Dr. Peter K. Rogan presented “Reversing chromatin accessibility differences that distinguish homologous mitotic meetaphase  chromosomes,”at the Gordon Research Conference on DNA Topoisomerases in Biology and Medicine at Sunday River in Newry, ME, United States.

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.

The paper can be accessed at:   abstract or  full text (web),  or downloadable pdf .

Please contact us if you would like a copy for non-commercial use.

Our previous preprint in bioRxiv on centromere detection has been published in F1000Research:

Subasinghe A, Samarabandu J, Li Y et al. Centromere detection of human metaphase chromosome images using a candidate based method. F1000Research 2016, 5:1565 (doi:10.12688/f1000research.9075.1)   URL Link.

Our paper, “Radiation Dose Estimation by Automated Cytogenetic Biodosimetry” by Peter K. Rogan, Yanxin Li, Ruth Wilkins, Farrah N. Flegal, and Joan H. M. Knoll, has been accepted for publication in the journal Radiation Protection Dosimetry.

Figure 1. Representative processed metaphase image in ADCI:

• 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.

Video interview with Eliseos John Mucaki and Peter K Rogan by Cara Campbell on CTV News-London Ontario. April 29, 2016.

(http://london.ctvnews.ca/video?clipId=857860&binId=1.1137789&playlistPageNum=1)

The University of Western Ontario has issued a press release about our studies describing a unified framework for prioritization of mutations in breast and ovarian cancer.

Other press outlets:

• EurekAlert
• Medical Xpress
• Science Newsline
• Science Codex
• NewsUnited
• MyInforms
• My-News-Site
• BioPortfolio
• Wn.com
• Bioengineer.org
• Ovarian cancer and us
• HealthyTips
• SistersNetwork
• Sci24h
• Spjnews

Discovery Grant from Natural Sciences and Engineering Research Council received for “Unravelling the metaphase epigenome: Differences in DNA accessibility between homologous chromosomes.” Joan Knoll, Principal Applicant and Peter Rogan, Co-applicant have been awarded 5 years of funding (2016-21) for this project.

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)

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)

Peer-reviewed article in Early View in the journal Human Mutation:   Link

Cytognomix pre-qualifies for funding from Build in Canada Innovation Program. Our proposal to test the Automated Dicentric Chromosome Identifier with Health Canada and Canadian Nuclear Laboratories has been approved by Public Works and Government Services Canada (link).

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.

Paper:  LI_et_al-2016-Microscopy_Research_and_Technique

Paper:   Caminskyetal_2016 uncorrectedproof

Our new paper on interpretation of gene variants in inherited breast and ovarian cancer has been accepted for publication in the journal, Human Mutation as a Research Article.

“Prioritizing variants in complete Hereditary Breast and Ovarian Cancer (HBOC) genes in patients lacking known BRCA mutations,” by Natasha G. Caminsky¹, Eliseos J. Mucaki¹, Ami M. Perri¹, Ruipeng Lu², Joan HM. Knoll^3,4 and Peter K. Rogan1,2,4,5.

¹Department of Biochemistry, Schulich School of Medicine and Dentistry, Western University, London, Canada, N6A 2C1, ²Department of Computer Science, Faculty of Science, Western University, London, Canada, N6A 2C1, ³Department of Pathology and Laboratory Medicine, Schulich School of Medicine and Dentistry, Western University, London, Canada, N6A 2C1, ⁴Cytognomix Inc. London, Canada, ⁵Department of Oncology, Schulich School of Medicine and Dentistry, Western University, London, Canada, N6A 2C1

A preprint of this article is published at http://biorxiv.org/content/early/2016/02/09/039206

We have added new capabilities to Variant Effect Predictor. Exome sequencing reveals many variants that have little or no effect on phenotype. You can remove these variants in MutationForecaster with our new stringency filters. Different default levels of filtering are offered. These can also be customized based on allele frequencies, predicted SIFT, Polyphen, variant type (eg. synonymous change), or protein coding domain containing the variant.

Yanxin Li¹, Joan H. Knoll^2,3, Ruth Wilkins⁴, Farrah N. Flegal⁵, and Peter K. Rogan^1,3*    Automated Discrimination of Dicentric and Monocentric Chromosomes by Machine Learning-based Image Processing. Departments of ¹Biochemistry, and ²Pathology and Laboratory Medicine, Schulich School of Medicine and Dentistry, University of Western Ontario, ³Cytognomix Inc., ⁴Health Canada, and ⁵Canadian Nuclear Laboratories.

in the journal Microscopy Research and Technique.

Abstract:  Dose from radiation exposure can be estimated from dicentric chromosome (DC) frequencies in metaphase cells of peripheral blood lymphocytes.  We automated DC detection by extracting features in Giemsa-stained metaphase chromosome images and classifying objects by machine learning (ML).  DC detection involves i) intensity thresholded segmentation of metaphase objects, ii) chromosome separation by watershed transformation and elimination of inseparable chromosome clusters, fragments and staining debris using a morphological decision tree filter, iii) determination of chromosome width and centreline, iv) derivation of centromere candidates and v) distinction of DCs from monocentric chromosomes (MC) by ML. Centromere candidates are inferred from 14 image features input to a Support Vector Machine (SVM). 16 features derived from these candidates are then supplied to a Boosting classifier and a second SVM which determines whether a chromosome is either a DC or MC. The SVM was trained with 292 DCs and 3135 MCs, and then tested with cells exposed to either low (1 Gy) or high (2-4 Gy) radiation dose.  Results were then compared with those of 3 experts. True positive rates (TPR) and positive predictive values (PPV) were determined for the tuning parameter, sigma. At larger sigma,  PPV decreases and TPR increases.  At high dose, for sigma= 1.3, TPR = 0.52 and PPV = 0.83, while at sigma= 1.6, the TPR = 0.65 and PPV = 0.72.  At low dose and sigma = 1.3, TPR = 0.67 and PPV = 0.26. The algorithm differentiates DCs from MCs, overlapped chromosomes and other objects with acceptable accuracy over a wide range of radiation exposures.

A preprint of the paper is available at bioRxiv: http://biorxiv.org/content/early/2016/01/19/037309