NanoPARE: parallel analysis of RNA 5′ ends from low-input RNA is a research paper published in Genome Research (2018). On theSindex it has a DataRank of 2.2. It has been cited 75 times, with 56 citing works in its 1-hop citation network.
Diverse RNA 5' ends are generated through both transcriptional and post-transcriptional processes. These important modes of gene regulation often vary across cell types and can contribute to the diversification of transcriptomes and thus cellular differentiation. Therefore, the identification of primary and processed 5' ends of RNAs is important for their functional characterization. Methods have been developed to profile either RNA 5' ends from primary transcripts or the products of RNA degradation genome-wide. However, these approaches either require high amounts of starting RNA or are performed in the absence of paired gene-body mRNA-seq data. This limits current efforts in RNA 5' end annotation to whole tissues and can prevent accurate RNA 5' end classification due to biases in the data sets. To enable the accurate identification and precise classification of RNA 5' ends from standard and low-input RNA, we developed a next-generation sequencing-based method called nanoPARE and associated software. By integrating RNA 5' end information from nanoPARE with gene-body mRNA-seq data from the same RNA sample, our method enables the identification of transcription start sites at single-nucleotide resolution from single-cell levels of total RNA, as well as small RNA-mediated cleavage events from at least 10,000-fold less total RNA compared to conventional approaches. NanoPARE can therefore be used to accurately profile transcription start sites, noncapped RNA 5' ends, and small RNA targeting events from individual tissue types. As a proof-of-principle, we utilized nanoPARE to improve Arabidopsis thaliana RNA 5' end annotations and quantify microRNA-mediated cleavage events across five different flower tissues.
FAIR checklist signals are shown for context only and do not affect DataRank scoring.
Base Score Contribution
0.650
From this paper's citation signal
Citation Network Contribution
1.5
From 43 citing papers with measurable signal
Ranked by citation count — the same ordering the engine uses when summing log1p(Cq) over citers.
DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 30% comes from its base citations and 70% from the citation network (43 citing papers contributed measurable signal).
Citers are pulled from OpenAlex sorted by cited_by_count:descand capped per paper, so when the cap binds we keep the highest-signal references and the score is reproducible across reruns.
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