Abstract Background The 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. Results In 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 [43]http://mlg.hit.edu.cn/lncrna2function. Conclusions The 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. Background Thousands of human long non-coding RNAs (lncRNAs) have been identified and emerging studies have revealed that lncRNAs play important roles in a wide range of biological processes [[44]1,[45]2] and diseases [[46]3,[47]4]. However, functions of most human lncRNAs are still elusive. Functions of a lncRNA may be determined by loss- and gain-of-function biological experiments [[48]5,[49]6]. However, this is not straightforward since it is difficult to knock down a lncRNA expressed as multiple isoforms. Alternatively, computational exploration of human lncRNA functions is helpful to guide further studies on lncRNAs. Currently, computational investigation of lncRNA functions is still at its early development stage, since it is a considerable challenge due to the characteristics of lncRNAs, e.g., many lncRNA gene sequences are not conserved and do not contain conserved sequence motifs [[50]7], which makes it difficult to infer potential functions of lncRNAs based on their sequences alone. In addition, few available molecular interaction data of new identified lncRNAs also hamper the lncRNA functional annotations [[51]8,[52]9]. Since genes with similar expression patterns across multiple conditions may share similar functions [[53]10] or be involved in related biological pathways [[54]11], identifying protein-coding genes that are co-expressed with lncRNAs may help to assign functions to the lncRNAs. By analyzing lncRNA-mRNA co-expression pattern, Guttman et al. identified several sets of mouse lncRNAs associated with protein-coding gene sets of distinct GO functional categories [[55]12]. In addition, two recent studies separately constructed a mouse co-expressed lncRNA-mRNA network using mouse microarray data and assigned functions to 340 and 1,625 mouse lncRNAs [[56]13,[57]14]. Despite accumulating insights into the mouse lncRNA functions, more than 10,000 human lncRNAs remain to be functionally characterized. Firstly, given a single human lncRNA gene, it needs to be established whether it executes crucial biological functions. Secondly, given a set of human lncRNA genes such as differential lncRNAs between cancer and normal samples, it is an important downstream task to identify significantly enriched function terms. Thirdly, given an important functional term such as a Wnt signalling pathway, how to know which lncRNAs may be involved in the pathway. Here, based on the expression correlation between lncRNAs and protein-coding genes inferred from RNA-seq data of 19 human normal tissues, we functionally annotated 9,625 human lncRNAs with significantly enriched functional terms among the co-expressed protein-coding genes, and developed a user-friendly web interface for the lncRNA community to obtain the lncRNAs associated with a specific functional term, the functional terms associated with a specific lncRNA, or to assign functions to a set of human lncRNAs of interest. Methods Data sources We downloaded: (1) genomic coordinates of all human lncRNA genes and protein-coding genes from the GENCODE V15 [[58]15], (2) paired-end RNA-Seq data of 19 human normal tissues from the Human Body Map 2 project (ArrayExpress accession no. E-MTAB-513) and another study (GEO accession no. [59]GSE30554), (3) GO assignments for the proteins of the human UniProtKB Complete Proteome from the website of the Gene Ontology Project [[60]16], (4) 4,380 human biological pathways from the ConsensusPathDB database which integrated 12 pathway databases [[61]17]. Workflow of LncRNA2Function The schematic workflow of lncRNA2Function is shown in Figure [62]1. Firstly, RNA-Seq reads sequenced in 19 human normal tissues were firstly mapped to human genome (hg19) using tophat with the default parameters [[63]18], and expression values of all human lncRNA and protein-coding genes in the 19 tissues were computed using cufflinks with the default parameter [[64]19]. Secondly, the Pearson Correlation