Wenbin Li

Bio: Wenbin Li is an academic researcher from University of Texas at San Antonio. The author has contributed to research in topics: Animal data. The author has an hindex of 2, co-authored 2 publications receiving 128 citations. Previous affiliations of Wenbin Li include Northeast Agricultural University.

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

Abstract: 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/.

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

Abstract: 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/.

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

Abstract: 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.

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

Abstract: 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.

Root precursors of microRNAs in wild emmer and modern wheats show major differences in response to drought stress

Abstract: MicroRNAs, small regulatory molecules with significant impacts on the transcriptional network of all living organisms, have been the focus of several studies conducted mostly on modern wheat cultivars. In this study, we investigated miRNA repertoires of modern durum wheat and its wild relatives, with differing degrees of drought tolerance, to identify miRNA candidates and their targets involved in drought stress response. Root transcriptomes of Triticum turgidum ssp. durum variety Kiziltan and two Triticum turgidum ssp. dicoccoides genotypes TR39477 and TTD-22 under control and drought conditions were assembled from individual RNA-Seq reads and used for in silico identification of miRNAs. A total of 66 miRNAs were identified from all species, across all conditions, of which 46 and 38 of the miRNAs identified from modern durum wheat and wild genotypes, respectively, had not been previously reported. Genotype- and/or stress-specific miRNAs provide insights into our understanding of the complex drought response. Particularly, miR1435, miR5024, and miR7714, identified only from drought-stress roots of drought-tolerant genotype TR39477, can be candidates for future studies to explore and exploit the drought response to develop tolerant varieties.

MicroRNAs and drought responses in sugarcane

Abstract: There is a growing demand for renewable energy, and sugarcane is a promising bioenergy crop. In Brazil, the largest sugarcane producer in the world, sugarcane plantations are expanding into areas where severe droughts are common. Recent evidence has highlighted the role of miRNAs in regulating drought responses in several species, including sugarcane. This review summarizes the data from miRNA expression profiles observed in a wide array of experimental conditions using different sugarcane cultivars that differ in their tolerance to drought. We uncovered a complex regulation of sugarcane miRNAs in response to drought and discussed these data with the miRNA profiles observed in other plant species. The predicted miRNA targets revealed different transcription factors, proteins involved in tolerance to oxidative stress, cell modification, as well as hormone signaling. Some of these proteins might regulate sugarcane responses to drought, such as reduction of internode growth and shoot branching and increased leaf senescence. A better understanding on the regulatory network from miRNAs and their targets under drought stress has a great potential to contribute to sugarcane improvement, either as molecular markers as well as by using biotechnological approaches.