ENdb: a manually curated database of experimentally supported enhancers for human and mouse

TFTG: A comprehensive database for human transcription factors and their targets

Transcription factors (TFs) are major contributors to gene transcription, especially in controlling cell-specific gene expression and disease occurrence and development. Uncovering the relationship between TFs and their target genes is critical to understanding the mechanism of action of TFs. With the development of high-throughput sequencing techniques, a large amount of TF-related data has accumulated, which can be used to identify their target genes. In this study, we developed TFTG (Transcription Factor and Target Genes) database (http://tf.liclab.net/TFTG), which aimed to provide a large number of available human TF-target gene resources by multiple strategies, besides performing a comprehensive functional and epigenetic annotations and regulatory analyses of TFs. We identified extensive available TF-target genes by collecting and processing TF-associated ChIP-seq datasets, perturbation RNA-seq datasets and motifs. We also obtained experimentally confirmed relationships between TF and target genes from available resources. Overall, the target genes of TFs were obtained through integrating the relevant data of various TFs as well as fourteen identification strategies. Meanwhile, TFTG was embedded with user-friendly search, analysis, browsing, downloading and visualization functions. TFTG is designed to be a convenient resource for exploring human TF-target gene regulations, which will be useful for most users in the TF and gene expression regulation research.

VCAT: an integrated variant function annotation tools

The development of sequencing technology has promoted discovery of variants in the human genome. Identifying functions of these variants is important for us to link genotype to phenotype, and to diagnose diseases. However, it usually requires researchers to visit multiple databases. Here, we presented a one-stop webserver for variant function annotation tools (VCAT, https://biomed.nscc-gz.cn/zhaolab/VCAT/ ) that is the first one connecting variant to functions via the epigenome, protein, drug and RNA. VCAT is also the first one to make all annotations visualized in interactive charts or molecular structures. VCAT allows users to upload data in VCF format, and download results via a URL. Moreover, VCAT has annotated a huge number (1,262,041,068) of variants collected from dbSNP, 1000 Genomes projects, gnomAD, ICGC, TCGA, and HPRC Pangenome project. For these variants, users are able to searcher their functions, related diseases and drugs from VCAT. In summary, VCAT provides a one-stop webserver to explore the potential functions of human genomic variants including their relationship with diseases and drugs.

CREdb: A comprehensive database of Cis-Regulatory Elements and their activity in human cells and tissues

BACKGROUND

Cis-regulatory elements (CREs) play a pivotal role in gene expression regulation, allowing cells to serve diverse functions and respond to external stimuli. Understanding CREs is essential for personalized medicine and disease research, as an increasing number of genetic variants associated with phenotypes and diseases overlap with CREs. However, existing databases often focus on subsets of regulatory elements and present each identified instance of element individually, confounding the effort to obtain a comprehensive view. To address this gap, we have created CREdb, a comprehensive database with over 10 million human regulatory elements across 1,058 cell types and 315 tissues harmonized from different data sources. We curated and aligned the cell types and tissues to standard ontologies for efficient data query.

RESULTS

Data from 11 sources were curated and mapped to standard ontological terms. 11,223,434 combined elements are present in the final database, and these were merged into 5,666,240 consensus elements representing the combined ranges of the individual elements informed by their overlap. Each consensus element contains curated metadata including the number of elements supporting it and a hash linking to the source databases. The inferred activity of each consensus element in various cell-type and tissue context is also provided. Examples presented here show the potential utility of CREdb in annotating non-coding genetic variants and informing chromatin accessibility profiling analysis.

CONCLUSIONS

We developed CREdb, a comprehensive database of CREs, to simplify the analysis of CREs by providing a unified framework for researchers. CREdb compiles consensus ranges for each element by integrating the information from all instances identified across various source databases. This unified database facilitates the functional annotation of non-coding genetic variants and complements chromatin accessibility profiling analysis. CREdb will serve as an important resource in expanding our knowledge of the epigenome and its role in human diseases.

