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.
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- 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.
Manuscript describing accurate genomic signatures of radiation exposure will be published shortly by F1000Research. Jonathan ZL Zhao, Eliseos J Mucaki, Peter K Rogan. Predicting Exposure to Ionizing Radiation by Biochemically-Inspired Genomic Machine Learning, F1000Research, in press. Abstract: Background: Gene signatures derived from transcriptomic data using machine learning methods have shown promise for biodosimetry testing. These […]
Ali, S, Li Y, Shirley B, Wilkins R, Flegal F, Rogan PK, Knoll JHM. Population scale biodosimetry with the Automated Dicentric Chromosome Identifier and Dose Estimator (ADCI) software system. [Platform] Rogan PK, Zhao JZL, and Mucaki EJ. Predicting exposure to ionizing radiation by biochemically-inspired genomic machine learning.[Poster] Li Y, Shirley B, Wilkins R, Flegal, F, […]
May 4, 2015. Comment on PMID 23348723. Prediction of mutant mRNA splice isoforms by information theory-based exon definition.
Peter Rogan 2015 May 04 6:14 p.m. The Logic and Formulation of Exon Definition for Splice and Splicing Regulatory Sites with Negative Information Content. PK Rogan, EJ Mucaki Update on: Mucaki EJ, 2013 and the Automated Splice Site and Exon Definition Analysis server (ASSEDA). In Mucaki EJ, 2013, we described a method of predicting the overall strength of an exon […]
Oct. 1, 2017. Comment on PubMed PMID 28949076: Rules and tools to predict the splicing effects of exonic and intronic mutations. In: PubMed Commons [Internet]. Bethesda (MD): National Library of Medicine; 2017 Sep 26
Peter Rogan2017 Oct 01 8:57 p.m. We would like to alert readers to the fact that information theory-based splicing mutation analysis has been used to analyze a wide range of variants (in/dels and SNVs) that affect splicing in introns and exons in peer reviewed studies. These tools have been used analyze mutations that alter branchpoint […]
Dec. 7, 2017. Rogan PK, Mucaki EJ. Comment on PMID 29185120: Characterization of a novel germline BRCA1 splice variant, c.5332+4delA. In: PubMed Commons [Internet]. Bethesda (MD): National Library of Medicine; 2017 Nov 28 [cited 2017 Dec 7].
Peter Rogan2017 Dec 07 5:24 p.m. We have analyzed this mutation with the Automated Splice Site and Exon Definition Analysis server (ASSEDA). The 1 nt deletion in the splice donor of exon 20 reduces the strength of this site from 11.5 -> 4.1 bits. (100/[27.4 bits] = 0.6% binding affinity) The information theory-based approach used in […]
Dec 12, 2017. Comment on PubMed PMID 23169495: Analysis of the effects of rare variants on splicing identifies alterations in GABAA receptor genes in autism spectrum disorder individuals.
Rogan PK, Mucaki EJ. Comment on PMID 23169495: Analysis of the effects of rare variants on splicing identifies alterations in GABAA receptor genes in autism spectrum disorder individuals. In: PubMed Commons [Internet]. Bethesda (MD): National Library of Medicine; 2012 Nov 21 [cited 2017 Dec 12]. Peter Rogan2017 Dec 12 09:53 a.m Regarding GABRQ:c.306G>C: Whereas none of the splicing analysis […]
Jan. 13 and 21, 2018. Comments on PubMed PMID 29280214: Thorough in silico and in vitro cDNA analysis of 21 putative BRCA1 and BRCA2 splice variants and a complex tandem duplication in BRCA2, allowing the identification of activated cryptic splice donor sites in BRCA2 exon 11.
We have posted a comment in PubMed Commons about Baert et al. “Thorough in silico and in vitro cDNA analysis of 21 putative BRCA1 and BRCA2 splice variants and a complex tandem duplication in BRCA2, allowing the identification of activated cryptic splice donor sites in BRCA2 exon 11.” (2017) (doi: 10.1002/humu.23390). The updated comments can […]
Mucaki et al. Predicting Response to Platin Chemotherapy Agents with Biochemically-inspired Machine Learning. bioRxiv. https://doi.org/10.1101/231712 Abstract Selection of effective drugs that accurately predict chemotherapy response could improve cancer outcomes. We derive optimized gene signatures for response to common platinum-based drugs, cisplatin, carboplatin, and oxaliplatin, and respectively validate each with bladder, ovarian, and colon cancer patient […]