Coefficients (PCC) of all lncRNA-mRNA gene pairs were computed, and a set of significantly co-expressed protein-coding genes was thus obtained for each human lncRNA (significant: the absolute value of the Pearson correlation coefficient >0.9 and adjusted P-value < 0.05). Thirdly, each lncRNA was functionally annotated with significantly enriched GO terms and biological pathways among the set of co-expressed protein-coding genes. Finally, a web interface was developed to facilitate researchers to browse or search the functions associated with a given lncRNA or lncRNAs associated with a specific function, or to functionally annotate a set of lncRNA genes of interest. Figure 1. Figure 1 [65]Open in a new tab Schematic workflow of the LncRNA2Function. GO and pathway enrichment analysis of human lncRNAs Given a single human lncRNA gene, we obtained a set of protein-coding genes that were significantly co-expressed with the lncRNA. The lncRNA was then functionally annotated with significantly enriched GO and pathway terms among the set of co-expressed protein-coding genes. The enrichment analysis was separately executed for each term (denoted as T), and a P-value of each term was calculated by the hypergeometric test: [MATH: p=∑i=mmin(n,M)MiN-Mn< mo class="MathClass-bin">-iNn :MATH] (1) Herein, N is the number of all protein-coding genes in human genome, M is the number of protein-coding genes that were annotated in the functional term T, n is the number of protein-coding genes that were significantly co-expressed with the lncRNA, and m is the number of protein-coding genes that were both significantly co-expressed with the lncRNA and annotated in the functional term T. For each GO term, protein-coding genes directly belong to it as well as those belong to any of its offspring terms are all considered as its annotated genes. Since the statistical analysis is not appropriate to problems with small sample size, those GO and pathway terms with less than 5 annotated protein-coding genes and those lncRNAs with less than 5 co-expressed protein-coding genes were excluded form the enrichment analysis. Given a set of human lncRNA genes of interest, LncRNA2Function first identify a set of protein-coding genes, each of which are significantly co-expressed with one or more of the given lncRNAs across 19 human normal tissues. Then, the set of lncRNAs are functionally annotated with the enriched GO and pathway terms among the set of co-expressed protein-coding genes. If researchers input a large number of lncRNAs, the LncRNA2Function may obtain thousands of co-expressed protein-coding genes, some of which are co-expressed with only one of the lncRNAs. To improve the accuracy of functional assignments to the set of lncRNAs, users can select the protein-coding genes that are co-expressed with at least K lncRNAs (the K can be assigned based on the size of the set of lncRNAs. The default K is 1). There are two commonly used methods for controlling false discovery rate (FDR), the Benjamini-Yekutieli (BY) method [[66]20] and the Benjamini-Hochberg (BH) method [[67]21]. The former is suitable for positively related multiple hypothesis tests whereas the later is suitable for independent multiple hypothesis tests [[68]22]. Since the hierarchical GO terms are often dependent, we chose the BY method to correct the P-values from the GO enrichment analysis, and the BH method to correct the P-values from the pathway enrichment analysis. The significant cut-off of corrected P-value was set as 0.05. Results and discussion Functional annotations of a single human lncRNA We obtained 5,232,299 significantly co-expressed pairs between 9,625 human lncRNA genes and 10,919 protein-coding genes. Each of the 9,625 lncRNAs was functionally annotated with significantly enriched GO terms and biological pathways among its co-expressed protein-coding genes. Consequently, we obtained 614,174 associations between 5,735 lncRNA genes and 3,890 GO terms, and 240,050 associations between 6,062 lncRNAs and 3,034 biological pathways. To understand the major functions of lncRNAs, we ranked GO biological processes and biological pathways according to the number of