GraSSRep: Graph-Based Self-Supervised Learning for Repeat Detection in Metagenomic Assembly

• URL: http://arxiv.org/abs/2402.09381v1
• Date: Wed, 14 Feb 2024 18:26:58 GMT
• Title: GraSSRep: Graph-Based Self-Supervised Learning for Repeat Detection in Metagenomic Assembly
• Authors: Ali Azizpour, Advait Balaji, Todd J. Treangen and Santiago Segarra
• Abstract summary: Repetitive DNA (repeats) poses significant challenges for accurate and efficient genome assembly and sequence alignment. GraSSRep is a self-supervised learning framework to classify DNA sequences into repetitive and non-repetitive categories. GraSSRep combines sequencing features with pre-defined and learned graph features to achieve state-of-the-art performance in repeat detection.
• Score: 24.55141372357102
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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