ArticleJournal of the Royal Society, Interface2022
Exact Bayesian inference for the detection of graft-mobile transcripts from sequencing data.
Article in Journal of the Royal Society, Interface, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
6 citing papers in PubMed, 1 synthesis or guideline pooled it, 11 citations in OpenAlex.
- Re-analysis of mobile mRNA datasets raises questions about the extent of long-distance mRNA communication.Nature plants · 2025Pooled it
- PHR-Mediated Pi Starvation Response Mobile Messenger RNAs Represent Noncoding Transcripts in Recipient Tissues.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Advances in understanding the graft healing mechanism: a review of factors and regulatory pathways.Horticulture research · 2024Article
- The mRNA mobileome: challenges and opportunities for deciphering signals from the noise.The Plant cell · 2023Review
- Long distance signalling and epigenetic changes in crop grafting.Frontiers in plant science · 2023Review
- Exact Bayesian inference for the detection of graft-mobile transcripts from sequencing data.Journal of the Royal Society, Interface · 2022Article
Corrections and comments
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Authors and funding
7 authors at 3 institutions in 2 countries.
Funding
Abstract
The long-distance transport of messenger RNAs (mRNAs) has been shown to be important for several developmental processes in plants. A popular method for identifying travelling mRNAs is to perform RNA-Seq on grafted plants. This approach depends on the ability to correctly assign sequenced mRNAs to the genetic background from which they originated. The assignment is often based on the identification of single-nucleotide polymorphisms (SNPs) between otherwise identical sequences. A major challenge is therefore to distinguish SNPs from sequencing errors. Here, we show how Bayes factors can be computed analytically using RNA-Seq data over all the SNPs in an mRNA. We used simulations to evaluate the performance of the proposed framework and demonstrate how Bayes factors accurately identify graft-mobile transcripts. The comparison with other detection methods using simulated data shows how not taking the variability in read depth, error rates and multiple SNPs per transcript into account can lead to incorrect classification. Our results suggest experimental design criteria for successful graft-mobile mRNA detection and show the pitfalls of filtering for sequencing errors or focusing on single SNPs within an mRNA.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.