Rabiee, H. R. | Search bioRxiv

bioRxiv2018

CANCERSIGN: a user-friendly and robust tool for identification and classification of mutational signatures and patterns in cancer genomes

Analyses of large somatic mutation datasets, using advanced computational algorithms, have revealed at least 30 independent mutational signatures in tumor samples. These studies have been instrumental in identification and quantification of responsible endogenous and exogenous molecular processes against cancer. The quantitative approach used to deconvolute mutational signatures is becoming an integral part of cancer research. Therefore, development of a stand-alone tool with a user-friendly graphical interface for analysis of cancer mutational signatures is necessary. In this manuscript, we introduce CANCERSIGN as an open access1 bioinformatics tool that uses raw mutation data (BED files) as input, and generates 3-mer and 5-mer mutational signatures. Additionally, this tool enables users to perform clustering on tumor samples based on the raw mutation counts as well as using the proportion of mutational signatures in each sample. Using this tool, we analysed all the whole genome somatic mutation datasets of International Cancer Genome Consortium (ICGC) samples and identified a number of novel signatures.

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bioRxiv2018

Deep Learning Based Proarrhythmia Analysis Using Field Potentials Recorded from Human Pluripotent Stem Cells Derived Cardiomyocytes

AimAn early characterization of drug-induced cardiotoxicity may be possible by combining comprehensive in vitro pro-arrhythmia assay and deep learning techniques. The goal of this study was to develop a deep learning method to automatically detect irregular beating rhythm as well as abnormal waveforms of field potentials in an in vitro cardiotoxicity assay using human pluripotent stem cell (hPSC) derived cardiomyocytes and multi-electrode array (MEA) system.\n\nMethods and ResultsWe included field potential waveforms from 380 experiments which obtained by application of some cardioactive drugs on healthy and/or patient-specific induced pluripotent stem cells derived cardiomyocytes (iPSC-CM). We employed convolutional and recurrent neural networks, in order to develop a new method for automatic classification of field potential recordings without using any hand-engineered features. In the proposed method, a preparation phase was initially applied to split 60-second long recordings into a series of 5-second long windows. Thereafter, the classification phase comprising of two main steps was designed. In the first step, 5-second long windows were classified using a designated convolutional neural network (CNN). In the second step, the results of 5-second long window assessments were used as the input sequence to a recurrent neural network (RNN). The output was then compared to electrophysiologist-level arrhythmia (irregularity or abnormal waveforms) detection, resulting in 0.84 accuracy, 0.84 sensitivity, 0.85 specificity, and 0.88 precision.\n\nConclusionA novel deep learning approach based on a two-step CNN-RNN method can be used for automated analysis of \"irregularity or abnormal waveforms\" in an in vitro model of cardiotoxicity experiments.

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