Help&FAQs
1. What Information is Available in the KnockTF?
2. What Datasets are Included in the KnockTF?
3. Database Content and Construction
4. How to Use the KnockTF?
4.1 Browse
4.2 Search
4.3 Analysis
4.3.1 Subnetwork Analysis
4.3.2 TF Enrichment
4.3.3 TF Enrichment(GSEA)
4.3.4 Pathway downstream analysis
4.4 Download
4.5 Statistics
5. Frequently Asked Questions
6. Development Environment
1. What Information is Available in the KnockTF?
We develop a comprehensive human and mouse gene expression profile
database with TF knockdown/knockout
(KnockTF), which provides a large number of available resources of
human and mouse gene expression
profile datasets associated with TF knockdown/knockout and annotates TFs and their target genes in a
tissue/cell type-specific manner.
The current version of KnockTF has 1093 manually curated RNA-seq
and microarray datasets associated with
573 TFs disrupted by different knockdown/knockout techniques and
across multiple tissue/cell types.
KnockTF not only provides comprehensive gene expression information about target genes of TFs of
interest, but also collects upstream pathway information of TFs and various functional annotation
and
analysis results of downstream target genes, including GSEA, GO enrichment, KEGG pathway enrichment,
hierarchical clustering analysis and differentially expressed analysis. KnockTF further
provides details
about TFs binding to promoters, super-enhancers and typical enhancers of target genes. In the
analysis
section, a TF-differentially expressed gene network is constructed and used to perform network
analyses
for gene sets of interest, such as subnetwork location, topological analysis, TF enrichment analysis
and
TF upstream pathway analysis. KnockTF will help elucidate TF-related functions and tap potential
biological effects.
2. What Datasets are Included in the KnockTF?
The current version of KnockTF has 1093 manually curated RNA-seq and
microarray datasets associated with 573 TFs disrupted by different
knockdown/knockout techniques and across multiple tissue/cell types. KnockTF provides a
conveniently, user-friendly interface for querying, browsing, analyzing and downloading detailed
information about gene expression profile datasets of TF knockdown/knockout.
For more detailed statistics, please see the "Statistics" page.
For more detailed statistics, please see the "Statistics" page.
3. Database Content and Construction
KnockTF not only provides comprehensive gene expression information about target genes
before/after knockdown/knockout of TFs of interest, but also collects upstream pathway
information of TFs and various functional annotation and analysis results of downstream target
genes, including Gene Set Enrichment Analysis, Gene ontology enrichment, KEGG pathway
enrichment, hierarchical clustering analysis and differentially expressed analysis. Furthermore,
KnockTF provides detailed information about TFs binding to promoters, super-enhancers and
typical enhancers of target genes. In addition, KnockTF constructs a TF-differentially expressed
gene network and performs network analyses for gene sets of interest, such as subnetwork
location, topological analysis, gsea ananlysis, GO/KEGG pathway downstream analysis and
hypergeometric enrichment.
4.How to Use the KnockTF?
The "Browse" page is organized as an interactive table for quickly searching for TF
knockdown/knockout datasets and customizing filters using "Species",
"Data Source", "Biosample Type", "Tissue Type", "TF Superclass" and "TF". Users can click the "Show entries" in a dropdown menu to
change the number of records displayed per page. To view details of a given TF
knockdown/knockout dataset, users click on "Dataset ID".
KnockTF supports four different searching modes: "Search by TF",
"Search by Target Gene", "Search by
Knock-Method", "Search by Tissue Type" and "Search by Target Gene (BLAST)". Brief search
results are presented as a table in the result page.
As an example, users can click Search→Search by TF→CREB1, the database will automatically display TF Class and TF Superclass: Basic leucine zipper factors (bZIP) and Basic domains. Search results are shown on the next page, including Dataset ID, TF, Knock-Method, Biosample Type, Tissue Type, Biosample Name, Profile ID, Platform, The Number of Control, The Number of Treat and Pubmed ID. Users can click the "Dataset ID" to view details about each TF knockdown/knockout dataset.
As an example, users can click Search→Search by TF→CREB1, the database will automatically display TF Class and TF Superclass: Basic leucine zipper factors (bZIP) and Basic domains. Search results are shown on the next page, including Dataset ID, TF, Knock-Method, Biosample Type, Tissue Type, Biosample Name, Profile ID, Platform, The Number of Control, The Number of Treat and Pubmed ID. Users can click the "Dataset ID" to view details about each TF knockdown/knockout dataset.
The "Search result of Dataset" page presents TF overview, TF-target gene network, target
gene information, upstream pathway annotation of TF and various functional annotation and
analysis results of downstream target genes, including Gene Set Enrichment Analysis (GSEA),
Gene ontology (GO) enrichment, KEGG pathway enrichment, hierarchical clustering analysis and
differentially expressed analysis.
Users can further click a target gene name, eg. "CNGA3", to view
detailed descriptions of a gene in new page, including gene overview, differentially
expressed (FC≥3/2 & FC≤2/3) target gene-TF pairs, annotation of TF binding regions of target
gene of interest and gene expression atlas from different data sources.
