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In monocots other than maize (Zea mays) and rice (Oryza sativa), the repertoire and diversity of microRNAs (miRNAs) and the populations of phased, secondary, small interfering RNAs (phasiRNAs) are poorly characterized. To remedy this, we sequenced small RNAs (sRNA) from vegetative and dissected inflorescence tissue in 28 phylogenetically diverse monocots and from several early-diverging angiosperm lineages, as well as publicly available data from 10 additional monocot species. We annotated miRNAs, small interfering RNAs (siRNAs) and phasiRNAs across the monocot phylogeny, identifying miRNAs apparently lost or gained in the grasses relative to other monocot families, as well as a number of transfer RNA fragments misannotated as miRNAs. Using our miRNA database cleaned of these misannotations, we identified conservation at the 8th, 9th, 19th, and 3′-end positions that we hypothesize are signatures of selection for processing, targeting, or Argonaute sorting. We show that 21-nucleotide (nt) reproductive phasiRNAs are far more numerous in grass genomes than other monocots. Based on sequenced monocot genomes and transcriptomes, DICER-LIKE5, important to 24-nt phasiRNA biogenesis, likely originated via gene duplication before the diversification of the grasses. This curated database of phylogenetically diverse monocot miRNAs, siRNAs, and phasiRNAs represents a large collection of data that should facilitate continued exploration of sRNA diversification in flowering plants.

The Lemnaceae family comprises aquatic plants of angiosperms gaining attention due to their utility in wastewater treatment, and rapid production of biomass that can be used as feed, fuel, or food. Moreover, it can serve as a model species for neotenous growth and environmental adaptation. The latter properties are subject to post-transcriptional regulation of gene expression, meriting investigation of how miRNAs in Spirodela polyrhiza, the most basal and most thoroughly sequenced member of the family, are expressed under different growth conditions. To further scientific understanding of its capacity to adapt to environmental cues, we measured miRNA expression and processing of their target sequences under different temperatures, and in the presence of abscisic acid, copper, kinetin, nitrate, and sucrose. Using two small RNA sequencing experiments and one degradome sequencing experiment, we provide evidence for 108 miRNAs. Sequencing cleaved mRNAs validated 42 conserved miRNAs with 83 targets and 24 novel miRNAs regulating 66 targets and created a list of 575 predicted and verified targets. These analyses revealed condition-induced changes in miRNA expression and cleavage activity, and resulted in the addition of stringently reviewed miRNAs to miRBase. This combination of small RNA and degradome sequencing provided not only high confidence predictions of conserved and novel miRNAs and targets, but also a view of the post-transcriptional regulation of adaptations. A unique aspect is the role of miR156 and miR172 expression and activity in its clonal propagation and neoteny. Additionally, low levels of 24 nt sRNAs were observed, despite the lack of recent retrotransposition.

Small RNAs are ubiquitous, versatile repressors and include (1) microRNAs (miRNAs), processed from mRNA forming stem-loops; and (2) small interfering RNAs (siRNAs), the latter derived in plants by a process typically requiring an RNA-dependent RNA polymerase. We constructed and analyzed an expression atlas of soybean (Glycine max) small RNAs, identifying over 500 loci generating 21-nucleotide phased siRNAs (phasiRNAs; from PHAS loci), of which 483 overlapped annotated protein-coding genes. Via the integration of miRNAs with parallel analysis of RNA end (PARE) data, 20 miRNA triggers of 127 PHAS loci were detected. The primary class of PHAS loci (208 or 41% of the total) corresponded to NB-LRR genes; some of these small RNAs preferentially accumulate in nodules. Among the PHAS loci, novel representatives of TAS3 and noncanonical phasing patterns were also observed. A noncoding PHAS locus, triggered by miR4392, accumulated preferentially in anthers; the phasiRNAs are predicted to target transposable elements, with their peak abundance during soybean reproductive development. Thus, phasiRNAs show tremendous diversity in dicots. We identified novel miRNAs and assessed the veracity of soybean miRNAs registered in miRBase, substantially improving the soybean miRNA annotation, facilitating an improvement of miRBase annotations and identifying at high stringency novel miRNAs and their targets.

