eccDNAMamba: A Pre-Trained Model for Ultra-Long eccDNA Sequence Analysis

• URL: http://arxiv.org/abs/2506.18940v1
• Date: Sun, 22 Jun 2025 17:50:57 GMT
• Title: eccDNAMamba: A Pre-Trained Model for Ultra-Long eccDNA Sequence Analysis
• Authors: Zhenke Liu, Jien Li, Ziqi Zhang,
• Abstract summary: Extrachromosomal circular DNA (eccDNA) plays key regulatory roles and contributes to oncogene overexpression in cancer.<n>No pre-trained models currently support full-length circular eccDNA for downstream analysis.<n>eccDNAMamba is the first bidirectional state-space encoder tailored for circular DNA sequences.
• Score: 5.86106644437914
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Extrachromosomal circular DNA (eccDNA) plays key regulatory roles and contributes to oncogene overexpression in cancer through high-copy amplification and long-range interactions. Despite advances in modeling, no pre-trained models currently support full-length circular eccDNA for downstream analysis. Existing genomic models are either limited to single-nucleotide resolution or hindered by the inefficiency of the quadratic attention mechanism. Here, we introduce eccDNAMamba, the first bidirectional state-space encoder tailored for circular DNA sequences. It combines forward and reverse passes for full-context representation learning with linear-time complexity, and preserves circular structure through a novel augmentation strategy. Tested on two real-world datasets, eccDNAMamba achieves strong classification performance and scales to sequences up to 200 Kbp, offering a robust and efficient framework for modeling circular genomes. Our codes are available at https://github.com/zzq1zh/GenAI-Lab.

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