m7GPredictor: An improved machine learning-based model for predicting internal m7G modifications using sequence properties.

As one of the most important post-transcriptional modifications, the N7-methylguanosine (m7G) plays a key role in many RNA processing events. The accurate identification of m7G is crucial for elucidating its biological significance and future application in the medical field. In this study, a machine learning-based model was developed for the prediction of internal m7G sites, and five different feature extraction methods (Pseudo dinucleotide composition, Pseudo k-tuple composition, K monomeric units, Ksnpf frequency, and Nucleotide chemical property) were used in the feature extraction.

HLMethy: a machine learning-based model to identify the hidden labels of mA candidates.

We developed a machine learning-based model to identify the hidden labels of mA candidates from noisy m6A-seq data. Peak-calling approaches, such as MeRIP-seq or mA-seq, are commonly used to map mA modifications. However, these technologies can only map mA sites with 100-200 nt resolution and cannot reveal the precise location or the number of modified residues in a transcript.