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CREsted is a Python package for training sequence-based deep learning models on scATAC-seq data, for capturing enhancer code and for designing cell type-specific sequences.
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SCENIC+ is a python package to build gene regulatory networks (GRNs) using combined or separate single-cell gene expression (scRNA-seq) and single-cell chromatin accessibility (scATAC-seq) data.
Coda: a convolutional denoising algorithm for genome-wide ChIP-seq data
a software package for analysis and exploration of single-cell RNA-seq datasets
R toolkit for inference, visualization and analysis of cell-cell communication from single-cell data
MultiNicheNet: a flexible framework for differential cell-cell communication analysis from multi-sample multi-condition single-cell transcriptomics data
A package for including transposable elements in differential enrichment analysis of sequencing datasets.
Differential expression of RNA-seq data using the Negative Binomial
TENET is a tool for reconstructing gene regulatory networks from pseudo-time ordered single-cell transcriptomic data.
Regulatory Genomics Toolbox: Python library and set of tools for the integrative analysis of high throughput regulatory genomics data.
Functions for identifying and characterizing continuous developmental trajectories in single-cell data.
R package for extracting and visualizing mutational patterns in base substitution catalogues
identifying mutational significance in cancer genomes
pySCENIC is a lightning-fast python implementation of the SCENIC pipeline (Single-Cell rEgulatory Network Inference and Clustering) which enables biologists to infer transcription factors, gene reg…