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1. Impact of DNA sequences on DNA ‘opening’ by the Rad4/XPC nucleotide excision repair complex

   https://doi.org/10.1016/j.dnarep.2021.103194  

   Paul, Debamita; Mu, Hong; Tavakoli, Amirrasoul; Dai, Qing; Chakraborty, Sagnik; He, Chuan; Ansari, Anjum; Broyde, Suse; Min, Jung-Hyun (November 2021, DNA Repair)

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2. Nick-seq for single-nucleotide resolution genomic maps of DNA modifications and damage

   https://doi.org/10.1093/nar/gkaa473  

   Cao, Bo; Wu, Xiaolin; Zhou, Jieliang; Wu, Hang; Liu, Lili; Zhang, Qinghua; DeMott, Michael S; Gu, Chen; Wang, Lianrong; You, Delin; et al (June 2020, Nucleic Acids Research)

   Abstract DNA damage and epigenetic marks are well established to have profound influences on genome stability and cell phenotype, yet there are few technologies to obtain high-resolution genomic maps of the many types of chemical modifications of DNA. Here we present Nick-seq for quantitative, sensitive, and accurate mapping of DNA modifications at single-nucleotide resolution across genomes. Pre-existing breaks are first blocked and DNA modifications are then converted enzymatically or chemically to strand-breaks for both 3′-extension by nick-translation to produce nuclease-resistant oligonucleotides and 3′-terminal transferase tailing. Following library preparation and next generation sequencing, the complementary datasets are mined with a custom workflow to increase sensitivity, specificity and accuracy of the map. The utility of Nick-seq is demonstrated with genomic maps of site-specific endonuclease strand-breaks in purified DNA from Eschericia coli, phosphorothioate epigenetics in Salmonella enterica Cerro 87, and oxidation-induced abasic sites in DNA from E. coli treated with a sublethal dose of hydrogen peroxide. Nick-seq applicability is demonstrated with strategies for >25 types of DNA modification and damage. 

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3. The owl sensor: a ‘fragile’ DNA nanostructure for the analysis of single nucleotide variations

   https://doi.org/10.1039/c8nr01107a  

   Karadeema, Rebekah J.; Stancescu, Maria; Steidl, Tyler P.; Bertot, Sophia C.; Kolpashchikov, Dmitry M. (January 2018, Nanoscale)

   Analysis of single nucleotide variations (SNVs) in DNA and RNA sequences is instrumental in healthcare for the detection of genetic and infectious diseases and drug-resistant pathogens. Here we took advantage of the developments in DNA nanotechnology to design a hybridization sensor, named the ‘owl sensor’, which produces a fluorescence signal only when it complexes with fully complementary DNA or RNA analytes. The novelty of the owl sensor operation is that the selectivity of analyte recognition is, at least in part, determined by the structural rigidity and stability of the entire DNA nanostructure rather than exclusively by the stability of the analyte–probe duplex, as is the case for conventional hybridization probes. Using two DNA and two RNA analytes we demonstrated that owl sensors differentiate SNVs in a wide temperature range of 5 °C–32 °C, a performance unachievable by conventional hybridization probes including the molecular beacon probe. The owl sensor reliably detects cognate analytes even in the presence of 100 times excess of single base mismatched sequences. The approach, therefore, promises to add to the toolbox for the diagnosis of SNVs at ambient temperatures. 

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4. Nucleosome recognition and DNA distortion by the Chd1 remodeler in a nucleotide-free state

   https://doi.org/10.1038/s41594-021-00719-x  

   Nodelman, Ilana M.; Das, Sayan; Faustino, Anneliese M.; Fried, Stephen D.; Bowman, Gregory D.; Armache, Jean-Paul (February 2022, Nature Structural & Molecular Biology)
5. Moss enables high sensitivity single-nucleotide variant calling from multiple bulk DNA tumor samples

   https://doi.org/10.1038/s41467-021-22466-9  

   Zhang, Chuanyi; El-Kebir, Mohammed; Ochoa, Idoia (April 2021, Nature Communications)

   Abstract Intra-tumor heterogeneity renders the identification of somatic single-nucleotide variants (SNVs) a challenging problem. In particular, low-frequency SNVs are hard to distinguish from sequencing artifacts. While the increasing availability of multi-sample tumor DNA sequencing data holds the potential for more accurate variant calling, there is a lack of high-sensitivity multi-sample SNV callers that utilize these data. Here we report Moss, a method to identify low-frequency SNVs that recur in multiple sequencing samples from the same tumor. Moss provides any existing single-sample SNV caller the ability to support multiple samples with little additional time overhead. We demonstrate that Moss improves recall while maintaining high precision in a simulated dataset. On multi-sample hepatocellular carcinoma, acute myeloid leukemia and colorectal cancer datasets, Moss identifies new low-frequency variants that meet manual review criteria and are consistent with the tumor’s mutational signature profile. In addition, Moss detects the presence of variants in more samples of the same tumor than reported by the single-sample caller. Moss’ improved sensitivity in SNV calling will enable more detailed downstream analyses in cancer genomics. 

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