Genome-wide prediction of DNA mutation effect on nucleosome positions for yeast synthetic genomics

Genetically modified genomes are often used today in many areas of fundamental and applied research. In many studies, coding or noncoding regions are modified in order to change protein sequences or gene expression levels. Modifying one or several nucleotides in a genome can also lead to unexpected changes in the epigenetic regulation of genes. When designing a synthetic genome with many mutations, it would thus be very informative to be able to predict the effect of these mutations on chromatin. We develop here a deep learning approach that quantifies the effect of every possible single mutation on nucleosome positions on the full Saccharomyces cerevisiae genome. This type of annotation track can be used when designing a modified S. cerevisiae genome. We further highlight how this track can provide new insights on the sequence-dependent mechanisms that drive nucleosomes' positions in vivo.

Automated summary

Genomics is widely used in current research, including modifying coding or non-coding regions to alter gene expression levels. Single nucleotide mutations can lead to unexpected changes in epigenetic regulation of genes. Deep learning methods can help predict the impact of mutations on chromatin, providing guidance for designing synthetic genomes.