Sept. 7, 2019. Pan cancer article and database update

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 ( 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 mRNA splicing mutations. These are now indicated in the ValidspliceMut ( query results. The majority of variants cause aberrant splicing or are likely aberrant. Interestingly, a significant fraction of variants that have the same information, expression, population frequency properties as ClinVar variants designated as “benign” cause allele-specific alternative splicing.

Figure 1 of Pan-cancer repository of validated natural and cryptic mRNA splicing mutations. F1000Research 2019, 7:1908

March 20, 2019. Presentation at the 2019 American College of Medical Genetics and Genomics annual conference

The following  paper has been accepted for presentation:

 “Pan-Cancer Repository of Validated Natural and Cryptic mRNA Splicing Mutations”, 

Category: “Laboratory genetics and genomics”, Abstract Poster Number: 754     (link to Abstract)

Where: Exhibit hall, Washington Convention Center, ACMG Clinical Genetics Meeting in Seattle, Washington

When: April 2 – 6, 2019;  Poster presentation time: Friday, 4/5 from 10:30am-12:00pm

This work will be available as an ePoster AS WELL AS being presented in printed format on a poster board during the Annual Meeting.  Details to access the ePoster will be available soon.





July 13, 2014. Presentations at the 2014 American Society of Human Genetics Conference

Cytognomix will be presenting several papers at the upcoming ASHG annual meeting (October 18-22, 2014, San Diego):

Using information theory to analyze and predict splicing mutations in rare and common diseases: performance and best practices. N.GCaminsky, E. Mucaki and P.K. Rogan

Reversing differences in chromatin accessibility that distinguish homologous mitotic metaphase chromosomes. W.A. Khan, P.K. Rogan, J.H.M. Knoll

Automated Dicentric Chromosome Identification by Machine Learning-based Image Processing. P.K. Rogan, Y. Li, A. Subasinghe, J. Samarabandu, R. Wilkins and J.H. Knoll

Towards the minimal breast cancer genome and its relevance to chemotherapy. S.N. Dorman, J.H. Knoll, K. Baranova, C. Viner, P.K. Rogan

The FANCM c.5791C>T nonsense mutation (rs144567652) induces exon skipping and is a risk factor for familial breast cancer. Paolo Peterlongo ,  Francesca Damiola, Eliseos Mucaki,  Valentina Dall’Olio ,Sara Pizzamiglio  , Irene Catucci ,  Anders Kvist , Paolo Verderio, Mara Colombo , Loris Bernard ,  Hans Ehrencrona, Laura Caleca, Valeria Pensotti , Sylvie Mazoyer, Peter K. Rogan ,Paolo Radice


Please contact us if you would like to meet or discuss this work.

January 4, 2013. Paper: “Predicting mRNA transcript isoforms derived from splicing mutations”, ASSEDA server

Volume 34, Issue 4

“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; 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: 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[4], 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)

How to: Protocol for Geostatistical Determination of Radiation Dosimetry Maps of Population-Scale Exposures 

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:

Overview of Cloud version- ADCI_Online

Presentation to the International Atomic Energy Agency (CRP E35010)

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,    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. F1000Research7(233), p.233.   Link to open access article:

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:

Pan-cancer repository of validated natural and cryptic mRNA.ePoster

Interactive Website: Gene signatures for chemotherapy drug response

Demo (Windows): Automated Dicentric Chromosome Identifier and Dose Estimator  

Review on information theory-based splicing mutation analysis:

Caminsky et al. 2014, Videos describing this paper: short and long versions.

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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.