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

arXiv:2402.09381v1 Announce Type: new
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 …

abstract alignment arxiv assembly challenges communities cs.lg data detection dna dynamics gene genome graph graph-based loss self-supervised learning supervised learning transfer true type