GraSSRep: Graph-Based Self-Supervised Learning for Repeat Detection in
  Metagenomic Assembly
Ali Azizpour Advait Balaji Todd J. Treangen Santiago Segarra
Repetitive DNA (repeats) poses significant challenges for accurate and
efficient genome assembly and sequence alignment. This is particularly true for
metagenomic data, where genome dynamics such as horizontal gene transfer, gene
duplication, and gene loss/gain complicate accurate genome assembly from
metagenomic communities. Detecting repeats is a crucial first step in
overcoming these challenges. To address this issue, we propose GraSSRep, a
novel approach that leverages the assembly graph's structure through graph
neural networks (GNNs) within a self-supervised learning framework to classify
DNA sequences into repetitive and non-repetitive categories. Specifically, we
frame this problem as a node classification task within a metagenomic assembly
graph. In a self-supervised fashion, we rely on a high-precision (but
low-recall) heuristic to generate pseudo-labels for a small proportion of the
nodes. We then use those pseudo-labels to train a GNN embedding and a random
forest classifier to propagate the labels to the remaining nodes. In this way,
GraSSRep combines sequencing features with pre-defined and learned graph
features to achieve state-of-the-art performance in repeat detection. We
evaluate our method using simulated and synthetic metagenomic datasets. The
results on the simulated data highlight our GraSSRep's robustness to repeat
attributes, demonstrating its effectiveness in handling the complexity of
repeated sequences. Additionally, our experiments with synthetic metagenomic
datasets reveal that incorporating the graph structure and the GNN enhances our
detection performance. Finally, in comparative analyses, GraSSRep outperforms
existing repeat detection tools with respect to precision and recall.