The home page contains tool entrance, schematic diagram and brief introduction to help users understand our database.
1. Main functions of the database are provided in menu bar form (boxed in blue).
2. The brief introduction of LncTarD 2.0.
3. A quick search for lncRNAs, cancers and functions.
4. Entrance to the single cell web tools.
5. Entrance to the RNA-seq/microarray web tools.
6. Quick browse of the regulatory mechanism of functional lncRNA-targets and single cell analysis module.
7. Overview of the images that Single Cell Web Tools can provide.
8. Overview of the images that RNA-seq Web Tools can provide.
9. New features of LncTarD 2.0 compared to LncTarD.

User can query the LncTarD 2.0 database through Quick Search and Advanced Search.
Quick Search page: users can input a lncRNA or disease name or biological function to search functional lncRNA-target regulations (step 3 in the figure of '2. Home page help'). In this page, only one term could be used, for combined search, please go to “Search” module.
Advanced Search page: advanced search provides multiple options for customized search of the interested contents. Users can query the interested disease-associated functional lncRNA-target regulations by combining different key words, including disease, function, drug, lncRNA, target gene and regulatory mechanism. LncTarD 2.0 also offers fuzzy keyword searching capabilities, which enables easy searching by returning the closest possible matching records (see figure below).
For example:
1) Input an interested disease name to query related functional lncRNA-target regulations.
2) Input an interested biological function to query related functional lncRNA-target regulations.
3) Input an interested drug. Obtaining functional lncRNA-target regulations related with drug-resistance or drug-sensitivity.
4) LncRNA-mediated regulatory mechanisms including transcriptional regulation, epigenetic regulation, chromatin looping, ceRNA or sponge, interacting with mRNA and interacting with protein could be used to obtain the results.
5) Input your interested lncRNAs or lncRNA aliases to query related regulations.
6) Input your target genes to query related regulations. Users can also input both an lncRNA and target gene to search for their regulations.

The browse page is built based on standardized classification scheme of human disease (according to Disease Ontology database) and different hierarchical classification. To browse the functional lncRNA-target regulations related a particular disease (such as Lung cancer), please click “DiseaseCategory” (step 1 in figure below), then choose “Cancer” and corresponding cancer type “Lung cancer” (step 2, 3 in figure below). The lung cancer-related lncRNA-target regulations will be returned. If we are interested a particular subtype of lung cancer (such as Lung adenocarcinoma), please click “Lung adenocarcinoma” (step 4 in figure below). The lung adenocarcinoma-related lncRNA-target regulations will show on table format on the top panel (step 5 in figure below), as well as a functional network on the bottom panel (step 6 in figure below). Similarly, the users can browse all entries associated with an interested event (Genes, Functions and Drugs) in a similar way.

Showing the functional lncRNA-target network in lung adenocarcinoma.

In addition, browser page also provides statistics information about functional lncRNA-target regulations and human diseases.

LncTarD 2.0 results are organized in a data table, with a single association record on each line that contains disease name, lncRNA name, target name, influenced biological functions and regulatory mechanism.
For example, in Advanced Search page, we input "angiogenesis" as an interested biological function. Click the "Search" button to submit. We obtained 153 functional lncRNA-target regulations which are involved in angiogenesis in human diseases (see figure below).

DiseaseName: The associated disease.
Regulator: The name of regulator gene, such as MALTA1.
Target: The name of target gene.
RegulationDirection: The regulator negatively or positively regulated the target gene.
InfluencedFunctions: The biological function positively (+) or negatively (-) affected by the lncRNA-target regulation in human disease.
RegulatoryMechanism: lncRNA-mediated regulatory mechanism in human disease.
Web Tools: Clickable links for accessing the analysis results of each lncRNA-target regulation in TCGA, GEO (if available), CTC and Single Cell datasets. For TCGA, GEO and CTC links, their differentially expression patterns, Pearson correlation coefficient and a scatter plot of lncRNA-target expression in TCGA pan-cancers, GEO datasets (if available) and CTC datasets will also be shown. As for scRNA-seq links, the cluster analysis, expression, differential expression, and expression correlation of the lncRNA-target regulation in different cell types are provided.
More Details: Clickable links for accessing the detail information of each lncRNA-target regulation. After clicking the links, the details of the functional lncRNA-target regulation will be displayed as on a new page. The results of the Web Tools are also provided on the new page.

