Deep learning approach for predicting functional Z-DNA regions using omics data.
Deep learning approach for predicting functional Z-DNA regions using omics data.
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DOI：
10.1038/s41598-020-76203-1
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发表时间：
2020-11-05
影响因子：
4.6
通讯作者：
Poptsova M
中科院分区：
文献类型：
--
作者：
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.
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影响因子：
3.7
作者：
Handoko L;Kaczkowski B;Hon CC;Lizio M;Wakamori M;Matsuda T;Ito T;Jeyamohan P;Sato Y;Sakamoto K;Yokoyama S;Kimura H;Minoda A;Umehara T
通讯作者：
Umehara T
影响因子：
14.9
作者：
The Gene Ontology Consortium
通讯作者：
The Gene Ontology Consortium
影响因子：
14.9
作者：
BRAATEN, DC;THOMAS, JR;DURSO, M
通讯作者：
DURSO, M
模因套件：用于发现和搜索的工具。
影响因子：
14.9
作者：
Bailey TL;Boden M;Buske FA;Frith M;Grant CE;Clementi L;Ren J;Li WW;Noble WS
通讯作者：
Noble WS
影响因子：
11.4
作者：
HO, PS;ELLISON, MJ;RICH, A
通讯作者：
RICH, A