Inferring characteristics of reproductive phased, secondary, small interfering RNAs (phasiRNAs) in grasses using Machine Learning

Small RNAs (sRNAs) in plant range in size from 21 to 24 nucleotides, and play important roles in biological processes such as development, epigenetics modification, and plant defense. They can be partitioned into three major classes: microRNAs (miRNAs); heterochromatic small interfering RNAs (hc-siRNAs); and phased, secondary, small interfering RNAs (phasiRNAs) (Fei et al., 2013). Our study focuses on phasiRNAs, for which the knowledge about functionality is still limited. We and others have shown that maize anthers (male reproductive organs), express two classes of phasiRNAs (21- and 24-nt) during different developmental time points. Other data suggest these phasiRNAs are required for fertility.

Given the important role grasses such as maize and rice play as a prime food-source in many countries and as influential factors in the global economy, we aim to identify and understand the function of grass-specific phasiRNAs in maize and rice development. To this end, we use the framework of Hidden Markov models (HMMs) in order to model both phasiRNA and non-phasiRNA sequences, and to distinguish between the two types of these sRNAs. We performed ANOVA with Dunnett's method, demonstrating that the probability assigned by the resulting HMMs to phasiRNAs (21/24-nt) from rice and maize is significantly different from that assigned to other genomic sequences of similar length. Future work will include, k-mer based alignment-free, classification to distinguish phasiRNA sequences from non-phasiRNA sequences using other machine learning classifier(s), aiming to extract patterns (i.e., sequence-composition based features, GC content) occurring in phasiRNAs to provide further insight into their biological function.