Misclassification of patients with underlying disorders by otherwise accurate radiation gene signatures compromises their utility for population-scale radiation exposure assessment. Underlying conditions modify the normal baseline values of biomarkers used for diagnostic analysis of radiation exposure. The collective frequency of these conditions would confound efforts to assess radiation exposures in a mass casualty event, affecting determination of eligibility for radiation-mitigating therapies.
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])
Revised version of our review article on mRNA splicing in F1000Research has been highlighted in the Faculty of 1000 blog:
The blog entry includes links to our YouTube video:
Short version (3:42)
Long version (9:41)
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 Cyto Visualization 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.
Don’t want to run your own analyses? Let us do it for you with our Bespoke Analysis Service.
“Prediction of mutant mRNA splice isoforms by information theory-based exon definition,” by Eliseos Mucaki, Ben Shirley and Peter Rogan has been accepted for publication by the journal Human Mutation.
Abstract. Mutations that affect mRNA splicing often produce multiple mRNA isoforms, resulting in complex molecular phenotypes. Definition of an exon and its inclusion in mature mRNA relies on joint recognition of both acceptor and donor splice sites. This study predicts cryptic and exon skipping isoforms in mRNA produced by splicing mutations from the combined information contents (Ri, which measures binding site affinity) and distribution of the splice sites defining these exons. The total information content of an exon (Ri,total) is the sum of the Ri values of its acceptor and donor splice sites, adjusted for the distance separating these sites, ie. the gap surprisal. Differences between total exon information contents (ΔRi,total) are predictive of the relative abundance of these exons in distinct processed mRNAs. Constraints on splice site and exon selection are used to eliminate non-conforming and poorly expressed isoforms. Molecular phenotypes are computed by the Automated Splice Site and Exon Definition Analysis server (ASSEDA; http://splice.uwo.ca). Predictions of splicing mutations were highly concordant (85.2%; n=61) with published expression data. In silico exon definition analysis will contribute to streamlining assessment of abnormal and normal splice isoforms resulting from mutations.
Update: The paper is now available online from the Journal website: DOI: 10.1002/humu.22277 and is cited on PubMed.
Update 2: John Mucaki has produced a Video Tutorial on using the ASSEDA server on YouTube.
Update 3: The accepted paper has now been copyedited, typeset and published online: http://onlinelibrary.wiley.com/doi/10.1002/humu.22277/abstract. Supplementary data are available as well. (2-21-2013)
Update 4: Annual subscriptions to the Automated Splice Site and Exon Definition server are available through Cytognomix (2-22-2013).
Update 5: The paper has been highlighted in the April 2013 issue of the Journal, where it appeared. Bing Yu, University of Sydney, authored the commentary (Vol 34, page v).
Update 6: Mucaki EJ., Shirley BC, and Rogan PK. Prediction of Mutant mRNA Splice Isoforms by Information Theory-Based Exon Definition has been published in print. Human Mutation, April 2013, Volume 34 (4), pages 557–565. The journal has made the paper FREE for anyone to download.
Links to the latest CytoGnomix products:
Applications and consulting in Geostatistical Epidemiology
Monitoring and discriminating infectious disease hotspots from high disease burden regions, eg. for COVID-19:
Zenodo repository: Geostatistical Analysis of SARS-CoV-2 Positive Cases in the United States
Defence Canada IDEaS project: Locating emerging COVID19 hotspots in Ontario after community transmission by time-correlated, geospatial analysis
Addressing large scale radiation incidents and accidents:
Article in PLOS One: Meeting radiation dosimetry capacity requirements of population-scale exposures …. (Funded by High performance computing consortium: SOSCIP and CytoGnomix)
Large scale Radiation Biodosimetry
Capacity of supercomputer version of Automated Dicentric Chromosome Identifier and Dose Estimator (ADCI) software: Automated Cytogenetic Biodosimetry at Population-Scale and Radiation, Radiation, 2021 (link to published article).
Scalable, democratized access to ADCI:
Gene Expression Signatures for Radiation Biodosimetry
Mucaki, E.J., Shirley, B.C. and Rogan, P.K., 2021. Improved radiation expression profiling in blood by sequential application of sensitive and specific gene signatures. International Journal of Radiation Biology, doi.org/10.1080/09553002.2021.1998709 Link to pdf: Improved radiation expression profiling…
Zhao, J.Z., Mucaki, E.J. and Rogan, P.K., 2018. Predicting ionizing radiation exposure using biochemically-inspired genomic machine learning. F1000Research, 7(233), p.233. Link to open access article: https://f1000research.com/articles/7-233
Large Scale Repository of Cancer Splicing Mutations
Pan-cancer repository of validated natural and cryptic mRNA splicing mutations (a major public resource of mRNA splicing mutations validated according to multiple lines of evidence of abnormal gene expression. )
Article in F1000Research: Pan-Cancer repository of …..
Presentation at the 2019 American College of Medical Genetics Annual Meeting: