Super-enhancers are important for controlling and defining the expression of cell-specific genes. With research on human disease and biological processes, human H3K27ac ChIP-seq datasets are accumulating rapidly, creating the urgent need to collect and process these data comprehensively and efficiently. More importantly, many studies showed that super-enhancer-associated single nucleotide polymorphisms (SNPs) and transcription factors (TFs) strongly influence human disease and biological processes. Here, we developed a comprehensive human super-enhancer database (SEdb, http://www.licpathway.net/sedb) that aimed to provide a large number of available resources on human super-enhancers. The database was annotated with potential functions of super-enhancers in the gene regulation. The current version of SEdb documented a total of 331 601 super-enhancers from 542 samples. Especially, unlike existing super-enhancer databases, we manually curated and classified 410 available H3K27ac samples from >2000 ChIP-seq samples from NCBI GEO/SRA. Furthermore, SEdb provides detailed genetic and epigenetic annotation information on super-enhancers. Information includes common SNPs, motif changes, expression quantitative trait locus (eQTL), risk SNPs, transcription factor binding sites (TFBSs), CRISPR/Cas9 target sites and Dnase I hypersensitivity sites (DHSs) for in-depth analyses of super-enhancers. SEdb will help elucidate super-enhancer-related functions and find potential biological effects.

Long non-coding RNAs (lncRNAs) have been proven to play important roles in transcriptional processes and various biological functions. Establishing a comprehensive collection of human lncRNA sets is urgent work at present. Using reference lncRNA sets, enrichment analyses will be useful for analyzing lncRNA lists of interest submitted by users. Therefore, we developed a human lncRNA sets database, called LncSEA, which aimed to document a large number of available resources for human lncRNA sets and provide annotation and enrichment analyses for lncRNAs. LncSEA supports >40 000 lncRNA reference sets across 18 categories and 66 sub-categories, and covers over 50 000 lncRNAs. We not only collected lncRNA sets based on downstream regulatory data sources, but also identified a large number of lncRNA sets regulated by upstream transcription factors (TFs) and DNA regulatory elements by integrating TF ChIP-seq, DNase-seq, ATAC-seq and H3K27ac ChIP-seq data. Importantly, LncSEA provides annotation and enrichment analyses of lncRNA sets associated with upstream regulators and downstream targets. In summary, LncSEA is a powerful platform that provides a variety of types of lncRNA sets for users, and supports lncRNA annotations and enrichment analyses. The LncSEA database is freely accessible at http://bio.liclab.net/LncSEA/index.php.

Transcription factors (TFs) and their target genes have important functions in human diseases and biological processes. Gene expression profile analysis before and after knockdown or knockout is one of the most important strategies for obtaining target genes of TFs and exploring TF functions. Human gene expression profile datasets with TF knockdown and knockout are accumulating rapidly. Based on the urgent need to comprehensively and effectively collect and process these data, we developed KnockTF (http://www.licpathway.net/KnockTF/index.html), a comprehensive human gene expression profile database of TF knockdown and knockout. KnockTF provides a number of resources for human gene expression profile datasets associated with TF knockdown and knockout and annotates TFs and their target genes in a tissue/cell type-specific manner. The current version of KnockTF has 570 manually curated RNA-seq and microarray datasets associated with 308 TFs disrupted by different knockdown and knockout techniques and across multiple tissue/cell types. KnockTF collects upstream pathway information of TFs and functional annotation results of downstream target genes. It provides details about TFs binding to promoters, super-enhancers and typical enhancers of target genes. KnockTF constructs a TF-differentially expressed gene network and performs network analyses for genes of interest. KnockTF will help elucidate TF-related functions and potential biological effects.

Super-enhancers (SEs) have prominent roles in biological and pathological processes through their unique transcriptional regulatory capability. To date, several SE databases have been developed by us and others. However, these existing databases do not provide downstream or upstream regulatory analyses of SEs. Pathways, transcription factors (TFs), SEs, and SE-associated genes form complex regulatory networks. Therefore, we designed a novel web server, SEanalysis, which provides comprehensive SE-associated regulatory network analyses. SEanalysis characterizes SE-associated genes, TFs binding to target SEs, and their upstream pathways. The current version of SEanalysis contains more than 330 000 SEs from more than 540 types of cells/tissues, 5042 TF ChIP-seq data generated from these cells/tissues, DNA-binding sequence motifs for ∼700 human TFs and 2880 pathways from 10 databases. SEanalysis supports searching by either SEs, samples, TFs, pathways or genes. The complex regulatory networks formed by these factors can be interactively visualized. In addition, we developed a customizable genome browser containing >6000 customizable tracks for visualization. The server is freely available at http://licpathway.net/SEanalysis.

Long noncoding RNAs (lncRNAs) have important regulatory roles in cancer biology. Although some lncRNAs have well‐characterized functions, the vast majority of this class of molecules remains functionally uncharacterized. To systematically pinpoint functional lncRNAs, a computational approach was proposed for identification of lncRNA‐mediated competing endogenous RNAs (ceRNAs) through combining global and local regulatory direction consistency of expression. Using esophageal squamous cell carcinoma (ESCC) as model, we further identified many known and novel functional lncRNAs acting as ceRNAs (ce‐lncRNAs). We found that most of them significantly regulated the expression of cancer‐related hallmark genes. These ce‐lncRNAs were significantly regulated by enhancers, especially super‐enhancers (SEs). Landscape analyses for lncRNAs further identified SE‐associated functional ce‐lncRNAs in ESCC, such as HOTAIR, XIST, SNHG5, and LINC00094. THZ1, a specific CDK7 inhibitor, can result in global transcriptional downregulation of SE‐associated ce‐lncRNAs. We further demonstrate that a SE‐associated ce‐lncRNA, LINC00094 can be activated by transcription factors TCF3 and KLF5 through binding to SE regions and promoted ESCC cancer cell growth. THZ1 downregulated expression of LINC00094 through inhibiting TCF3 and KLF5. Our data demonstrated the important roles of SE‐associated ce‐lncRNAs in ESCC oncogenesis and might serve as targets for ESCC diagnosis and therapy.