  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.
