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.
June 7, 2018. Presentations at upcoming international conferences
Population scale biodosimetry with the Automated Dicentric Chromosome Identifier and Dose Estimator (ADCI) software system. (Platform) Rogan PK, Ali, S, Li Y, Shirley B, Wilkins R, Flegal F, Cooke R, Peerlaproulx T, Waller E, Knoll JHM. EPR Biodose 2018, June 11-15, Munich Germany Optimization of image selection in Automated Dicentric Chromosome Analysis. […]
May 30, 2018. Radiation gene signature study approved and indexed in PubMed.
May 29, 2018. Change to cytognomix.org URL
As of today, we have transitioned the website cytognomix.org to: http://academic.cytognomix.com The site contains our published articles, lectures and presentations about human genetics and molecular biology. All of the legacy content at this site (1980-2007) will be preserved on the new site. Please update your browser bookmarks to reflect this change.
March 29, 2018. Mutation Forecaster: Key to discovery of mutations in novel ALS gene
CytoGnomix’s Mutation Forecaster: Key to discovery of mutations in novel ALS gene. Nicolas et al. Genome-wide Analyses Identify KIF5A as a Novel ALS Gene. Neuron 97:1268-1283.e6, 2018 http://www.cell.com/neuron/fulltext/S0896-6273(18)30148-X From our subscriber, Dr. John Landers (U. Mass. School of Medicine): “We used the application ASSEDA (Automated Splice Site and Exon Definition Analyses) to predict any mutant […]
April 9, 2018. Upcoming release of Automated Dicentric Chromosome Identifier and Dose Estimator (ADCI)
We have just completed porting Windows ADCI from the MinGW C++ (32 bit) to Microsoft’s C++ (64 bit) compiled version. The release of this software in summer 2018 will contain this new version (v 2.0). ADCI now has access to 8 Gb of runtime memory, which should allow twice as many samples to be batch […]
March 23, 2018. New preprint about target gene regulation by transcription factors
Clustered, information-dense transcription factor binding sites identify genes with similar tissue-wide expression profiles. BioRxiv, 2018. doi: https://doi.org/10.1101/283267
March 13, 2018. Oral presentation on chemotherapy response in Best Poster session at ESHG 2018
On behalf of the Scientific Programme Committee of the European Conference of Human Genetics 2018 taking place in Milan, Italy from June 16 to June 19, 2018, we are pleased to inform you that the abstract entitled: ‘Comprehensive prediction of responses to chemotherapies by biochemically-inspired machine learning’ (Control No. 2018-A-2095-ESHG) was among the best scored […]
February 28, 2018. New publication on radiation biodosimetry based upon machine learning
We have published a new approach to devise gene signatures to detect radiation exposure (human, murine), and to quantify levels of exposure (murine): Zhao JZL, Mucaki EJ and Rogan PK. Predicting ionizing radiation exposure using biochemically-inspired genomic machine learning. F1000Research 2018, 7:233 (doi: 10.12688/f1000research.14048.1)