lncRNAs associated with each of them. Among the top ranked 200 GO biological processes and pathways, we found that lncRNAs play roles in many important biological processes, including defense response to bacterium, DNA packaging, meiosis, developmental process, metabolic process, cell cycle process, cell adhesion, cell differentiation, Jak-STAT signaling pathway and PI3K-Akt signaling pathway. A part of the enriched functions of lncRNAs have been validated by published studies [[69]23-[70]26]. Case studies Due to the lack of a large gold standard dataset of known human lncRNA functions, five well-studied lncRNAs were used as the examples to show the usefulness of LncRNA2Function. Case study 1: HOTAIR The HOTAIR is a well-studied lncRNA. Rinn et al. found that the HOTAIR interacts with the Polycomb repressive complex 2 (PRC2) to modify chromatin and repress transcription of the HOX genes, which regulate development [[71]27]. Niinuma et al. revealed that overexpression of HOTAIR was strongly associated with high-risk grade and metastasis among gastrointestinal stromal tumors (GIST) specimens, and knockdown of HOTAIR suppressed GIST cell invasiveness [[72]28]. In addition, Gupta et al. demonstrated that the lncRNA HOTAIR is increased in expression in primary breast tumors and metastases, and enforced expression of HOTAIR in epithelial cancer cells leaded to altered histone H3 lysine 27 methylation, gene expression, and increased cancer invasiveness and metastasis in a manner dependent on PRC2. Conversely, loss of HOTAIR can inhibit breast cancer invasiveness [[73]26]. To examine whether our LncRNA2Function can functionally annotate the lncRNA HOTAIR with development and metastasis-related functional terms, we applied the LncRNA2Function to the HOTAIR, and found that it was annotated with 99 GO biological processes and 33 pathways (The significant Corrected P-value cutoff is 0.05). Of the 99 GO biological processes, 77.8% (77/99) are involved in the development and morphogenesis as expected (The top 20 GO development-related biological processes are shown in Table [74]1), and 9.1% (9/99) are involved in the cell invasion and metastasis, such as cell migration (GO:0016477), cell adhesion (GO:0007155), biological adhesion (GO:0022610) and cell motility (GO:0048870). In addition, Of the 33 biological pathways, 72.7% (24/33) are involved in the cell invasion and metastasis (Table [75]2), such as focal adhesion, beta1 integrin cell surface interactions, NCAM1 interactions, Syndecan-1-mediated signaling events, PI3K-Akt signaling pathway and cell surface interactions at the vascular wall. Taken together, these results demonstrated that our LncRNA2Function can successfully recall the known functions of a well-studied lncRNA HOTAIR and suggested that it is applicable to infer potential functions of new identified lncRNAs. Table 1. The top 20 biological processes assigned to the development-regulating HOTAIR by LncRNA2Function. GO term Background frequency Sample frequency P-value Corrected P-value System development 3253/20447 38/74 1.63E-12 2.37E-08 Anatomical structure morphogenesis 1884/20447 28/74 2.45E-11 7.10E-08 Tissue development 1183/20447 23/74 1.33E-11 7.10E-08 Embryonic skeletal system development 120/20447 10/74 1.73E-11 7.10E-08 Anatomical structure development 3717/20447 39/74 2.07E-11 7.10E-08 Skeletal system development 388/20447 14/74 1.01E-10 2.44E-07 Organ morphogenesis 790/20447 18/74 2.89E-10 4.73E-07 Multicellular organismal development 3830/20447 38/74 2.56E-10 4.73E-07 Developmental process 4248/20447 40/74 2.93E-10 4.73E-07 Organ development 2271/20447 29/74 3.63E-10 5.27E-07 Skeletal system morphogenesis 189/20447 10/74 1.54E-09 1.72E-06 Multicellular organismal process 5336/20447 44/74 1.39E-09 1.72E-06 Single-multicellular organism process 5125/20447 43/74 1.51E-09 1.72E-06 Extracellular matrix organization 204/20447 10/74 3.23E-09 3.28E-06 Extracellular structure organization 205/20447 10/74 3.39E-09 3.28E-06 Head development 52/20447 6/74 3.26E-08 2.96E-05 Embryonic skeletal system morphogenesis 91/20447 7/74 3.86E-08 3.30E-05 Single-organism developmental process 3161/20447 31/74 4.39E-08 3.54E-05 