NetProphet 2.0: mapping transcription factor networks by exploiting scalable data resources
Creators: name: Yiming Kang
name: Hien-Haw Liow
name: Maier, Ezekiel
name: Brent, Michael
Publisher: name: F1000Research
Year: 2017

description: Motivation: Cells process information, in part, through transcription factor (TF) networks, which control the rates at which individual genes produce their products. A TF network map is a graph that indicates which TFs bind and directly regulate each gene. Previous work has described both network mapping algorithms that rely exclusively on gene expression data and “integrative” algorithms that exploit a wide range of data sources including chromatin immunoprecipitation sequencing (ChIP-seq) of many TFs, genome-wide chromatin marks, and binding specificities for many TFs determined <i>in vitro</i>. However, such resources are available only for a few major model systems and cannot be easily replicated for new organisms or cell types.<br><br>Results: We present NetProphet 2.0, a “data light” algorithm for TF network mapping (Fig. 1A) and show that it is more accurate at identifying direct targets of TFs than other, similarly data light algorithms (Fig. 1B). It work by exploiting three principles. First, combining multiple approaches to network mapping from expression data can improve accuracy relative to the constituent approaches. Second, TFs with similar DNA binding domains bind similar sets of target genes. Third, even a noisy, preliminary network map can be used to infer DNA binding specificities from promoter sequences and these inferred specificities can be used to further improve the accuracy of the network map. The result is the only algorithm that has been shown to be accurate on animal networks.<br><br>Approach: NetProphet 2.0 comprises 6 modules (Fig. 1A), 5 of which take advantage of information derived from gene expression profiling or genome sequencing. The output of each module is a score matrix (TFs x genes). The score vector (row) for a TF represents the strength of evidence that the TF regulates each potential target gene. A discrete graph structure can be constructed by thresholding edge scores.<br><br>Module A (Fig. 1Aa) is NetProphet 1.0, as previously described. Module B (Fig. 1Ab) constructs an independent network from the gene expression data by using Bayesian Additive Regression Trees (BART), a machine learning algorithm. For each gene, Module B trains a separate BART model to predict its RNA level as a function of the RNA levels of all TFs. It then simulates the effect on the predicted RNA level when each TF’s RNA level is varied between its min and max observed levels. The difference between these predicted gene expression levels is the score of the TF-target pair.<br><br>Module C (Fig. 1Ac) capitalizes on the fact that TFs with similar DNA binding domains (DBDs) tend to bind similar sets of target genes. It replaces the score matrix row for each TF by a weighted average of rows for other TFs with similar DBDs. Each row is weighted according to how similar the DBD of its TF is to the DBD of the row being replaced. The predicted amino acid sequence of the DBD can be obtained from automated genome annotation. The outputs of modules A and B are independently passed through Module C, then combined into a single score matrix by Module D (Fig. 1Ad), which uses quantile normalization to match their score distributions.<br><br>Module E infers the DNA-binding motif of each TF as the motif whose presence in a promoter best distinguishes high scoring (likely) target genes from low scoring (unlikely) target genes. Module F scans the inferred motifs over the promoters of all genes and computes a score reflecting the strength of evidence that the TF binds the promoter. The resulting score matrix is then combined with the input score matrix by using module D again. In a final step, the combined matrix is passed through module C again.<br>Availability: https://github.com/yiming-kang/NetProphet_2.0.