KnockTF constructed a TF-differentially expressed gene (DEG) network, as follows. First, for each TF knockdown/knockout dataset, we extracted DEGs under the threshold of FC≥3/2 & FC≤2/3 and formed TF-DEG pairs that were ranked based on significant levels of DEGs. Second, we combined all TF-DEG pairs for the 570 TF knockdown/knockout datasets. If a TF-DEG pair appeared multiple times in different TF knockdown/knockout datasets, we removed duplications and retained its minimum rank. Then, we reordered all nonredundant TF-DEG pairs and constructed a TF-DEG network with TFs and their DEGs as nodes and TF-DEG pairs as edges. The rank of TF-DEG pairs represented the importance of the regulatory intensity of TFs on target genes. TF-target relationships supported by the ChIP-seq data were also marked and recorded for TF-DEG pairs. Topological features such as degree, betweenness and closeness of all nodes in the TF-DEG network were computed.
4.3.1 Subnetwork Analysis
Users can submit a gene list to locate a transcriptional regulatory subnetwork. The subnetwork consists of submitted genes and their one-step neighbors within TF-DEG network. TF-target gene relationships supported by the ChIP-seq data have bold edges in the subnetwork. Users can choose subnetwork size displays by filtering the number of the most important TF-DEG pairs. KnockTF also provides topological features of subnetwork genes including degree, betweenness and closeness.
4.3.2 TF Enrichment
Users can submit a gene list and set (FDR-adjusted) P-value for TF enrichment. KnockTF maps submitted genes to the TF-DEG network and performs hypergeometric test between submitted genes and all DEGs regulated by each TF. The TFs under the threshold of (FDR-adjusted) P-value user sets are considered the most important TFs that significantly regulate the submitted genes.
4.3.3 TF Enrichment(GSEA)
Users can set different thresholds as needed for analysis, such as pvalueCutoff and pAdjustCutoff. User can obtain the analysis results within a short time and download results (files and graphs) as needed.
4.3.4 Pathway downstream analysis
The user performs pathway enrichment analysis by inputting the gene of interest and the related threshold to obtain the related transcription factors in the pathway. We constructed and analyzed the network of downstream target genes using the enriched transcription factors.
Gene expression profile of each dataset, differential expression information of genes, the
promoter/super-enhancer/typical enhancer regions and corresponding TF binding information of
target genes are provided for downloading in the "Download" page. In addition, KnockTF
supports export of query results for each search result page.
The datasets are respectively classified by different knock-methods and biosample types in
the "Statistics" page. The number of DEGs in each TF knockdown/knockout dataset is also
provided.
5. Frequently Asked Questions
5.1 Why are showing no results available somewhere?
Reply: Because there is no corresponding function annotation or no significant annotation results for this TF/gene.5.2 Why the same TF disrupted by the same knockdown/knockout technique appears in different datasets?
Reply: Because these datasets may be from different profiles, different platforms or different biosamples. In KnockTF, there are two data sources. If data source is GEO, five conditions are used to uniquely determine a dataset, including TF, knockdown/knockout technique, biosample name, series (GSE) and platform (GPL); if data source is ENCODE, four conditions are used to uniquely determine a dataset, including TF, knockdown/knockout technique, biosample name and experiment accession.5.3 Which kinds of species are stored in the database?
Reply: The current version of KnockTF stores the most abundant human and mouse gene expression profile datasets of TF knockdown/knockout. There may be a lot of TF knockdown/knockout data about other species in other data sources. In the next version of KnockTF, we will manually curate more TF knockdown/knockout data with more species and enrich the kinds of species information.5.4 Why might web pages load slowly?
Reply: KnockTF has advanced storage technology and sufficient bandwidth to meet the needs of most users for the speed of web page loading. However, it is not excluded that few users have poor user experience due to network reasons.
6.Development Environment
The current version of KnockTF was developed using MySQL 8.0.18 (http://www.mysql.com) and runs
on a Linux-based Apache Web server (http://www.apache.org). PHP 7.2.24 (http://www.php.net) was
used for server-side scripting. The interactive interface was designed and built using Bootstrap
v4.3.1 (https://getbootstrap.com/) and JQuery v3.2.1 (http://jquery.com). ECharts
(https://www.echartsjs.com/) and Highcharts (https://www.highcharts.com.cn/) were used as a
graphical visualization framework. Analysis related functions use version R4.2.0.
The blast tool uses NCBI version 2.13.0. The above environment is established using a docker.
We recommend to use a modern web browser that supports the
HTML5 standard, such as Firefox, Google Chrome, Safari, Opera or IE 9.0+ for the best display.
The knockTF database is freely available to the research community using the web link (http://www.licpathway.net/KnockTF/index.php). Users are not required to register or login to access features in the database.
The knockTF database is freely available to the research community using the web link (http://www.licpathway.net/KnockTF/index.php). Users are not required to register or login to access features in the database.