Deep learning approach for predicting functional Z-DNA regions using omics data

Scientific Reports. 2020. Vol. 10. P. 19134.

Beknazarov N., Jin S., Poptsova M.

Computational methods to predict Z-DNA regions are in high demand to understand the functional role of Z-DNA. The previous state-of-the-art method Z-Hunt is based on statistical mechanical and energy considerations about B- to Z-DNA transition using sequence information. Z-DNA CHiP-seq experiment results showed little overlap with Z-Hunt predictions implying that sequence information only is not sufficient to explain emergence of Z-DNA at different genomic locations. Adding epigenetic and other functional genomic mark-ups to DNA sequence level can help revealing the functional Z-DNA sites. Here we take advantage of the deep learning approach that can analyze and extract information from large volumes of molecular biology data. We developed a machine learning approach DeepZ that aggregates information from genome-wide maps of epigenetic markers, transcription factor and RNA polymerase binding sites, and chromosome accessibility maps. With the developed model we not only verify the experimental Z-DNA predictions, but also generate the whole-genome annotation, introducing new possible Z-DNA regions, which have not yet been found in experiments and can be of interest to the researchers from various fields.

Keywords: машинное обучение, machine learning, deep learning, глубинное обучение, DNA secondary structures, вторичные структуры ДНК, epigenetics, сверточные нейронные сети, эпигенетика, RNN, рекуррентные нейронные сети, non-B DNA, Z-DNA, глубинные нейронные сети, CNN, Z-ДНК

Publication based on the results of: Analysis of functional genomic elements (2020)