MultiScale-CNN-4mCPred: a multi-scale CNN and adaptive embedding-based method for mouse genome DNA N4-methylcytosine prediction

Abstract

N4-methylcytosine (4mC) is an important epigenetic mechanism, which regulates many cellular processes such as cell differentiation and gene expression. The knowledge about the 4mC sites is a key foundation to exploring its roles. Due to the limitation of techniques, precise detection of 4mC is still a challenging task. In this paper, we presented a multi-scale convolution neural network (CNN) and adaptive embedding-based computational method for predicting 4mC sites in mouse genome, which was referred to as MultiScale-CNN-4mCPred. The MultiScale-CNN-4mCPred used adaptive embedding to encode nucleotides, and then utilized multi-scale CNNs as well as long short-term memory to extract more in-depth local properties and contextual semantics in the sequences. The MultiScale-CNN-4mCPred is an end-to-end learning method, which requires no sophisticated feature design. The MultiScale-CNN-4mCPred reached an accuracy of 81.66% in the 10-fold cross-validation, and an accuracy of 84.69% in the independent test, outperforming state-of-the-art methods. We implemented the proposed method into a user-friendly web application which is freely available at: http://www.biolscience.cn/MultiScale-CNN-4mCPred/.

Biochemical and structural characterization of the first-discovered metazoan DNA cytosine-N4 methyltransferase from the bdelloid rotifer Adineta vaga

“…8 B ), we observed nearly every residue from 79 to 324 (with broken density at residues 220–225) of N4CMT 79–324 in space group C 222 1 . For N4CMT 61–324 , the first seven residues ( 61 , 62 , 63 , 64 , 65 , 66 , 67 ) were disordered, and residues 69 to 80 formed an additional helix, away from both the active site (where sinefungin binds) and the dimer interface ( Fig. 6 , B and C ).…”

“…There is some evidence for 4mC in the DNA of mammals ( 62 ) and plants ( 63 ), and a number of papers have reported computational methods for predicting where 4mC is likely to occur ( e.g. , ( 64 , 65 )). However, the gene for the first DNA MTase generating 4mC in a metazoan genome (N4-cytosine methyltransferase, N4CMT) was only reported and partially characterized in 2022, in tiny freshwater invertebrates called bdelloid rotifers ( 35 ).…”

Mus4mCPred: Accurate Identification of DNA N4-Methylcytosine Sites in Mouse Genome Using Multi-View Feature Learning and Deep Hybrid Network

“…For the fairness and credibility of the experiment, the dataset used in this study is the same as 4mCPred-EL [22], i4mC-Mouse [23], Mouse4mC-BGRU [25], and MultiScale-CNN-4mCPred [26]. These datasets were constructed using the MethSMRT database [28], which is a database specifically for methylation data and contains genomic methylation information from a variety of biological samples.…”

“…Mouse4mC-BGRU employs k-mer tokenization for encoding and inputs features into a bidirectional gated recurrent unit (GRU) to automatically extract both long-term and short-term dependencies within DNA sequences, thereby learning contextual information. Recently, a new method called MultiScale-CNN-4mCPred [26] has emerged, which combines convolutional neural networks with different kernel sizes and long short-term memory (LSTM) to capture features of different scales and contextual information for predicting 4mC sites in mouse genes, thus improving prediction accuracy.…”

Applying a Recurrent Neural Network-Based Deep Learning Model for Gene Expression Data Classification

“…CNNs have emerged as a pivotal tool for classifying gene expression data, thanks to their autonomous feature-learning capability, which curtails the need for manual intervention in high-dimensional genomic data extraction [5][6][7]. By recognizing patterns effectively, they capture both local and global spatial hierarchies of gene expression profiles, a key aspect in identifying complex biological states.…”