Clustering and conservation patterns of human microRNAs

MicroRNAs (miRNAs) are ~22 nt-long non-coding RNA molecules, believed to play important roles in gene regulation. We present a comprehensive analysis of the conservation and clustering patterns of known miRNAs in human. We show that human miRNA gene clustering is significantly higher than expected at random. A total of 37% of the known human miRNA genes analyzed in this study appear in clusters of two or more with pairwise chromosomal distances of at most 3000 nt. Comparison of the miRNA sequences with their homologs in four other organisms reveals a typical conservation pattern, persistent throughout the clusters. Furthermore, we show enrichment in the typical conservation patterns and other miRNA-like properties in the vicinity of known miRNA genes, compared with random genomic regions. This may imply that additional, yet unknown, miRNAs reside in these regions, consistent with the current recognition that there are overlooked miRNAs. Indeed, by comparing our predictions with cloning results and with identified miRNA genes in other mammals, we corroborate the predictions of 18 additional human miRNA genes in the vicinity of the previously known ones. Our study raises the proportion of clustered human miRNAs that are <3000 nt apart to 42%. This suggests that the clustering of miRNA genes is higher than currently acknowledged, alluding to its evolutionary and functional implications.     

A naïve Bayesian classifier for identifying plant microRNAs

MicroRNAs (miRNAs) are important regulatory molecules in eukaryotic organisms. Existing methods for the identification of mature miRNA sequences in plants rely extensively on the search for stem–loop structures, leading to high false negative rates. Here, we describe a probabilistic method for ranking putative plant miRNAs using a naïve Bayes classifier and its publicly available implementation. We use a number of properties to construct the classifier, including sequence length, number of observations, existence of detectable predicted miRNA* sequences, the distribution of nearby reads and mapping multiplicity. We apply the method to small RNA sequence data from soybean, peach, Arabidopsis and rice and provide experimental validation of several predictions in soybean. The approach performs well overall and strongly enriches for known miRNAs over other types of sequences. By utilizing a Bayesian approach to rank putative miRNAs, our method is able to score miRNAs that would be eliminated by other methods, such as those that have low counts or lack detectable miRNA* sequences. As a result, we are able to detect several soybean miRNA candidates, including some that are 24 nucleotides long, a class that is almost universally eliminated by other methods.     

Reexamining microRNA Site Accessibility in Drosophila: A Population Genomics Study

Kertesz et al. (Nature Genetics 2008) described PITA, a miRNA target prediction algorithm based on hybridization energy and site accessibility. In this note, we used a population genomics approach to reexamine their data and found that the PITA algorithm had lower specificity than methods based on evolutionary conservation at comparable levels of sensitivity.We also showed that deeply conserved miRNAs tend to have stronger hybridization energies to their targets than do other miRNAs. Although PITA had higher specificity in predicting targets than a naïve seed-match method, this signal was primarily due to the use of a single cutoff score for all miRNAs and to the observed correlation between conservation and hybridization energy. Overall, our results clarify the accuracy of different miRNA target prediction algorithms in Drosophila and the role of site accessibility in miRNA target prediction.     

Phylogenetic shadowing and computational identification of human microRNA genes

We sequenced 122 miRNAs in 10 primate species to reveal conservation characteristics of miRNA genes. Strong conservation is observed in stems of miRNA hairpins and increased variation in loop sequences. Interestingly, a striking drop in conservation was found for sequences immediately flanking the miRNA hairpins. This characteristic profile was employed to predict novel miRNAs using cross-species comparisons. Nine hundred and seventy-six candidate miRNAs were identified by scanning whole-genome human/mouse and human/rat alignments. Most of the novel candidates are conserved also in other vertebrates (dog, cow, chicken, opossum, zebrafish). Northern blot analysis confirmed the expression of mature miRNAs for 16 out of 69 representative candidates. Additional support for the expression of 179 novel candidates can be found in public databases, their presence in gene clusters, and literature that appeared after these predictions were made. Taken together, these results suggest the presence of significantly higher numbers of miRNAs in the human genome than previously estimated.     

Partitioning of Minimotifs Based on Function with Improved Prediction Accuracy

Background

Minimotifs are short contiguous peptide sequences in proteins that are known to have a function in at least one other protein. One of the principal limitations in minimotif prediction is that false positives limit the usefulness of this approach. As a step toward resolving this problem we have built, implemented, and tested a new data-driven algorithm that reduces false-positive predictions.

Methodology/Principal Findings

Certain domains and minimotifs are known to be strongly associated with a known cellular process or molecular function. Therefore, we hypothesized that by restricting minimotif predictions to those where the minimotif containing protein and target protein have a related cellular or molecular function, the prediction is more likely to be accurate. This filter was implemented in Minimotif Miner using function annotations from the Gene Ontology. We have also combined two filters that are based on entirely different principles and this combined filter has a better predictability than the individual components.

Conclusions/Significance

Testing these functional filters on known and random minimotifs has revealed that they are capable of separating true motifs from false positives. In particular, for the cellular function filter, the percentage of known minimotifs that are not removed by the filter is ∼4.6 times that of random minimotifs. For the molecular function filter this ratio is ∼2.9. These results, together with the comparison with the published frequency score filter, strongly suggest that the new filters differentiate true motifs from random background with good confidence. A combination of the function filters and the frequency score filter performs better than these two individual filters.     

