Deep-2'-O-Me: Predicting 2'-O-methylation sites by Convolutional Neural Networks.

Area of Science:

• Molecular Biology
• Bioinformatics
• Computational Biology

Background:

• 2 -O-methylation (2 -O-me) is a crucial RNA modification affecting RNA metabolism and function.
• Existing methods like Nmseq identify 2 -O-me locations, but high-resolution computational prediction tools are lacking.

Purpose of the Study:

• To develop a high-resolution computational tool for predicting 2 -O-me sites in RNA sequences.
• To leverage deep learning and sequence embedding for accurate RNA modification prediction.

Main Methods:

• Utilized dna2vec, a biological sequence embedding method, to learn feature representations of pre-mRNA sequences.
• Employed a Convolutional Neural Network (CNN) to fine-tune features for accurate 2 -O-me site classification.
• Trained the model using data from Nm-seq experiments.