Haixiu Yang

In silico detection and characteristics of novel microRNA genes in the Equus caballus genome using an integrated ab initio and comparative genomic approach.

The importance of microRNAs at the post-transcriptional regulation level has recently been recognized in both animals and plants. We used the simple but effective sequential method of first Blasting known animal miRNAs against the horse genome and then using the located candidates to search for novel miRNAs by RNA folding method in the vicinity (+ -500 bp) of the candidates. Here, a total of 407 novel horse miRNA genes including 354 mature miRNAs were identified, of these, 75 miRNAs were grouped into 32 families based on seed sequence identity. MiRNA genes tend to be present as clusters in some chromosomes, and 146 miRNA genes accounted for 36% of the total were observed as part of polycistronic transcripts. Detailed analysis of sequence characteristics in novel horse and all previous known animal miRNAs were carried out. Our study will provide a reference point for further study on miRNAs identification in animals and improve the understanding of genome in horse.

Subpathway-GM: identification of metabolic subpathways via joint power of interesting genes and metabolites and their topologies within pathways

Various ‘omics’ technologies, including microarrays and gas chromatography mass spectrometry, can be used to identify hundreds of interesting genes, proteins and metabolites, such as differential genes, proteins and metabolites associated with diseases. Identifying metabolic pathways has become an invaluable aid to understanding the genes and metabolites associated with studying conditions. However, the classical methods used to identify pathways fail to accurately consider joint power of interesting gene/metabolite and the key regions impacted by them within metabolic pathways. In this study, we propose a powerful analytical method referred to as Subpathway-GM for the identification of metabolic subpathways. This provides a more accurate level of pathway analysis by integrating information from genes and metabolites, and their positions and cascade regions within the given pathway. We analyzed two colorectal cancer and one metastatic prostate cancer data sets and demonstrated that Subpathway-GM was able to identify disease-relevant subpathways whose corresponding entire pathways might be ignored using classical entire pathway identification methods. Further analysis indicated that the power of a joint genes/metabolites and subpathway strategy based on their topologies may play a key role in reliably recalling disease-relevant subpathways and finding novel subpathways.

Topologically inferring risk-active pathways toward precise cancer classification by directed random walk

MOTIVATION The accurate prediction of disease status is a central challenge in clinical cancer research. Microarray-based gene biomarkers have been identified to predict outcome and outperform traditional clinical parameters. However, the robustness of the individual gene biomarkers is questioned because of their little reproducibility between different cohorts of patients. Substantial progress in treatment requires advances in methods to identify robust biomarkers. Several methods incorporating pathway information have been proposed to identify robust pathway markers and build classifiers at the level of functional categories rather than of individual genes. However, current methods consider the pathways as simple gene sets but ignore the pathway topological information, which is essential to infer a more robust pathway activity. RESULTS Here, we propose a directed random walk (DRW)-based method to infer the pathway activity. DRW evaluates the topological importance of each gene by capturing the structure information embedded in the directed pathway network. The strategy of weighting genes by their topological importance greatly improved the reproducibility of pathway activities. Experiments on 18 cancer datasets showed that the proposed method yielded a more accurate and robust overall performance compared with several existing gene-based and pathway-based classification methods. The resulting risk-active pathways are more reliable in guiding therapeutic selection and the development of pathway-specific therapeutic strategies. AVAILABILITY DRW is freely available at http://210.46.85.180:8080/DRWPClass/

DisSim: an online system for exploring significant similar diseases and exhibiting potential therapeutic drugs

The similarity of pair-wise diseases reveals the molecular relationships between them. For example, similar diseases have the potential to be treated by common therapeutic chemicals (TCs). In this paper, we introduced DisSim, an online system for exploring similar diseases, and comparing corresponding TCs. Currently, DisSim implemented five state-of-the-art methods to measure the similarity between Disease Ontology (DO) terms and provide the significance of the similarity score. Furthermore, DisSim integrated TCs of diseases from the Comparative Toxicogenomics Database (CTD), which can help to identify potential relationships between TCs and similar diseases. The system can be accessed from http://123.59.132.21:8080/DisSim.

