scholarly journals Deciphering the ‘m6A code’ via quantitative profiling of m6A at single-nucleotide resolution

2019 ◽  
Author(s):  
Miguel Angel Garcia-Campos ◽  
Sarit Edelheit ◽  
Ursula Toth ◽  
Ran Shachar ◽  
Ronit Nir ◽  
...  

AbstractN6-methyladenosine (m6A) is the most abundant modification on mRNA, and is implicated in critical roles in development, physiology and disease. The ability to map m6A using immunoprecipitation-based approaches has played a critical role in dissecting m6A functions and mechanisms of action. Yet, these approaches are of limited specificity, unknown sensitivity, and unable to quantify m6A stoichiometry. These limitations have severely hampered our ability to unravel the factors determining where m6A will be deposited, to which levels (the ‘m6A code’), and to quantitatively profile m6A dynamics across biological systems. Here, we used the RNase MazF, which cleaves specifically at unmethylated RNA sites, to develop MASTER-seq for systematic quantitative profiling of m6A sites at 16-25% of all m6A sites at single nucleotide resolution. We established MASTER-seq for orthogonal validation andde novodetection of m6A sites, and for tracking of m6A dynamics in yeast gametogenesis and in early mammalian differentiation. We discover that antibody-based approaches severely underestimate the number of m6A sites, and that both the presence of m6A and its stoichiometry are ‘hard-coded’ via a simple and predictable code within the extended sequence composition at the methylation sites. This code accounts for ~50% of the variability in methylation levels across sites, allows excellentde novoprediction of methylation sites, and predicts methylation acquisition and loss across evolution. We anticipate that MASTER-seq will pave the path towards a more quantitative investigation of m6A biogenesis and regulation in a wide variety of systems, including diverse cell types, stimuli, subcellular components, and disease states.

2011 ◽  
Vol 29 (8) ◽  
pp. 723-730 ◽  
Author(s):  
Yingrui Li ◽  
Hancheng Zheng ◽  
Ruibang Luo ◽  
Honglong Wu ◽  
Hongmei Zhu ◽  
...  

2019 ◽  
Author(s):  
Hongyang Li ◽  
Yuanfang Guan

AbstractDecoding the cell type-specific transcription factor (TF) binding landscape at single-nucleotide resolution is crucial for understanding the regulatory mechanisms underlying many fundamental biological processes and human diseases. However, limits on time and resources restrict the high-resolution experimental measurements of TF binding profiles of all possible TF-cell type combinations. Previous computational approaches either can not distinguish the cell-context-dependent TF binding profiles across diverse cell types, or only provide a relatively low-resolution prediction. Here we present a novel deep learning approach, Leopard, for predicting TF-binding sites at single-nucleotide resolution, achieving the median area under receiver operating characteristic curve (AUROC) of 0.994. Our method substantially outperformed state-of-the-art methods Anchor and FactorNet, improving the performance by 19% and 27% respectively despite evaluated at a lower resolution. Meanwhile, by leveraging a many-to-many neural network architecture, Leopard features hundred-fold to thousand-fold speedup compared to current many-to-one machine learning methods.


2019 ◽  
Author(s):  
Bo Cao ◽  
Xiaolin Wu ◽  
Jieliang Zhou ◽  
Hang Wu ◽  
Michael S. DeMott ◽  
...  

AbstractHere we present the Nick-seq platform for quantitative mapping of DNA modifications and damage at single-nucleotide resolution across genomes. Pre-existing breaks are blocked and DNA structures converted to strand-breaks for 3’-extension by nick-translation to produce nuclease-resistant oligonucleotides, and 3’-capture by terminal transferase tailing. Libraries from both products are subjected to next-generation sequencing. Nick-seq is a generally applicable method illustrated with quantitative profiling of single-strand-breaks, phosphorothioate modifications, and DNA oxidation.


FEBS Letters ◽  
1988 ◽  
Vol 234 (2) ◽  
pp. 295-299 ◽  
Author(s):  
M. Vojtíšková ◽  
S. Mirkin ◽  
V. Lyamichev ◽  
O. Voloshin ◽  
M. Frank-Kamenetskii ◽  
...  

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