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1. DNA 5-methylcytosine detection and methylation phasing using PacBio circular consensus sequencing

   https://doi.org/10.1038/s41467-023-39784-9  

   Ni, Peng; Nie, Fan; Zhong, Zeyu; Xu, Jinrui; Huang, Neng; Zhang, Jun; Zhao, Haochen; Zou, You; Huang, Yuanfeng; Li, Jinchen; et al (December 2023, Nature Communications)

   Abstract Long single-molecular sequencing technologies, such as PacBio circular consensus sequencing (CCS) and nanopore sequencing, are advantageous in detecting DNA 5-methylcytosine in CpGs (5mCpGs), especially in repetitive genomic regions. However, existing methods for detecting 5mCpGs using PacBio CCS are less accurate and robust. Here, we present ccsmeth, a deep-learning method to detect DNA 5mCpGs using CCS reads. We sequence polymerase-chain-reaction treated and M.SssI-methyltransferase treated DNA of one human sample using PacBio CCS for training ccsmeth. Using long (≥10 Kb) CCS reads, ccsmeth achieves 0.90 accuracy and 0.97 Area Under the Curve on 5mCpG detection at single-molecule resolution. At the genome-wide site level, ccsmeth achieves >0.90 correlations with bisulfite sequencing and nanopore sequencing using only 10× reads. Furthermore, we develop a Nextflow pipeline, ccsmethphase, to detect haplotype-aware methylation using CCS reads, and then sequence a Chinese family trio to validate it. ccsmeth and ccsmethphase can be robust and accurate tools for detecting DNA 5-methylcytosines. 

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2. Semiconservative transmission of DNA N ⁶ -adenine methylation in a unicellular eukaryote

   https://doi.org/10.1101/gr.277843.123  

   Sheng, Yalan; Wang, Yuanyuan; Yang, Wentao; Wang, Xue Qing; Lu, Jiuwei; Pan, Bo; Nan, Bei; Liu, Yongqiang; Ye, Fei; Li, Chun; et al (May 2024, Genome Research)

   Although DNAN⁶-adenine methylation (6mA) is best known in prokaryotes, its presence in eukaryotes has recently generated great interest. Biochemical and genetic evidence supports that AMT1, an MT-A70 family methyltransferase (MTase), is crucial for 6mA deposition in unicellular eukaryotes. Nonetheless, the 6mA transmission mechanism remains to be elucidated. Taking advantage of single-molecule real-time circular consensus sequencing (SMRT CCS), here we provide definitive evidence for semiconservative transmission of 6mA inTetrahymena thermophila. In wild-type (WT) cells, 6mA occurs at the self-complementary ApT dinucleotide, mostly in full methylation (full-6mApT); after DNA replication, hemi-methylation (hemi-6mApT) is transiently present on the parental strand, opposite to the daughter strand readily labeled by 5-bromo-2′-deoxyuridine (BrdU). In ΔAMT1cells, 6mA predominantly occurs as hemi-6mApT. Hemi-to-full conversion in WT cells is fast, robust, and processive, whereas de novo methylation in ΔAMT1cells is slow and sporadic. InTetrahymena, regularly spaced 6mA clusters coincide with the linker DNA of nucleosomes arrayed in the gene body. Importantly, in vitro methylation of human chromatin by the reconstituted AMT1 complex recapitulates preferential targeting of hemi-6mApT sites in linker DNA, supporting AMT1's intrinsic and autonomous role in maintenance methylation. We conclude that 6mA is transmitted by a semiconservative mechanism: full-6mApT is split by DNA replication into hemi-6mApT, which is restored to full-6mApT by AMT1-dependent maintenance methylation. Our study dissects AMT1-dependent maintenance methylation and AMT1-independent de novo methylation, reveals a 6mA transmission pathway with a striking similarity to 5-methylcytosine (5mC) transmission at the CpG dinucleotide, and establishes 6mA as a bona fide eukaryotic epigenetic mark. 