The network visualization aims to intuitively display the lncRNA-target regulation in human disease, their influenced biological functions, the regulatory mechanisms and related drugs. This network is illustrated with ECharts plugins. Three layout presentations are available: “Radial Tree” (by default), “Horizontal Tree” (i.e. Left to Right Tree) and “Vertical Tree” (i.e. Top to Bottom Tree).
For example, the figure below using “Radial Tree” layout shows the functional lncRNA-target regulations which are involved in angiogenesis in human diseases.

Now we change the layout. The figure below using “Horizontal Tree” layout shows the functional lncRNA-target regulations which are involved in angiogenesis in human diseases.

Introduction
Detailed information of the Single Cell Web Tools contained complex functions for mining single cell datasets is displayed:
1. Main functions of the Single Cell Web Tools are provided in menu bar form (boxed in blue).
2. Detailed introduction of main functions of Single Cell Web Tools.
3. Detailed information of single cell datasets.

Pan-cancer analysis module of key lncRNA-target
1.Differential regulations page:
1).Search bar:
This function provides the landscape of the proportion of cells with high activity scores for differential lncRNA-target regulations in cells across 31 single-cell datasets (>500 cells). To analyze the differences in lncRNA-target regulation between different datasets, you should select a cancer single-cell dataset and input an interested lncRNA name first, and then click the “Plot” button to retrieve the differential lncRNA-target regulations. If no interested lncRNA is inputted, all differential lncRNA-target regulations between a given single-cell scRNA-seq and other scRNA-seq datasets are displayed.

scRNA-seq Dataset:Select a cancer single-cell dataset to obtain differential lncRNA-target regulations.
LncRNA/Gene Symbol:Select a lncRNA to obtain key lncRNA-target regulations for analysis. (optional)
2).Differential regulations in cells across single-cell datasets
The dot plot shown below presents top differential regulations for the selected dataset. To show the differential lncRNA-target regulations in different datasets, first, the CSN method was used to identify whether the lncRNA-target regulation existed in the cells. According to the results, the cells were divided into two groups for differential expression analysis. The results of differential expression are shown in the table below. The Wilcoxon rank sum test was used to identify genes whose expression was significantly associated with the regulation (|logFC|>1, FDR<0.05), and AUCell was used to determine the activity of each regulon (set of differential expression genes) as the activity of that regulation in the cell. Finally, Wilcoxon rank sum test was used to identify the significance of the difference between the selected dataset and other datasets. At the same time, to determine the 'on/off' activity of each regulon in each dataset, we used '0.5* Max (AUC scores)' for each regulon as a threshold to binarize the regulon activity scores. The size and color of each point in the dot plot below correspond to the ratio of the lncRNA regulatory relationship to the total number of cells in the 'on' state in the dataset. The order of pairs of lncRNA regulatory relations was determined based on the number and size of datasets that differed significantly from the selected ones as shown in the figure below. Finally, Wilcoxon rank sum test was used to identify differential lncRNA-target regulations by comparing the activity scores between a given single-cell dataset and other datasets.

3).Search result table:
All differential lncRNA-target regulations in the select single-cell dataset are listed in a table below the dot plot, with each lncRNA-target regulation in one row, including disease name, lncRNA name, target name, influenced biological functions and regulatory mechanism. Users can click “Web Tools” column to get lncRNA and target analysis tools. Use the search box in the upper left corner to quickly retrieve items of interest. Use the buttons in the upper right corner to reformat the table and export data.

