Reconstruction of short genomic sequences with graph convolutional networks

Genome reconstruction, without prior knowledge about the sequence we are reconstructing, is performed with tools called de novo genome assemblers. These tools rely on numerous heuristics and usually provide a fragmented reconstruction, even for sequences shorter than the entire genomes or chromosomes. One of the most common approaches to de novo assembly, called Overlap-Layout-Consensus, constructs a graph from short overlapping fragments, which heuristics then simplify and find a path through. In this work, we explore how graph neural networks (GNNs) can assist with this task, and show that the GNN-based Layout phase can reconstruct longer sequences than naive search algorithms or heuristics deployed in de novo assemblers, with no significant difference in compute time on sequences up to 10 Mbp in length.

In 46th MIPRO ICT and Electronics Convention (MIPRO)