38 Int. J. Bioinformatics Research and Applications, Vol. 17, No. 1, 2021 Copyright © 2021 Inderscience Enterprises Ltd. A hybrid method for differentially expressed genes identification and ranking from RNA-Seq data Mohammad Samir Farooqi* Centre for Agricultural Bioinformatics, Indian Agricultural Statistics Research Institute, Library Avenue, Pusa, New Delhi, 110012, Indıa Email: samirfarooqi8@gmail.com *Corresponding author Devendra Kumar Department of Statistics, Central University Haryana, Jant-Pali, Mahendergarh District, Pali, Haryana, 123031, Indıa Email: devendrastats@gmail.com Dwijesh Chandra Mishra and Anil Rai Centre for Agricultural Bioinformatics, Indian Agricultural Statistics Research Institute, Library Avenue, Pusa, New Delhi, 110012, Indıa Email: dwij.mishra@gmail.com Email: anilrai64@gmail.com Niraj Kumar Singh Department of Statistics, AIAS, Amity University, Noida, UP, 201313, India Email: nksingh@amity.edu Abstract: RNA-Seq has gained immense popularity and emerged as a potential high-throughput platform for identification of differentially expressed (DE) genes. In order to estimate the nature of differential genes, it is important to find statistical distributional property of the data. In the present study we propose a new hybrid model (NBPFCROS) based on parametric and non-parametric statistic for the identification of DE genes. The NBP model based on Compound mixture of Poisson–gamma distribution is used as a parametric statistic and Fold change value derived using fold change rank ordering statistics (FCROS) algorithm is used as non-parametric statistic, we used a gene significance score pi-value by combining expression fold change (f value) and statistical significance (p-value). The performance of