Inferring potential microRNA-microRNA associations based on targeting propensity and connectivity in the context of protein interaction network.

MicroRNAs (miRNAs) are a group of small non-coding RNAs that play important regulatory roles at the post-transcriptional level. Although several computational methods have been developed to compare miRNAs, it is still a challenging and a badly needed task with the availability of various biological data resources. In this study, we proposed a novel graph theoretic property based computational framework and method, called miRFunSim, for quantifying the associations between miRNAs based on miRNAs targeting propensity and proteins connectivity in the integrated protein-protein interaction network. To evaluate the performance of our method, we applied the miRFunSim method to compute functional similarity scores of miRNA pairs between 100 miRNAs whose target genes have been experimentally supported and found that the functional similarity scores of miRNAs in the same family or in the same cluster are significantly higher compared with other miRNAs which are consistent with prior knowledge. Further validation analysis on experimentally verified miRNA-disease associations suggested that miRFunSim can effectively recover the known miRNA pairs associated with the same disease and achieve a higher AUC of 83.1%. In comparison with similar methods, our miRFunSim method can achieve more effective and more reliable performance for measuring the associations of miRNAs. We also conducted the case study examining liver cancer based on our method, and succeeded in uncovering the candidate liver cancer related miRNAs such as miR-34 which also has been proven in the latest study.

A novel network-based method for measuring the functional relationship between gene sets

MOTIVATION In the functional genomic era, a large number of gene sets have been identified via high-throughput genomic and proteomic technologies. These gene sets of interest are often related to the same or similar disorders or phenotypes, and are commonly presented as differentially expressed gene lists, co-expressed gene modules, protein complexes or signaling pathways. However, biologists are still faced by the challenge of comparing gene sets and interpreting the functional relationships between gene sets into an understanding of the underlying biological mechanisms. RESULTS We introduce a novel network-based method, designated corrected cumulative rank score (CCRS), which analyzes the functional communication and physical interaction between genes, and presents an easy-to-use web-based toolkit called GsNetCom to quantify the functional relationship between two gene sets. To evaluate the performance of our method in assessing the functional similarity between two gene sets, we analyzed the functional coherence of complexes in functional catalog and identified protein complexes in the same functional catalog. The results suggested that CCRS can offer a significant advance in addressing the functional relationship between different gene sets compared with several other available tools or algorithms with similar functionality. We also conducted the case study based on our method, and succeeded in prioritizing candidate leukemia-associated protein complexes and expanding the prioritization and analysis of cancer-related complexes to other cancer types. In addition, GsNetCom provides a new insight into the communication between gene modules, such as exploring gene sets from the perspective of well-annotated protein complexes. AVAILABILITY AND IMPLEMENTATION GsNetCom is a freely available web accessible toolkit at http://bioinfo.hrbmu.edu.cn/GsNetCom.

Integration of Multiple Genomic and Phenotype Data to Infer Novel miRNA-Disease Associations

MicroRNAs (miRNAs) play an important role in the development and progression of human diseases. The identification of disease-associated miRNAs will be helpful for understanding the molecular mechanisms of diseases at the post-transcriptional level. Based on different types of genomic data sources, computational methods for miRNA-disease association prediction have been proposed. However, individual source of genomic data tends to be incomplete and noisy; therefore, the integration of various types of genomic data for inferring reliable miRNA-disease associations is urgently needed. In this study, we present a computational framework, CHNmiRD, for identifying miRNA-disease associations by integrating multiple genomic and phenotype data, including protein-protein interaction data, gene ontology data, experimentally verified miRNA-target relationships, disease phenotype information and known miRNA-disease connections. The performance of CHNmiRD was evaluated by experimentally verified miRNA-disease associations, which achieved an area under the ROC curve (AUC) of 0.834 for 5-fold cross-validation. In particular, CHNmiRD displayed excellent performance for diseases without any known related miRNAs. The results of case studies for three human diseases (glioblastoma, myocardial infarction and type 1 diabetes) showed that all of the top 10 ranked miRNAs having no known associations with these three diseases in existing miRNA-disease databases were directly or indirectly confirmed by our latest literature mining. All these results demonstrated the reliability and efficiency of CHNmiRD, and it is anticipated that CHNmiRD will serve as a powerful bioinformatics method for mining novel disease-related miRNAs and providing a new perspective into molecular mechanisms underlying human diseases at the post-transcriptional level. CHNmiRD is freely available at http://www.bio-bigdata.com/CHNmiRD.

