CNN6mA: Interpretable neural network model based on position-specific CNN and cross-interactive network for 6mA site prediction

N6-methyladenine (6mA) plays a critical role in various epigenetic processing including DNA replication, DNA repair, silencing, transcription, and diseases such as cancer. To understand such epigenetic mechanisms, 6 mA has been detected by high-throughput technologies on a genome-wide scale at single-base resolution, to-gether with conventional methods such as immunoprecipitation, mass spectrometry and capillary electro-phoresis, but these experimental approaches are time-consuming and laborious. To complement these problems, we have developed a CNN-based 6 mA site predictor, named CNN6mA, which proposed two new architectures: a position-specific 1-D convolutional layer and a cross-interactive network. In the position -specific 1-D convolutional layer, position-specific filters with different window sizes were applied to an in-quiry sequence instead of sharing the same filters over all positions in order to extract the position-specific features at different levels. The cross-interactive network explored the relationships between all the nu-cleotide patterns within the inquiry sequence. Consequently, CNN6mA outperformed the existing state-of-the-art models in many species and created the contribution score vector that intelligibly interpret the prediction mechanism. The source codes and web application in CNN6mA are freely accessible at https:// github.com/kuratahiroyuki/CNN6mA.git and http://kurata35.bio.kyutech.ac.jp/CNN6mA/, respectively.(c) 2022 The Author(s). Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology. This is an open access article under the CC BY license (http://creativecommons.org/ licenses/by/4.0/).

Automated summary

N6-methyladenine (6mA) plays crucial roles in various epigenetic processes and diseases. To understand these mechanisms, researchers have developed a CNN-based 6mA site predictor, CNN6mA, which outperforms existing models and provides intelligible interpretation of the prediction mechanism through new architectures.