TGMI: an efficient algorithm for identifying pathway regulators through evaluation of triple-gene mutual interaction

Abstract

Despite their important roles, the regulators for most metabolic pathways and biological processes remain elusive. Presently, the methods for identifying metabolic pathway and biological process regulators are intensively sought after. We developed a novel algorithm called triple-gene mutual interaction (TGMI) for identifying these regulators using high-throughput gene expression data. It first calculated the regulatory interactions among triple gene blocks (two pathway genes and one transcription factor (TF)), using conditional mutual information, and then identifies significantly interacted triple genes using a newly identified novel mutual interaction measure (MIM), which was substantiated to reflect strengths of regulatory interactions within each triple gene block. The TGMI calculated the MIM for each triple gene block and then examined its statistical significance using bootstrap. Finally, the frequencies of all TFs present in all significantly interacted triple gene blocks were calculated and ranked. We showed that the TFs with higher frequencies were usually genuine pathway regulators upon evaluating multiple pathways in plants, animals and yeast. Comparison of TGMI with several other algorithms demonstrated its higher accuracy. Therefore, TGMI will be a valuable tool that can help biologists to identify regulators of metabolic pathways and biological processes from the exploded high-throughput gene expression data in public repositories.

Transcriptional and Post-transcriptional Regulation of Lignin Biosynthesis Pathway Genes in Populus

“…This suggests that the molecular regulation of lignin biosynthesis is not unidirectional and is more complex than that was previously reported. Recently, Gunasekara et al (2018) developed a novel algorithm called triple-gene mutual interaction (TGMI) for identifying the pathway regulators using high-throughput gene expression data, which calculates the mutual interaction measure for each triple gene grouping (two pathway genes and one TF) and then examines its statistical significance using bootstrap. Implementing this algorithm, Gunasekara et al (2018) analyzed pathway regulators of lignin biosynthesis using a compendium dataset that comprised 128 microarray samples from Arabidopsis stem tissues under short-day conditions.…”

“…Recently, Gunasekara et al (2018) developed a novel algorithm called triple-gene mutual interaction (TGMI) for identifying the pathway regulators using high-throughput gene expression data, which calculates the mutual interaction measure for each triple gene grouping (two pathway genes and one TF) and then examines its statistical significance using bootstrap. Implementing this algorithm, Gunasekara et al (2018) analyzed pathway regulators of lignin biosynthesis using a compendium dataset that comprised 128 microarray samples from Arabidopsis stem tissues under short-day conditions. In this review, we also applied the TGMI algorithm to identify regulators of lignin biosynthesis in Populus based on the tissue-specific Populus Gene Expression Atlas and AspWood datasets (209 RNA-Seq samples in total).…”

Identification of biological pathway and process regulators using sparse partial least squares and triple-gene mutual interaction

“…and then normalized with the robust multi-array analysis (RMA) algorithm in affy package. This compendium data set was also used in our previous studies 7 . The annotation information of all genes was acquired from the Arabidopsis Information Resource website (TAIR) ( https://www.arabidopsis.org/ ), and the list of all transcription factors was acquired from the PlantTFDB website ( http://planttfdb.cbi.pku.edu.cn/ ).…”

“…Triple-gene mutual interaction (TGMI) 7 calculates the mutual information and conditional mutual information among a triple-gene block (Two pathway genes and one TF) using high-throughput gene expression data, and then evaluates if there are causal relationships among the triple genes. The significance of causal relationships was determined by bootstrapping.…”

“…SPLS regression performs well even when the sample size is much smaller than the number of variables. TGMI is specially developed for identifying the pathway regulators by evaluating all combined triple gene blocks using a novel mutual interaction measure (MIM) calculated with mutual information and conditional mutual information 7 . MIM represents the regulatory strength exerted by a transcription factor (TF) on two combined pathway genes in a triple gene block, and it can facilitate the recognition process of true causal relationships between TFs and pathway genes.…”