Qinghua Jiang

miR2Disease: A manually curated database for microRNA deregulation in human disease

‘miR2Disease’, a manually curated database, aims at providing a comprehensive resource of microRNA deregulation in various human diseases. The current version of miR2Disease documents 1939 curated relationships between 299 human microRNAs and 94 human diseases by reviewing more than 600 published papers. Around one-seventh of the microRNA–disease relationships represent the pathogenic roles of deregulated microRNA in human disease. Each entry in the miR2Disease contains detailed information on a microRNA–disease relationship, including a microRNA ID, the disease name, a brief description of the microRNA–disease relationship, an expression pattern of the microRNA, the detection method for microRNA expression, experimentally verified target gene(s) of the microRNA and a literature reference. miR2Disease provides a user-friendly interface for a convenient retrieval of each entry by microRNA ID, disease name, or target gene. In addition, miR2Disease offers a submission page that allows researchers to submit established microRNA–disease relationships that are not documented. Once approved by the submission review committee, the submitted records will be included in the database. miR2Disease is freely available at http://www.miR2Disease.org.

Prioritization of disease microRNAs through a human phenome-microRNAome network

BackgroundThe identification of disease-related microRNAs is vital for understanding the pathogenesis of diseases at the molecular level, and is critical for designing specific molecular tools for diagnosis, treatment and prevention. Experimental identification of disease-related microRNAs poses considerable difficulties. Computational analysis of microRNA-disease associations is an important complementary means for prioritizing microRNAs for further experimental examination.ResultsHerein, we devised a computational model to infer potential microRNA-disease associations by prioritizing the entire human microRNAome for diseases of interest. We tested the model on 270 known experimentally verified microRNA-disease associations and achieved an area under the ROC curve of 75.80%. Moreover, we demonstrated that the model is applicable to diseases with which no known microRNAs are associated. The microRNAome-wide prioritization of microRNAs for 1,599 disease phenotypes is publicly released to facilitate future identification of disease-related microRNAs.ConclusionsWe presented a network-based approach that can infer potential microRNA-disease associations and drive testable hypotheses for the experimental efforts to identify the roles of microRNAs in human diseases.

An Evaluation of Copy Number Variation Detection Tools from Whole-Exome Sequencing Data

Copy number variation (CNV) has been found to play an important role in human disease. Next‐generation sequencing technology, including whole‐genome sequencing (WGS) and whole‐exome sequencing (WES), has become a primary strategy for studying the genetic basis of human disease. Several CNV calling tools have recently been developed on the basis of WES data. However, the comparative performance of these tools using real data remains unclear. An objective evaluation study of these tools in practical research situations would be beneficial. Here, we evaluated four well‐known WES‐based CNV detection tools (XHMM, CoNIFER, ExomeDepth, and CONTRA) using real data generated in house. After evaluation using six metrics, we found that the sensitive and accurate detection of CNVs in WES data remains challenging despite the many algorithms available. Each algorithm has its own strengths and weaknesses. None of the exome‐based CNV calling methods performed well in all situations; in particular, compared with CNVs identified from high coverage WGS data from the same samples, all tools suffered from limited power. Our evaluation provides a comprehensive and objective comparison of several well‐known detection tools designed for WES data, which will assist researchers in choosing the most suitable tools for their research needs.

LncRNA2Function: a comprehensive resource for functional investigation of human lncRNAs based on RNA-seq data

