ArticleNucleic acids research2020
NATpare: a pipeline for high-throughput prediction and functional analysis of nat-siRNAs.
Article in Nucleic acids research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed.
- Genome-wide view and characterization of natural antisense transcripts in Cannabis Sativa L.Plant molecular biology · 2024Article
- The Multiverse of Plant Small RNAs: How Can We Explore It?International journal of molecular sciences · 2022Review
- Biogenesis, Functions, Interactions, and Resources of Non-Coding RNAs in Plants.International journal of molecular sciences · 2022Review
- RNA Drug Delivery Using Biogenic Nanovehicles for Cancer Therapy.Frontiers in pharmacology · 2021Review
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Authors and funding
3 authors.
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Abstract
Natural antisense transcript-derived small interfering RNAs (nat-siRNAs) are a class of functional small RNA (sRNA) that have been found in both plant and animals kingdoms. In plants, these sRNAs have been shown to suppress the translation of messenger RNAs (mRNAs) by directing the RNA-induced silencing complex (RISC) to their sequence-specific mRNA target(s). Current computational tools for classification of nat-siRNAs are limited in number and can be computationally infeasible to use. In addition, current methods do not provide any indication of the function of the predicted nat-siRNAs. Here, we present a new software pipeline, called NATpare, for prediction and functional analysis of nat-siRNAs using sRNA and degradome sequencing data. Based on our benchmarking in multiple plant species, NATpare substantially reduces the time required to perform prediction with minimal resource requirements allowing for comprehensive analysis of nat-siRNAs in larger and more complex organisms for the first time. We then exemplify the use of NATpare by identifying tissue and stress specific nat-siRNAs in multiple Arabidopsis thaliana datasets.
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