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
September 7, 2015. Video presentation of Molecular Oncology article on chemotherapy response
We have published a video synopsis of : Genomic signatures for paclitaxel and gemcitabine resistance in breast cancer derived by machine learning. Stephanie N. Dorman, Katherina Baranova, Joan H.M. Knoll, Brad L. Urquhart, Gabriella Mariani, Maria Luisa Carcangiu, Peter K. Rogan. Molecular Oncology, in press. DOI: http://dx.doi.org/10.1016/j.molonc.2015.07.006
August 28, 2015. Article on chemotherapy gene signature published
The uncorrected proofs of our new paper: Genomic signatures for paclitaxel and gemcitabine resistance in breast cancer derived by machine learning. Dorman et al. Mol. Oncol. 2015 DOI: 10.1016/j.molonc.2015.07.006 are now available online at this link (http://dx.doi.org/10.1016/j.molonc.2015.07.006).
Aug. 25, 2015. Top viewed article in Molecular Cytogenetics
Our article: Reversing chromatin accessibility differences that distinguish homologous mitotic metaphase chromosomes. Wahab Khan, Peter Rogan, Joan Knoll. Molecular Cytogenetics 2015, 8:65 was published on August 13th. In less than two weeks, it has become the most viewed article in this journal for the past month, averaging 55 per day. Update: as of Sept. 6, the article is still the […]
August 13, 2015. New paper on metaphase epigenetics published
The next exciting installment of the “story” about differential accessibility of metaphase chromosomes has been published. Cytognomix’s single copy FISH technology was key to making these observations. Reversing chromatin accessibility differences that distinguish homologous mitotic metaphase chromosomes. Khan et. al. Molecular Cytogenetics 2015, 8:65 (http://www.molecularcytogenetics.org/content/8/1/65)
July 31, 2015. Chemotherapy resistance in breast cancer manuscript accepted for publication
Genomic signatures for paclitaxel and gemcitabine resistance in breast cancer derived by machine learning Authors: Stephanie N. Dormana, Katherina Baranovaa, Joan H.M. Knollb,c,d, Brad L. Urquharte, Gabriella Marianif, Maria Luisa Carcangiuf, Peter K. Rogana,d,g,h* aDepartment of Biochemistry, Schulich School of Medicine and Dentistry, University of Western Ontario, London, ON, Canada, bDepartment of Pathology and Laboratory Medicine, […]
July 11, 2015. Presentation at RegGen Satellite Meeting at 2015 ISMB Annual Meeting
We presented: “Discovery of Primary, Cofactor, and Novel Transcription Factor Binding Site Motifs by Recursive, Thresholded Entropy Minimization” by Ruipeng Lu, Eliseos Mucaki, and Peter Rogan at the Regulatory Genomics Special Interest Group meeting in Dublin, Ireland: Link to abstract
July 8, 2015. New paper using Cytognomix’s single copy FISH probes
Khan WA, Rogan PK, Knoll JH. Reversing chromatin accessibility differences that distinguish homologous mitotic metaphase chromosomes. Molecular Cytogenetics, in press. Stay tuned for posts providing details and links to the manuscript once it is available online at the journal website.
July 3, 2015. New publication on breast cancer gene mutation
FANCM c.5791C>T nonsense mutation (rs144567652) induces exon skipping, affects DNA repair activity, and is a familial breast cancer risk factor. Peterlongo et al. Hum Mol Genet. 2015 Jun 30. pii: ddv251. In this paper, we use information theory to demonstrate a new mechanism for disease mutations. It turns out that this a fairly common type […]
