Shuilin Jin

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.

Predicting disease-related genes using integrated biomedical networks

BackgroundIdentifying the genes associated to human diseases is crucial for disease diagnosis and drug design. Computational approaches, esp. the network-based approaches, have been recently developed to identify disease-related genes effectively from the existing biomedical networks. Meanwhile, the advance in biotechnology enables researchers to produce multi-omics data, enriching our understanding on human diseases, and revealing the complex relationships between genes and diseases. However, none of the existing computational approaches is able to integrate the huge amount of omics data into a weighted integrated network and utilize it to enhance disease related gene discovery.ResultsWe propose a new network-based disease gene prediction method called SLN-SRW (Simplified Laplacian Normalization-Supervised Random Walk) to generate and model the edge weights of a new biomedical network that integrates biomedical data from heterogeneous sources, thus far enhancing the disease related gene discovery.ConclusionsThe experiment results show that SLN-SRW significantly improves the performance of disease gene prediction on both the real and the synthetic data sets.

Genetic variant rs763361 regulates multiple sclerosis CD226 gene expression

In a recent study, Gross et al. investigate the presence, distribution, and function of natural killer (NK) cells in three different compartments to characterize the role of NK cells in multiple sclerosis (MS) (1). Their results indicate that NK cells played an important role in controlling T-cell activity in vivo. In addition, Gross et al. found reduced expression of the activating NK-cell receptor DNAM-1 ( CD226 ) in MS. Gross et al. compare their findings to those from human genetic-association studies. Interestingly, a nonsynonymous variant, Gly307Ser (rs763361), in the CD226 gene was identified to be significantly associated with several autoimmune diseases, including MS, which further supported Gross et al.’s findings as described in their discussion (1, 2). The … [↵][1]2To whom correspondence should be addressed. Email: qhjiang{at}hit.edu.cn. [1]: #xref-corresp-1-1

Cis-eQTLs regulate reduced LST1 gene and NCR3 gene expression and contribute to increased autoimmune disease risk

In PNAS, Yau et al. (1) identify a conserved 33-kb haplotype Ltab-Ncr3 across five genes, lymphotoxin-α ( Lta ), Tnf , lymphotoxin-β ( Ltb ), leukocyte-specific transcript 1 ( Lst1 ), and natural cytotoxicity-triggering receptor 3 ( Ncr3 ) in the MHC-III region in wild rats. The higher Ltb and Ncr3 expression, the lower Lst1 expression, and the expression of a shorter splice variant of Lst1 were associated with reduced arthritis severity in rats (1). Yau et al. (1) further analyzed the expression levels of LTB , LST1 , and NCR3 using whole-blood samples from 32 patients with rheumatoid arthritis (RA) and 92 healthy controls (1). They identify significantly increased expression of these three genes in RA cases (1). The mild RA cases also showed lower expression of LST1 and higher expression of … [↵][1]1To whom correspondence may be addressed. Email: hjw{at}tmu.edu.cn or qhjiang{at}hit.edu.cn. [1]: #xref-corresp-1-1

Annotating the Function of the Human Genome with Gene Ontology and Disease Ontology

Increasing evidences indicated that function annotation of human genome in molecular level and phenotype level is very important for systematic analysis of genes. In this study, we presented a framework named Gene2Function to annotate Gene Reference into Functions (GeneRIFs), in which each functional description of GeneRIFs could be annotated by a text mining tool Open Biomedical Annotator (OBA), and each Entrez gene could be mapped to Human Genome Organisation Gene Nomenclature Committee (HGNC) gene symbol. After annotating all the records about human genes of GeneRIFs, 288,869 associations between 13,148 mRNAs and 7,182 terms, 9,496 associations between 948 microRNAs and 533 terms, and 901 associations between 139 long noncoding RNAs (lncRNAs) and 297 terms were obtained as a comprehensive annotation resource of human genome. High consistency of term frequency of individual gene (Pearson correlation = 0.6401, p = 2.2e − 16) and gene frequency of individual term (Pearson correlation = 0.1298, p = 3.686e − 14) in GeneRIFs and GOA shows our annotation resource is very reliable.

Alzheimer’s Disease rs11767557 Variant Regulates EPHA1 Gene Expression Specifically in Human Whole Blood

Large-scale genome-wide association studies have reported EPHA1 rs11767557 variant to be associated with Alzheimers disease (AD) risk in the European population. However, it is still unclear how this variant functionally contributes to the underlying disease pathogenesis. The rs11767557 variant is located approximately 3 kb upstream of EPHA1 gene. We think that rs11767557 may modify the expression of nearby genes such as EPHA1 and further cause AD risk. Until now, the potential association between rs11767557 and the expression of nearby genes has not been reported in previous studies. Here, we evaluate the potential expression association between rs11767557 and EPHA1 using multiple large-scale eQTLs datasets in human brain tissues and the whole blood. The results show that rs11767557 variant could significantly regulate EPHA1 gene expression specifically in human whole blood. These findings may further provide important supplementary information about the regulating mechanisms of rs11767557 variant in AD risk.

Autoimmune disease variants regulate GSDMB gene expression in human immune cells and whole blood

There are four gasdermin (GSDM) proteins, including GSDMA, GSDMB, GSDMC, and GSDMD, in the human genome (1). Genome-wide association studies have reported genetic variants at 17q12.2.1 loci, including GSDMA , GSDMB , and ORDML3 genes, to be associated with kinds of autoimmune diseases, including asthma, type 1 diabetes, inflammatory bowel disease (IBD), and rheumatoid arthritis (1). However, the potential genetic mechanisms are unknown (1). In a recent study in PNAS, Chao et al. (1) identified that the GSDMB gene SNPs (rs2305479 G > A and rs2305480 C > T), which are associated with an increased susceptibility to asthma and IBD, could alter the structure of GSDMB, a sulfatide and phosphoinositide binding protein. In their discussion, Chao et al. (1) describe that GSDMB may … [↵][1]1To whom correspondence may be addressed. Email: liuguiyou1981{at}163.com or qhjiang{at}hit.edu.cn. [1]: #xref-corresp-1-1