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Short-read DNA sequencing instruments can yield over 10<sup>12</sup> bases per run, typically composed of reads 150 bases long. Despite this high throughput, <italic>de novo</italic> assembly algorithms have difficulty reconstructing contiguous genome sequences using short reads due to both repetitive and difficult-to-sequence regions in these genomes. Some of the short read assembly challenges are mitigated by scaffolding assembled sequences using paired-end reads. However, unresolved sequences in these scaffolds appear as &#x201C;gaps&#x201D;. Here, we introduce GapPredict &#x2013; An implementation of a proof of concept that uses a character-level language model to predict unresolved nucleotides in scaffold gaps. We benchmarked GapPredict against the state-of-the-art gap-filling tool Sealer, and observed that the former can fill 65.6&#x0025; of the sampled gaps that were left unfilled by the latter with high similarity to the reference genome, demonstrating the practical utility of deep learning approaches to the gap-filling problem in genome assembly.

GapPredict – A Language Model for Resolving Gaps in Draft Genome Assemblies