DeepM6ASeq-EL: prediction of human N6-methyladenosine (m6A) sites with LSTM and ensemble learning

N6-methyladenosine (m(6)A) is a prevalent methylation modification and plays a vital role in various biological processes, such as metabolism, mRNA processing, synthesis, and transport. Recent studies have suggested that m(6)A modification is related to common diseases such as cancer, tumours, and obesity. Therefore, accurate prediction of methylation sites in RNA sequences has emerged as a critical issue in the area of bioinformatics. However, traditional high-throughput sequencing and wet bench experimental techniques have the disadvantages of high costs, significant time requirements and inaccurate identification of sites. But through the use of traditional experimental methods, researchers have produced many large databases of m(6)A sites. With the support of these basic databases and existing deep learning methods, we developed an m(6)A site predictor named DeepM6ASeq-EL, which integrates an ensemble of five LSTM and CNN classifiers with the combined strategy of hard voting. Compared to the state-of-the-art prediction method WHISTLE (average AUC 0.948 and 0.880), the DeepM6ASeq-EL had a lower accuracy in m(6)A site prediction (average AUC: 0.861 for the full transcript models and 0.809 for the mature messenger RNA models) when tested on six independent datasets.

Automated summary

N6-methyladenosine (m(6)A) is a prevalent methylation modification that is related to common diseases such as cancer, tumors, and obesity. Accurate prediction of m(6)A methylation sites in RNA sequences has become a critical issue in bioinformatics. Researchers developed an m(6)A site predictor called DeepM6ASeq-EL, which integrates LSTM and CNN classifiers with the strategy of hard voting. However, its accuracy in m(6)A site prediction is lower compared to the state-of-the-art method WHISTLE.