DiseaseName: The associated disease.
Regulator: The name of regulator gene, such as an lncRNA.
Target: The name of target gene.
InfluencedFunctions: The biological function positively (+) or negatively (-) affected by the lncRNA-target regulation in human disease.
RegulatoryMechanism: lncRNA-mediated regulatory mechanism in human disease, including transcriptional regulation, epigenetic regulation, chromatin looping, ceRNA or sponge, interacting with mRNA and interacting with protein.
Web Tools: Clickable links for accessing the analysis results of each lncRNA-target regulation in TCGA, GEO (if available), CTC and Single Cell datasets. For TCGA, GEO and CTC links, their differentially expression patterns, Pearson correlation coefficient and a scatter plot of lncRNA-target expression in TCGA pan-cancers, GEO datasets (if available) and CTC datasets will also be shown. As for scRNA-seq links, the cluster analysis, expression, differential expression, and expression correlation of the lncRNA-target regulation in different cell types are provided.
More Details: Clickable links for accessing the detail information of each lncRNA-target regulation. After clicking the links, the details of the functional lncRNA-target regulation will be displayed on a new page. The results of the Web Tools are also provided on the new page.
Single cell tools of Single Cell database.

Select a cancer single-cell data set to obtain lncrNA-target-regulated single-cell analysis.
Sub-populations of cells identified in different single-cell datasets
To demonstrate the expression of the functional lncRNA-target regulation in singlecell data, tSNE and UMAP were first used to reduce dimension of single-cell data and identify cell types (as figure below shows). Cell types are distinguished by color, and the legend illustrations indicate the most specific genes and selected data sets for each cancer cell type.

Gene expression level in different cell subtypes of the functional lncRNA-target regulation based on single cell dataset was shown if available.
The figure below shows the normalized expression levels of genes in each cell.

Differential expression analysis of lncRNA-target regulation among different cell sub-populations
The boxplot shows the expression distribution of genes (as figure below shows), Statistical analyses were using wilcoxon rank sum test.

The distribution of lncRNA-target gene associations in cell sub-populations was demonstrated
In addition, to show the expression correlation of functional lncRNA-target regulations in human disease, gene association between lncRNA and key target in each of 50 single cell datasets are shown. And the bar plot shows the proportion of expression correlation in different cell types.
4).Network Result
At the bottom of the table, users can show the functional lncRNA-target network based on the table contents by clicking the "Network Display" button.

2.Differential expression page:
1).Search bar:
This function allows users to obtain differential expression information of lncRNAs and target gene in 31 single-cell datasets. Wilcoxon rank sum test was used to identify differential lncRNA and genes by comparing expression levels between a given single-cell scRNA-seq and other scRNA-seq datasets. Users first select “Pan-cancer analysis module”, followed by “Differential expression”, then select a cancer single-cell dataset and input an interested lncRNA name , after that click the “Plot” button to retrieve the landscape of differential expression of lncRNAs and target genes. If no interested lncRNA is inputted, all landscape of differential expression between a given single-cell scRNA-seq and other scRNA-seq datasets are displayed.

scRNA-seq Dataset: Select a cancer single-cell dataset for differential expression analysis.
LncRNA/Gene Symbol: Select a lncRNA for differential expression analysis. (optional)
2).Differential expression in cells across single-cell datasets
We provide heatmap of differential expressed lncRNAs among diverse cell population in 31 single-cell datasets are displayed. Users can select a cancer single-cell dataset for differential expression analysis.

3).Search result table
We provide a table with a gene record in each row, including gene name, gene type, average expression value, differential P-value, and FC value. Users can click “Plot” to see the expression level of the gene in each dataset.

4).differential expression of lncRNA-target in single cell datasets
The boxplot shows the differential expression levels of gene in 31 single-cell datasets. At the same time, we provided a table to show all lncRNA-target regulations of the selected gene. Users can click “Web Tools” to get more lncRNA and target analysis tools or “More Details” to get more detailed information. In addition, users can click "Network Display" to show the functional lncRNA-target network.

3.Functional states page:
1).Search bar:
This function provides the landscape of the proportion of cells with high activity scores (> 0.5*max (AUC scores)) for differential lncRNA-target regulations associated with a given cell state (such as stemness, epithelial-mesenchymal transition, angiogenesis and inflammation) across 31 single-cell datasets with 21 human cancers. Wilcoxon rank sum test was used to identify differential lncRNA-target regulations by comparing the activity scores between a given cell functional state and other cell functional states.
Users first select “Pan-cancer analysis module”, followed by “Functional states”. Then select a cell states, a cancer single-cell dataset and an interested lncRNA name, and then click the “Plot” button to retrieve the landscape of differential lncRNA-target regulations associated with a given cell state. If no interested lncRNA is inputted, all landscape of differential lncRNA-target regulations between a given scRNA-seq dataset and other scRNA-seq datasets are displayed.

