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Detection of m6A from direct RNA sequencing using a Multiple Instance Learning framework
RNA modifications such as m6A methylation form an additional layer of complexity in the transcriptome. Nanopore direct RNA sequencing captures this information in the raw current signal for each RNA molecule, enabling the detection of RNA modifications using supervised machine learning. However, experimental approaches provide only site-level training data, whereas the modification status for each single RNA molecule is missing. Here we present m6Anet, a neural network-based method thatdoi:10.1101/2021.09.20.461055 fatcat:rlwszw4rpzhrboohu5z6xdyg7m