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