Identification of microRNA precursor with the degenerate K-tuple or Kmer strategy.

A microRNA (miRNA) is a small non-coding RNA molecule, functioning in transcriptional and post-transcriptional regulation of gene expression. The human genome may encode over 1000 miRNAs. Albeit poorly characterized, miRNAs are widely deemed as important regulators of biological processes. Aberrant expression of miRNAs has been observed in many cancers and other disease states, indicating that they are deeply implicated with these diseases, particularly in carcinogenesis. Therefore, it is important for both basic research and miRNA-based therapy to discriminate the real pre-miRNAs from the false ones (such as hairpin sequences with similar stem-loops). Particularly, with the avalanche of RNA sequences generated in the post-genomic age, it is highly desired to develop computational sequence-based methods for effectively identifying the human pre-miRNAs. Here, we propose a predictor called "iMiRNA-PseDPC", in which the RNA sequences are formulated by a novel feature vector called "pseudo distance-pair composition" (PseDPC) with 10 types of structure statuses. Rigorous cross-validations on a much larger and more stringent newly constructed benchmark data-set showed that our approach has remarkably outperformed the existing ones in either prediction accuracy or efficiency, indicating the new predictor is quite promising or at least may become a complementary tool to the existing predictors in this area. For the convenience of most experimental scientists, a user-friendly web server for the new predictor has been established at http://bioinformatics.hitsz.edu.cn/iMiRNA-PseDPC/, by which users can easily get their desired results without the need to go through the mathematical details. It is anticipated that the new predictor may become a useful high throughput tool for genome analysis particularly in dealing with large-scale data.

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iMiRNA-PseDPC: microRNA precursor identification with a pseudo distance-pair composition approach

Artificial Intelligence federates numerous scientific fields in the aim of developing machines able to assist human operators performing complex treatments; most of which demand high cognitive skills (e.g. learning or decision processes). Central to this quest is to give machines the ability to estimate the likeness or similarity between things in the way human beings estimate the similarity between stimuli. In this context, this book focuses on semantic measures: approaches designed for comparing semantic entities such as units of language, e.g. words, sentences, or concepts and instances defined into knowledge bases. The aim of these measures is to assess the similarity or relatedness of such semantic entities by taking into account their semantics, i.e. their meaning; intuitively, the words tea and coffee, which both refer to stimulating beverage, will be estimated to be more semantically similar than the words toffee (confection) and coffee, despite that the last pair has a higher syntactic similarity. The two state-of-the-art approaches for estimating and quantifying semantic similarities/relatedness of semantic entities are presented in detail: the first one relies on corpora analysis and is based on Natural Language Processing techniques and semantic models while the second is based on more or less formal, computer-readable and workable forms of knowledge such as semantic networks, thesauri or ontologies. Semantic measures are widely used today to compare units of language, concepts, instances or even resources indexed by them (e.g., documents, genes). They are central elements of a large variety of Natural Language Processing applications and knowledge-based treatments, and have therefore naturally been subject to intensive and interdisciplinary research efforts during last decades. Beyond a simple inventory and categorization of existing measures, the aim of this monograph is to convey novices as well as researchers of these domains toward a better understanding of semantic similarity estimation and more generally semantic measures. To this end, we propose an in-depth characterization of existing proposals by discussing their features, the assumptions on which they are based and empirical results regarding their performance in particular applications. By answering these questions and by providing a detailed discussion on the foundations of semantic measures, our aim is to give the reader key knowledge required to: (i) select the more relevant methods according to a particular usage context, (ii) understand the challenges offered to this field of study, (iii) distinguish room of improvements for state-of-the-art approaches and (iv) stimulate creativity toward the development of new approaches. In this aim, several definitions, theoretical and practical details, as well as concrete applications are presented.

Semantic Similarity from Natural Language and Ontology Analysis

Phosphorus (P) plays important roles in plant growth and development. MicroRNAs involved in P signaling have been identified in Arabidopsis and rice, but P-responsive microRNAs and their targets in soybean leaves and roots are poorly understood. Using high-throughput sequencing-by-synthesis (SBS) technology, we sequenced four small RNA libraries from leaves and roots grown under phosphate (Pi)-sufficient (+Pi) and Pi-depleted (-Pi) conditions, respectively, and one RNA degradome library from Pi-depleted roots at the genome-wide level. Each library generated ∼21.45−28.63 million short sequences, resulting in ∼20.56−27.08 million clean reads. From those sequences, a total of 126 miRNAs, with 154 gene targets were computationally predicted. This included 92 new miRNA candidates with 20-23 nucleotides that were perfectly matched to the Glycine max genome 1.0, 70 of which belong to 21 miRNA families and the remaining 22 miRNA unassigned into any existing miRNA family in miRBase 18.0. Under both +Pi and -Pi conditions, 112 of 126 total miRNAs (89%) were expressed in both leaves and roots. Under +Pi conditions, 12 leaf- and 2 root-specific miRNAs were detected; while under -Pi conditions, 10 leaf- and 4 root-specific miRNAs were identified. Collectively, 25 miRNAs were induced and 11 miRNAs were repressed by Pi starvation in soybean. Then, stem-loop real-time PCR confirmed expression of four selected P-responsive miRNAs, and RLM-5’ RACE confirmed that a PHO2 and GmPT5, a kelch-domain containing protein, and a Myb transcription factor, respectively are targets of miR399, miR2111, and miR159e-3p. Finally, P-responsive cis-elements in the promoter regions of soybean miRNA genes were analyzed at the genome-wide scale. Leaf- and root-specific miRNAs, and P-responsive miRNAs in soybean were identified genome-wide. A total of 154 target genes of miRNAs were predicted via degradome sequencing and computational analyses. The targets of miR399, miR2111, and miR159e-3p were confirmed. Taken together, our study implies the important roles of miRNAs in P signaling and provides clues for deciphering the functions for microRNA/target modules in soybean.

