Sreelatha Ganapathy1, Aparna Muraleedharan2, Puthumangalathu Savithri Sathidevi3, Parkash Chand4, Ravi Philip Rajkumar5. 1. Department of Electronics & Communication Engineering, National Institute of Technology Calicut, India. Electronic address: sreelatha_pec12@nitc.ac.in. 2. Department of Anatomy, Pondicherry Institute of Medical Sciences (PIMS), Kalapet Puducherry, India. 3. Department of Electronics & Communication Engineering, National Institute of Technology Calicut, India. 4. Department of Anatomy, Jawaharlal Institute of Postgraduate Medical Education & Research (JIPMER), Puducherry, India. 5. Department of Psychiatry, Jawaharlal Institute of Postgraduate Medical Education & Research (JIPMER), Puducherry, India.
Abstract
BACKGROUND AND OBJECTIVE: DNA damage analysis plays an important role in determining the approaches for treatment and prevention of various diseases like cancer, schizophrenia and other heritable diseases. Comet assay is a sensitive and versatile method for DNA damage analysis. The main objective of this work is to implement a fully automated tool for the detection and quantification of DNA damage by analysing comet assay images. METHODS: The comet assay image analysis consists of four stages: (1) classifier (2) comet segmentation (3) comet partitioning and (4) comet quantification. Main features of the proposed software are the design and development of four comet segmentation methods, and the automatic routing of the input comet assay image to the most suitable one among these methods depending on the type of the image (silver stained or fluorescent stained) as well as the level of DNA damage (heavily damaged or lightly/moderately damaged). A classifier stage, based on support vector machine (SVM) is designed and implemented at the front end, to categorise the input image into one of the above four groups to ensure proper routing. Comet segmentation is followed by comet partitioning which is implemented using a novel technique coined as modified fuzzy clustering. Comet parameters are calculated in the comet quantification stage and are saved in an excel file. RESULTS: Our dataset consists of 600 silver stained images obtained from 40 Schizophrenia patients with different levels of severity, admitted to a tertiary hospital in South India and 56 fluorescent stained images obtained from different internet sources. The performance of "CometQ", the proposed standalone application for automated analysis of comet assay images, is evaluated by a clinical expert and is also compared with that of a most recent and related software-OpenComet. CometQ gave 90.26% positive predictive value (PPV) and 93.34% sensitivity which are much higher than those of OpenComet, especially in the case of silver stained images. The results are validated using confusion matrix and Jaccard index (JI). Comet assay images obtained after DNA damage repair by incubation in the nutrient medium were also analysed, and CometQ showed a significant change in all the comet parameters in most of the cases. CONCLUSIONS: Results show that CometQ is an accurate and efficient tool with good sensitivity and PPV for DNA damage analysis using comet assay images.
BACKGROUND AND OBJECTIVE: DNA damage analysis plays an important role in determining the approaches for treatment and prevention of various diseases like cancer, schizophrenia and other heritable diseases. Comet assay is a sensitive and versatile method for DNA damage analysis. The main objective of this work is to implement a fully automated tool for the detection and quantification of DNA damage by analysing comet assay images. METHODS: The comet assay image analysis consists of four stages: (1) classifier (2) comet segmentation (3) comet partitioning and (4) comet quantification. Main features of the proposed software are the design and development of four comet segmentation methods, and the automatic routing of the input comet assay image to the most suitable one among these methods depending on the type of the image (silver stained or fluorescent stained) as well as the level of DNA damage (heavily damaged or lightly/moderately damaged). A classifier stage, based on support vector machine (SVM) is designed and implemented at the front end, to categorise the input image into one of the above four groups to ensure proper routing. Comet segmentation is followed by comet partitioning which is implemented using a novel technique coined as modified fuzzy clustering. Comet parameters are calculated in the comet quantification stage and are saved in an excel file. RESULTS: Our dataset consists of 600 silver stained images obtained from 40 Schizophreniapatients with different levels of severity, admitted to a tertiary hospital in South India and 56 fluorescent stained images obtained from different internet sources. The performance of "CometQ", the proposed standalone application for automated analysis of comet assay images, is evaluated by a clinical expert and is also compared with that of a most recent and related software-OpenComet. CometQ gave 90.26% positive predictive value (PPV) and 93.34% sensitivity which are much higher than those of OpenComet, especially in the case of silver stained images. The results are validated using confusion matrix and Jaccard index (JI). Comet assay images obtained after DNA damage repair by incubation in the nutrient medium were also analysed, and CometQ showed a significant change in all the comet parameters in most of the cases. CONCLUSIONS: Results show that CometQ is an accurate and efficient tool with good sensitivity and PPV for DNA damage analysis using comet assay images.
Authors: Joe R Delaney; Chandni B Patel; Jaidev Bapat; Christian M Jones; Maria Ramos-Zapatero; Katherine K Ortell; Ralph Tanios; Mina Haghighiabyaneh; Joshua Axelrod; John W DeStefano; Isabelle Tancioni; David D Schlaepfer; Olivier Harismendy; Albert R La Spada; Dwayne G Stupack Journal: PLoS Genet Date: 2020-01-10 Impact factor: 5.917