Biomedical subjects

Jin-Wu Nam

Publications and source records attributed to Jin-Wu Nam.

5 recordsLinked to original sources

PubMed2006

miTarget: microRNA target gene prediction using a support vector machine.

BACKGROUND: MicroRNAs (miRNAs) are small noncoding RNAs, which play significant roles as posttranscriptional regulators. The functions of animal miRNAs are generally based on complementarity for their 5' components. Although several computational miRNA target-gene prediction methods have been proposed, they still have limitations in revealing actual target genes. RESULTS: We implemented miTarget, a support vector machine (SVM) classifier for miRNA target gene prediction. It uses a radial basis function kernel as a similarity measure for SVM features, categorized by structural, thermodynamic, and position-based features. The latter features are introduced in this study for the first time and reflect the mechanism of miRNA binding. The SVM classifier produces high performance with a biologically relevant data set obtained from the literature, compared with previous tools. We predicted significant functions for human miR-1, miR-124a, and miR-373 using Gene Ontology (GO) analysis and revealed the importance of pairing at positions 4, 5, and 6 in the 5' region of a miRNA from a feature selection experiment. We also provide a web interface for the program. CONCLUSION: miTarget is a reliable miRNA target gene prediction tool and is a successful application of an SVM classifier. Compared with previous tools, its predictions are meaningful by GO analysis and its performance can be improved given more training examples.

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PubMed2006

ProMiR II: a web server for the probabilistic prediction of clustered, nonclustered, conserved and nonconserved microRNAs.

ProMiR is a web-based service for the prediction of potential microRNAs (miRNAs) in a query sequence of 60-150 nt, using a probabilistic colearning model. Identification of miRNAs requires a computational method to predict clustered and nonclustered, conserved and nonconserved miRNAs in various species. Here we present an improved version of ProMiR for identifying new clusters near known or unknown miRNAs. This new version, ProMiR II, integrates additional evidence, such as free energy data, G/C ratio, conservation score and entropy of candidate sequences, for more controllable prediction of miRNAs in mouse and human genomes. It also provides a wider range of services, e.g. the prediction of miRNA genes in long nonrelated sequences such as viral genomes. Importantly, we have validated this method using several case studies. All data used in ProMiR II are structured in the MySQL database for efficient analysis. The ProMiR II web server is available at http://cbit.snu.ac.kr/~ProMiR2/.

Animals↗

PubMed2006

Molecular basis for the recognition of primary microRNAs by the Drosha-DGCR8 complex.

The Drosha-DGCR8 complex initiates microRNA maturation by precise cleavage of the stem loops that are embedded in primary transcripts (pri-miRNAs). Here we propose a model for this process that is based upon evidence from both computational and biochemical analyses. A typical metazoan pri-miRNA consists of a stem of approximately 33 bp, with a terminal loop and flanking segments. The terminal loop is unessential, whereas the flanking ssRNA segments are critical for processing. The cleavage site is determined mainly by the distance (approximately 11 bp) from the stem-ssRNA junction. Purified DGCR8, but not Drosha, interacts with pri-miRNAs both directly and specifically, and the flanking ssRNA segments are vital for this binding to occur. Thus, DGCR8 may function as the molecular anchor that measures the distance from the dsRNA-ssRNA junction. Our current study thus facilitates the prediction of novel microRNAs and will assist in the rational design of small hairpin RNAs for RNA interference.

Animals↗

PubMed2006

Genomics of microRNA.

Discovered just over a decade ago, microRNA (miRNA) is now recognized as one of the major regulatory gene families in eukaryotic cells. Hundreds of miRNAs have been found in animals, plants and viruses, and there are certainly more to come. Through specific base-pairing with mRNAs, these tiny approximately 22-nt RNAs induce mRNA degradation or translational repression, or both. Because a miRNA can target numerous mRNAs, often in combination with other miRNAs, miRNAs operate highly complex regulatory networks. In this article, we summarize the current status of miRNA gene mining and miRNA expression profiling. We also review up-to-date knowledge of miRNA gene structure and the biogenesis mechanism. Our focus is on animal miRNAs.

Animals↗

PubMed2005

Human microRNA prediction through a probabilistic co-learning model of sequence and structure.

MicroRNAs (miRNAs) are small regulatory RNAs of approximately 22 nt. Although hundreds of miRNAs have been identified through experimental complementary DNA cloning methods and computational efforts, previous approaches could detect only abundantly expressed miRNAs or close homologs of previously identified miRNAs. Here, we introduce a probabilistic co-learning model for miRNA gene finding, ProMiR, which simultaneously considers the structure and sequence of miRNA precursors (pre-miRNAs). On 5-fold cross-validation with 136 referenced human datasets, the efficiency of the classification shows 73% sensitivity and 96% specificity. When applied to genome screening for novel miRNAs on human chromosomes 16, 17, 18 and 19, ProMiR effectively searches distantly homologous patterns over diverse pre-miRNAs, detecting at least 23 novel miRNA gene candidates. Importantly, the miRNA gene candidates do not demonstrate clear sequence similarity to the known miRNA genes. By quantitative PCR followed by RNA interference against Drosha, we experimentally confirmed that 9 of the 23 representative candidate genes express transcripts that are processed by the miRNA biogenesis enzyme Drosha in HeLa cells, indicating that ProMiR may successfully predict miRNA genes with at least 40% accuracy. Our study suggests that the miRNA gene family may be more abundant than previously anticipated, and confer highly extensive regulatory networks on eukaryotic cells.

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