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.
MutationForecaster® combines 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 .
You can now experience our integrated suite of genome interpretation products through a free trial of MutationForecaster®. Once you register, analyze datasets that we have analyzed in our peer-reviewed publications with any of our software tools.
Ionizing radiation produces characteristic chromosome changes. The altered chromosomes contain two central constrictions, termed centromeres, instead of one (known as dicentric chromosomes [DCs]). Chromosome biodosimetry is approved by the IAEA for occupational radiation exposure, radiation emergencies, or monitoring long term exposures. In emergency responses to a range of doses, labs need efficient methods that identify DCs.
Cytognomix has developed a novel approach to find DCs that is independent of chromosome length, shape and structure from different laboratories (paper: 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 based algorithms with high sensitivity and specificity that 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 all types of commercial metaphase scanning systems, selects high quality cells for analysis, identifies dicentric chromosomes (removing false positives), 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 that others cannot with advanced, patented genomic probe and bioinformatic technologies. Cytognomix continues our long track record of creating technologies for genomic medicine. We anticipate and implement the needs of the biomedical and clinical genomics communities.
Browse the products section of the menu found in the header bar for more information regarding any of our services.
- Don’t want to run your own analyses on MutationForecaster®? Let us do it for you with our Bespoke Analysis Service.
- Customized genomic microarrays
- Ultrahigh resolution FISH probes:
- Microarray-based comparative genomic hybridization (aCGH) can use SC technology to increase reproducibility and reduce cost per sample.
Version 3 of Geostatistical Analysis of SARS-CoV-2 in the United States dataset published (http://doi.org/10.5281/zenodo.3950057). Contains hotspot and cluster analysis of daily case counts across all US counties from Marcy 25-July 13, 2020. #covid-19 #SARSCov2 #geostatistics #hotspot
CytoGnomix has coauthored a new article on collaborative gene variant interpretation applied to selection of cancer therapy: Shruti Rao, Beth Pitel, Alex H Wagner, Simina M Boca, Matthew McCoy, Ian King, Samir Gupta, Ben Ho Park, Jeremy L Warner, James Chen, Peter K Rogan, Debyani Chakravarty, Malachi Griffith, Obi L Griffith, Subha Madhava. Collaborative, Multidisciplinary Evaluation of Cancer Variants Through Virtual Molecular Tumor Boards Informs Local Clinical Practices. DOI: […]
We have consolidated our geostatistical biodosimetry contributions at Grow Kudos. We have been developing a new application of this technology for tracking hotspots of infections during the current COVID-19 pandemic. This animation shows geostatistically significant hotspot counties in the US relative to 3 nearest neighbors from March through June 2020. Please stay tuned for more:
April 24, 2020. Schulich School of Medicine and Dentistry highlights PLOS ONE study on radiation dose estimation and COVID19
Rogan PK, Mucaki EJ, Lu R, Shirley BC, Waller E, Knoll JHM (2020) Meeting radiation dosimetry capacity requirements of population-scale exposures by geostatistical sampling. PLoS ONE 15(4): e0232008. https://doi.org/10.1371/journal.pone.0232008
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 […]