2020
DOI: 10.1038/s41598-020-76203-1
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Deep learning approach for predicting functional Z-DNA regions using omics data

Abstract: 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 geno… Show more

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Cited by 38 publications
(52 citation statements)
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“…Maria Poptsova [ 10 ] presented computational approaches based on the DeepZ machine learning algorithms to study factors that alter the B to Z flip energetics in vivo and to describe the pathways involved. Structural studies on Z-RNA formation by Quentin Vicens [ 23 ] and Beat Vögeli [ 9 ] outlined some of the different requirements for Z-RNA formation compared to Z-DNA formation, pointing at a widespread distribution of Z-RNA across transcriptomes.…”
Section: Meeting Summarymentioning
confidence: 99%
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“…Maria Poptsova [ 10 ] presented computational approaches based on the DeepZ machine learning algorithms to study factors that alter the B to Z flip energetics in vivo and to describe the pathways involved. Structural studies on Z-RNA formation by Quentin Vicens [ 23 ] and Beat Vögeli [ 9 ] outlined some of the different requirements for Z-RNA formation compared to Z-DNA formation, pointing at a widespread distribution of Z-RNA across transcriptomes.…”
Section: Meeting Summarymentioning
confidence: 99%
“…These advances were informed by biochemical, biophysical and structural approaches that established the conditions and base modifications necessary to flip from the right-to left-handed conformation under physiological conditions [9]. Computational analyses established that the localization of the repeats that form Z-DNA is not random [10], consistent with its selection for or against particular outcomes. This inaugural ABZ meeting (https://abz2021.bio, accessed 16 July 2021) was convened to bring together the leading researchers within the different disciplines that study the Z-conformation, with an intent to celebrate and collaborate.…”
Section: Introductionmentioning
confidence: 99%
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“…Many biochemical and biophysical in vitro experiments have been conducted to better characterise Z-DNA behaviour at close to physiological conditions [ 27 , 28 ] and also to better understand switching between B and Z-DNA [ 29 , 30 ]. Furthermore, several bioinformatic searches have been performed to predict Z-DNA-prone sequence motifs in the genomic DNA of some model organisms, including humans [ 31 , 32 ]. Z-DNA structures can be formed only in specific double-stranded sequences with alternating purine–pyrimidine tracks, which has been determined by crystallography in various nucleotide repeats, where specifically Z-DNA containing GC repeats have been shown to have increased stability [ 33 ].…”
Section: Introductionmentioning
confidence: 99%
“…Whereas poly(deoxyguanylic-deoxycytidylic) acid [poly(dG-dC).poly(dG-dC)], further abbreviated in this paper to [poly(dG-dC)], can undergo a B-to Z-DNA transition at highsalt conditions, bromination of poly(dG-dC) [Br-poly(dG-dC)] produces a synthetic DNA in which the Z-DNA conformation is present at low-salt conditions [3]. Algorithms to predict the presence of Z-DNA on the basis of thermodynamic considerations indicate that a variety of sequences, and not just alternating pyrimidine-purine tracts, can form Z-DNA in the genome [12][13][14][15][16].…”
Section: Introductionmentioning
confidence: 99%