Topology of the human and mouse m6A RNA methylomes revealed by m6A-seq

By D. Dominissini, S. Moshitch-Moshkovitz, Schraga Schwartz, M. Salmon-Divon, L. Ungar, Sivan Osenberg, K. Cesarkas, J. Jacob-Hirsch, N. Amariglio, M. Kupiec and 2 more. Nature, 2012

4,382 citations recorded by the source corpus.

Subfields: Cancer Research, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/715852403fb0d8cd839f0ee3d98b9e61b08ca149

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In Cancer Research

1. Chuan He
2. Howard Y. Chang
3. Pedro J. Batista
4. Kate D. Meyer
5. Samie R. Jaffrey
6. Y. Yue
7. Jianzhao Liu
8. P. He

In Molecular Biology

1. Chuan He
2. Samie R. Jaffrey
3. P. He
4. Kate D. Meyer
5. Howard Y. Chang
6. Hailing Shi
7. Pedro J. Batista
8. Jiangbo W

Citations in the corpus

• FMRP Modulates Neural Differentiation through m6A-Dependent mRNA Nuclear Export (2019, background): “RNAmethylation on N(6)-adenosine is emerging as a vital mechanism regulating RNAmetabolism and function (Dominissini et al., 2012; Meyer et al., 2012; Peer et al., 2017).”
• m6A RNA Methylation Regulates the Self-Renewal and Tumorigenesis of Glioblastoma Stem Cells (2017, methodology): “Peak distribution analysis revealed strong enrichment of m6A peaks near the stop codon (Figure 7F) as previously described (Dominissini et al., 2012; Meyer et al., 2012).”
• YTHDC1 mediates nuclear export of N6-methyladenosine methylated mRNAs (2017, background): “YTHDC1 affects subcellular mRNA localization independent of premRNA splicing Both m6A and YTHDC1 have been shown to affect alternative splicing patterns in mRNA (Alarcón et al., 2015; Dominissini et al., 2012; Liu et al., 2015; Xiao et al., 2016).”
• YTH Domain: A Family of N6-methyladenosine (m6A) Readers (2018, methodology): “Although YTH domaincontaining proteins are putative RBPs, their exact binding ligands had remained unknown until two reports discovered that mammalian YTH family members are the candidates of m(6)A readers [36,37].”
• SRAMP: prediction of mammalian N6-methyladenosine (m6A) sites based on sequence-derived features (2016, background): “MCC = TP × TN − FP × FN √ (TP + FP) × (TP + FN) × (TN + FP) × (TN + FN) (6)”