  Short-read DNA sequencing instruments can yield over 1e+12 bases per run,
typically composed of reads 150 bases long. Despite this high throughput, de
novo 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 "gaps". Here, we introduce
GapPredict, a tool 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% of the sampled gaps that were left unfilled by the latter, demonstrating
the practical utility of deep learning approaches to the gap-filling problem in
genome sequence assembly.