Phased, secondary siRNAs (phasiRNAs) are found widely in plants, from protein-coding transcripts and long, non-coding RNAs; animal piRNAs are also phased. Integrated methods characterizing “PHAS” loci are unavailable, and existing methods are quite limited and inefficient in handling large volumes of sequencing data. The PHASIS suite described here provides complete tools for the computational characterization of PHAS loci, with an emphasis on plants, in which these loci are numerous. Benchmarked comparisons demonstrate that PHASIS is sensitive, highly scalable and fast. Importantly, PHASIS eliminates the requirement of a sequenced genome and PARE/degradome data for discovery of phasiRNAs and their miRNA triggers.

Summary: We describe miTRATA, the first web-based tool for microRNA Truncation and Tailing Analysis—the analysis of 3′ modifications of microRNAs including the loss or gain of nucleotides relative to the canonical sequence. miTRATA is implemented in Python (version 3) and employs parallel processing modules to enhance its scalability when analyzing multiple small RNA (sRNA) sequencing datasets. It utilizes miRBase, currently version 21, as a source of known microRNAs for analysis. miTRATA notifies user(s) via email to download as well as visualize the results online. miTRATA’s strengths lie in (i) its biologist-focused web interface, (ii) improved scalability via parallel processing and (iii) its uniqueness as a webtool to perform microRNA truncation and tailing analysis. Availability and implementation: miTRATA is developed in Python and PHP. It is available as a web-based application from https://wasabi.dbi.udel.edu/∼apps/ta/. Contact: meyers@dbi.udel.edu Supplementary information: Supplementary data are available at Bioinformatics online.

MicroRNAs are ubiquitous in plant genomes but vary greatly in their abundance within and conservation among plant lineages. To gain insight into the evolutionary birth/death dynamics of microRNA families, we sequenced small RNA and 5′-end PARE libraries generated from two closely related species of Gossypium. Here, we demonstrate that 33 microRNA families, with similar copy numbers and average evolutionary rates, are conserved in the two congeneric cottons. Analysis of the presence/absence of these microRNA families in other land plants sheds light on their depth of phylogenetic origin and lineage-specific loss/gain. Conserved microRNA families in Gossypium exhibit a striking interspecific asymmetry in expression, potentially connected to relative proximity to neighboring transposable elements. A complex correlated expression pattern of microRNA target genes with their controlling microRNAs indicates that possible functional divergence of conserved microRNA families can also exist even within a single plant genus.

Parallel analysis of RNA ends (PARE) is a technique utilizing high-throughput sequencing to profile uncapped, mRNA cleavage or decay products on a genome-wide basis. Tools currently available to validate miRNA targets using PARE data employ only annotated genes, whereas important targets may be found in unannotated genomic regions. To handle such cases and to scale to the growing availability of PARE data and genomes, we developed a new tool, 'sPARTA' (small RNA-PARE target analyzer) that utilizes a built-in, plant-focused target prediction module (aka 'miRferno'). sPARTA not only exhibits an unprecedented gain in speed but also it shows greater predictive power by validating more targets, compared to a popular alternative. In addition, the novel 'seed-free' mode, optimized to find targets irrespective of complementarity in the seed-region, identifies novel intergenic targets. To fully capitalize on the novelty and strengths of sPARTA, we developed a web resource, 'comPARE', for plant miRNA target analysis; this facilitates the systematic identification and analysis of miRNA-target interactions across multiple species, integrated with visualization tools. This collation of high-throughput small RNA and PARE datasets from different genomes further facilitates re-evaluation of existing miRNA annotations, resulting in a 'cleaner' set of microRNAs.