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3. High-throughput and high-accuracy single-cell RNA isoform analysis using PacBio circular consensus sequencing

   https://doi.org/10.1038/s41467-023-38324-9  

   Shi, Zhuo-Xing; Chen, Zhi-Chao; Zhong, Jia-Yong; Hu, Kun-Hua; Zheng, Ying-Feng; Chen, Ying; Xie, Shang-Qian; Bo, Xiao-Chen; Luo, Feng; Tang, Chong; et al (December 2023, Nature Communications)

   Abstract Although long-read single-cell RNA isoform sequencing (scISO-Seq) can reveal alternative RNA splicing in individual cells, it suffers from a low read throughput. Here, we introduce HIT-scISOseq, a method that removes most artifact cDNAs and concatenates multiple cDNAs for PacBio circular consensus sequencing (CCS) to achieve high-throughput and high-accuracy single-cell RNA isoform sequencing. HIT-scISOseq can yield >10 million high-accuracy long-reads in a single PacBio Sequel II SMRT Cell 8M. We also report the development of scISA-Tools that demultiplex HIT-scISOseq concatenated reads into single-cell cDNA reads with >99.99% accuracy and specificity. We apply HIT-scISOseq to characterize the transcriptomes of 3375 corneal limbus cells and reveal cell-type-specific isoform expression in them. HIT-scISOseq is a high-throughput, high-accuracy, technically accessible method and it can accelerate the burgeoning field of long-read single-cell transcriptomics. 

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4. Revealing long-range heterogeneous organization of nucleoproteins with 6mA footprinting by ipdTrimming

   https://doi.org/10.1186/s13059-025-03592-9  

   Yang, Wentao; Wang, Xue Qing; Wei, Fan; Yu, Jingqi; Liu, Yifan; Dou, Yali (May 2025, Genome Biology)

   Enabled by long-read sequencing technologies, particularly Single Molecule, Real-Time sequencing, N6-methyladenine (6mA) footprinting is a transformative methodology for revealing the heterogenous and dynamic distribution of nucleosomes and other DNA-binding proteins. Here, we present ipdTrimming, a novel 6mA-calling pipeline that outperforms existing tools in both computational efficiency and accuracy. Utilizing this optimized experimental and computational framework, we are able to map nucleosome positioning and transcription factor occupancy in nuclear DNA and establish high-resolution, long-range binding events in mitochondrial DNA. Our study highlights the potential of 6mA footprinting to capture coordinated nucleoprotein binding and to unravel epigenetic heterogeneity. 

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5. DeepSignal: detecting DNA methylation state from Nanopore sequencing reads using deep-learning

   https://doi.org/10.1093/bioinformatics/btz276  

   Ni, Peng; Huang, Neng; Zhang, Zhi; Wang, De-Peng; Liang, Fan; Miao, Yu; Xiao, Chuan-Le; Luo, Feng; Wang, Jianxin; Elofsson, Arne (April 2019, Bioinformatics)

   Abstract Motivation The Oxford Nanopore sequencing enables to directly detect methylation states of bases in DNA from reads without extra laboratory techniques. Novel computational methods are required to improve the accuracy and robustness of DNA methylation state prediction using Nanopore reads. Results In this study, we develop DeepSignal, a deep learning method to detect DNA methylation states from Nanopore sequencing reads. Testing on Nanopore reads of Homo sapiens (H. sapiens), Escherichia coli (E. coli) and pUC19 shows that DeepSignal can achieve higher performance at both read level and genome level on detecting 6 mA and 5mC methylation states comparing to previous hidden Markov model (HMM) based methods. DeepSignal achieves similar performance cross different DNA methylation bases, different DNA methylation motifs and both singleton and mixed DNA CpG. Moreover, DeepSignal requires much lower coverage than those required by HMM and statistics based methods. DeepSignal can achieve 90% above accuracy for detecting 5mC and 6 mA using only 2× coverage of reads. Furthermore, for DNA CpG methylation state prediction, DeepSignal achieves 90% correlation with bisulfite sequencing using just 20× coverage of reads, which is much better than HMM based methods. Especially, DeepSignal can predict methylation states of 5% more DNA CpGs that previously cannot be predicted by bisulfite sequencing. DeepSignal can be a robust and accurate method for detecting methylation states of DNA bases. Availability and implementation DeepSignal is publicly available at https://github.com/bioinfomaticsCSU/deepsignal. Supplementary information Supplementary data are available at bioinformatics online. 

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