scRNA-seq Dataset: Select a cancer single-cell dataset for differential regulation analysis.
Cell states: Select a cell functional state for analysis.
LncRNA/Gene Symbol: Select a lncRNA for drawing the point diagram. (optional)
2).Differential regulations in cell status across single-cell datasets
The bubble triangle plot shows the percentage of the number of cells with lncRNA-target regulation high activity scores between datasets for the selected cell state. (figure below) To show the differential lncRNA-target regulations in different datasets under the selected cell state, we used the same approach as 'Differential regulations', including CSN, Wilcoxon rank sum test and AUCell. The results of differential expression are shown in the table below.

3).Search result table
The table below shows the details of the searched lncRNA. We provided a table, with a single association record on each line that contains disease name, lncRNA name, target name, influenced biological functions and regulatory mechanism. Users can click “Web Tools” to get more lncRNA and target analysis tools or “More Details” to get more detailed information of the lncRNA-target regulation. In addition, users can click "Network Display" to show the functional lncRNA-target network.

Cancer-specific analysis module of key lncRNA-target
1.Differential regulations page:
1).Search bar:
This function provides the differences in the distribution of lncRNA-target regulation activity scores among cancer cell sub-populations and shows the proportion of cells with high activity scores (> 0.5*max (AUC scores)). It will help to understand the roles of lncRNA-target regulations in intra-tumor heterogeneity at the single-cell level. To analyze the distribution difference, you should select a cancer single-cell dataset, a cancer cell sub-population and an interested lncRNA first, and then click the “Plot” button. If no interested lncRNA is inputted, all differential lncRNA-target regulations between a given cancer cell sub-population and other cancer cell sub-populations are displayed.

scRNA-seq Dataset: Select a cancer single-cell dataset for analysis.
Cell Subtype: Select a cancer cell sub-population for differential regulations analysis.
LncRNA/Gene Symbol: Select a lncRNA to obtain key lncRNA-target regulations for analysis. (optional)
2).Differential regulations in cell sub-populations
The dot plot shown below presents top differential regulations for the selected cell sub-populations in the selected dataset. (figure below) To show the differential lncRNA-target regulations in different cell sub-populations, we used the same approach as 'Differential regulations', including CSN, Wilcoxon rank sum test and AUCell. The results of differential expression are shown in the table below.

3).Search result table
All differential lncRNA-target regulations for the selected cell sub-populations in the select single-cell dataset are listed in a table below the dot plot, with each lncRNA-target regulation in one row, including disease name, lncRNA name, target name, influenced biological functions and regulatory mechanism. Users can click “Web Tools” column to get lncRNA and target analysis tools. Use the search box in the upper left corner to quickly retrieve items of interest. Use the buttons in the upper right corner to reformat the table and export data.

A detailed explanation of each item in the table is provided in '5. Search/Browse result table' or 'Differential regulations'.
4).Network result
At the bottom of the table, users can show the functional lncRNA-target network based on the table contents by clicking the "Network Display" button.

2.Differential expression page:
1).Search bar:
This function allows users to obtain differential expression information of lncRNAs and target gene in sub-populations. Wilcoxon rank sum test was used to identify differential lncRNA and target genes by comparing expression levels in different cancer cell sub-populations.
Users first selects “Cancer-specific analysis” module, followed by “Differential expression”, after that input a cancer single-cell dataset, a cell subtype and an interested lncRNA, and then click the “Plot” button to retrieve the differential expression of lncRNAs and target genes across cancer cell sub-populations. If no interested lncRNA is inputted, all landscape of differential expression between different cancer cell sub-populations are displayed.

scRNA-seq Dataset: Select a cancer single-cell dataset for analysis.
Cell Subtype: Select a cancer cell sub-population for differential regulations analysis.
LncRNA/Gene Symbol: Select a lncRNA for differential expression analysis. (optional)
2).Differential expression in cell sub-populations
We provide heatmap of differential expressed lncRNAs among diverse cell population in sub-populations is displayed. Users can select a lncRNA, a cancer single-cell datasets or a cell subtype for differential expression analysis.