Identifying Reproducible Transcription Regulator Coexpression Patterns with Single Cell Transcriptomics

The proliferation of single cell transcriptomics has potentiated our ability to unveil patterns that reflect dynamic cellular processes, rather than cell type compositional effects that emerge from bulk tissue samples. In this study, we leverage a broad collection of single cell RNA-seq data to identify the gene partners whose expression is most coordinated with each human and mouse transcription regulator (TR). We assembled 120 human and 103 mouse scRNA-seq datasets from the literature (>28 million cells), constructing a single cell coexpression network for each. We aimed to understand the consistency of TR coexpression profiles across a broad sampling of biological contexts, rather than examine the preservation of context-specific signals. Our workflow therefore explicitly prioritizes the patterns that are most reproducible across cell types. Towards this goal, we characterize the similarity of each TR's coexpression within and across species. We create single cell coexpression rankings for each TR, demonstrating that this aggregated information recovers literature curated targets on par with ChIP-seq data. We then combine the coexpression and ChIP-seq information to identify candidate regulatory interactions supported across methods and species. Finally, we highlight interactions for the important neural TR ASCL1 to demonstrate how our compiled information can be adopted for community use.

Molecular mechanisms reconstruction from single-cell multi-omics data with HuMMuS

MOTIVATION

The molecular identity of a cell results from a complex interplay between heterogeneous molecular layers. Recent advances in single-cell sequencing technologies have opened the possibility to measure such molecular layers of regulation.

RESULTS

Here, we present HuMMuS, a new method for inferring regulatory mechanisms from single-cell multi-omics data. Differently from the state-of-the-art, HuMMuS captures cooperation between biological macromolecules and can easily include additional layers of molecular regulation. We benchmarked HuMMuS with respect to the state-of-the-art on both paired and unpaired multi-omics datasets. Our results proved the improvements provided by HuMMuS in terms of transcription factor (TF) targets, TF binding motifs and regulatory regions prediction. Finally, once applied to snmC-seq, scATAC-seq and scRNA-seq data from mouse brain cortex, HuMMuS enabled to accurately cluster scRNA profiles and to identify potential driver TFs.

AVAILABILITY AND IMPLEMENTATION

HuMMuS is available at https://github.com/cantinilab/HuMMuS.

Enhancer-MDLF: a novel deep learning framework for identifying cell-specific enhancers

Enhancers, noncoding DNA fragments, play a pivotal role in gene regulation, facilitating gene transcription. Identifying enhancers is crucial for understanding genomic regulatory mechanisms, pinpointing key elements and investigating networks governing gene expression and disease-related mechanisms. Existing enhancer identification methods exhibit limitations, prompting the development of our novel multi-input deep learning framework, termed Enhancer-MDLF. Experimental results illustrate that Enhancer-MDLF outperforms the previous method, Enhancer-IF, across eight distinct human cell lines and exhibits superior performance on generic enhancer datasets and enhancer-promoter datasets, affirming the robustness of Enhancer-MDLF. Additionally, we introduce transfer learning to provide an effective and potential solution to address the prediction challenges posed by enhancer specificity. Furthermore, we utilize model interpretation to identify transcription factor binding site motifs that may be associated with enhancer regions, with important implications for facilitating the study of enhancer regulatory mechanisms. The source code is openly accessible at https://github.com/HaoWuLab-Bioinformatics/Enhancer-MDLF.

A body map of super-enhancers and their function in pig

Introduction

Super-enhancers (SEs) are clusters of enhancers that act synergistically to drive the high-level expression of genes involved in cell identity and function. Although SEs have been extensively investigated in humans and mice, they have not been well characterized in pigs.

Methods

Here, we identified 42,380 SEs in 14 pig tissues using chromatin immunoprecipitation sequencing, and statistics of its overall situation, studied the composition and characteristics of SE, and explored the influence of SEs characteristics on gene expression.

Results

We observed that approximately 40% of normal enhancers (NEs) form SEs. Compared to NEs, we found that SEs were more likely to be enriched with an activated enhancer and show activated functions. Interestingly, SEs showed X chromosome depletion and short interspersed nuclear element enrichment, implying that SEs play an important role in sex traits and repeat evolution. Additionally, SE-associated genes exhibited higher expression levels and stronger conservation than NE-associated genes. However, genes with the largest SEs had higher expression levels than those with the smallest SEs, indicating that SE size may influence gene expression. Moreover, we observed a negative correlation between SE gene distance and gene expression, indicating that the proximity of SEs can affect gene activity. Gene ontology enrichment and motif analysis revealed that SEs have strong tissue-specific activity. For example, the CORO2B gene with a brain-specific SE shows strong brain-specific expression, and the phenylalanine hydroxylase gene with liver-specific SEs shows strong liver-specific expression.

