ActivePathways - Integrative Pathway Enrichment Analysis of Multivariate Omics Data

Framework for analysing multiple omics datasets in the context of molecular pathways, biological processes and other types of gene sets. The package uses p-value merging to combine gene- or protein-level signals, followed by ranked hypergeometric tests to determine enriched pathways and processes. Genes can be integrated using directional constraints that reflect how the input datasets are expected interact with one another. This approach allows researchers to interpret a series of omics datasets in the context of known biology and gene function, and discover associations that are only apparent when several datasets are combined. The recent version of the package is part of the following publication: Directional integration and pathway enrichment analysis for multi-omics data. Slobodyanyuk M^, Bahcheli AT^, Klein ZP, Bayati M, Strug LJ, Reimand J. Nature Communications (2024) <doi:10.1038/s41467-024-49986-4>.

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8.77 score 120 stars 2 dependents 54 scripts 507 downloads

ActiveDriverWGS - A Driver Discovery Tool for Cancer Whole Genomes

A method for finding enrichments of somatic single nucleotide variants (SNVs) and small insertions-deletions (Indels) in functional elements in the human genome. 'ActiveDriverWGS' detects coding and noncoding cancer driver elements using whole genome sequencing data. The method is part of the publication H. Zhu et al. (2020) <doi:10.1016/j.molcel.2019.12.027> "Candidate Cancer Driver Mutations in Distal Regulatory Elements and Long-Range Chromatin Interaction Networks" in Molecular Cell.

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3.38 score 12 scripts 601 downloads

ActiveDriver - Finding Cancer Driver Proteins with Enriched Mutations in Post-Translational Modification Sites

A mutation analysis tool that discovers cancer driver genes with frequent mutations in protein signalling sites such as post-translational modifications (phosphorylation, ubiquitination, etc). The Poisson generalised linear regression model identifies genes where cancer mutations in signalling sites are more frequent than expected from the sequence of the entire gene. Integration of mutations with signalling information helps find new driver genes and propose candidate mechanisms to known drivers. Reference: Systematic analysis of somatic mutations in phosphorylation signaling predicts novel cancer drivers. Juri Reimand and Gary D Bader. Molecular Systems Biology (2013) 9:637 <doi:10.1038/msb.2012.68>.

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2.00 score 6 scripts 235 downloads

mExplorer - Identifying Master Gene Regulators from Gene Expression and DNA-Binding Data

The method 'm:Explorer' associates a given list of target genes (e.g. those involved in a biological process) to gene regulators such as transcription factors. Transcription factors that bind DNA near significantly many target genes or correlate with target genes in transcriptional (microarray or RNAseq data) are selected. Selection of candidate master regulators is carried out using multinomial regression models, likelihood ratio tests and multiple testing correction. Reference: m:Explorer: multinomial regression models reveal positive and negative regulators of longevity in yeast quiescence. Juri Reimand, Anu Aun, Jaak Vilo, Juan M Vaquerizas, Juhan Sedman and Nicholas M Luscombe. Genome Biology (2012) 13:R55 <doi:10.1186/gb-2012-13-6-r55>.

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1.00 score 191 downloads