3).Search result table
We provide a table with a gene record in each row, including gene name, gene type, average expression value, differential P-value, and FC value. Users can click “Plot” to see the expression level of the gene in each cell subtype.

4).differential expression of lncRNA-target in cell sub-populations
The boxplot shows the differential expression levels of gene in each cell subtype in the current dataset. At the same time, we provided a table to show all lncRNA-target regulations of the selected gene. Users can filter the results using the search box in the upper left corner (step 1 in figure below) or click “Web Tools” to get more lncRNA and target analysis tools (step 2 in figure below) or “More Details” to get more detailed information of the lncRNA-target regulation (step 3 in figure below). In addition, users can click "Network Display" to show the functional lncRNA-target network (step 4 in figure below).

3.Functional states page:
1).Search bar:
This function provides the cell proportion with high activity scores (> 0.5*max (AUC scores)) of differential lncRNA-target regulations associated with a given cell state (such as stemness, epithelial-mesenchymal transition, angiogenesis and inflammation) among cancer cell sub-populations in a single-cell data sets. Wilcoxon rank sum test was used to identify differential lncRNA-target regulations by comparing the activity scores between a given cell functional state and other cell functional states.
Users first select “Cancer-special analysis module”, followed by “Functional states”. Then select a cancer single-cell dataset, a cell states and an interested lncRNA name, and then click the “Plot” button to retrieve the landscape of differential lncRNA-target regulations associated with a given cell state. If no interested lncRNA is inputted, all landscape of differential lncRNA-target regulations between a given cell functional state and other cell functional states are displayed.

scRNA-seq Dataset: Select a cancer single-cell dataset for analysis.
Cell states: Select a cell functional state for analysis.
LncRNA/Gene Symbol: Select a lncRNA to obtain lncRNA-target regulations for analysis. (optional)
2).Differential regulations in diverse cell status
The bubble triangle plot shows the proportion of cells with high activity scores by differentially lncRNA-target regulation in cell state subsets associated with a given cell state in the selected single-cell dataset. (figure below) To show the differential lncRNA-target regulations in different cell sub-population in the selected dataset under the selected cell state, , we used the same approach as 'Differential regulations', including CSN, Wilcoxon rank sum test and AUCell. The results of differential expression are shown in the table below.

3).Search result table
The figure below shows the details of the searched lncRNA. We provided a table, with a single association record on each line that contains disease name, lncRNA name, target name, influenced biological functions and regulatory mechanism. Users can filter the results using the search box in the upper left corner (step 1 in figure below) or click “Web Tools” to get more lncRNA and target analysis tools (step 2 in figure below) or click “More Details” to get more detailed information of the lncRNA-target regulation of the lncRNA-target regulation (step 3 in figure below). In addition, users can click "Network Display" to show the functional lncRNA-target network (step 4 in figure below).

Introduction.
Detailed information of the RNA-seq Web Tools contained complex functions for mining TCGA, GEO and CTC datasets is displayed:
1.Main functions of the RNA-seq Web Tools are provided in menu bar form (step 1 in figure below).
2.Detailed introduction of main functions of RNA-seq Web Tools (step 2 in figure below).

Search bar and search result table.
The search bar of all three analysis tools provides gene-centric searches. TCGA and CTC analysis modules are gene-centered retrieval, while GEO analysis module is gene and disease retrieval to show the expression patterns of lncRNA and target genes in multiple sets of data under the disease.
The table below shows the details of the searched lncRNA. We provided a data table, with a single association record on each line that contains disease name, lncRNA name, target name, regulated direction, expression pattern of lncRNA, influenced biological functions and regulatory mechanism. Users can click Plot to get more lncRNA and taget anlaysis result. Users also can click More Details to get more details.

1): TCGA and GEO analysis module provides the following four analyses:
1).Differentially expressed patterns of the functional lncRNA-target regulation was shown if available. Differential genes expression between tumor samples and normal samples using DEseq2 in TCGA analysis module. Differential genes expression between tumor samples and normal samples using limma in GEO analysis module.
Table: the table shows differential expression pattern of lncRNAs and targets.
FC: A base-2 logarithm to fold changes of genes expression between tumor samples and normal samples.
FDR: False discovery rate of genes expression between tumor samples and normal samples using DEseq2 or limma.

