Abstract
High throughput techniques such as RNA-seq or microarray analysis have proven to be invaluable for the characterization of global transcriptional gene activity changes due to external stimuli or diseases. Differential gene expression analysis (DGEA) is the first step in the course of data interpretation, typically producing lists of dozens to thousands of differentially expressed genes. To further guide the interpretation of these lists, different pathway analysis approaches have been developed. These tools typically rely on the classification of genes into sets of genes, such as pathways, based on the interactions between the genes and their function in a common biological process. Regardless of technical differences, these methods do not properly account for cross talk between different pathways and rely on binary separation into differentially expressed gene and unaffected genes based on an arbitrarily set p-value cut-off. To overcome this limitation, we developed a novel approach to identify concertedly modulated sub-graphs in the global cell signaling network, based on the DGEA results of all genes tested. Thereby, expression patterns of genes are integrated according to the topology of their interactions and allow potentially to read the flow of information from the perturbation source to the effectors. The described software, named Modulated Sub-graph Finder (MSF) is freely available at https: //github.com/Modulated-Subgraph-Finder/MSF.
Highlights
High throughput sequencing techniques have been widely used to yield differentially expressed genes (DEG) (Malone & Oliver, 2011)
Differential gene expression analysis (DGEA) informs about the magnitude of expression changes between the conditions which are often expressed as fold change, sign of fold change and the confidence level of observing an authentic change, often expressed as p-value
Case Study To demonstrate its usefulness, Modulated Sub-graph Finder (MSF) is applied to an RNA-seq data set of primary human monocytederived macrophages (MDMs) infected with Ebola virus (GSE84188) (Olejnik et al, 2017)
Summary
High throughput sequencing techniques have been widely used to yield differentially expressed genes (DEG) (Malone & Oliver, 2011). Differential gene expression analysis (DGEA) informs about the magnitude of expression changes between the conditions which are often expressed as fold change, sign of fold change and the confidence level of observing an authentic change, often expressed as p-value. These DEGs information is further interpreted to extract meaningful biological insights. Genes that could be involved in the response to a particular stimuli or maybe the cause of a disease To this end, pathway-based analysis has become an important tool to further interpret the results of a DGEA and to acquire understandings of the perturbations in a biological system.
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