psRNATarget: a plant small RNA target analysis server (2017 release)

Dai, Xinbin; Zhuang, Zhaohong; Zhao, Patrick Xuechun

doi:10.1093/nar/gky316

Cited by 1,069 publications

(741 citation statements)

References 20 publications

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“…Putative miRNA targets were predicted using psRNATarget 62 with a maximum expectation score of 2.5, allowing no gaps. miRNA origin from target signature was defined for miRNAs when the stem outside the guide region exhibited extended homology (of at least five consecutive bases) to the targeted gene.…”

Section: Bioinformatic Analysismentioning

confidence: 99%

Unravelling the developmental and functional significance of an ancient Argonaute duplication

Fridrich

Modepalli

Lewandowska

et al. 2020

Preprint

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microRNAs (miRNAs), base-pair to messenger RNA targets and guide Argonaute proteins to mediate their silencing. This target regulation is considered crucial for animal physiology and development.However, this notion is based exclusively on studies in bilaterians, which comprise almost all lab model animals. To fill this glaring phylogenetic gap, we characterized the functions of two Argonaute paralogs in the sea anemone Nematostella vectensis of the phylum Cnidaria, which is separated from bilaterians by ~600 million years. Using genetic manipulation, Argonaute-immunoprecipitations and high-throughput sequencing we provide experimental evidence for the developmental importance of miRNAs in a nonbilaterian animal. Additionally, we uncover unexpected differential distribution of distinct miRNAs between the two Argonautes and the ability of one of them to load additional types of small RNAs. This enables us to postulate a novel model for evolution of miRNA precursors in sea anemones and their relatives, revealing alternative trajectories for metazoan miRNA evolution.

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Section: Bioinformatic Analysismentioning

confidence: 99%

Unravelling the developmental and functional significance of an ancient Argonaute duplication

Fridrich

Modepalli

Lewandowska

et al. 2020

Preprint

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“…In the case of seed‐restricted matches, programs such as miRanda, TargetScan, PITA, psRNATarget, and others are useful for getting the initial lists of predicted targets, from which it is easy to identify which transcripts had a potential binding site to which miRNAs. The predicted lists generated by different programs might differ from each other and such comparisons of their performances have been previously discussed .…”

Section: What Can Be Done?mentioning

confidence: 99%

“…Discoveries of miRNA‐regulated processes rely on identification of genes regulated by specific miRNAs. For this purpose, target identification methods were developed . These programs rely on principles of miRNA–target interaction in plants and animals and were discussed in contexts of ways of improving predictability of real targets and in contexts of comparing their accuracies .…”

Section: Introductionmentioning

confidence: 99%

Too Many False Targets for MicroRNAs: Challenges and Pitfalls in Prediction of miRNA Targets and Their Gene Ontology in Model and Non‐model Organisms

2019

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Short (“seed”) or extended base pairing between microRNAs (miRNAs) and their target RNAs enables post‐transcriptional silencing in many organisms. These interactions allow the computational prediction of potential targets. In model organisms, predicted targets are frequently validated experimentally; hence meaningful miRNA‐regulated processes are reported. However, in non‐models, these reports mostly rely on computational prediction alone. Many times, further bioinformatic analyses such as Gene Ontology (GO) enrichment are based on these in silico projections. Here such approaches are reviewed, their caveats are highlighted and the ease of picking false targets from predicted lists is demonstrated. Discoveries that shed new light on how miRNAs evolved to regulate targets in various phyletic groups are discussed, in addition to the pitfalls of target identification in non‐model organisms. The goal is to prevent the misuse of bioinformatic tools, as they cannot bypass the biological understanding of miRNA–target regulation.

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“…The sRNAs were filtered by fold changes between ≤ -2 and ≥ +2. The significance of the differentially expressed sRNAs was evaluated with q-value of ≤ 0.05. miRNA target RNAs were identified using the psRNATarget (Dai et al, 2018) prediction V2 tool (http://plantgrn.noble.org/psRNATarget/) from protein coding and non-coding transcripts present in Araport11. An expectation value of less than 2.5 was considered as a cut-off for true miRNA targets where mRNAs harbouring a lower number of mismatches to the reverse and complementary miRNA obtain lower score values.…”

Section: Analysis Of Srnamentioning

confidence: 99%

Impact of small RNAs in retrograde signalling pathways inArabidopsis thaliana

Habermann

Tiwari

Krantz

et al. 2019

Preprint

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Chloroplast perturbations activate retrograde signalling pathways causing dynamic changes of gene expression. Besides transcriptional control of gene expression different classes of small non-coding RNAs (sRNAs) act in gene expression control, but comprehensive analyses regarding their role in retrograde signalling is lacking. We performed sRNA profiling in response to norflurazon (NF) that provokes retrograde signals in A. thaliana wild type and the two retrograde signalling mutants gun1 and gun5. The RNA samples were also used for mRNA and long non-coding RNA (lncRNA) profiling to link altered sRNA levels to changes of their cognate target RNAs. We identified 122 sRNAs from all known sRNA classes that were responsive to NF in wild type. Strikingly, 140 and 213 sRNAs were found to be differentially regulated in both mutants indicating a retrograde control of these sRNAs. Concomitant with the changes in sRNA expression we detected about 1500 differentially expressed mRNAs in the NF treated wild type and around 900 and 1400 mRNAs that were differentially regulated in the gun1 and gun5 mutant with a high proportion (~30%) of genes encoding plastid proteins. Furthermore, around 20% of predicted miRNA targets code for plastid localised proteins. The analyses of sRNA-target pairs identified pairs with an anticorrelated expression as well pairs showing other expressional relations pointing to a role of sRNAs in balancing transcriptional changes upon retrograde signals. Based on the comprehensive changes in sRNA expression we assume a considerable impact of sRNAs in retrograde-dependent transcriptional changes to adjust plastidic and nuclear gene expression. Significance statementPerturbations of plastid functions trigger retrograde signalling to adjust plastidic and nuclear gene expression, however, the role of small non-coding RNAs acting as regulators in these pathways is not well understood. We analysed small non-coding RNA expression in response to retrograde signals in A. thaliana wild type and two retrograde signalling mutants and identified members of all known small non-coding RNA classes pointing to a functional role of these RNA classes in retrograde pathways.

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psRNATarget: a plant small RNA target analysis server (2017 release)

Cited by 1,069 publications

References 20 publications

Unravelling the developmental and functional significance of an ancient Argonaute duplication

Unravelling the developmental and functional significance of an ancient Argonaute duplication

Too Many False Targets for MicroRNAs: Challenges and Pitfalls in Prediction of miRNA Targets and Their Gene Ontology in Model and Non‐model Organisms

Impact of small RNAs in retrograde signalling pathways inArabidopsis thaliana

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