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.
We present a new approach for estimating exposures in radiation incidents or accidents using geolocated dosimetry data: Rogan P, Mucaki E, Lu R, Shirley B, Waller E, and Knoll J. Meeting radiation dosimetry capacity requirements of population-scale exposures with geostatistical sampling, PLoS ONE 15(4): e0232008. [https://dx.doi.org/10.1371/journal.pone.0232008]. The MedRxiv version contains a Note Added in Proof […]
Mucaki, E.J., Shirley, B.C. and Rogan, P.K., 2020. Expression changes confirm genomic variants predicted to result in allele-specific, alternative mRNA splicing. Frontiers in Genetics, 11: 109 (https://www.frontiersin.org/articles/10.3389/fgene.2020.00109/full). (preprint in bioRxiv, 549089 [doi: https://doi.org/10.1101/549089])
Nov. 18, 2019. Final version of review article on prediction of chemotherapy response by machine learning now published
Multigene signatures of responses to chemotherapy derived by biochemically-inspired machine learning Journal:Molecular Genetics and Metabolism Volume 128, Issues 1–2, September–October 2019, Pages 45-52 PK Rogan. link to Article Rogan Mol Genet Metab 128_45-52_2019 (pdf)
We have published another revision to our pan-cancer splicing mutation database paper: Shirley BC, Mucaki EJ and Rogan PK. Pan-cancer repository of validated natural and cryptic mRNA splicing mutations F1000Research 2019, 7:1908 (https://f1000research.com/articles/7-1908/v3). The paper is already indexed in PubMed. In this version, we derive a simplified variant classification scheme with ClinVar designations calibrated to the molecular phenotypes of […]
New review article in Molecular Genetics and Metabolism about predicting responses to chemotherapy: Multigene signatures of responses to chemotherapy derived by biochemically inspired machine learning Published: https://doi.org/10.1016/j.ymgme.2019.08.005 Abstract: Pharmacogenomic responses to chemotherapy drugs can be modeled by supervised machine learning of expression and copy number of relevant gene combinations. Such biochemical evidence can form the […]
“Automated Cytogenetic Biodosimetry at Population-Scale” PK Rogan, R Lu, E Mucaki, S Ali, B Shirley, Y Li, R Wilkins, F Norton, O Sevriukova, D Pham, E Ainsbury, J Moquat, R Cooke, T Peerlaproulx, E Waller, JHM Knoll https://www.biorxiv.org/content/10.1101/718973v1 (doi: https://doi.org/10.1101/718973) Introduction The dicentric chromosome (DC) assay accurately quantifies exposure to radiation, however manual and semi-automated assignment of DCs has limited its use for a potential large-scale radiation incident. The Automated Dicentric Chromosome Identifier and Dose Estimator Chromosome (ADCI) software automates unattended DC detection and determines radiation exposures, fulfilling […]
July 4, 2019. Presentations describing interlaboratory comparison of radiation exposure determination by automated cytogenetic biodosimetry
We will be presenting: Determination of radiation exposure levels by fully automated dicentric chromosome analysis: Results from IAEA MEDBIODOSE (CRP E35010) interlaboratory comparison at both the 19th International Congress of Radiation Research (Aug. 25-29, 2019) and the 12th International Symposium on Chromosome Aberrations (Aug. 27, 2019) in Manchester, UK. This study compared the performance of […]
We will present a new geostatistical approach to reduce biodosimetry workload in a large scale nuclear event at the International Congress of Radiation Research in Manchester UK, 25-20 August, 2019: (link to full abstract […]