2). The boxplot shows the expression distribution of genes (TPM value). * Fold change>2 and FDR < 0.05, Statistical analyses were using DEseq2 or limma.

3).Expression correlation of functional lncRNA-target regulation was also shown if available.
In addition, to show the dynamic expression correlation of functional lncRNA-target regulations in human disease, Pearson correlation coefficients between lncRNA and key target in each of cancer dataset (33 TCGA cancer types, 351 GEO microarray data) are shown. After clicking interested cancer type in heatmap, a scatter plot of lncRNA-target expression will be shown if available (as figure below shows).

2): CTC analysis module provides the following four analyses:
1).Differentially expressed patterns of the functional lncRNA-target regulation was shown if available. Limma was used to analyze differential gene expression between circulating tumor cell samples and normal blood samples. If no normal blood samples were available in a CTC dataset, differential expression analysis was performed between circulating tumor cells in that dataset and white blood cells transcriptome profiles of healthy peripheral-blood subsets.
Table: the table shows differential expression pattern of lncRNAs and targets.
FC: A base-2 logarithm to fold changes of genes expression between circulating tumor cell samples and normal blood samples.
FDR: False discovery rate of genes expression between circulating tumor cell samples and normal blood samples using limma.

2). The boxplot shows the expression distribution of genes. * Fold change>2 and FDR < 0.05, statistical analyses were using limma.

3).Expression correlation of functional lncRNA-target regulation was also shown if available.
In addition, to show the dynamic expression correlation of functional lncRNA-target regulations in human disease, Pearson correlation coefficients between lncRNA and key target in each of CTC datasets are shown. After clicking interested cancer type in heatmap, a scatter plot of lncRNA-target expression will be shown if available (as figure below shows).

To further learn and explore the interested functional lncRNA-target regulation in human disease, user can click "More Details" in the result table to obtain the corresponding metadata.
1) Basic information

Disease Name: The regulation of related diseases.
Regulator: The regulator in this regulation.
Target: The target in this regulation.
Clinical application: the lncRNA-target regulation is involved in metastasis, circulating, drug-resistance, recurrence and prognosis.
Regulator dysregulation in Circulating Tumor Cells: differential expression of regulatory gene in circulating tumor cells (CTCs) from cancer patients. The R package limma was used to analyze the differential expression of regulators and target genes in circulating tumor cell samples and normal blood samples. If no normal blood samples were available in a CTC dataset, differential expression analysis was performed between circulating tumor cells in that dataset and white blood cells transcriptome profiles of healthy peripheral-blood subsets. The regulator and target gene with |logFC| > 1 and FDR < 0.05 were considered to be significantly differentially expressed in CTCs. For example, the regulatory gene A1BG-AS1 is significantly down-regulated in circulating tumor cells, especially in breast cancer, melanoma, non-small cell lung cancer and other cancers.

Target dysregulation in Circulating Tumor Cells: differential expression of target gene in circulating tumor cells (CTCs) from cancer patients. For example, SMAD7, the target gene of A1BG-AS1, was also significantly down-regulated in circulating tumor cells, especially in breast cancer, melanoma, non-small cell lung cancer and other cancers.

