Reproductive phasiRNAs in grasses are compositionally distinct from other classes of small RNAs

Abstract

Little is known about the characteristics and function of reproductive phased, secondary, small interfering RNAs (phasiRNAs) in the Poaceae, despite the availability of significant genomic resources, experimental data, and a growing number of computational tools. We utilized machine-learning methods to identify sequence-based and positional features that distinguish phasiRNAs in rice and maize from other small RNAs (sRNAs). We developed Random Forest classifiers that can distinguish reproductive phasiRNAs from other sRNAs in complex sets of sequencing data, utilizing sequence-based (k-mers) and features describing position-specific sequence biases. The classification performance attained is > 80% in accuracy, sensitivity, specificity, and positive predicted value. Feature selection identified important features in both ends of phasiRNAs. We demonstrated that phasiRNAs have strand specificity and position-specific nucleotide biases potentially influencing AGO sorting; we also predicted targets to infer functions of phasiRNAs, and computationally assessed their sequence characteristics relative to other sRNAs. Our results demonstrate that machine-learning methods effectively identify phasiRNAs despite the lack of characteristic features typically present in precursor loci of other small RNAs, such as sequence conservation or structural motifs. The 5'-end features we identified provide insights into AGO-phasiRNA interactions. We describe a hypothetical model of competition for AGO loading between phasiRNAs of different nucleotide compositions.

From small RNA discoveries to a new paradigm in computational genomics?

“…Just the set of plant small RNAs consists of an array of different types of molecules that have been hierarchically classified based on their mode of biogenesis and their function (Axtell, ). In this issue of New Phytologist Patel et al . (pp. 851–864) tackle the problem of identifying and characterizing reproductive phased, secondary, small interfering RNAs (phasiRNAs) in the grasses.…”

“…One difficulty in studying these phasiRNAs has been to distinguish them from other small RNAs. Patel et al . show that their machine learning approach (a random forest classifier) can fairly accurately distinguish phasiRNAs from other types of small RNAs in input sets of tens of thousands of sequences. Furthermore, inspection of the successful classifier revealed the distinguishing features: distinct nucleotide preferences compared to other small RNAs were found in several positions.…”

“…The impressive amount of computational analysis by Patel et al . leads to experimentally testable hypotheses. For example, for verified phasiRNA–target pairs, substitution of critical RNA nucleotides should give the same phenotype as a complete knockout of the RNA gene.…”

“…I should think yes, and this immediately suggests a follow‐up question of how to best implement and disseminate such computational workflows. The currently accepted best protocol, adhered to by Patel et al ., is to carefully document all data set sources, software, and parameters. In practice, however, this still leads to ambiguity and much overhead for efforts to reproduce the current work, or to apply the workflow to new data sets, or with different machine learning components, for example.…”

“…The study of Patel et al . is an example of modern genome research, the likes of which will increasingly appear on the pages of this and peer journals. It is time for authors and journals to think deeply about how to adjust to this new world.…”