Chordate embryonic development 557/20447 13/74 9.05E-08 6.57E-05 Embryo development ending in birth or egg hatching 564/20447 13/74 1.05E-07 7.23E-05 [76]Open in a new tab Table 2. The metastasis-associated HOTAIR was annotated with metastasis-related GO and pathway terms by LncRNA2Function. Database Functional term Background frequency Sample frequency P-value Corrected P-value GO Locomotion 1022/20447 14/74 1.53E-05 0.003417 GO Cell migration 603/20447 10/74 6.07E-05 0.010887 GO Cell adhesion 790/20447 11/74 1.21E-04 0.020655 GO Biological adhesion 792/20447 11/74 1.24E-04 0.020663 GO Cell motility 664/20447 10/74 1.34E-04 0.021695 GO Positive regulation of cell-cell adhesion 33/20447 3/74 2.30E-04 0.032376 PID Beta1 integrin cell surface interactions 75/20447 7/74 9.92E-09 2.22E-06 Reactome Extracellular matrix organization 102/20447 7/74 8.54E-08 9.57E-06 KEGG ECM-receptor interaction 110/20447 7/74 1.44E-07 1.07E-05 INOH Integrin 141/20447 7/74 7.82E-07 4.01E-05 Wikipathways Focal Adhesion 203/20447 7/74 8.79E-06 2.46E-04 KEGG Focal adhesion 219/20447 7/74 1.44E-05 3.58E-04 PID Beta3 integrin cell surface interactions 47/20447 4/74 2.51E-05 5.61E-04 PID Syndecan-1-mediated signaling events 50/20447 4/74 3.21E-05 6.53E-04 PID Integrin cell surface interactions 58/20447 4/74 5.78E-05 0.001079 PID Integrins in angiogenesis 73/20447 4/74 1.42E-04 0.002454 Reactome Integrin cell surface interactions 88/20447 4/74 2.93E-04 0.004373 KEGG PI3K-Akt signaling pathway 361/20447 7/74 3.29E-04 0.004606 Reactome Cell surface interactions at the vascular wall 104/20447 4/74 5.53E-04 0.006566 Reactome Signaling by PDGF 187/20447 5/74 5.86E-04 0.006566 Reactome NCAM1 interactions 45/20447 3/74 5.79E-04 0.006566 Reactome NCAM signaling for neurite out-growth 72/20447 3/74 0.002268 0.023101 Reactome Platelet Adhesion to exposed collagen 22/20447 2/74 0.002848 0.025533 PID VEGFR3 signaling in lymphatic endothelium 25/20447 2/74 0.003673 0.031645 Reactome Basigin interactions 26/20447 2/74 0.003969 0.032935 KEGG TGF-beta signaling pathway 92/20447 3/74 0.004537 0.036298 PID Wnt signaling network 29/20447 2/74 0.004924 0.038039 Reactome Degradation of the extracellular matrix 32/20447 2/74 0.005974 0.043169 Reactome Activation of Matrix Metalloproteinases 32/20447 2/74 0.005974 0.043169 PID Alpha4 beta1 integrin signaling events 34/20447 2/74 0.006725 0.046835 [77]Open in a new tab Case study 2: HCP5 The lncRNA HCP5 was found to be associated with AIDS [[78]29-[79]31]. Rodriguez-Novoa et al. analyzed a total of 245 HIV patients and found a good correlation between HLA-B*5701 and HCP5 (negative and positive predictive values of 100% and 93%, respectively). Colombo et al. analyzed that 1,103 singles infected with human immunodeficiency virus (HIV) and concluded that HCP5 genotyping could serve as a simple screening tool for ABC-HSR, particularly in settings where sequence-based HLA typing is not available. To assess whether the HCP5 can be correctly predicted to have immune-related functions, we applied our LncRNA2Function to it and found that HCP5 was annotated with 549 GO biological processes terms and 270 biological pathways. As expected, most of them are indeed immune system and response functional terms, which are strongly associated with the development of AIDS. The top 20 GO biological terms assigned to the HCP5 are shown in Table [80]3 while the top 20 biological pathways assigned to the HCP5 are shown in Table [81]4. Table 3. The top 20 biological processes assigned to the AIDS-related lncRNA HCP5 by LncRNA2Function. GO term Background frequency Sample frequency P-value Corrected P-value Immune system process 1581/20447 208/458 1.0E-109 3.49E-105 Immune response 867/20447 148/458 1.57E-90 2.70E-86 Defense response 968/20447 144/458 2.37E-79 2.72E-75 Regulation of immune system process 879/20447 131/458 1.16E-71 9.95E-68 Regulation of immune response 527/20447 105/458 9.62E-70 6.62E-66 Response to stimulus 6195/20447 312/458 9.35E-64 5.36E-60 Cell activation 557/20447 89/458 3.11E-50 1.53E-46 Leukocyte activation 344/20447 73/458 5.00E-50 2.15E-46 Regulation of response to stimulus 2379/20447 173/458 1.23E-48 4.72E-45 