December 1, 2016. Faux controversies in variant genomic analysis

Recently, we published 2 papers describing our unifying framework for non-coding mutation analysis (Mucaki et al. BMC Medical Genomic, 2016;, and Caminsky et al. Human Mutation, 2016;  Among the results were SNP analyses of transcription factor binding site mutations. These gene regions are very rich in variation, but only a small percentage of variants significantly alter the strengths of transcription factor binding sites. Knowing which sites are affected is important for mutation detection in these regions. The information theory-based models on which these SNP interpretations were based were obtained using a new approach just published in Nucleic Acids Research:
(Lu et al. 2016;

I am still scratching my head about the  current controversy regarding interpretation of VUS in breast cancer and other genetic diseases. I think the current focus on database discrepancies or differences in coding interpretation between commercial providers misses the key point. The pathogenic mutation yields in most exon-based sequencing studies alone are really quite poor. The amount and scope of non-coding variation completely dwarfs what is seen in coding regions.  It is a more likely explanation for significant amount of the missing heritability in inherited predisposition and congenital disease than the discrepancies in coding sequences.

I am not claiming that the variants we prioritize with our framework are definitively pathogenic, but do believe that strategies that are narrowly focused on the genetic code itself won’t advance the field or help patients much.  Clinical molecular geneticists seriously consider sequencing beyond coding regions and trying to interpret the variants detected in the regions.  The incremental costs to do this aren’t exorbitant, and the excuse of ignorance about the meaning of such variants is simply not valid any longer.

Many non-coding mutations have been proven ‘anecdotally’; studies have not been designed to determine the incidence of these types of mutations, in part due to the higher densities of variants in non-coding regions, identifying the clinically relevant ones is more daunting. This has been compounded by the lack of bioinformatic and genomic methods to generate a reliable and comprehensive and high throughput validation of variants outside of coding regions with adverse functional consequences .  Suffice it to say, there are many individual reports in the published literature, but they are not generally being systemically uncovered because of the narrow focus on changes in coding regions that affect amino acid sequences.

The problem is not only where the variants reside, but an overly conservative philosophy that fails to consider other interpretations for the effects of variants, even within coding regions. It’s not just non-coding regions that contain missing pathogenic variants, but also coding variants where the change in the amino acid code may not be the source of the disease pathology. There are actually numerous examples of this phenomenon (and a number of good reviews eg. Cartegni et al (, however most genetic testing labs (commercial or academic) do not look for them proactively. This is the problem of overreliance on databases. If the authors of a paper describing a mutation are solely focused on changes in the amino acid code (most are), the cited reference will miss this

This is an example of a breast cancer predisposing mutation that affects mRNA processing (ie. exon skipping) even though it produces a premature termination of translation or stop codon: Peterlongo et al. 2014 ( You can appreciate that if the exon containing the stop codon is spliced out prior to translation, then that particular stop codon is not activated.
Another example is this rare mutation causing  multiple Acyl-CoA dehydrogenation deficiency (Olsen et al. 2014; ​While the change appears to result in a missense mutation, it simultaneously introduces multiple RNA binding protein binding sequences for proteins that suppress exon recognition and weakens overlapping binding sequences that enhance recognition of the same exon. The result is that the exon is skipped during mRNA splicing, and the missense change is never introduced into the protein because the exon skipping event alters the reading frame of the mRNA.
In our recent review article (, we compile 203 published examples of cryptic splicing mutations involving many different disorders analyzed by information theory with experimental validation. Some of the activated cryptic splice sites are exonic and others are non-coding, ie. intronic.

There is inevitably some bias against the reporting of intronic cryptic splicing mutations, because these sequences are not routinely determined in either research or clinical studies. Besides these classes, our studies also identify variants that alter transcription factor binding site strength and mRNA stability (in untranslated regions of mRNAs).

The  exchange of mutation information about inherited breast cancer among various testing companies (except Myriad) has increased confidence in mutation interpretation. Those with rare mutations that are not shared among multiple patients do not benefit from this exchange. But these are generally  based almost entirely on variants that cause amino acid substitutions or nonsense codons. I contend that such exercises, while very useful, are simply not scalable to the true volumes of all variants found in genes, and they ignore other mechanisms of pathogenicity such as those described above.

To reiterate, my argument is that current clinical molecular diagnostic practices will continue to leave many patients without known pathogenic mutations. Until this point of view changes and we seriously focus on functional and bioinformatic methods to analyze and prioritize VUSs thoughout genes, there will be a lot of frustration about the lack of results among the companies, academics and the patients they are purporting to help. We should also question whether the cost of testing can be justified, with the knowledge that a significant amount of genetic real estate is not being sequenced nor interpreted.

