Abstract

With the emergence of PacBio HiFi and ultra-long ONT reads, the efforts to assemble genomes of various species in a de novo manner have significantly increased. This is manifested in projects including the T2T consortium project, the Human Pangenome Project and the Vertebrate Genome Project, which strive to assemble a large number of genomes with contemporary tools and data. However, even with all the recent advances in sequencing technologies, manual curation of the assembly genomes is still necessary. At the same time, most de novo assembly tools rely on graph-simplification heuristics, which have remained largely unchanged in recent years. Moreover, heuristics parameters have been hand-crafted using several genomes for which a high-quality reference was available during the time of development.

We implemented an entirely novel approach for resolving assembly graphs into genomes, one based on graph neural networks. We evaluated our method on different types of reads and with initial assembly graphs produced in a different way, comparing it against state-of-the-art de novo assemblers used in the field. Moreover, the preliminary results indicate the modularity and the adaptability of our approach, which should generalize better with every new genome assembly released, requiring minimal adjustments to the existing pipeline.

hybrid talk: https://cl-cam-ac-uk.zoom.us/j/93934749405?pwd=M0ZERUZleklON01vZlpXbzhqTld0Zz09

ID riunione: 939 3474 9405 Passcode: 374750