Positive regulation of immune system process 522/20447 84/458 1.39E-47 4.77E-44 Response to stress 2747/20447 181/458 4.76E-45 1.26E-41 Signal transduction 3612/20447 205/458 4.25E-42 1.04E-38 Positive regulation of immune response 331/20447 63/458 4.17E-40 9.56E-37 Cellular response to stimulus 4596/20447 231/458 5.11E-40 1.10E-36 Lymphocyte activation 276/20447 58/458 1.81E-39 3.66E-36 Innate immune response 474/20447 72/458 6.13E-39 1.17E-35 Positive regulation of response to stimulus 1154/20447 106/458 6.43E-37 1.16E-33 T cell activation 176/20447 46/458 5.40E-36 9.28E-33 Single organism signaling 4081/20447 208/458 1.23E-35 1.92E-32 Immune response-regulating signaling pathway 218/20447 49/458 6.76E-35 1.01E-31 [82]Open in a new tab Table 4. The top 20 pathways assigned to AIDS-related lncRNA HCP5 by our LncRNA2Function. Pahtway database Pahtway name Background frequency Sample frequency P-value Corrected P-value Reactome Immune System 1177/20447 98/458 1.66E-30 2.14E-27 KEGG Natural killer cell mediated cytotoxicity 219/20447 38/458 6.00E-23 3.87E-20 PID Immunoregulatory interactions between a Lymphoid and a non-Lymphoid cell 104/20447 28/458 1.06E-22 4.54E-20 Reactome Immunoregulatory interactions between a Lymphoid and a non-Lymphoid cell 202/20447 35/458 3.36E-21 1.08E-18 Reactome Adaptive Immune System 772/20447 66/458 4.68E-21 1.21E-18 NetPath TCR 252/20447 36/458 6.84E-19 1.47E-16 KEGG Chemokine signaling pathway 195/20447 30/458 7.56E-17 1.39E-14 PID Generation of second messenger molecules 15/20447 11/458 7.96E-16 1.28E-13 KEGG Osteoclast differentiation 174/20447 27/458 2.20E-15 3.16E-13 PID TCR signaling in naive CD4+ T cells 80/20447 19/458 1.03E-14 1.32E-12 PID TCR signaling in naive CD8+ T cells 63/20447 17/458 2.63E-14 3.09E-12 PID IL12-mediated signaling events 81/20447 18/458 1.80E-13 1.94E-11 KEGG Cytokine-cytokine receptor interaction 291/20447 31/458 6.97E-13 6.42E-11 Reactome Innate Immune System 542/20447 43/458 6.96E-13 6.42E-11 KEGG Hematopoietic cell lineage 114/20447 20/458 9.26E-13 7.97E-11 KEGG T cell receptor signaling pathway 116/20447 20/458 1.30E-12 1.05E-10 Reactome Cell surface interactions at the vascular wall 104/20447 19/458 1.62E-12 1.23E-10 PID Cell surface interactions at the vascular wall 42/20447 13/458 4.26E-12 3.06E-10 Reactome Class A/1 (Rhodopsin-like receptors) 319/20447 31/458 7.85E-12 5.33E-10 PID Fc-epsilon receptor I signaling in mast cells 64/20447 15/458 8.34E-12 5.38E-10 [83]Open in a new tab Case study 3: HULC The lncRNA HULC is highly upregulated in liver cancer and plays an important role in tumorigenesis [[84]32]. Depletion of HULC resulted in a significant deregulation of several genes involved in liver cancer [[85]33], and colorectal carcinomas that metastasize to the livers but not to lymph nodes experience an up-regulation of HULC in all the samples tested (n = 8), with a strong-to-moderate expression in six out of eight [[86]34]. To examine whether the HULC was predicted to have liver-related functions, we analyzed it using our lncRNA2Function. Expectedly, LncRNA2Function also works well to functionally annotate the HULC. The results showed that it was annotated with 373 GO biological processes and 383 biological pathways (the significant P-value cutoff is 0.05). Of the 373 GO biological processes and 383 pathways, over 80% are involved in the known liver-related biological functions, such as metabolic function, bile secretion, lipid transport and homeostasis, cholesterol homeostasis, regulation of blood coagulation, protein-lipid complex subunit organization, detoxification, Immune defense and complement activation. The Figure [87]2 shows the top 25 GO functional terms assigned to the HULC, and the Table [88]5 shows the top 20 pathways enriched in protein-coding genes that are co-expressed with the liver-related lncRNA HULC. Figure 2. Figure 2 [89]Open in a new tab The top 25 statistically significant enriched GO biological processes assigned to liver-related HULC are associated with the metabolic function of liver. Table 5. Top 20 pathways enriched in protein-coding genes that are co-expressed with the liver-related lncRNA HULC. Pahtway database