MIReNA: finding microRNAs with high accuracy and no learning at genome scale and from deep sequencing data

RNA-mediated silencing in Algae: biological roles and tools for analysis of gene function

Algae are a large group of aquatic, typically photosynthetic, eukaryotes that include species from very diverse phylogenetic lineages, from those similar to land plants to those related to protist parasites. The recent sequencing of several algal genomes has provided insights into the great complexity of these organisms. Genomic information has also emphasized our lack of knowledge of the functions of many predicted genes, as well as the gene regulatory mechanisms in algae. Core components of the machinery for RNA-mediated silencing show widespread distribution among algal lineages, but they also seem to have been lost entirely from several species with relatively small nuclear genomes. Complex sets of endogenous small RNAs, including candidate microRNAs and small interfering RNAs, have now been identified by high-throughput sequencing in green, red, and brown algae. However, the natural roles of RNA-mediated silencing in algal biology remain poorly understood. Limited evidence suggests that small RNAs may function, in different algae, in defense mechanisms against transposon mobilization, in responses to nutrient deprivation and, possibly, in the regulation of recently evolved developmental processes. From a practical perspective, RNA interference (RNAi) is becoming a promising tool for assessing gene function by sequence-specific knockdown. Transient gene silencing, triggered with exogenously synthesized nucleic acids, and/or stable gene repression, involving genome-integrated transgenes, have been achieved in green algae, diatoms, yellow-green algae, and euglenoids. The development of RNAi technology in conjunction with system level "omics" approaches may provide the tools needed to advance our understanding of algal physiological and metabolic processes.

miRFam: an effective automatic miRNA classification method based on n-grams and a multiclass SVM

Background

MicroRNAs (miRNAs) are ~22 nt long integral elements responsible for post-transcriptional control of gene expressions. After the identification of thousands of miRNAs, the challenge is now to explore their specific biological functions. To this end, it will be greatly helpful to construct a reasonable organization of these miRNAs according to their homologous relationships. Given an established miRNA family system (e.g. the miRBase family organization), this paper addresses the problem of automatically and accurately classifying newly found miRNAs to their corresponding families by supervised learning techniques. Concretely, we propose an effective method, miRFam, which uses only primary information of pre-miRNAs or mature miRNAs and a multiclass SVM, to automatically classify miRNA genes.

Results

An existing miRNA family system prepared by miRBase was downloaded online. We first employed n-grams to extract features from known precursor sequences, and then trained a multiclass SVM classifier to classify new miRNAs (i.e. their families are unknown). Comparing with miRBase's sequence alignment and manual modification, our study shows that the application of machine learning techniques to miRNA family classification is a general and more effective approach. When the testing dataset contains more than 300 families (each of which holds no less than 5 members), the classification accuracy is around 98%. Even with the entire miRBase15 (1056 families and more than 650 of them hold less than 5 samples), the accuracy surprisingly reaches 90%.

Conclusions

Based on experimental results, we argue that miRFam is suitable for application as an automated method of family classification, and it is an important supplementary tool to the existing alignment-based small non-coding RNA (sncRNA) classification methods, since it only requires primary sequence information.

Availability

The source code of miRFam, written in C++, is freely and publicly available at: http://admis.fudan.edu.cn/projects/miRFam.htm.