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Genome-wide identification of soybean microRNAs and their targets reveals their organ-specificity and responses to phosphate starvation

BackgroundMicroRNAs (miRNAs) are a novel class of non-coding small RNAs. In mammalian cells, miRNAs repress the translation of messenger RNAs (mRNAs) or degrade mRNAs. miRNAs play important roles in development and differentiation, and they are also implicated in aging, and oncogenesis. Predictions of targets of miRNAs suggest that they may regulate more than one-third of all genes. The overall functions of mammalian miRNAs remain unclear. Combinatorial regulation by transcription factors alone or miRNAs alone offers a wide range of regulatory programs. However, joining transcriptional and post-transcriptional regulatory mechanisms enables higher complexity regulatory programs that in turn could give cells evolutionary advantages. Investigating coordinated regulation of genes by miRNAs and transcription factors (TFs) from a statistical standpoint is a first step that may elucidate some of their roles in various biological processes.ResultsHere, we studied the nature and scope of coordination among regulators from the transcriptional and miRNA regulatory layers in the human genome. Our findings are based on genome wide statistical assessment of regulatory associations ("interactions") among the sets of predicted targets of miRNAs and sets of putative targets of transcription factors. We found that combinatorial regulation by transcription factor pairs and miRNA pairs is much more abundant than the combinatorial regulation by TF-miRNA pairs. In addition, many of the strongly interacting TF-miRNA pairs involve a subset of master TF regulators that co-regulate genes in coordination with almost any miRNA. Application of standard measures for evaluating the degree of interaction between pairs of regulators show that strongly interacting TF-miRNA, TF-TF or miRNA-miRNA pairs tend to include TFs or miRNAs that regulate very large numbers of genes. To correct for this potential bias we introduced an additional Bayesian measure that incorporates not only how significant an interaction is but also how strong it is. Putative pairs of regulators selected by this procedure are more likely to have biological coordination. Importantly, we found that the probability of a TF-miRNA pair forming feed forward loops with its common target genes (where the miRNA simultaneously suppresses the TF and many of its targets) is increased for strongly interacting TF-miRNA pairs.ConclusionGenes are more likely to be co-regulated by pairs of TFs or pairs of miRNAs than by pairs of TF-miRNA, perhaps due to higher probability of evolutionary duplication events of shorter DNA sequences. Nevertheless, many gene sets are reciprocally regulated by strongly interacting pairs of TF-miRNA, which suggests an effective mechanism to suppress functionally related proteins. Moreover, the particular type of feed forward loop (with two opposing modes where the TF activates its target genes or the miRNA simultaneously suppresses this TF and the TF-miRNA joint target genes) is more prevalent among strongly interacting TF-miRNA pairs. This may be attributed to a process that prevents waste of cellular resources or a mechanism to accelerate mRNA degradation.

Inter- and Intra-Combinatorial Regulation by Transcriptions Factors and MicroRNAs.

Motivation: Compared with sequence and structure similarity, functional similarity is more informative for understanding the biological roles and functions of genes. Many important applications in computational molecular biology require functional similarity, such as gene clustering, protein function prediction, protein interaction evaluation and disease gene prioritization. Gene Ontology (GO) is now widely used as the basis for measuring gene functional similarity. Some existing methods combined semantic similarity scores of single term pairs to estimate gene functional similarity, whereas others compared terms in groups to measure it. However, these methods may make error-prone judgments about gene functional similarity. It remains a challenge that measuring gene functional similarity reliably.

Result: We propose a novel method called SORA to measure gene functional similarity in GO context. First of all, SORA computes the information content (IC) of a term making use of semantic specificity and coverage. Second, SORA measures the IC of a term set by means of combining inherited and extended IC of the terms based on the structure of GO. Finally, SORA estimates gene functional similarity using the IC overlap ratio of term sets. SORA is evaluated against five state-of-the-art methods in the file on the public platform for collaborative evaluation of GO-based semantic similarity measure. The carefully comparisons show SORA is superior to other methods in general. Further analysis suggests that it primarily benefits from the structure of GO, which implies expressive information about gene function. SORA offers an effective and reliable way to compare gene function.