Pahtway name Background frequency Sample frequency P-value Corrected P-value Reactome Metabolism 1394/20447 128/390 6.17E-54 6.23E-51 KEGG Metabolic pathways 1256/20447 109/390 7.95E-43 4.01E-40 KEGG Retinol metabolism 69/20447 29/390 5.66E-32 1.91E-29 KEGG Complement and coagulation cascades 87/20447 30/390 6.18E-30 1.56E-27 EHMN Androgen and estrogen biosynthesis and metabolism 90/20447 30/390 2.02E-29 4.07E-27 Reactome Biological oxidations 151/20447 36/390 2.71E-29 4.55E-27 KEGG Drug metabolism - cytochrome P450 76/20447 28/390 5.84E-29 8.41E-27 KEGG Metabolism of xenobiotics by cytochrome P450 87/20447 29/390 1.78E-28 2.24E-26 KEGG Chemical carcinogenesis 86/20447 28/390 3.33E-27 3.74E-25 EHMN Tyrosine metabolism 113/20447 30/390 4.29E-26 4.33E-24 EHMN Xenobiotics metabolism 54/20447 23/390 9.33E-26 8.56E-24 Reactome Metabolism of amino acids and derivatives 159/20447 32/390 1.09E-23 9.20E-22 Wikipathways metapathway biotransformation 190/20447 34/390 2.43E-23 1.88E-21 EHMN Linoleate metabolism 75/20447 24/390 2.81E-23 2.03E-21 Wikipathways cytochrome P450 68/20447 23/390 5.40E-23 3.63E-21 Wikipathways Complement and Coagulation Cascades 60/20447 22/390 5.97E-23 3.77E-21 EHMN Leukotriene metabolism 102/20447 26/390 2.87E-22 1.70E-20 Reactome Phase 1 - Functionalization of compounds 74/20447 23/390 5.01E-22 2.81E-20 KEGG Drug metabolism - other enzymes 53/20447 20/390 2.85E-21 1.51E-19 Reactome Xenobiotics 15/20447 13/390 3.68E-21 1.86E-19 [90]Open in a new tab Case study 4: H19 H19 is an important lncRNA that play roles in the infertility [[91]35] and multiple cancers such as breast cancer [[92]36,[93]37], cervical cancer [[94]38], liver cancer [[95]39,[96]40] and bladder cancer [[97]41]. For example, Korucuoglu et al. revealed that H19 expression was lower in the infertility group as compared to the control group (4-fold change, P < 0.0001), and Lottin et al. showed that over-expression of H19 transcript is associated with cells exhibiting higher tumorigenic phenotypes and promotes tumor progression. We applied the LncRNA2Function to the lncRNA H19 and found that it was annotated with 6 GO biological processes and 31 biological pathways. The GO terms includes female pregnancy (GO: 0007565), estrogen biosynthetic process (GO:0006703), growth hormone receptor signaling pathway (GO:0060396), cellular response to growth hormone stimulus (GO:0071378) and JAK-STAT cascade involved in growth hormone signaling pathway (GO:0060397), which suggest that H19 may play roles in infertility or breast cancer by participating in these biological processes. In addition, the cancer-related lncRNA H19 was correctly annotated with many important caner pathways, such as PI3K-Akt signaling pathway, GPCR signaling-G alpha s Epac and ERK pathway, Nuclear signaling by ERBB4 pathway, Akt signaling pathway and JAK-STAT-Core cancer pathway. These results suggest that our LncRNA2Function correctly recall the known functions of H19. Case study 5: PCA3 The lncRNA prostate cancer antigen 3 (PCA3) is a highly specific biomarker upregulated and plays crucial roles in prostate cancer (PCa) [[98]42-[99]45]. Clarke et al. found that up-regulation of two new PCA3 isoforms in PCa tissues improves discrimination between PCa and benign prostatic hyperplasia (BPH). In 2012, the US Food and Drug Administration approved the use of the lncRNA PCA3 for the detection of prostate cancer. To test whether our LncRNA2Function can annotate the PCA3 with prostate-related functions, we applied the LncRNA2Function to the PCA3. LncRNA2Function first identified 77 protein-coding genes that are co-expressed with the PCA3 and then annotated it with only one pathway named 'Regulation of Androgen receptor activity' (corrected P-value: 0.020385). This pathway has 62 genes, which includes 4 protein-coding genes that are co-expressed with the PCA3. These four genes are HOXB13, KLK3, KLK2 and SPDEF that have been validated to be useful in the diagnosis and monitoring of prostatic carcinoma and be suitable target for developing specific cancer therapies. Consequently, lncRNA2Function can correctly predict the functions of PCA3 by its co-expressed protein-coding genes. Functional