Interactive Web-based Annotation of Plant MicroRNAs with iwa-miRNA

MicroRNAs (miRNAs) are important regulators of gene expression. The large-scale detection and profiling of miRNAs have been accelerated with the development of high-throughput small RNA sequencing (sRNA-Seq) techniques and bioinformatics tools. However, generating high-quality comprehensive miRNA annotations remains challenging due to the intrinsic complexity of sRNA-Seq data and inherent limitations of existing miRNA prediction tools. Here, we present iwa-miRNA, a Galaxy-based framework that can facilitate miRNA annotation in plant species by combining computational analysis and manual curation. iwa-miRNA is specifically designed to generate a comprehensive list of miRNA candidates, bridging the gap between already annotated miRNAs provided by public miRNA databases and new predictions from sRNA-Seq datasets. It can also assist users in selecting promising miRNA candidates in an interactive mode, contributing to the accessibility and reproducibility of genome-wide miRNA annotation. iwa-miRNA is user-friendly and can be easily deployed as a web application for researchers without programming experience. With flexible, interactive, and easy-to-use features, iwa-miRNA is a valuable tool for the annotation of miRNAs in plant species with reference genomes. We also illustrate the application of iwa-miRNA for miRNA annotation using data from plant species with varying genomic complexity. The source codes and web server of iwa-miRNA are freely accessible at http://iwa-miRNA.omicstudio.cloud/.     

myMIR: a genome-wide microRNA targets identification and annotation tool

miRNA target genes prediction represents a crucial step in miRNAs functional characterization. In this context, the challenging issue remains predictions accuracy and recognition of false positive results. In this article myMIR, a web based system for increasing reliability of miRNAs predicted targets lists, is presented. myMIR implements an integrated pipeline for computing ranked miRNA::target lists and provides annotations for narrowing them down. The system relies on knowledge base data, suitably integrated in order to extend the functional characterization of targeted genes to miRNAs, by highlighting the search on over-represented annotation terms. Validation results show a dramatic reduction in the quantity of predictions and an increase in the sensitivity, when compared to other methods. This improves the predictions accuracy and allows the formulation of novel hypotheses on miRNAs functional involvement.     

Grass microRNA gene paleohistory unveils new insights into gene dosage balance in subgenome partitioning after whole-genome duplication

The recent availability of plant genome sequences, combined with a robust evolutionary scenario of the modern monocot and eudicot karyotypes from their diploid ancestors, offers an opportunity to gain insights into microRNA (miRNA) gene paleohistory in plants. Characterization and comparison of miRNAs and associated protein-coding targets in plants allowed us to unravel (1) contrasted genome conservation patterns of miRNAs in monocots and eudicots after whole-genome duplication (WGD), (2) an ancestral miRNA founder pool in the monocot genomes dating back to 100 million years ago, (3) miRNA subgenome dominance during the post-WGD diploidization process with selective miRNA deletion complemented with possible transposable element-mediated return flows, and (4) the miRNA/target interaction-directed differential loss/retention of miRNAs following the gene dosage balance rule. Together, our data suggest that overretained miRNAs in grass genomes may be implicated in connected gene regulations for stress responses, which is essential for plant adaptation and useful for crop variety innovation.     

Naïve Bayes for microRNA target predictions--machine learning for microRNA targets

MOTIVATION: Most computational methodologies for miRNA:mRNA target gene prediction use the seed segment of the miRNA and require cross-species sequence conservation in this region of the mRNA target. Methods that do not rely on conservation generate numbers of predictions, which are too large to validate. We describe a target prediction method (NBmiRTar) that does not require sequence conservation, using instead, machine learning by a na?ve Bayes classifier. It generates a model from sequence and miRNA:mRNA duplex information from validated targets and artificially generated negative examples. Both the 'seed' and 'out-seed' segments of the miRNA:mRNA duplex are used for target identification. RESULTS: The application of machine-learning techniques to the features we have used is a useful and general approach for microRNA target gene prediction. Our technique produces fewer false positive predictions and fewer target candidates to be tested. It exhibits higher sensitivity and specificity than algorithms that rely on conserved genomic regions to decrease false positive predictions.     

miRRim: a novel system to find conserved miRNAs with high sensitivity and specificity

The identification of novel miRNAs has significant biological and clinical importance. However, none of the known miRNA features alone is sufficient for accurately detecting novel miRNAs. The aim of this paper is to integrate these features in a straightforward manner for detecting miRNAs with better accuracy. Since most miRNA regions are highly conserved among vertebrates for the ability to form stable hairpin structures, we implemented a hidden Markov model that outputs multidimensional feature vectors composed of both evolutionary features and secondary structural ones. The proposed method, called miRRim, outperformed existing ones in terms of detection/prediction performance: The total number of predictions was smaller than with existing methods when the number of miRNAs detected was adjusted to be the same. Moreover, there were several candidates predicted only by our method that are clustered with the known miRNAs, suggesting that our method is able to detect novel miRNAs. Genomic coordinates of predicted miRNA can be obtained from http://mirrim.ncrna.org/.