Availability: The web service of SORA is freely available at http://nclab.hit.edu.cn/SORA/.

Contact: maozuguo@hit.edu.cn

Measuring gene functional similarity based on group-wise comparison of GO terms

Motivation: MicroRNAs (miRNAs) are a set of short (21–24 nt) non-coding RNAs that play significant roles as post-transcriptional regulators in animals and plants. While some existing methods use comparative genomic approaches to identify plant precursor miRNAs (pre-miRNAs), others are based on the complementarity characteristics between miRNAs and their target mRNAs sequences. However, they can only identify the homologous miRNAs or the limited complementary miRNAs. Furthermore, since the plant premiRNAs are quite different from the animal pre-miRNAs, all the ab initio methods for animals cannot be applied to plants. Therefore, it is essential to develop a method based on machine learning to classify real plant pre-miRNAs and pseudo genome hairpins. Results: A novel classification method based on support vector machine (SVM) is proposed specifically for predicting plant pre-miRNAs. To make efficient prediction, we extract the pseudo hairpin sequences from the protein coding sequences of Arabidopsis thaliana and Glycine max, respectively. These pseudo pre-miRNAs are extracted in this study for the first time. A set of informative features are selected to improve the classification accuracy. The training samples are selected according to their distributions in the high-dimensional sample space. Our classifier PlantMiRNAPred achieves >90% accuracy on the plant datasets from eight plant species, including A.thaliana, Oryza sativa, Populus trichocarpa, Physcomitrella patens, Medicago truncatula, Sorghum bicolor, Zea mays and G.max. The superior performance of the proposed classifier can be attributed to the extracted plant pseudo pre-miRNAs, the selected training dataset and the carefully selected features. The ability of PlantMiRNAPred to discern real and pseudo pre-miRNAs provides a viable method for discovering new nonhomologous plant pre-miRNAs. Availability: The web service of PlantMiRNAPred, the training datasets, the testing datasets and the selected features are freely available at http://nclab.hit.edu.cn/PlantMiRNAPred/.

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PlantMiRNAPred: efficient classification of real and pseudo plant pre-miRNAs.

Background 
MicroRNAs (miRNAs) are a set of short (19∼24 nt) non-coding RNAs that play significant roles as posttranscriptional regulators in animals and plants. The ab initio prediction methods show excellent performance for discovering new pre-miRNAs. While most of these methods can distinguish real pre-miRNAs from pseudo pre-miRNAs, few can predict the positions of miRNAs. Among the existing methods that can also predict the miRNA positions, most of them are designed for mammalian miRNAs, including human and mouse. Minority of methods can predict the positions of plant miRNAs. Accurate prediction of the miRNA positions remains a challenge, especially for plant miRNAs. This motivates us to develop MaturePred, a machine learning method based on support vector machine, to predict the positions of plant miRNAs for the new plant pre-miRNA candidates.
Methodology/Principal Findings 
A miRNA:miRNA* duplex is regarded as a whole to capture the binding characteristics of miRNAs. We extract the position-specific features, the energy related features, the structure related features, and stability related features from real/pseudo miRNA:miRNA* duplexes. A set of informative features are selected to improve the prediction accuracy. Two-stage sample selection algorithm is proposed to combat the serious imbalance problem between real and pseudo miRNA:miRNA* duplexes. The prediction method, MaturePred, can accurately predict plant miRNAs and achieve higher prediction accuracy compared with the existing methods. Further, we trained a prediction model with animal data to predict animal miRNAs. The model also achieves higher prediction performance. It further confirms the efficiency of our miRNA prediction method.
Conclusions 
The superior performance of the proposed prediction model can be attributed to the extracted features of plant miRNAs and miRNA*s, the selected training dataset, and the carefully selected features. The web service of MaturePred, the training datasets, the testing datasets, and the selected features are freely available at http://nclab.hit.edu.cn/maturepred/.

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MaturePred: efficient identification of microRNAs within novel plant pre-miRNAs.

This paper aims to provide researchers with a computational background, a comprehensive discussion on the importance of miRNA (microRNA), the related high-throughput technology and databases, the current development of computational algorithms for elucidating miRNA regulatory functions, and the prospective directions on related topics. Some topics discussed were about miRNA target recognition, silencing, expression profiling, transfection experiments, target predictions, databases, microarray data, biochemical approaches, rule-based algorithms, data-driven algorithms, prediction algorithms, transcription factors, and other computational techniques.

Maozu Guo

Papers

Measuring gene functional similarity based on group-wise comparison of GO terms

PlantMiRNAPred: efficient classification of real and pseudo plant pre-miRNAs.

MaturePred: efficient identification of microRNAs within novel plant pre-miRNAs.

Understanding MicroRNA Regulation: A computational perspective