mutations that alter transcriptional regulation
Cytognomix‘s goal to enable complete gene or genome bioinformatic mutation interpretation for our customers and partners. We will be introducing multiple new types of mutation analyses to our MutationForecaster product over the coming year. We will be introducing a new type of mutation analysis to the MutationForecaster product next week. It will still use the Shannon […]
November 28, 2016. Article on transcription factor binding sites published in Nucleic Acids Research
Citation: Lu R, Mucaki E and Rogan PK. Discovery and Validation of Information Theory-Based Transcription Factor and Cofactor Binding Site Motifs, Nucleic Acids Research. DOI: 10.1093/nar/gkw1036 (pdf) Download: Copyright licence (CC-BY) Manuscript with Figures – Lu, Mucaki and Rogan, Nucl. Acids Res. 2016 Response to peer reviewers Supplementary Methods Supplementary – Table S1; Table S2; Table S3; Table […]
October 19, 2016. Publication in Atlas of Science for the layperson
The Atlas of Science has published a simplified description for the lay public of our 2016 study of gene variants in hereditary breast and ovarian cancer in BMC Medical Genomics (citation below). Please see: Focusing on the most relevant gene variants in inherited breast and ovarian cancer by Eliseos Mucaki and Peter Rogan. (http://atlasofscience.org/focusing-on-the-most-relevant-gene-variants-in-inherited-breast-and-ovarian-cancer/#more-16892) Original technical […]
Sept. 23, 2016. Notice of Allowance of claims for US patent application
Cytognomix has received a notice of allowance of all claims for US Pat. App. Ser. No. 13/744,459: Stable gene targets in breast cancer and use thereof for optimizing therapy Inventors: Peter K. Rogan and Joan H.M. Knoll The patent is based on our previous publication: Park et al. Structural and genic characterization of stable genomic regions […]