Regulatory Type: There are three regulatory types including association, binding/interaction and regulation. The lncRNA-target relationships obtained based on knockdown or overexpression of a specific lncRNA (association). There are molecular interaction or binding sites between lncRNA and targets (binding/interaction). Other lncRNA-target relationships are regarded to regulation.
Level of Regulation: If regulatory type of lncRNA-target is binding/interaction, we recorded the molecular level of interaction. For example, RNA-RNA represents the lncRNA interacting with mRNA or miRNA.
Regulation Direction: LncRNA expression changes in this regulation. Positive /negative represents increase/decrease, and E/F represents expression/function.
Experimental method for lncRNA-target: the method of experimental validation for each lncRNA-target regulation.
Expression Pattern: An experimentally supported dysregulation state of this lncRNA in the disease.
Experimental method for lncRNA expression: method of experimental validation for lncRNA expression dysregulation.
Data Accession: data accession for lncRNA microarray or lncRNA sequence data.
Influenced Function: The biological functions affected by this regulation. +/- indicates promotion/inhibition of this biological function, respectively.
Regulatory Mechanism: The regulatory mechanism of this lncRNA-target regulation.
Drugs: The lncRNA-target regulation contribute to chemoresistance or chemosensitivity of the drug.
Cancer Stem Cell: The association between the functional lncRNA-target regulation and cancer stem cell.
Hallmark: The cancer hallmarks influenced by the functional lncRNA-target regulation.
Disease Category: The category of diseases based on Disease Ontology.
Disease Name2: The subtype of diseases based on Disease Ontology.
Regulator Type: The gene type of the regulator.
Target Type: The gene type of the target.
Regulatory EnsembleID: The Ensemble ID of the regulator.
Target EnsembleID: The Ensemble ID of the target.
Regulator EntrezID: The Entrez ID of the regulator.
Target EntrezID: The Entrez ID of the target.
Regulator Aliases: The aliases of lncRNAs or TFs.
Target Aliases: The aliases of genes.
Regulator Location: The genomic location of regulator.
Target Location: The genomic location of target.
Evidence: The detailed descriptions of the functional lncRNA-target regulation in human disease according to the reference.
PubMed: The pubmed ID of the reference.


2) RNA-seq tools
Differentially expressed patterns of the functional lncRNA-target regulation based on TCGA dataset was shown if available.
Table: the table shows differential expression pattern of lncRNAs and targets.
FC: A base-2 logarithm to fold changes of genes expression between tumor samples and normal samples
FDR: False discovery rate of genes expression between tumor samples and normal samples using DEseq2.

The boxplot shows the expression distribution of genes (TPM value). * Fold change > 2 and FDR < 0.05, Statistical analyses were using DEseq2.

Expression correlation of functional lncRNA-target regulation based on TCGA dataset was also shown if available.
In addition, to show the dynamic expression correlation of functional lncRNA-target regulations in human disease, Pearson correlation coefficients between lncRNA and key target in each of 33 TCGA cancer types are shown. After clicking interested cancer type in heatmap, a scatter plot of lncRNA-target expression will be shown if available (as figure below shows).

3) Single cell tools

Select a cancer single-cell dataset to obtain lncRNA-target-regulated single-cell analysis.
Sub-populations of cells identified in different single-cell datasets.
To demonstrate the expression of the functional lncRNA-target regulation in single-cell data, tSNE and UMAP were first used to reduce dimension of single-cell data and identify cell types (as figure below shows). Cell types are distinguished by color, and the legend illustrations indicate the most specific genes and selected datasets for each cancer cell type.

Gene expression level in different cell subtypes of the functional lncRNA-target regulation based on single cell dataset was shown if available.
The figure below shows the normalized expression levels of genes in each cell.

Differential expression analysis of lncRNA-target regulation among different cell sub-populations.
The boxplot shows the expression distribution of genes (as figure below shows), Statistical analyses were using wilcoxon rank sum test. The table shows differential expression pattern of lncRNAs and targets.

The distribution of lncRNA-target gene associations in cell sub-populations was demonstrated.
In addition, to show the expression correlation of functional lncRNA-target regulations in human disease, gene association between lncRNA and key target in each of 73 single-cell datasets are shown. And the bar plot shows the proportion of expression correlation in different cell types.

To download data in the LncTarD 2.0, select the menu 'Download'. LncTarD 2.0 provides downloadable file in TEXT format. This file contains key targets and important biological functions driven by disease-related lncRNAs and lncRNA-mediated regulatory mechanisms in human diseases.
Additionally, differentially expressed patterns of lncRNAs and targets, expression correlation and significance level between lncRNA and key target in 33 TCGA cancer types are also provided.

LncTarD 2.0 offers a submission page that enables other researchers to submit novel functional lncRNA-target regulation data in human diseases. Once approved by the submission review committee, the submitted record will be included in the LncTarD 2.0 database and made available to the public in the update release.