###### Abstract  
Understanding condition-specific biological mechanisms from RNA-seq data requires comprehensive analysis of gene expression data, from the gene to the network level. However, this requires computational expertise, which limits the accessibility of data analysis for understanding biological mechanisms. Therefore, the development of an easy-to-use and comprehensive analysis system is essential. In response to this issue, we present TFNetPropX, a user-friendly web-based platform designed to perform gene-level, gene-set-level, and network-level analysis of RNA-seq data under two different conditions. TFNetPropX performs comprehensive analysis, from DEG analysis to network propagation, to predict TF-affected genes with a single request, and provides users with an interactive web-based visualization of the results. To demonstrate the utility of our system, we performed analysis on two TF knockout RNA-seq datasets and effectively reproduced biologically significant findings. We believe that our system will make it easier for biological researchers to gain insights from different perspectives, allowing them to develop diverse hypotheses and analyses. network propagation; web-based tool; gene expression; RNA-seq; TF knockout; bioinformatics; network biology +
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analysis (GSEA), effectively extracts insights from gene expression data, facilitating the identification of the underlying biological processes or pathways associated with specific phenotypes [8]. Enrichr serves as a gene set search engine, providing the ability to query an extensive collection of annotated gene sets. It is a tool that integrates knowledge from many projects to provide a unified understanding of mammalian genes and gene sets. The tool is available on the web and also provides an API [9]. The Kyoto Encyclopedia of Genes and Genomes (KEGG) is a comprehensive knowledge base designed to facilitate the biological interpretation of molecular datasets. It provides comprehensive insights into diverse biological systems, covering data on genes, proteins, pathways, and compounds. KEGG is particularly valuable for gene-set-level analyses as it contains detailed information on the interactions between genes involved in specific biological functions, making it a widely used resource in this context [10]. Biological network analysis examines the interaction and organization of genes in forming functional modules that carry out essential physiological processes. Gene regulatory network analysis, which examines the regulatory interactions between transcription factors (TFs) and target genes (TGs) to modulate gene expression, is a common approach to analyzing RNA-seq data [11; 12]. The STRING database provides comprehensive data on protein-protein interactions, consolidating information from diverse sources such as experimental studies, co-expression data, therapeutic knowledge, and computational biological text mining. The STRING protein-protein interaction (PPI) network is used by many network-based methods and provides insight into interactions across a wide range of biological species [13]. When constructing a TF-TG network, several databases offer predictions of TF-TG interactions, including resources such as TRANSFAC, TRRUST, and hTFtarget [14; 15; 16]. These databases present TF-TG target information using various approaches, including predictions derived from the literature dedicated to TF-TG interactions, motif-based predictions, aggregation of experimental evidence, and compilation of relevant datasets. Various network construction and analysis algorithms have been developed to facilitate the interpretation of complex biological mechanisms at the network scale. Among these methods, network propagation is emerging as an important approach to identifying important biological modules and genes in the analysis of genomic, transcriptomic, and proteomic data using networks [17; 18; 19].