IntNetLncSim: an integrative network analysis method to infer human lncRNA functional similarity

Increasing evidence indicated that long non-coding RNAs (lncRNAs) were involved in various biological processes and complex diseases by communicating with mRNAs/miRNAs each other. Exploiting interactions between lncRNAs and mRNA/miRNAs to lncRNA functional similarity (LFS) is an effective method to explore function of lncRNAs and predict novel lncRNA-disease associations. In this article, we proposed an integrative framework, IntNetLncSim, to infer LFS by modeling the information flow in an integrated network that comprises both lncRNA-related transcriptional and post-transcriptional information. The performance of IntNetLncSim was evaluated by investigating the relationship of LFS with the similarity of lncRNA-related mRNA sets (LmRSets) and miRNA sets (LmiRSets). As a result, LFS by IntNetLncSim was significant positively correlated with the LmRSet (Pearson correlation γ2=0.8424) and LmiRSet (Pearson correlation γ2=0.2601). Particularly, the performance of IntNetLncSim is superior to several previous methods. In the case of applying the LFS to identify novel lncRNA-disease relationships, we achieved an area under the ROC curve (0.7300) in experimentally verified lncRNA-disease associations based on leave-one-out cross-validation. Furthermore, highly-ranked lncRNA-disease associations confirmed by literature mining demonstrated the excellent performance of IntNetLncSim. Finally, a web-accessible system was provided for querying LFS and potential lncRNA-disease relationships: http://www.bio-bigdata.com/IntNetLncSim.

ESEA: Discovering the Dysregulated Pathways based on Edge Set Enrichment Analysis

Pathway analyses are playing an increasingly important role in understanding biological mechanism, cellular function and disease states. Current pathway-identification methods generally focus on only the changes of gene expression levels; however, the biological relationships among genes are also the fundamental components of pathways, and the dysregulated relationships may also alter the pathway activities. We propose a powerful computational method, Edge Set Enrichment Analysis (ESEA), for the identification of dysregulated pathways. This provides a novel way of pathway analysis by investigating the changes of biological relationships of pathways in the context of gene expression data. Simulation studies illustrate the power and performance of ESEA under various simulated conditions. Using real datasets from p53 mutation, Type 2 diabetes and lung cancer, we validate effectiveness of ESEA in identifying dysregulated pathways. We further compare our results with five other pathway enrichment analysis methods. With these analyses, we show that ESEA is able to help uncover dysregulated biological pathways underlying complex traits and human diseases via specific use of the dysregulated biological relationships. We develop a freely available R-based tool of ESEA. Currently, ESEA can support pathway analysis of the seven public databases (KEGG; Reactome; Biocarta; NCI; SPIKE; HumanCyc; Panther).

Subpathway-GMir: identifying miRNA-mediated metabolic subpathways by integrating condition-specific genes, microRNAs, and pathway topologies.

MicroRNAs (miRNAs) regulate disease-relevant metabolic pathways. However, most current pathway identification methods fail to consider miRNAs in addition to genes when analyzing pathways. We developed a powerful method called Subpathway-GMir to construct miRNA-regulated metabolic pathways and to identify miRNA-mediated subpathways by considering condition-specific genes, miRNAs, and pathway topologies. We used Subpathway-GMir to analyze two liver hepatocellular carcinomas (LIHC), one stomach adenocarcinoma (STAD), and one type 2 diabetes (T2D) data sets. Results indicate that Subpathway-GMir is more effective in identifying phenotype-associated metabolic pathways than other methods and our results are reproducible and robust. Subpathway-GMir provides a flexible platform for identifying abnormal metabolic subpathways mediated by miRNAs, and may help to clarify the roles that miRNAs play in a variety of diseases. The Subpathway-GMir method has been implemented as a freely available R package.