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CREsted-paper
Public- 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.
- 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 regulatory networks and cell types from single-cell RNA-seq data.
scenicplus_core
Public- Tools for working with scATAC-seq fragment files
Nova-ST
Public- DeepBrain: a collection of vertebrate sequence-based enhancer models aimed at understanding brain cell type enhancer code across and within species
pycistarget
PublicPUMATAC_tutorial
Publicscforest
Publicbiopython
PublicSpatialNF
Public- A scalable python-based framework for gene regulatory network inference using tree-based ensemble regressors.
cisTopic
Publicctxcore
PublicAUCell
PublicRcisTarget
PublicscATAC-seq_benchmark
Public