Inferring gene correlation networks from transcription factor binding sites

Abstract

Gene expression is a highly regulated biological process that is fundamental to the existence of phenotypes of any living organism. The regulatory relations are usually modeled as a network; simply, every gene is modeled as a node and relations are shown as edges between two related genes. This paper presents a novel method for inferring correlation networks, networks constructed by connecting coexpressed genes, through predicting co-expression level from genes promoter's sequences. According to the results, this method works well on biological data and its outcome is comparable to the methods that use microarray as input. The method is written in C++ language and is available upon request from the corresponding author.

CrossHub: a tool for multi-way analysis of The Cancer Genome Atlas (TCGA) in the context of gene expression regulation mechanisms

“…Supporting the results of gene DE profiling with ChIP-Seq data significantly improves this approach, allowing us to refine TF target lists predicted only from gene DE profiling after TF knockdown/activation ( 68 ). Expression correlation analysis also represents an option, with either gene-TF ( 69 , 70 ) or correlation of expression of multiple genes regulated by a common TF ( 71 , 72 ).…”

Characterization of Transcriptional Activities

“…Similar to the reason why genes within the same operon is likely to be expressionally correlated, a set of genes activated by the same set of transcription factors are also likely to be expressionally correlated. Binding of transcription factors to the promoter may affect chromatin accessibility (Lamparter et al, 2017), leading to correlated expression patterns from genes affected by the same transcription factors (Mahdevar et al, 2013). A study (Zhang and Li, 2017) examining more than 1000 human transcription factors found that transcription factor usage can be used to predict gene expression level (r 2 of up to 0.617).…”

Inferring Weighted Directed Association Networks from Multivariate Time Series with the Small-Shuffle Symbolic Transfer Entropy Spectrum Method

“…Of course, there are many more methods for association networks inference and we have not mentioned above, such as neural network [28], SparCC (Sparse Correlations for Compositional data) [29], S-estimator [30,31], Maximal Information Coefficient (MIC) [32], Local Similarity Analysis (LSA) [33,34], Transfer Entropy [35][36][37], and so on. They all showed some excellent performance through experiment and observation.…”