Peter K. Rogan

November 30, 2016. Contract award to Cytognomix by the Government of Canada.

Cytognomix receives contract from the Build in Canada Innovation Program from the Government of Canada to test our novel ADCI software to estimate effects of exposure to ionizing radation. The project will be a collaboration with Health Canada and Canadian Nuclear Laboratories. ADCI determines the biological dose received without manual review and is suitable for evaluation of exposures in a mass casualty event.


November 27, 2016. New patent issued in Germany

US Patent No. 8,605,981 on CytoGnomix’s centromere finding algorithm, which is a key component of the Automated Dicentric Chromosome Identifier and Dose Estimator (ADCI) software, was awarded in 2013. On November 8th 2016, German patent application No. 11 2011 103 687.6 on the same invention was granted as Patent No. 11 2011103687. We note that both of the major manufacturers of automated cytogenetic image capture systems are German and we look forward to working with them.


November 12, 2016. MutationForecaster detects mutations that alter transcriptional regulation

Cytognomix‘s goal to enable complete gene or genome bioinformatic mutation interpretation for our customers and partners. We will be introducing multiple new types of mutation analyses to our MutationForecaster product over the coming year. 
We will be introducing a new type of mutation analysis to the MutationForecaster product next week. It will still use the Shannon pipeline framework to present results of genome-wide variant analysis, except that instead of splicing mutations. it will identify transcription factor binding site mutations in gene promoters
We have recently published 2 large patient-based studies where we have prioritized these and other types mutations for inherited breast cancer:
Caminsky NG, Mucaki EJ, Perri AM, Lu R, Knoll JHM and Rogan PK. Prioritizing variants in complete Hereditary Breast and Ovarian Cancer (HBOC) genes in patients lacking known BRCA mutations. Human Mutation, 37:640-52, 2016 
Mucaki, E*, Caminsky N*, Perri A, Lu R, Laederach A, Halvorsen, M, Knoll, JHM, Rogan PK. A unified analytic framework for prioritization of non-coding variants of uncertain significance in heritable breast and ovarian cancer, BMC Medical Genomics, 9:19, 2016.
The information theory based models of these transcription factor binding sites have been validated by 4 different approaches. These are described in this article (link), which will be published next week in the journal, Nucleic Acids Research


November 28, 2016. Article on transcription factor binding sites published in Nucleic Acids Research


Lu R, Mucaki E and Rogan PK. Discovery and Validation of Information Theory-Based Transcription Factor and Cofactor Binding Site Motifs,  Nucleic Acids Research. DOI: 10.1093/nar/gkw1036  (pdf)



Copyright licence (CC-BY)

Manuscript with Figures – Lu, Mucaki and Rogan, Nucl. Acids Res. 2016

Response to peer reviewers

Supplementary Methods

Supplementary – Table S1Table S2;  Table S3Table S4;  Table S5;  Table S6;  Table S7Table S8


October 19, 2016. Publication in Atlas of Science for the layperson

The Atlas of Science  has published a simplified description for the lay public of our 2016  study of gene variants in hereditary breast and ovarian cancer in BMC Medical Genomics (citation below).

Please see:  Focusing on the most relevant gene variants in inherited breast and ovarian cancer by  Eliseos Mucaki and Peter Rogan.


Original technical paper: A unified analytic framework for prioritization of non-coding variants of uncertain significance in heritable breast and ovarian cancer. Mucaki EJ, Caminsky NG, Perri AM, Lu R, Laederach A, Halvorsen M, Knoll JH, Rogan PK BMC Med Genomics. 2016 Apr 11

Sept. 23, 2016. Notice of Allowance of claims for US patent application

Cytognomix has received a notice of allowance of all claims for US Pat. App. Ser. No. 13/744,459:

Stable gene targets in breast cancer and use thereof for optimizing therapy

Inventors: Peter K. Rogan and Joan H.M. Knoll

The patent is based on our previous publication:

 Park et al. Structural and genic characterization of stable genomic regions in breast cancer: relevance to chemotherapy 2012.










