N7-Methylguanosine
(m7G) is a crucial post-transcriptional
RNA modification that plays a pivotal role in regulating gene expression.
Accurately identifying m7G sites is a fundamental step
in understanding the biological functions and regulatory mechanisms
associated with this modification. While whole-genome sequencing is
the gold standard for RNA modification site detection, it is a time-consuming,
expensive, and intricate process. Recently, computational approaches,
especially deep learning (DL) techniques, have gained popularity in
achieving this objective. Convolutional neural networks and recurrent
neural networks are examples of DL algorithms that have emerged as
versatile tools for modeling biological sequence data. However, developing
an efficient network architecture with superior performance remains
a challenging task, requiring significant expertise, time, and effort.
To address this, we previously introduced a tool called autoBioSeqpy,
which streamlines the design and implementation of DL networks for
biological sequence classification. In this study, we utilized autoBioSeqpy
to develop, train, evaluate, and fine-tune sequence-level DL models
for predicting m7G sites. We provided detailed descriptions
of these models, along with a step-by-step guide on their execution.
The same methodology can be applied to other systems dealing with
similar biological questions. The benchmark data and code utilized
in this study can be accessed for free at .