GEREA: Prediction of Gene Expression Regulators from Transcriptome Profiling Data to Transition Networks

Abstract

Background: Mammalian genes are regulated at the transcriptional and post-transcriptional levels. These mechanisms may involve the direct promotion or inhibition of transcription via a regulator or post-transcriptional regulation through factors such as micro (mi)RNAs. Objective: This study aimed to construct gene regulation relationships modulated by causality inference-based miRNA-(transition factor)-(target gene) networks and analyze gene expression data to identify gene expression regulators. Methods: Mouse gene expression regulation relationships were manually curated from literature using a text mining method which was then employed to generate miRNA-(transition factor)-(target gene) networks. An algorithm was then introduced to identify gene expression regulators from transcriptome profiling data by applying enrichment analysis to these networks. Results: A total of 22,271 mouse gene expression regulation relationships were curated for 4,018 genes and 242 miRNAs. GEREA software was developed to perform the integrated analyses. We applied the algorithm to transcriptome data for synthetic miR-155 oligo-treated mouse CD4+ T-cells and confirmed that miR-155 is an important network regulator. The software was also tested on publicly available transcriptional profiling data for Salmonella infection, resulting in the identification of miR-125b as an important regulator. Conclusion: The causality inference-based miRNA-(transition factor)-(target gene) networks serve as a novel resource for gene expression regulation research, and GEREA is an effective and useful adjunct to the currently available methods. The regulatory networks and the algorithm implemented in the GEREA software package are available under a free academic license at website : http://www.thua45.cn/gerea.

Flaver: mining transcription factors in genome-wide transcriptome profiling data using weighted rank correlation statistics

“…Numerous state of art TFBS prediction tools such as Grit [1], FIMO [4] and Pscan [5] were available, as well as the success of experimental Chip-Seq [6] technique provided multiple solutions for requirement (ii). The 1 st generation enrichment analysis based on the Fisher’s exact test and its variants [7-10] and the 2 ed generation enrichment analysis based on the Kolmogorov– Smirnov test variant, the GSEA tool [11], have partially solved the requirement (iii). All of these software require gene-sets and a gene-list as input sources.…”

“…All of these software require gene-sets and a gene-list as input sources. The input for the 1 st generation enrichment analysis tools are as simple as gene vectors for both genesets and gene-list [7-10]. The input for the 2 ed generation enrichment analysis tools are the gene-sets which are vectors of genes similar to the 1 st generation tool, and a gene-list ranked by expression values, which is different [11].…”