August 31, 2016. New publication on predicting outcomes of hormone and chemotherapy in breast cancer

Rezaeian I, Mucaki EJ, Baranova K et al. Predicting Outcomes of Hormone and Chemotherapy in the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) Study by Biochemically-inspired Machine Learning. F1000Research 2016, 5:2124 (doi:10.12688/f1000research.9417.1)
Figure 2

July 29, 2016. The MutationForecaster Value Proposition

MutationForecaster is catching on. Researchers, clinicians and commercial laboratories are realizing the value of being able to detect and interpret mutations that other platforms miss.  Cytognomix has picked up multiple new subscribers from Germany, Switzerland, Australia,  China, and Canada this year, and subscription renewals from last year. Cytognomix continues to push the envelope, for the first time publishing papers describing a Unified framework for analyzing gene variants in non-coding and coding gene regions  and applying this framework in a large clinical study of inherited breast and ovarian cancer. These reports have led to invitations to contribute our unique expertise to interpretation of results of large inherited cancer genetic studies in the United States and in France.  These ongoing projects are showing that the effects of  mutations we predict by information theory-based approaches can be confirmed with corresponding  gene expression studies in collaborators’ laboratories. What are we working on next for the MutationForecaster suite?  

  • Adding to our Interactive Report generator to summarize key findings (currently available at MutationForecaster).
  • Incorporating our  Unified Analytical Framework for complete gene and genome sequence analysis.
  • Bespoke Consulting Services to assist you with variant analysis using our software products

This will give our customers will have access to our latest for analysis, filter and interpret their own data.  Wouldn’t you like access to these capabilities?  Subscribe! NGS sequencing itself may be more accessible and economical today than it has ever been.  What we’ve learned from our complete gene sequencing projects is that this success comes with rapidly expanding collections of gene variants, many of which have never been reported before or have been found only rarely.  Comprehensive sequencing significantly magnifies the challenges of accurate genome interpretation.  Our approach allows you to focus these large collections on only the most functionally relevant variants for review, experimental validation, and prioritization. See what others think of  MutationForecaster to gain access to our patented technologies. They are only available from Cytognomix.

May 6, 2016. Upcoming public presentations

  • Peter K. Rogan, Yanxin Li, Ruth Wilkins, Farrah Flegal, Joan HM Knoll. Radiation Dose Estimation by Automated Cytogenetic Biodosimetry, Great Lakes/Canadian Bioinformatics Conference (CCBC/GLBIO). May 16, 2016. University of Toronto (Platform Presentation).
  • Peter K. Rogan. Radiation Dose Estimation by Automated Cytogenetic Biodosimetry. Platform presentation. Great Lakes Chromosome Conference. May 20, 2016. University of Toronto.
  • Peter K. Rogan. Cisplatin Response Prediction in Recurrent Bladder Cancer using Biochemically-inspired Machine Learning. Oral and Poster presentations. 3rd International Molecular Pathological Epidemiology Meeting. May 13, 2016. Dana-Farber Cancer Institute, Boston.
  • Rezaeian I, Mucaki E, Baranova K,  Quang HP, Angelov D, Ilie L, Ngom A, Rueda L, Rogan PK. Predicting outcome of hormone and chemotherapy in the METABRIC breast cancer study. Great Lakes/Canadian Bioinformatics Conference  (GLBIO/CCBC). May 16, 2016. University of Toronto.
  • Baranova K, Mucaki EJ, Angelov D, Lizotte D, and Rogan PK. Cisplatin Response Prediction in Recurrent Bladder Cancer using Biochemically-inspired Machine Learning. Great Lakes/Canadian Bioinformatics Conference (GLBIO/CCBC). May 16, 2016. University of Toronto.
  • Lu R and Rogan PK. Predicting cis-regulation in human promoters by information density-based clustering of heterotypic transcription factor binding sites. Great Lakes/Canadian Bioinformatics Conference  (GLBIO/CCBC). May 16, 2016. University of Toronto.

April 11, 2016. New paper on analysis of variants of uncertain significance in hereditary breast & ovarian cancer



Our paper, which describes a generalized information theory-based approach for mutation analysis of protein-nucleic binding sites, has been published:

Mucaki, E, Caminsky N, Perri A, Lu R, Laederach A, Halvorsen, M, Knoll, JHM, Rogan PK. A unified analytic framework for prioritization of non-coding variants of uncertain significance in heritable breast and ovarian cancer, BMC Medical Genomics, 9:19, 2016. DOI: 10.1186/s12920-016-0178-5.   (link to paper)   (PubMed citation)



March 29, 2016. New publication on cost effectiveness of gene expression microarray testing in cancer diagnosis

Through a pan-Canadian collaboration led by Greg Zaric, we have published:

Cost-effectiveness of using a gene expression profiling test to aid in identifying the primary tumour in patients with cancer of unknown primary. M B Hannouf, E Winquist, S M Mahmud, M Brackstone, S Sarma, G Rodrigues, P Rogan, J S Hoch and G S Zaric.

The Pharmacogenomics Journal advance online publication 29 March 2016;  doi: 10.1038/tpj.2015.94  (Link)