vulcan
Vulcan (VirtUaL ChIP-Seq Analysis through Networks) is a package that interrogates gene regulatory networks to infer cofactors significantly enriched in a differential binding signature coming from ChIP-Seq data. In order to do so, our package combines strategies from different BioConductor packages: DESeq for data normalization, ChIPpeakAnno and DiffBind for annotation and definition of ChIP-Seq genomic peaks, csaw to define optimal peak width and viper for applying a regulatory network over a differential binding signature.
- Bioconductor
- https://bioconductor.org/packages/vulcan
Source attribution
- Bioconductor — vulcan
Related resources
MIRit is an R package that provides several methods for investigating the relationships between miRNAs and genes in different biological conditions. In particular, MIRit allows to explore the functions of dysregulated miRNAs, and makes it possible to identify miRNA-gene regulatory axes that control biological pathways, thus enabling the users to unveil the complexity of miRNA biology. MIRit is an all-in-one framework that aims to help researchers in all the central aspects of an integrative miRNA-mRNA analyses, from differential expression analysis to network characterization.
This package implements the Ensemble of Gene Set Enrichment Analyses (EGSEA) method for gene set testing. EGSEA algorithm utilizes the analysis results of twelve prominent GSE algorithms in the literature to calculate collective significance scores for each gene set.
Provides an interface to several normalization and statistical testing packages for RNA-Seq gene expression data. Additionally, it creates several diagnostic plots, performs meta-analysis by combinining the results of several statistical tests and reports the results in an interactive way.
DEsubs is a network-based systems biology package that extracts disease-perturbed subpathways within a pathway network as recorded by RNA-seq experiments. It contains an extensive and customizable framework covering a broad range of operation modes at all stages of the subpathway analysis, enabling a case-specific approach. The operation modes refer to the pathway network construction and processing, the subpathway extraction, visualization and enrichment analysis with regard to various biological and pharmacological features. Its capabilities render it a tool-guide for both the modeler and experimentalist for the identification of more robust systems-level biomarkers for complex diseases.
SGC is a semi-supervised pipeline for gene clustering in gene co-expression networks. SGC consists of multiple novel steps that enable the computation of highly enriched modules in an unsupervised manner. But unlike all existing frameworks, it further incorporates a novel step that leverages Gene Ontology information in a semi-supervised clustering method that further improves the quality of the computed modules.
Inference of Genetic Variants Driving Cellullar Phenotypes by the DIGGIT algorithm