Software

MOJITOO : a fast and universal method for integration of multimodal single cell data. MOJITOO uses canonical correlation analysis for a fast and parameter free detection of a shared representation of cells from multimodal single cell data. Moreover, estimated canonical components can be used for interpretation, i.e. association of modality specific molecular features with the latent space.

CrossTalkeR is a framework for network analysis and visualisation of LR networks. CrossTalkeR identifies relevant ligands, receptors and cell types contributing to changes in cell communication when contrasting two biological states: disease vs. homeostasis. A case study on scRNA-seq of human myeloproliferative neoplasms reinforces the strengths of CrossTalkeR for characterisation of changes in cellular crosstalk in disease state. See our webpage.

HINT & HINT-ATAC

HINT (Hmm-based IdeNtification of Transcription factor footprints) is an hmm-based framework that uses open chromatin data (DNase-seq, ATAC-seq and histone modification ChIP-seq) in order to find transcription factor footprints in the DNA. HINT makes part of the Regulatory Genomics Toolbox suite. Please find more information about the tool in the RGT website.

ODIN & THOR are HMM-based approach to detect and analyse differential peaks in pairs of ChIP-seq data. ODIN is the first differential peak caller that performs genomic signal processing, peak calling and p-value calculation in an integrated framework. THOR extends ODIN by supporting replicates and further normalization approaches.

The Regulatory Genomics Toobox is a toolbox for the integrative analysis of regulatory and expression data from ChIP-Seq, RNA-Seq and biological sequences. The tool provides several functionalities required for regulatory analysis as Chip-Seq peak calling, transcription factor binding site detection and association to further experimental data as gene expression.

Contributed Software

Clever – Click Enumerating Variant Finder

Pymix  – Mixture model estimation package in python.

GHMM – A General Hidden Markov Model library in C.