Discussion

In this study, we illustrated a body map of SEs and explored their functions in pigs, providing information on the composition and tissue-specific patterns of SEs. This study can serve as a valuable resource of gene regulatory and comparative analyses to the scientific community and provides a theoretical reference for genetic control mechanisms of important traits in pigs.

Cis-Cardio: A comprehensive analysis platform for cardiovascular-relavant cis-regulation in human and mouse

Cis-regulatory elements are important molecular switches in controlling gene expression and are regarded as determinant hubs in the transcriptional regulatory network. Collection and processing of large-scale cis-regulatory data are urgent to decipher the potential mechanisms of cardiovascular diseases from a cis-regulatory element aspect. Here, we developed a novel web server, Cis-Cardio, which aims to document a large number of available cardiovascular-related cis-regulatory data and to provide analysis for unveiling the comprehensive mechanisms at a cis-regulation level. The current version of Cis-Cardio catalogs a total of 45,382,361 genomic regions from 1,013 human and mouse epigenetic datasets, including ATAC-seq, DNase-seq, Histone ChIP-seq, TF/TcoF ChIP-seq, RNA polymerase ChIP-seq, and Cohesin ChIP-seq. Importantly, Cis-Cardio provides six analysis tools, including region overlap analysis, element upstream/downstream analysis, transcription regulator enrichment analysis, variant interpretation, and protein-protein interaction-based co-regulatory analysis. Additionally, Cis-Cardio provides detailed and abundant (epi-) genetic annotations in cis-regulatory regions, such as super-enhancers, enhancers, transcription factor binding sites (TFBSs), methylation sites, common SNPs, risk SNPs, expression quantitative trait loci (eQTLs), motifs, DNase I hypersensitive sites (DHSs), and 3D chromatin interactions. In summary, Cis-Cardio is a valuable resource for elucidating and analyzing regulatory cues of cardiovascular-specific cis-regulatory elements. The platform is freely available at http://www.licpathway.net/Cis-Cardio/index.html.

TF-Prioritizer: a Java pipeline to prioritize condition-specific transcription factors

BACKGROUND

Eukaryotic gene expression is controlled by cis-regulatory elements (CREs), including promoters and enhancers, which are bound by transcription factors (TFs). Differential expression of TFs and their binding affinity at putative CREs determine tissue- and developmental-specific transcriptional activity. Consolidating genomic datasets can offer further insights into the accessibility of CREs, TF activity, and, thus, gene regulation. However, the integration and analysis of multimodal datasets are hampered by considerable technical challenges. While methods for highlighting differential TF activity from combined chromatin state data (e.g., chromatin immunoprecipitation [ChIP], ATAC, or DNase sequencing) and RNA sequencing data exist, they do not offer convenient usability, have limited support for large-scale data processing, and provide only minimal functionality for visually interpreting results.

RESULTS

We developed TF-Prioritizer, an automated pipeline that prioritizes condition-specific TFs from multimodal data and generates an interactive web report. We demonstrated its potential by identifying known TFs along with their target genes, as well as previously unreported TFs active in lactating mouse mammary glands. Additionally, we studied a variety of ENCODE datasets for cell lines K562 and MCF-7, including 12 histone modification ChIP sequencing as well as ATAC and DNase sequencing datasets, where we observe and discuss assay-specific differences.

CONCLUSION

TF-Prioritizer accepts ATAC, DNase, or ChIP sequencing and RNA sequencing data as input and identifies TFs with differential activity, thus offering an understanding of genome-wide gene regulation, potential pathogenesis, and therapeutic targets in biomedical research.

