circTAIL-seq, a targeted method for deep analysis of RNA 3' tails, reveals transcript-specific differences by multiple metrics.

Citation data:

RNA (New York, N.Y.), ISSN: 1469-9001, Vol: 22, Issue: 3, Page: 477-86

Publication Year:
2016
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PMID:
26759453
DOI:
10.1261/rna.054494.115
PMCID:
PMC4748824
Author(s):
Gazestani, Vahid H, Hampton, Marshall, Abrahante, Juan E, Salavati, Reza, Zimmer, Sara L
Publisher(s):
Cold Spring Harbor Laboratory
Tags:
Biochemistry, Genetics and Molecular Biology
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article description
Post-transcriptionally added RNA 3' nucleotide extensions, or tails, impose numerous regulatory effects on RNAs, including effects on RNA turnover and translation. However, efficient methods for in-depth tail profiling of a transcript of interest are still lacking, hindering available knowledge particularly of tail populations that are highly heterogeneous. Here, we developed a targeted approach, termed circTAIL-seq, to quantify both major and subtle differences of heterogeneous tail populations. As proof-of-principle, we show that circTAIL-seq quantifies the differences in tail qualities between two selected Trypanosoma brucei mitochondrial transcripts. The results demonstrate the power of the developed method in identification, discrimination, and quantification of different tail states that the population of one transcript can possess. We further show that circTAIL-seq can detect the tail characteristics for variants of transcripts that are not easily detectable by conventional approaches, such as degradation intermediates. Our findings are not only well supported by previous knowledge, but they also expand this knowledge and provide experimental evidence for previous hypotheses. In the future, this approach can be used to determine changes in tail qualities in response to environmental or internal stimuli, or upon silencing of genes of interest in mRNA-processing pathways. In summary, circTAIL-seq is an effective tool for comparing nonencoded RNA tails, especially when the tails are extremely variable or transcript of interest is low abundance.

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