ArticleNucleic acids research2018
PAREsnip2: a tool for high-throughput prediction of small RNA targets from degradome sequencing data using configurable targeting rules.
Article in Nucleic acids research, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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Who cites it
28 citing papers in PubMed.
- Small RNAs big impact: a review on microRNA-mediated tolerance in wheat under terminal heat.Functional & integrative genomics · 2025Review
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- Detection of Ethylene Signaling Using Bioinformatics Tools in Plants.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Comparative RNA profiling identifies stage-specific phasiRNAs and coexpressed Argonaute genes in Bambusoideae and Pooideae species.The Plant cell · 2024Article
- Conceptual Framework of Epigenetic Analyses of Plant Responses to Sedentary Endoparasitic Nematodes.Methods in molecular biology (Clifton, N.J.) · 2024Article
- Distribution of Small RNAs Along Transposable Elements in Vitis vinifera During Somatic Embryogenesis.Methods in molecular biology (Clifton, N.J.) · 2024Article
- Leaf rust responsive miRNA and their target genes in wheat.Functional & integrative genomics · 2022Article
- Article
- MicroRNA miR394 regulates flowering time in Arabidopsis thaliana.Plant cell reports · 2022Article
- The Multiverse of Plant Small RNAs: How Can We Explore It?International journal of molecular sciences · 2022Review
- Degradome comparison between wild and cultivated rice identifies differential targeting by miRNAs.BMC genomics · 2022Article
- POSTAR3: an updated platform for exploring post-transcriptional regulation coordinated by RNA-binding proteins.Nucleic acids research · 2022Article
- Analysis of Small RNA Sequencing Data in Plants.Methods in molecular biology (Clifton, N.J.) · 2022Article
- Computational Analysis of HTS Data and Its Application in Plant Pathology.Methods in molecular biology (Clifton, N.J.) · 2022Article
- Excess nitrogen responsive HvMADS27 transcription factor controls barley root architecture by regulating abscisic acid level.Frontiers in plant science · 2022Article
- noisyR: enhancing biological signal in sequencing datasets by characterizing random technical noise.Nucleic acids research · 2021Article
- fIdentification of B. napus small RNAs responsive to infection by a necrotrophic pathogen.BMC plant biology · 2021Article
- smartPARE: An R Package for Efficient Identification of True mRNA Cleavage Sites.International journal of molecular sciences · 2021Article
- Pi-starvation induced transcriptional changes in barley revealed by a comprehensive RNA-Seq and degradome analyses.BMC genomics · 2021Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Small RNAs (sRNAs) are short, non-coding RNAs that play critical roles in many important biological pathways. They suppress the translation of messenger RNAs (mRNAs) by directing the RNA-induced silencing complex to their sequence-specific mRNA target(s). In plants, this typically results in mRNA cleavage and subsequent degradation of the mRNA. The resulting mRNA fragments, or degradome, provide evidence for these interactions, and thus degradome analysis has become an important tool for sRNA target prediction. Even so, with the continuing advances in sequencing technologies, not only are larger and more complex genomes being sequenced, but also degradome and associated datasets are growing both in number and read count. As a result, existing degradome analysis tools are unable to process the volume of data being produced without imposing huge resource and time requirements. Moreover, these tools use stringent, non-configurable targeting rules, which reduces their flexibility. Here, we present a new and user configurable software tool for degradome analysis, which employs a novel search algorithm and sequence encoding technique to reduce the search space during analysis. The tool significantly reduces the time and resources required to perform degradome analysis, in some cases providing more than two orders of magnitude speed-up over current methods.
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