Double-strand break (DSB) repair is a critical cellular process which repairs breaks in both strands of the DNA double helix. Different repair mechanisms are tasked with repairing such breaks. Predicting deficiencies in repair mechanisms has been widely used for therapeutic purposes, such as targeting cancer cells that have specific DNA repair deficiencies. DSB repair, however, is not error-free, resulting in mutations. These mutations are also influenced by the DNA sequence surrounding the break site. To the best of our knowledge, sequence representations have not been considered when predicting DNA repair deficiencies. We hypothesise that higher-order information can be extracted from sequence representations. In this study, we research the problem of predicting Non-Homologous End Joining (NHEJ) repair deficiencies. Initially, we evaluate how accurately we can predict NHEJ repair deficiency using only the mutational outcome frequencies (mutational spectra). Afterwards, we examine how combining mutational spectra with representations of the sequence surrounding the break site can improve the prediction of NHEJ repair deficiency. We demonstrate that adding DNABERT sequence representations to mutational spectra features significantly improves prediction accuracy from 94.44% to 96.12%. We also show that even simple sequence representations, such as 1-mer frequencies, can lead to significant improvements. Our findings highlight the importance of including sequence representations with mutational spectra in repair deficiency prediction.

Recent advancements in quantification of repair outcomes of CRISPR-Cas9 mediated double-stranded DNA breaks (DSBs) have allowed for the use of machine learning for predicting the frequencies of these repair outcomes. Local DNA sequence context influences the frequencies of mutations that arise when DNA gets repaired after it is targeted by CRISPR (CRISPR outcomes). Contemporary models exploit this and can predict what the frequencies are of CRISPR outcomes at predetermined genomic loci. Predictions of such models are reasonably precise, but there may be opportunities for improvement in how the DNA sequence context is leveraged for making predictions. Some models only utilize a set of hand-crafted features, limiting the available information for the model. Other models do utilize broader sequence context but disregard sequence order or only predict a limited set of outcome classes. In this work we present an attention-based deep learning model that uses DNA sequence context to make fine-grained CRISPR outcome predictions. We present a custom input embedding for representing DSB repair outcomes and we expand on existing methods for analyzing attention-based models.