TF-Marker: a comprehensive manually curated database for transcription factors and related markers in specific cell and tissue types in human

Transcription factors (TFs) play key roles in biological processes and are usually used as cell markers. The emerging importance of TFs and related markers in identifying specific cell types in human diseases increases the need for a comprehensive collection of human TFs and related markers sets. Here, we developed the TF-Marker database (TF-Marker, http://bio.liclab.net/TF-Marker/), aiming to provide cell/tissue-specific TFs and related markers for human. By manually curating thousands of published literature, 5905 entries including information about TFs and related markers were classified into five types according to their functions: (i) TF: TFs which regulate expression of the markers; (ii) T Marker: markers which are regulated by the TF; (iii) I Marker: markers which influence the activity of TFs; (iv) TFMarker: TFs which play roles as markers and (v) TF Pmarker: TFs which play roles as potential markers. The 5905 entries of TF-Marker include 1316 TFs, 1092 T Markers, 473 I Markers, 1600 TFMarkers and 1424 TF Pmarkers, involving 383 cell types and 95 tissue types in human. TF-Marker further provides a user-friendly interface to browse, query and visualize the detailed information about TFs and related markers. We believe TF-Marker will become a valuable resource to understand the regulation patterns of different tissues and cells.

Epigenetic regulation of nuclear lamina-associated heterochromatin by HAT1 and the acetylation of newly synthesized histones

A central component of the epigenome is the pattern of histone post-translational modifications that play a critical role in the formation of specific chromatin states. Following DNA replication, nascent chromatin is a 1:1 mixture of parental and newly synthesized histones and the transfer of modification patterns from parental histones to new histones is a fundamental step in epigenetic inheritance. Here we report that loss of HAT1, which acetylates lysines 5 and 12 of newly synthesized histone H4 during replication-coupled chromatin assembly, results in the loss of accessibility of large domains of heterochromatin, termed HAT1-dependent Accessibility Domains (HADs). HADs are mega base-scale domains that comprise ∼10% of the mouse genome. HAT1 globally represses H3 K9 me3 levels and HADs correspond to the regions of the genome that display HAT1-dependent increases in H3 K9me3 peak density. HADs display a high degree of overlap with a subset of Lamin-Associated Domains (LADs). HAT1 is required to maintain nuclear structure and integrity. These results indicate that HAT1 and the acetylation of newly synthesized histones may be critical regulators of the epigenetic inheritance of heterochromatin and suggest a new mechanism for the epigenetic regulation of nuclear lamina-heterochromatin interactions.

Experiment level curation of transcriptional regulatory interactions in neurodevelopment

To facilitate the development of large-scale transcriptional regulatory networks (TRNs) that may enable in-silico analyses of disease mechanisms, a reliable catalogue of experimentally verified direct transcriptional regulatory interactions (DTRIs) is needed for training and validation. There has been a long history of using low-throughput experiments to validate single DTRIs. Therefore, we reason that a reliable set of DTRIs could be produced by curating the published literature for such evidence. In our survey of previous curation efforts, we identified the lack of details about the quantity and the types of experimental evidence to be a major gap, despite the theoretical importance of such details for the identification of bona fide DTRIs. We developed a curation protocol to inspect the published literature for support of DTRIs at the experiment level, focusing on genes important to the development of the mammalian nervous system. We sought to record three types of low-throughput experiments: Transcription factor (TF) perturbation, TF-DNA binding, and TF-reporter assays. Using this protocol, we examined a total of 1,310 papers to assemble a collection of 1,499 unique DTRIs, involving 251 TFs and 825 target genes, many of which were not reported in any other DTRI resource. The majority of DTRIs (965; 64%) were supported by two or more types of experimental evidence and 27% were supported by all three. Of the DTRIs with all three types of evidence, 170 had been tested using primary tissues or cells and 44 had been tested directly in the central nervous system. We used our resource to document research biases among reports towards a small number of well-studied TFs. To demonstrate a use case for this resource, we compared our curation to a previously published high-throughput perturbation screen and found significant enrichment of the curated targets among genes differentially expressed in the developing brain in response to Pax6 deletion. This study demonstrates a proof-of-concept for the assembly of a high resolution DTRI resource to support the development of large-scale TRNs.