annotation for a set of human lncRNAs High-throughput genomic technologies like lncRNA microarray and RNA-Seq usually generate hundreds of candidate lncRNA genes of interest, such as a cluster of co-expressed lncRNA genes across multiple conditions or a set of differentially expressed lncRNAs between cancer and normal samples. To manually map each lncRNA to functional terms is by far a simple task. Therefore, how to identify significantly enriched functions among the set of lncRNAs is an important downstream task for interpreting high-throughput experimental data. As a proof-of-concept, a set of liver-specific lncRNAs and a set of heart-specific lncRNAs inferred from RNA-Seq data of 19 human normal tissues were used as examples to show the functionality of our lncRNA2Function system in annotating a set of lncRNAs of interest, respectively. As expected, lncRNA2Function correctly assigned the functional terms to the two distinct sets of lncRNAs. Users can test these two sets or their own lncRNA sets at our 'LncRNA set analyzer' web interface [100]http://mlg.hit.edu.cn/lncrna2function/lncrna_enrich.jsp. Web interface of LncRNA2Function To facilitate researchers to access the functional annotations of lncRNA genes, we developed a web interface named 'LncRNA annotation browser', which is a user-friendly interface to browse or search lncRNAs associated with a specific functional term, or functional terms associated with a given lncRNA. To enable researchers to analyze a set of lncRNA genes of their interest, we implemented a web interface titled 'LncRNA set analyzer', which can help investigators to annotate a set of lncRNAs with Gene Ontology and 4,380 biological pathways curated from 12 pathway databases. In addition, we developed a web interface titled 'LncRNA expression viewer' to facilitate investigators to graphically view the expression dynamics of genes across multiple human normal tissues. Users can not only view expression value of a single lncRNA or protein-coding gene across 19 human normal tissues, but also simultaneously view the expression index of both lncRNA and protein-coding genes to learn about whether they are co-expressed across the 19 tissues. Furthermore, we provide a submission page that allows other researchers to submit known functional annotations of lncRNAs that are not documented in our LncRNA2Function system (Figure [101]3). They do not have to be an author on the original study to submit a record. Once approved by the submission review committee, the submitted records will be made available to the public in the coming release. LncRNA2Function is freely accessible at [102]http://mlg.hit.edu.cn/lncrna2function. Figure 3. Figure 3 [103]Open in a new tab Screenshot of web interface of LncRNA2Function. Conclusions Thousands of human lncRNAs have been identified in recent several years, while the vast majority of the lncRNAs remain to be functionally characterized. In this study, we functionally annotate 9,625 human lncRNAs with the enriched functions among the protein-coding genes that are co-expressed with each lncRNA. Furthermore, we developed a web interface, which facilitates researchers to search the functions of a specific lncRNA or the lncRNAs associated with a given functional term, or annotate functionally a set of human lncRNAs of interest. The lncRNA2Function will become an important tool for investigating functions of human lncRNAs. Competing interests The authors declare that they have no competing interests Authors' contributions YW and YL conceived and designed the experiments. QJ, RM, XW, SJ, TZ, RT and JP performed the experiments and analyzed the data. QJ and JW designed and developed the web interface. QJ, YW and YL wrote the paper. Contributor Information Qinghua Jiang, Email: qhjiang@hit.edu.cn. Rui Ma, Email: ruima@hit.edu.cn. Jixuan Wang, Email: coderChase@gmail.com. Xiaoliang Wu, Email: wuxiaoliang@hit.edu.cn. Shuilin Jin, Email: jinsl@hit.edu.cn. Jiajie Peng, Email: jiajiepeng@hit.edu.cn. Renjie Tan, Email: 11b903027@hit.edu.cn. Tianjiao Zhang, Email: ztj.hit@gmail.com. Yu Li, Email: liyugene@hit.edu.cn. Yadong Wang, Email: ydwang@hit.edu.cn. Acknowledgements