BackgroundThe GENCODE project has collected over 10,000 human long non-coding RNA (lncRNA) genes. However, the vast majority of them remain to be functionally characterized. Computational investigation of potential functions of human lncRNA genes is helpful to guide further experimental studies on lncRNAs.ResultsIn this study, based on expression correlation between lncRNAs and protein-coding genes across 19 human normal tissues, we used the hypergeometric test to functionally annotate a single lncRNA or a set of lncRNAs with significantly enriched functional terms among the protein-coding genes that are significantly co-expressed with the lncRNA(s). The functional terms include all nodes in the Gene Ontology (GO) and 4,380 human biological pathways collected from 12 pathway databases. We successfully mapped 9,625 human lncRNA genes to GO terms and biological pathways, and then developed the first ontology-driven user-friendly web interface named lncRNA2Function, which enables researchers to browse the lncRNAs associated with a specific functional term, the functional terms associated with a specific lncRNA, or to assign functional terms to a set of human lncRNA genes, such as a cluster of co-expressed lncRNAs. The lncRNA2Function is freely available at http://mlg.hit.edu.cn/lncrna2function.ConclusionsThe LncRNA2Function is an important resource for further investigating the functions of a single human lncRNA, or functionally annotating a set of human lncRNAs of interest.

LncRNA2Target: a database for differentially expressed genes after lncRNA knockdown or overexpression

Long non-coding RNAs (lncRNAs) have emerged as critical regulators of genes at epigenetic, transcriptional and post-transcriptional levels, yet what genes are regulated by a specific lncRNA remains to be characterized. To assess the effects of the lncRNA on gene expression, an increasing number of researchers profiled the genome-wide or individual gene expression level change after knocking down or overexpressing the lncRNA. Herein, we describe a curated database named LncRNA2Target, which stores lncRNA-to-target genes and is publicly accessible at http://www.lncrna2target.org. A gene was considered as a target of a lncRNA if it is differentially expressed after the lncRNA knockdown or overexpression. LncRNA2Target provides a web interface through which its users can search for the targets of a particular lncRNA or for the lncRNAs that target a particular gene. Both search types are performed either by browsing a provided catalog of lncRNA names or by inserting lncRNA/target gene IDs/names in a search box.

Predicting human microRNA-disease associations based on support vector machine

The identification of disease-related microRNAs is vital for understanding the pathogenesis of disease at the molecular level and may lead to the design of specific molecular tools for diagnosis, treatment and prevention. Experimental identification of disease-related microRNAs poses difficulties. Computational prediction of microRNA-disease associations is one of the complementary means. However, one major issue in microRNA studies is the lack of bioinformatics programs to accurately predict microRNA-disease associations. Herein, we present a machine-learning-based approach for distinguishing positive microRNA-disease associations from negative microRNA-disease associations. A set of features was extracted for each positive and negative microRNA-disease association, and a Support Vector Machine SVM classifier was trained, which achieved the area under the ROC curve of up to 0.8884 in 10-fold cross-validation procedure, indicating that the SVM-based approach described here can be used to predict potential microRNA-disease associations and formulate testable hypotheses to guide future biological experiments.

Alzheimer's Disease Variants with the Genome-Wide Significance are Significantly Enriched in Immune Pathways and Active in Immune Cells.

The existing large-scale genome-wide association studies (GWAS) datasets provide strong support for investigating the mechanisms of Alzheimer’s disease (AD) by applying multiple methods of pathway analysis. Previous studies using selected single nucleotide polymorphisms (SNPs) with several thresholds of nominal significance for pathway analysis determined that the threshold chosen for SNPs can reflect the disease model. Presumably, then, pathway analysis with a stringent threshold to define “associated” SNPs would test the hypothesis that highly associated SNPs are enriched in one or more particular pathways. Here, we selected 599 AD variants (P < 5.00E−08) to investigate the pathways in which these variants are enriched and the cell types in which these variants are active. Our results showed that AD variants are significantly enriched in pathways of the immune system. Further analysis indicated that AD variants are significantly enriched for enhancers in a number of cell types, in particular the B-lymphocyte, which is the most substantially enriched cell type. This cell type maintains its dominance among the strongest enhancers. AD SNPs also display significant enrichment for DNase in 12 cell types, among which the top 6 significant signals are from immune cell types, including 4 B cells (top 4 significant signals) and CD14+ and CD34+ cells. In summary, our results show that these AD variants with P < 5.00E−08 are significantly enriched in pathways of the immune system and active in immune cells. To a certain degree, the genetic predisposition for development of AD is rooted in the immune system, rather than in neuronal cells.

