iRSpot-SPI: Deep learning-based recombination spots prediction by incorporating secondary sequence information coupled with physio-chemical properties via Chou's 5-step rule and pseudo components

Abstract Meiotic recombination plays an important role in the process of genetic diversity generation. “Hotspots” are regions that show a higher rate of meiotic recombination, while the regions with a lower rate are called “cold spots”. It has a great effect on the genome evolution via gene conversion or mutagenesis. According to recent research, recombination is present in uneven distribution across the genome. Many computational methods have been developed using secondary sequence information or physiochemical properties of nucleotide descriptor for the prediction of hotspots and cold spots, which are computationally cheap and fast in performance rather than web-lab experiments, but the correlations between nucleotides pairs at different positions along DNA sequence is often ignored, which conceal a very important predictive information. In this study, we have proposed a deep neural network to predict recombination spots by fusing both the secondary sequence information and physio-chemical derived features. Our deep learning algorithm leverage's deep dense architecture by showing its effectiveness over the state-of-the-art methods with a classification accuracy of 90.04%, sensitivity of 92.21%, specificity of 92.11% and area under the curve of 0.9801. Moreover, it is anticipated, that our model will provide novel insight into basic research, drug designing, academic research and recombination spots studies particularly. All the methodology and python-based source code is publicly available for the users at https://github.com/zaheerkhancs/irSpot_SPI along with publicly accessible web server using the proposed predictor.