The capacity to computationally reconstruct gene regulatory networks using large-scale biological data is currently limited by the absence of a high confidence set of one-to-one regulatory interactions. Given the lengthy history of using small scale experimental assays to investigate individual interactions, we reason that a reliable collection of gene regulatory interactions could be compiled by systematically inspecting the published literature. To this end, we developed a curation protocol to examine and record evidence of regulatory interactions at the individual experiment level. Focusing on the area of brain development, we applied our pipeline to 1,310 publications. We identified 3,601 individual experiments, providing detailed information about 1,499 regulatory interactions. Many of these interactions have verified activity specifically in the embryonic brain. By capturing reports of regulatory interactions at this level of detail, we equip the users with more granular information than other similar resources, enabling more informed assessments of reliability.

Seeing the forest through the trees: prioritising potentially functional interactions from Hi-C

Eukaryotic genomes are highly organised within the nucleus of a cell, allowing widely dispersed regulatory elements such as enhancers to interact with gene promoters through physical contacts in three-dimensional space. Recent chromosome conformation capture methodologies such as Hi-C have enabled the analysis of interacting regions of the genome providing a valuable insight into the three-dimensional organisation of the chromatin in the nucleus, including chromosome compartmentalisation and gene expression. Complicating the analysis of Hi-C data, however, is the massive amount of identified interactions, many of which do not directly drive gene function, thus hindering the identification of potentially biologically functional 3D interactions. In this review, we collate and examine the downstream analysis of Hi-C data with particular focus on methods that prioritise potentially functional interactions. We classify three groups of approaches: structural-based discovery methods, e.g. A/B compartments and topologically associated domains, detection of statistically significant chromatin interactions, and the use of epigenomic data integration to narrow down useful interaction information. Careful use of these three approaches is crucial to successfully identifying potentially functional interactions within the genome.

Public data sources for regulatory genomic features

High-throughput technologies have led to a continuously growing amount of information about regulatory features in the genome. A wealth of data generated by large international research consortia is available from online databases. Disease-driven studies provide details on specific DNA elements or epigenetic modifications regulating gene expression in specific cellular and developmental contexts, but these results are usually only published in scientific articles. All this information can be helpful in interpreting variants in the regulatory genome. This review describes a selection of high-profile data sources providing information on the non-coding genome, as well as pitfalls and techniques to search and capture information from the literature.

VARAdb: a comprehensive variation annotation database for human

With the study of human diseases and biological processes increasing, a large number of non-coding variants have been identified and facilitated. The rapid accumulation of genetic and epigenomic information has resulted in an urgent need to collect and process data to explore the regulation of non-coding variants. Here, we developed a comprehensive variation annotation database for human (VARAdb, http://www.licpathway.net/VARAdb/), which specifically considers non-coding variants. VARAdb provides annotation information for 577,283,813 variations and novel variants, prioritizes variations based on scores using nine annotation categories, and supports pathway downstream analysis. Importantly, VARAdb integrates a large amount of genetic and epigenomic data into five annotation sections, which include ‘Variation information’, ‘Regulatory information’, ‘Related genes’, ‘Chromatin accessibility’ and ‘Chromatin interaction’. The detailed annotation information consists of motif changes, risk SNPs, LD SNPs, eQTLs, clinical variant-drug-gene pairs, sequence conservation, somatic mutations, enhancers, super enhancers, promoters, transcription factors, chromatin states, histone modifications, chromatin accessibility regions and chromatin interactions. This database is a user-friendly interface to query, browse and visualize variations and related annotation information. VARAdb is a useful resource for selecting potential functional variations and interpreting their effects on human diseases and biological processes.

Integrative Epigenomic Analysis of Transcriptional Regulation of Human CircRNAs

Circular RNAs (circRNAs) are evolutionarily conserved and abundant non-coding RNAs whose functions and regulatory mechanisms remain largely unknown. Here, we identify and characterize an epigenomically distinct group of circRNAs (TAH-circRNAs), which are transcribed to a higher level than their host genes. By integrative analysis of cistromic and transcriptomic data, we find that compared with other circRNAs, TAH-circRNAs are expressed more abundantly and have more transcription factors (TFs) binding sites and lower DNA methylation levels. Concordantly, TAH-circRNAs are enriched in open and active chromatin regions. Importantly, ChIA-PET results showed that 23–52% of transcription start sites (TSSs) of TAH-circRNAs have direct interactions with cis-regulatory regions, strongly suggesting their independent transcriptional regulation from host genes. In addition, we characterize molecular features of super-enhancer-driven circRNAs in cancer biology. Together, this study comprehensively analyzes epigenomic characteristics of circRNAs and identifies a distinct group of TAH-circRNAs that are independently transcribed via enhancers and super-enhancers by TFs. These findings substantially advance our understanding of the regulatory mechanism of circRNAs and may have important implications for future investigations of this class of non-coding RNAs.

