Coding genomes with gapped pattern graph convolutional network.

ABSTRACT:

Motivation

Genome sequencing technologies reveal a huge amount of genomic sequences. Neural network-based methods can be prime candidates for retrieving insights from these sequences because of their applicability to large and diverse datasets. However, the highly variable lengths of genome sequences severely impair the presentation of sequences as input to the neural network. Genetic variations further complicate tasks that involve sequence comparison or alignment.

Results

Inspired by the theory and applications of "spaced seeds," we propose a graph representation of genome sequences called "gapped pattern graph." These graphs can be transformed through a Graph Convolutional Network to form lower-dimensional embeddings for downstream tasks. On the basis of the gapped patter

SUBMITTER: Wang RH 

PROVIDER: S-EPMC11034989 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature