RNAWRE: a Resource of Writers, Readers and Erasers of RNA Modifications

Overview

Journal Database (Oxford)

Specialty Biology

Date 2020 Jul 2

PMID 32608478

Citations 15

Authors

Fulei Nie

Pengmian Feng

Xiaoming Song

Meng Wu

Qiang Tang

Wei Chen

Affiliations

Soon will be listed here.

Abstract

RNA modifications are involved in various kinds of cellular biological processes. Accumulated evidences have demonstrated that the functions of RNA modifications are determined by the effectors that can catalyze, recognize and remove RNA modifications. They are called 'writers', 'readers' and 'erasers'. The identification of RNA modification effectors will be helpful for understanding the regulatory mechanisms and biological functions of RNA modifications. In this work, we developed a database called RNAWRE that specially deposits RNA modification effectors. The current version of RNAWRE stored 2045 manually curated writers, readers and erasers for the six major kinds of RNA modifications, namely Cap, m1A, m6A, m5C, ψ and Poly A. The main modules of RNAWRE not only allow browsing and downloading the RNA modification effectors but also support the BLAST search of the potential RNA modification effectors in other species. We hope that RNAWRE will be helpful for the researches on RNA modifications. Database URL: http://rnawre.bio2db.com.

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References

Paramasivam A, Priyadharsini J, Raghunandhakumar S . N6-adenosine methylation (m6A): a promising new molecular target in hypertension and cardiovascular diseases. Hypertens Res. 2019; 43(2):153-154. DOI: 10.1038/s41440-019-0338-z. View

Liu K, Chen W . iMRM: a platform for simultaneously identifying multiple kinds of RNA modifications. Bioinformatics. 2020; 36(11):3336-3342. DOI: 10.1093/bioinformatics/btaa155. View

Chen T, Hao Y, Zhang Y, Li M, Wang M, Han W . m(6)A RNA methylation is regulated by microRNAs and promotes reprogramming to pluripotency. Cell Stem Cell. 2015; 16(3):289-301. DOI: 10.1016/j.stem.2015.01.016. View

Boccaletto P, Machnicka M, Purta E, Piatkowski P, Baginski B, Wirecki T . MODOMICS: a database of RNA modification pathways. 2017 update. Nucleic Acids Res. 2017; 46(D1):D303-D307. PMC: 5753262. DOI: 10.1093/nar/gkx1030. View

. UniProt: a worldwide hub of protein knowledge. Nucleic Acids Res. 2018; 47(D1):D506-D515. PMC: 6323992. DOI: 10.1093/nar/gky1049. View

DAVIS F, Allen F . Ribonucleic acids from yeast which contain a fifth nucleotide. J Biol Chem. 1957; 227(2):907-15. View

Fustin J, Doi M, Yamaguchi Y, Hida H, Nishimura S, Yoshida M . RNA-methylation-dependent RNA processing controls the speed of the circadian clock. Cell. 2013; 155(4):793-806. DOI: 10.1016/j.cell.2013.10.026. View

Sun T, Wu R, Ming L . The role of m6A RNA methylation in cancer. Biomed Pharmacother. 2019; 112:108613. DOI: 10.1016/j.biopha.2019.108613. View

Khoddami V, Yerra A, Mosbruger T, Fleming A, Burrows C, Cairns B . Transcriptome-wide profiling of multiple RNA modifications simultaneously at single-base resolution. Proc Natl Acad Sci U S A. 2019; 116(14):6784-6789. PMC: 6452723. DOI: 10.1073/pnas.1817334116. View

10.

Dominissini D, Nachtergaele S, Moshitch-Moshkovitz S, Peer E, Kol N, Ben-Haim M . The dynamic N(1)-methyladenosine methylome in eukaryotic messenger RNA. Nature. 2016; 530(7591):441-6. PMC: 4842015. DOI: 10.1038/nature16998. View

11.

Jonkhout N, Tran J, Smith M, Schonrock N, Mattick J, Novoa E . The RNA modification landscape in human disease. RNA. 2017; 23(12):1754-1769. PMC: 5688997. DOI: 10.1261/rna.063503.117. View

12.

Liu B . BioSeq-Analysis: a platform for DNA, RNA and protein sequence analysis based on machine learning approaches. Brief Bioinform. 2017; 20(4):1280-1294. DOI: 10.1093/bib/bbx165. View

13.

Zou Q, Xing P, Wei L, Liu B . Gene2vec: gene subsequence embedding for prediction of mammalian -methyladenosine sites from mRNA. RNA. 2018; 25(2):205-218. PMC: 6348985. DOI: 10.1261/rna.069112.118. View

14.

Cunningham F, Achuthan P, Akanni W, Allen J, Amode M, Armean I . Ensembl 2019. Nucleic Acids Res. 2018; 47(D1):D745-D751. PMC: 6323964. DOI: 10.1093/nar/gky1113. View

15.

Basith S, Manavalan B, Shin T, Lee G . SDM6A: A Web-Based Integrative Machine-Learning Framework for Predicting 6mA Sites in the Rice Genome. Mol Ther Nucleic Acids. 2019; 18:131-141. PMC: 6796762. DOI: 10.1016/j.omtn.2019.08.011. View

16.

Shen F, Huang W, Huang J, Xiong J, Yang Y, Wu K . Decreased N(6)-methyladenosine in peripheral blood RNA from diabetic patients is associated with FTO expression rather than ALKBH5. J Clin Endocrinol Metab. 2014; 100(1):E148-54. PMC: 5399497. DOI: 10.1210/jc.2014-1893. View

17.

Xu Y, Zhang S, Lin S, Guo Y, Deng W, Zhang Y . WERAM: a database of writers, erasers and readers of histone acetylation and methylation in eukaryotes. Nucleic Acids Res. 2016; 45(D1):D264-D270. PMC: 5210520. DOI: 10.1093/nar/gkw1011. View

18.

Lv H, Zhang Z, Li S, Tan J, Chen W, Lin H . Evaluation of different computational methods on 5-methylcytosine sites identification. Brief Bioinform. 2019; 21(3):982-995. DOI: 10.1093/bib/bbz048. View

19.

Camacho C, Coulouris G, Avagyan V, Ma N, Papadopoulos J, Bealer K . BLAST+: architecture and applications. BMC Bioinformatics. 2009; 10:421. PMC: 2803857. DOI: 10.1186/1471-2105-10-421. View

20.

Wang X, Lu Z, Gomez A, Hon G, Yue Y, Han D . N6-methyladenosine-dependent regulation of messenger RNA stability. Nature. 2013; 505(7481):117-20. PMC: 3877715. DOI: 10.1038/nature12730. View