Nucleotide resolution profiling of m(7)G tRNA modification by TRAC-Seq.
Nucleotide resolution profiling of m(7)G tRNA modification by TRAC-Seq.
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通过 TRAC-Seq 对 m(7)G tRNA 修饰进行核苷酸分辨率分析
DOI:
10.1038/s41596-019-0226-7
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发表时间:
2019-11
期刊:
影响因子:
14.8
通讯作者:
Gregory RI
中科院分区:
文献类型:
--
作者:
Lin S;Liu Q;Jiang YZ;Gregory RI
Precise identification of sites of RNA modification is key to studying the functional role of such modifications in the regulation of gene expression and for elucidating relevance to diverse physiological processes. tRNA reduction and cleavage sequencing (TRAC-Seq) is a chemically based approach for the unbiased global mapping of 7-methylguansine (m7G) modification of tRNAs at single-nucleotide resolution throughout the tRNA transcriptome. m7G TRAC-Seq involves the treatment of size-selected (<200 nt) RNAs with the demethylase AlkB to remove major tRNA modifications, followed by sodium borohydride (NaBH4) reduction of m7G sites and subsequent aniline-mediated cleavage of the RNA chain at the resulting abasic sites. The cleaved sites are subsequently ligated with adaptors for the construction of libraries for high-throughput sequencing. The m7G modification sites are identified using a bioinformatic pipeline that calculates the cleavage scores at individual sites on all tRNAs. Unlike antibody-based methods, such as methylated RNA immunoprecipitation and sequencing (meRIP-Seq) for enrichment of methylated RNA sequences, chemically based approaches, including TRAC-Seq, can provide nucleotide-level resolution of modification sites. Compared to the related method AlkAniline-Seq (alkaline hydrolysis and aniline cleavage sequencing), TRAC-Seq incorporates small RNA selection, AlkB demethylation, and sodium borohydride reduction steps to achieve specific and efficient single-nucleotide resolution profiling of m7G sites in tRNAs. The m7G TRAC-Seq protocol could be adapted to chemical cleavage-mediated detection of other RNA modifications. The protocol can be completed within ~9 d for four biological replicates of input and treated samples.
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影响因子:
14.9
作者:
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DOI:
10.1093/bioinformatics/btq033
发表时间:
2010-03-15
期刊:
Bioinformatics (Oxford, England)
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12.3
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通讯作者:
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