Learning to Untangle Genome Assembly with Graph Convolutional Networks

TL;DR

This paper introduces a graph convolutional network framework for genome assembly graph untangling, demonstrating improved accuracy and efficiency over traditional heuristics, and generalizing well across different chromosomes using simulated training data.

Contribution

The paper presents a novel deep learning approach using graph convolutional networks to resolve assembly graphs, reducing reliance on manual heuristics and improving generalization across genomes.

Findings

Model trained on simulated data resolves real chromosome graphs effectively.

Outperforms traditional heuristics in accuracy and assembly metrics.

Achieves more contiguous and accurate genome reconstructions.

Abstract

A quest to determine the complete sequence of a human DNA from telomere to telomere started three decades ago and was finally completed in 2021. This accomplishment was a result of a tremendous effort of numerous experts who engineered various tools and performed laborious manual inspection to achieve the first gapless genome sequence. However, such method can hardly be used as a general approach to assemble different genomes, especially when the assembly speed is critical given the large amount of data. In this work, we explore a different approach to the central part of the genome assembly task that consists of untangling a large assembly graph from which a genomic sequence needs to be reconstructed. Our main motivation is to reduce human-engineered heuristics and use deep learning to develop more generalizable reconstruction techniques. Precisely, we introduce a new learning framework to train a graph convolutional network to resolve assembly graphs by finding a correct path through them. The training is supervised with a dataset generated from the resolved CHM13 human sequence and tested on assembly graphs built using real human PacBio HiFi reads. Experimental results show that a model, trained on simulated graphs generated solely from a single chromosome, is able to remarkably resolve all other chromosomes. Moreover, the model outperforms hand-crafted heuristics from a state-of-the-art \textit{de novo} assembler on the same graphs. Reconstructed chromosomes with graph networks are more accurate on nucleotide level, report lower number of contigs, higher genome reconstructed fraction and NG50/NGA50 assessment metrics.