SilencerDB: a comprehensive database of silencers

Gene regulatory elements, including promoters, enhancers, silencers, etc., control transcriptional programs in a spatiotemporal manner. Though these elements are known to be able to induce either positive or negative transcriptional control, the community has been mostly studying enhancers which amplify transcription initiation, with less emphasis given to silencers which repress gene expression. To facilitate the study of silencers and the investigation of their potential roles in transcriptional control, we developed SilencerDB (http://health.tsinghua.edu.cn/silencerdb/), a comprehensive database of silencers by manually curating silencers from 2300 published articles. The current version, SilencerDB 1.0, contains (1) 33 060 validated silencers from experimental methods, and (ii) 5 045 547 predicted silencers from state-of-the-art machine learning methods. The functionality of SilencerDB includes (a) standardized categorization of silencers in a tree-structured class hierarchy based on species, organ, tissue and cell line and (b) comprehensive annotations of silencers with the nearest gene and potential regulatory genes. SilencerDB, to the best of our knowledge, is the first comprehensive database at this scale dedicated to silencers, with reliable annotations and user-friendly interactive database features. We believe this database has the potential to enable advanced understanding of silencers in regulatory mechanisms and to empower researchers to devise diverse applications of silencers in disease development.

MiRNA-Mediated Subpathway Identification and Network Module Analysis to Reveal Prognostic Markers in Human Pancreatic Cancer

Background

Pancreatic cancer (PC) remains one of the most lethal cancers. In contrast to the steady increase in survival for most cancers, the 5-year survival remains low for PC patients.

Methods

We describe a new pipeline that can be used to identify prognostic molecular biomarkers by identifying miRNA-mediated subpathways associated with PC. These modules were then further extracted from a comprehensive miRNA-gene network (CMGN). An exhaustive survival analysis was performed to estimate the prognostic value of these modules.

Results

We identified 105 miRNA-mediated subpathways associated with PC. Two subpathways within the MAPK signaling and cell cycle pathways were found to be highly related to PC. Of the miRNA-mRNA modules extracted from CMGN, six modules showed good prognostic performance in both independent validated datasets.

Conclusions

Our study provides novel insight into the mechanisms of PC. We inferred that six miRNA-mRNA modules could serve as potential prognostic molecular biomarkers in PC based on the pipeline we proposed.

Characterization of super-enhancer-associated functional lncRNAs acting as ceRNAs in ESCC

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.

TRlnc: a comprehensive database for human transcriptional regulatory information of lncRNAs

Long noncoding RNAs (lncRNAs) have been proven to play important roles in transcriptional processes and biological functions. With the increasing study of human diseases and biological processes, information in human H3K27ac ChIP-seq, ATAC-seq and DNase-seq datasets is accumulating rapidly, resulting in an urgent need to collect and process data to identify transcriptional regulatory regions of lncRNAs. We therefore developed a comprehensive database for human regulatory information of lncRNAs (TRlnc, http://bio.licpathway.net/TRlnc), which aimed to collect available resources of transcriptional regulatory regions of lncRNAs and to annotate and illustrate their potential roles in the regulation of lncRNAs in a cell type-specific manner. The current version of TRlnc contains 8 683 028 typical enhancers/super-enhancers and 32 348 244 chromatin accessibility regions associated with 91 906 human lncRNAs. These regions are identified from over 900 human H3K27ac ChIP-seq, ATAC-seq and DNase-seq samples. Furthermore, TRlnc provides the detailed genetic and epigenetic annotation information within transcriptional regulatory regions (promoter, enhancer/super-enhancer and chromatin accessibility regions) of lncRNAs, including common SNPs, risk SNPs, eQTLs, linkage disequilibrium SNPs, transcription factors, methylation sites, histone modifications and 3D chromatin interactions. It is anticipated that the use of TRlnc will help users to gain in-depth and useful insights into the transcriptional regulatory mechanisms of lncRNAs.