iRG-4mC: Neural Network Based Tool for Identification of DNA 4mC Sites in Rosaceae Genome

Abstract

DNA N4-Methylcytosine is a genetic modification process which has an essential role in changing different biological processes such as DNA conformation, DNA replication, DNA stability, cell development and structural alteration in DNA. Due to its negative effects, it is important to identify the modified 4mC sites. Further, methylcytosine may develop anywhere at cytosine residue, however, clonal gene expression patterns are most likely transmitted just for cytosine residues in strand-symmetrical sequences. For this reason many different experiments are introduced but they proved not to be viable choice due to time limitation and high expenses. Therefore, to date there is still need for an efficient computational method to deal with 4mC sites identification. Keeping it in mind, in this research we have proposed an efficient model for Fragaria vesca (F. vesca) and Rosa chinensis (R. chinensis) genome. The proposed iRG-4mC tool is developed based on neural network architecture with two encoding schemes to identify the 4mC sites. The iRG-4mC predictor outperformed the existing state-of-the-art computational model by an accuracy difference of 9.95% on F. vesca (training dataset), 8.7% on R. chinesis (training dataset), 6.2% on F. vesca (independent dataset) and 10.6% on R. chinesis (independent dataset). We have also established a webserver which is freely accessible for the research community.

A Grid Search-Based Multilayer Dynamic Ensemble System to Identify DNA N4—Methylcytosine Using Deep Learning Approach

“…In TNC, all samples of 41 nt produce 39 components with the equation of L − k + 1. Here, L stands for the sequence length, and k stands for the K-mer value as an integer [ 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 ]. ATG, TGC, GCG, and CGA are the four 3-mers that can be tokenized from the DNA sequence “ATGCGA,” for instance.…”

“…D.Y. Lim et al (2021) [ 15 ] developed the iRG-4mC tool, which is based on the CNN-LSTM model. The DNA sequence was encoded using a combination of one-hot encoding and nucleotide chemical properties in the proposed system (NCP).…”