15 Transcription Factor Network

Transcription factor (TF) networks identify transcription factors in a supplied set of genes and links them to their targets. Some transcription factors may also be the target of other transcription factors in the network. Genes which are the target of transcription factors are indicated by circles, transcription factors by triangles, and transcription factors which are also the target of another transcription factor (sometime self looping) are indicated by diamonds. Transcription factors and targets were taken from AnimalTFDB 3.0 (H. Hu et al. 2018) and RegNetwork (Liu et al. 2015) which includes both transcription factors and transcription co-factors. This section is highly experimental and the databases will be updated often. We will also plan to add additional databases to TF-target relationships.

A word of warning: Any database of TF to target relationships suffers from two problems: 1) They aggregate information from multiple sources, organisms, and cell lines. As such an interactions from a database may not exist in your biological system. 2) They are highly skewed toward a few well-studied transcription factors with many publications (Nfkb, AP1 family members, IRFs, GATA, Myc, etc.). It is likely that lists of targets for such transcription factors contain a fair number of false-positives.

This section builds two type of networks depending on the input. When a list of gene names is provided transcription factors and co-factors are identified in this list and and are linked to their targets within this list. This approach is useful to identify potential targets of transcription factors in a co-regulated set of genes. For example the user may perform differential expression analysis, select up or down regulated genes and use this list as an input.

On the backend rnaseqDRaMA uses the visNetwork package in R to visualize the network which is built on the vis.js JavaScript library.

Figure 15.1: Transcription factor networks of a group of co-regulated genes

Alternatively, when a single transcription factor is selected all its immediate targets are shown. The color of nodes reflects differential expression metrics (LogFC, P-values or FDR) and can be selected from the Select Variable for Node Color drop down menu.

To reposition a first select HALT/Manipulate, and then click on the node and drag it. Use Ctrl-click to select multiple nodes to move.

To export the network as a static image press black export pdf button in the lower right corner of the network window.

15.1 Transcription Factor Network Control Panel

Select by Gene Name: accepts comma-, space-, tab-, or semicolon-separated lists of genes. Mixing delimiters and cases is allowed. Click Select button to finalize selection.

Force Stabilization: In order to improve visualization, position of nodes and edges are adjusted in agreement with a physics model that assumes certain types of interactions between nodes and edges. For large networks, 'Force Stabilization' button improves the speed of stabilization; however, complex networks may fail to stabilize completely. The HALT/Manipulate button freezes the current state of the network and allows manual position adjustment.

Select Variable for Node Colors: Allows node coloring based on differential expression metrics.

Select Species: Select either mouse, human, or both subsets of TF-target interaction database.

Layout as Hierarchical: Toggle between force-directed or hierarchical network representations.

Curved Edges: Toggle between curved and straight network edges. While this is a matter of personal preference, curved edges look much better and have better properties for the physics engine that stabilizes the network.

Remove Self Loop: (TRUE by default). Because many transcription factors regulate their own expression (act as their own targets), self loops are often associated with the TF nodes. This option turns self-loops ON.

References

Hu, Hui, Ya-Ru Miao, Long-Hao Jia, Qing-Yang Yu, Qiong Zhang, and An-Yuan Guo. 2018. “AnimalTFDB 3.0: a comprehensive resource for annotation and prediction of animal transcription factors.” Nucleic Acids Research 47 (D1): D33–D38. doi:10.1093/nar/gky822.

Liu, Zhi-Ping, Canglin Wu, Hongyu Miao, and Hulin Wu. 2015. “RegNetwork: an integrated database of transcriptional and post-transcriptional regulatory networks in human and mouse.” Database 2015 (September). doi:10.1093/database/bav095.