Cell adhesion molecule pathway genes are regulated by cis-regulatory SNPs and show significantly altered expression in Alzheimer's disease brains.

We previously identified the cell adhesion molecule (CAM) pathway as a consistent signal in 2 Alzheimers disease (AD) genome-wide association studies (GWAS). However, the genetic mechanisms of the CAM pathway in AD are unclear. Here, we conducted pathway analysis using (1) Kyoto Encyclopedia of Genes and Genomes and Gene Ontology pathways; (2) 4 brain expression GWAS datasets; and (3) 2 whole-genome AD case-control expression datasets. Using the 4 brain expression GWAS datasets, we identified that genes regulated by cis-regulatory single-nucleotide polymorphisms (SNPs) were significantly enriched in the CAM pathway (p = 2.05E-06, p = 6.10E-07, p = 2.05E-06, and p = 1.47E-07 for each dataset). Interestingly, CAM is a significantly enriched pathway using down-regulated genes (raw p = 0.0235 and adjusted p = 0.0305) and all differentially expressed genes (raw p = 0.0105 and adjusted p = 0.0156) in dataset 5, and all differentially expressed genes (raw p = 0.0041 and adjusted p = 0.0062) in dataset 6. Collectively, our results show that CAM pathway genes are regulated by cis-regulatory SNPs and show significantly altered expression in AD. We believe that our results advance the understanding of AD mechanisms and will be useful for future genetic studies of AD.

Integrating genome-wide association studies and gene expression data highlights dysregulated multiple sclerosis risk pathways

Background: Much effort has been expended on identifying the genetic determinants of multiple sclerosis (MS). Existing large-scale genome-wide association study (GWAS) datasets provide strong support for using pathway and network-based analysis methods to investigate the mechanisms underlying MS. However, no shared genetic pathways have been identified to date. Objective: We hypothesize that shared genetic pathways may indeed exist in different MS-GWAS datasets. Methods: Here, we report results from a three-stage analysis of GWAS and expression datasets. In stage 1, we conducted multiple pathway analyses of two MS-GWAS datasets. In stage 2, we performed a candidate pathway analysis of the large-scale MS-GWAS dataset. In stage 3, we performed a pathway analysis using the dysregulated MS gene list from seven human MS case–control expression datasets. Results: In stage 1, we identified 15 shared pathways. In stage 2, we successfully replicated 14 of these 15 significant pathways. In stage 3, we found that dysregulated MS genes were significantly enriched in 10 of 15 MS risk pathways identified in stages 1 and 2. Conclusion: We report shared genetic pathways in different MS-GWAS datasets and highlight some new MS risk pathways. Our findings provide new insights on the genetic determinants of MS.

InteGO2: a web tool for measuring and visualizing gene semantic similarities using Gene Ontology

BackgroundThe Gene Ontology (GO) has been used in high-throughput omics research as a major bioinformatics resource. The hierarchical structure of GO provides users a convenient platform for biological information abstraction and hypothesis testing. Computational methods have been developed to identify functionally similar genes. However, none of the existing measurements take into account all the rich information in GO. Similarly, using these existing methods, web-based applications have been constructed to compute gene functional similarities, and to provide pure text-based outputs. Without a graphical visualization interface, it is difficult for result interpretation.ResultsWe present InteGO2, a web tool that allows researchers to calculate the GO-based gene semantic similarities using seven widely used GO-based similarity measurements. Also, we provide an integrative measurement that synergistically integrates all the individual measurements to improve the overall performance. Using HTML5 and cytoscape.js, we provide a graphical interface in InteGO2 to visualize the resulting gene functional association networks.ConclusionsInteGO2 is an easy-to-use HTML5 based web tool. With it, researchers can measure gene or gene product functional similarity conveniently, and visualize the network of functional interactions in a graphical interface. InteGO2 can be accessed via http://mlg.hit.edu.cn:8089/.