Dongrong Wen

Predicting coaxial helical stacking in RNA junctions

RNA junctions are important structural elements that form when three or more helices come together in space in the tertiary structures of RNA molecules. Determining their structural configuration is important for predicting RNA 3D structure. We introduce a computational method to predict, at the secondary structure level, the coaxial helical stacking arrangement in junctions, as well as classify the junction topology. Our approach uses a data mining approach known as random forests, which relies on a set of decision trees trained using length, sequence and other variables specified for any given junction. The resulting protocol predicts coaxial stacking within three- and four-way junctions with an accuracy of 81% and 77%, respectively; the accuracy increases to 83% and 87%, respectively, when knowledge from the junction family type is included. Coaxial stacking predictions for the five to ten-way junctions are less accurate (60%) due to sparse data available for training. Additionally, our application predicts the junction family with an accuracy of 85% for three-way junctions and 74% for four-way junctions. Comparisons with other methods, as well applications to unsolved RNAs, are also presented. The web server Junction-Explorer to predict junction topologies is freely available at: http://bioinformatics.njit.edu/junction.

Effective classification of microRNA precursors using feature mining and AdaBoost algorithms.

MicroRNAs play important roles in most biological processes, including cell proliferation, tissue differentiation, and embryonic development, among others. They originate from precursor transcripts (pre-miRNAs), which contain phylogenetically conserved stem-loop structures. An important bioinformatics problem is to distinguish the pre-miRNAs from pseudo pre-miRNAs that have similar stem-loop structures. We present here a novel method for tackling this bioinformatics problem. Our method, named MirID, accepts an RNA sequence as input, and classifies the RNA sequence either as positive (i.e., a real pre-miRNA) or as negative (i.e., a pseudo pre-miRNA). MirID employs a feature mining algorithm for finding combinations of features suitable for building pre-miRNA classification models. These models are implemented using support vector machines, which are combined to construct a classifier ensemble. The accuracy of the classifier ensemble is further enhanced by the utilization of an AdaBoost algorithm. When compared with two closely related tools on twelve species analyzed with these tools, MirID outperforms the existing tools on the majority of the twelve species. MirID was also tested on nine additional species, and the results showed high accuracies on the nine species. The MirID web server is fully operational and freely accessible at http://bioinformatics.njit.edu/MirID/ . Potential applications of this software in genomics and medicine are also discussed.

Pre-miRNA classification via combinatorial feature mining and boosting

MicroRNAs (miRNAs) are non-coding RNAs with approximately 22 nucleotides (nt) that are derived from precursor molecules. These precursor molecules or pre-miRNAs often fold into stem-loop hairpin structures. However, a large number of sequences with pre-miRNA-like hairpins can be found in genomes. It is a challenge to distinguish the real pre-miRNAs from other hairpin sequences with similar stem-loops (referred to as pseudo pre-miRNAs). Several computational methods have been developed to tackle this challenge. In this paper we propose a new method, called MirlD, for identifying and classifying microRNA precursors. We collect 74 features from the sequences and secondary structures of pre-miRNAs; some of these features are taken from our previous studies on non-coding RNA prediction while others were suggested in the literature. We develop a combinatorial feature mining algorithm to identify suitable feature sets. These feature sets are then used to train support vector machines to obtain classification models, based on which classifier ensemble is constructed. Finally we use a boosting algorithm to further enhance the accuracy of the classifier ensemble. Experimental results on a variety of species demonstrate the good performance of the proposed method, and its superiority over existing tools.

A computational approach to finding RNA tertiary motifs in genomic sequences: a case study.

Motif finding in DNA, RNA and proteins plays an important role in life science research. Recent patents concerning motif finding in biomolecular data are recorded in the DNA Patent Database which serves as a resource for policy makers and members of the general public interested in fields like genomics, genetics and biotechnology. In this paper, we present a computational approach to mining for RNA tertiary motifs in genomic sequences. Specifically, we describe a method, named CSminer, and show, as a case study, the application of CSminer to genome-wide search for coaxial helical stackings in RNA 3-way junctions. A coaxial helical stacking occurs in an RNA 3-way junction where two separate helical elements form a pseudocontiguous helix and provide thermodynamic stability to the RNA molecule as a whole. Experimental results demonstrate the effectiveness of our approach.

Toward an integrated RNA motif database

In this paper we present the design and implementation of an RNA structural motif database, called RmotifDB. The structural motifs stored in RmotifDB come from three sources: (1) collected manually from biomedical literature; (2) submitted by scientists around the world; and (3) discovered by a wide variety of motif mining methods. We present here a motif mining method in detail. We also describe the interface and search mechanisms provided by RmotifDB as well as techniques used to integrate RmotifDB with the Gene Ontology. The RmotifDB system is fully operational and accessible on the Internet at http://datalab.njit.edu/bioinfo/

Genome-Wide Prediction of Coaxial Helical Stacking Using Random Forests and Covariance Models

Developing effective artificial intelligence tools to find motifs in DNA, RNA and proteins poses a challenging yet important problem in life science research. In this paper, we present a computational approach for finding RNA tertiary motifs in genomic sequences. Specifically, we predict genomic coordinate locations for coaxial helical stackings in 3-way RNA junctions. These predictions are provided by our tertiary motif search package, named CSminer, which utilizes two versatile methodologies: random forests and covariance models. A coaxial helical stacking tertiary motif occurs in a 3-way RNA junction where two separate helical elements form a pseudocontiguous helix and provide thermodynamic stability to the RNA molecule as a whole. Our CSminer tool first uses a genome-wide search method based on covariance models to find a genomic region that may potentially contain a coaxial helical stacking tertiary motif. CSminer then uses a random forests classifier to predict whether the genomic region indeed contains the tertiary motif. Experimental results demonstrate the effectiveness of our approach.

Genome-wide search for coaxial helical stacking motifs

Motif finding in DNA, RNA and proteins plays an important role in life science research. In this paper, we present a computational approach to searching for RNA tertiary motifs in genomic sequences. Specifically, we describe a method, named CSminer, and show, as a case study, the application of CSminer to genome-wide search for coaxial helical stackings in RNA 3-way junctions. A coaxial helical stacking motif occurs in an RNA 3-way junction where two separate helical elements form a pseudocontiguous helix and provide thermodynamic stability to the RNA molecule as a whole. Experimental results demonstrate the effectiveness of our approach.

Design of an RNA structural motif database

In this paper we present the design and implementation of an RNA structural motif database, called RmotifDB. The structural motifs stored in RmotifDB come from three sources: collected manually from the biomedical literature; submitted by scientists around the world; discovered by a wide variety of motif mining methods. We present here a motif mining method in detail. We also describe the interface and search mechanisms provided by RmotifDB and report its current status. The RmotifDB system is fully operational and accessible on the web at http://datalab.njit.edu/bioinfo/.