A convolution based computational approach towards DNA N6-methyladenine site identification and motif extraction in rice genome

Abstract

DNA N6-methylation (6mA) in Adenine nucleotide is a post replication modification responsible for many biological functions. Automated and accurate computational methods can help to identify 6mA sites in long genomes saving significant time and money. Our study develops a convolutional neural network (CNN) based tool i6mA-CNN capable of identifying 6mA sites in the rice genome. Our model coordinates among multiple types of features such as PseAAC (Pseudo Amino Acid Composition) inspired customized feature vector, multiple one hot representations and dinucleotide physicochemical properties. It achieves auROC (area under Receiver Operating Characteristic curve) score of 0.98 with an overall accuracy of 93.97% using fivefold cross validation on benchmark dataset. Finally, we evaluate our model on three other plant genome 6mA site identification test datasets. Results suggest that our proposed tool is able to generalize its ability of 6mA site identification on plant genomes irrespective of plant species. An algorithm for potential motif extraction and a feature importance analysis procedure are two by products of this research. Web tool for this research can be found at: https://cutt.ly/dgp3QTR.

Epigenetic targeting of autophagy for cancer: DNA and RNA methylation

“…Simultaneously, this intricate interplay between 6mA, m6A, and autophagy, while academically intriguing, could also pave the way for transformative therapeutic interventions ( 172 ), offering a unique perspective on autophagy in cancer. The potential of 6mA in DNA, particularly in plants like rice, has been investigated, shedding light on its role in various biological functions ( 173 ). On the other hand, the role of m6A in RNA metabolism and its potential impact on autophagy presents an intriguing prospect for therapeutic interventions, especially in conditions marked by autophagy dysregulation ( 174 ).…”

ACNN-6mA Prediction of N6-Methyladenine Loci in Multiple Species Based on Rice Dataset Pre-training Model†

“…I6mA-Fuse 24 constructs five Random Forest models using five separate feature encoding methods: one-hot, di-nucleotide binary, k-space spectral nucleus, kmer, and EIIPs, and uses linear regression models to combine the prediction probability scores of five RF models based on a single encoding to predict the m6A sites of F. vesca and R. chinensis. I6mA-CNN 25 uses one-hot, dinucleotide binary, dinucleotide property and a hybrid encoding method to encode the sequence, and then conducts convolution and full join operations to obtain four models and perform fusion to predict methylation sites. Deep6mA 26 encodes the sequence as one pot, extracts, and abstracts the features of the sequence using a 5-layer convolutional neural network, and then uses LSTM to model the extracted abstract features in terms of time series, extracts the timing effect between high-level features, and then makes a full connection prediction.…”