Detection of m6A from direct RNA sequencing using a multiple instance learning framework

Abstract RNA modifications such as m6A methylation form an additional layer of complexity in the transcriptome. Nanopore direct RNA sequencing can capture 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 that leverages the multiple instance learning framework to specifically handle missing read-level modification labels in site-level training data. m6Anet outperforms existing computational methods, shows similar accuracy…

• AF
  Agency for Science, Technology and Research
• NU
  National University of Singapore
• GI
  Genome Institute of Singapore
• MR
  Medical Research Council
• NM
  National Medical Research Council