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

Citation:

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)

 

Download:

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.

(http://atlasofscience.org/focusing-on-the-most-relevant-gene-variants-in-inherited-breast-and-ovarian-cancer/#more-16892)

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.

graphicalabstractParketal

 

 

 

 

 

 

 

 

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

BMCMedGenomics

 

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)

March 10, 2016. New paper accepted on prioritization strategy for gene variants of uncertain significance in breast/ovarian cancer

Our paper:

“A unified analytic framework for prioritization of non-coding variants of uncertain significance in heritable breast and ovarian cancer,” by
Eliseos J. Mucaki; Natasha G. Caminsky; Ami M. Perri; Ruipeng Lu; Alain Laederach; Matthew Halvorsen; Joan H.M. Knoll; and Peter K. Rogan

has been accepted for publication in the journal, BMC Medical Genomics.

A preprint of this article is currently available at BioRxiv:  http://biorxiv.org/content/early/2015/11/11/031419.