Learning Genomic Sequence Representations using Graph Neural Networks over De Bruijn Graphs

• URL: http://arxiv.org/abs/2312.03865v1
• Date: Wed, 6 Dec 2023 19:23:53 GMT
• Title: Learning Genomic Sequence Representations using Graph Neural Networks over De Bruijn Graphs
• Authors: Kacper Kapu\'sniak, Manuel Burger, Gunnar R\"atsch, Amir Joudaki
• Abstract summary: Existing techniques often neglect intricate structural details, emphasizing mainly contextual information. We developed k-mer embeddings that merge contextual and string information by enhancing De Bruijn graphs with structural similarity connections. Our embeddings consistently outperform prior techniques for Edit Distance Approximation and Closest String Retrieval tasks.
• Score: 1.8024397171920885
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The rapid expansion of genomic sequence data calls for new methods to achieve robust sequence representations. Existing techniques often neglect intricate structural details, emphasizing mainly contextual information. To address this, we developed k-mer embeddings that merge contextual and structural string information by enhancing De Bruijn graphs with structural similarity connections. Subsequently, we crafted a self-supervised method based on Contrastive Learning that employs a heterogeneous Graph Convolutional Network encoder and constructs positive pairs based on node similarities. Our embeddings consistently outperform prior techniques for Edit Distance Approximation and Closest String Retrieval tasks.

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