Software – Single Cell Omics Germany

ChromSCape	Ref	R	Single-Cell Chromatin Landscape profiling, analysis of sparse single-cell chromatin profiling datasets in a Shiny App
CUT&RUNTools 2.0	Ref		Pipeline for single-cell and bulk-level CUT&RUN and CUT&Tag data analysis
Dr.seq2	Ref	R, Python	Quality control and analysis pipeline for parallel single cell transcriptome and epigenome data (including scATAC-seq and Drop-ChIP data)
scChromHMM	Ref	Rust	Fast and efficient tool to perform a genome wide Single cell Chromatin State Analysis using multimodal histone modification data
SCRAT	Ref	R	Single-Cell Regulome Analysis Tool (ATAC-seq, DNase-seq, ChIP-seq)

BAVARIA	Ref	Python	Batch-adversarial variational auto-encoder for simultaneous dimensionality reduction and integration of single-cell ATAC-seq datasets
ChraccR	Ref	R	Tools for the comprehensive analysis of (single cell) chromatin accessibility data, methods for data quality control, exploratory analyses and the identification and characterization of differentially accessible regions
epiScanpy	Ref	Python	Computational framework for the analysis of single-cell DNA methylation and single-cell ATAC-seq data
scipipeline		Snakemake	Pipeline for processing single-cell combinatorial indexing ATAC seq data
scOpen	Ref	Python	Chromatin-accessibility estimation of single-cell ATAC data
scregseg	Ref	Jupyter Notebook	Single-cell regulatory landscape segmentation, tool that facilitates the analysis of single cell ATAC-seq data by an HMM-based segmentation algorithm

ALLCools	Ref	Python	ALL methyl-Cytosine tools, Toolkit for single-cell DNA methylation analysis
Alleloscope	Ref	R	Method for allele-specific copy number estimation that can be applied to single cell DNA and ATAC sequencing data
APEC	Ref	Python	Accessibility Pattern-based Epigenomic Clustering, single-cell chromatin accessibility analysis toolkit
ArchR	Ref	R	Full-featured R package for processing and analyzing single-cell ATAC-seq data, providing the most extensive suite of scATAC-seq analysis tools and excelling in both speed and resource usage. ArchR also supports paired scATAC-seq and scRNA-seq analysis.
ATAC-CoGAPS	Ref	R	Framework to enable cross-study and cross-platform analysis of multiple scATAC-seq data sets through the application of the Bayesian Non-Negative Matrix Factorization algorithm CoGAPS in conjunction with the transfer learning program project R.
AtacWorks	Ref	Python	Deep learning toolkit for coverage track denoising and peak calling from low-coverage or low-quality ATAC-Seq data.
BIRD	Ref	C++, R	Big data Regression for predicting DNase I hypersensitivity, Software to predict chromatin accessibility based on gene expression data (scRNA-seq)
Brockman	Ref	R	Brockman Representation Of Chromatin by K-mers in Mark-Associated Nucleotides, tool for exploratory data analysis for highdimensional or single cell epigenomics
ChromA	Ref	Python	Chromatin Landscape Annotation Tool, probabilistic model to annotate chromatin regions into accessible or inaccessible, open or closed, based on their ATACseq profile. ChromA can process bulk datasets, single-cell or integrate information from a combination of both
ChromVAR	Ref	R	Analysis of sparse chromatin accessibility data from single cell or bulk ATAC or DNAse-seq data
Cicero	Ref	R	Tools for analyzing single-cell chromatin accessibility experiments
cisTopic	Ref	R	Simultaneously identify cell states and cis-regulatory topics from single cell epigenomics data
Copy-scAT	Ref	R	Copy number variant inference with single-cell ATAC seq, R package that uses single-cell epigenomic data to infer copy number variants (CNVs) that define cancer cells
CoRE-ATAC	Ref		Classification of Regulatory Elements with ATAC-seq, deep learning model for the functional classification of regulatory elements from single cell and bulk ATAC-seq data
DC3	Ref	Python	De-Convolution and Coupled-Clustering, method for the joint analysis of various bulk and single-cell data such as HiChIP, RNA-seq and ATAC-seq from the same heterogeneous cell population
deNOPA	Ref	Python	decoding nucleosome organization profile based on ATAC-seq data, prediction of nucleosome position with ultrasparse ATAC-seq data (single-cell data)
Destin	Ref	R	Toolkit for single-cell analysis of chromatin accessibility, scATAC-seq processing and cell clustering pipeline
Dr.seq2	Ref	R, Python	Quality control and analysis pipeline for parallel single cell transcriptome and epigenome data (including scATAC-seq and Drop-ChIP data)
EpiAnno	Ref	Python	Cell type annotation of single-cell epigenomic data via supervised Bayesian embedding
epiConv	Ref	R	Algorithm to cluster scATAC-seq data and detect differentially accessible peaks, epiConv learns the similarities (or distances) between single cells from their raw Tn5 insertion profiles by a convolution-based approach
FITs	Ref	Python	Forest of Imputation Trees, method to impute highly sparse and noisy data-sets from single cell epigenome profiling
MAESTRO	Ref	Snakemake	Model-based AnalysEs of Transcriptome and RegulOme, a comprehensive open-source computational workflow for the integrative analyses of scRNA-seq and scATAC-seq data from multiple platforms
MEDEA	Ref	Python	Motif Enrichment in Differential Elements of Accessibility, analysis of transcription factor binding motifs in accessible chromatin
proATAC		Python	Preprocessing pipeline for (sc)ATAC data, toolkit that performs robust and scalable preprocessing of ATAC-Seq data and implements the Buenrostro lab’s data processing and quality control pipeline for bulk ATAC-Seq, single cell ATAC-Seq, and droplet-based ATAC-Seq.
RA3	Ref	R	Reference-guided Approach for the Analysis of single-cell chromatin Acessibility data, analysis of high-dimensional and sparse single-cell epigenetic data using a reference-guided approach
scABC	Ref	R	Unsupervised clustering and analysis of scATAC-seq data
SAILER	Ref	Python	Scalable and accurate invariant representation learning for single-cell ATAC-seq processing and integration
SCALE	Ref	Python	Single-cell ATAC-seq analysis via Latent feature Extraction
SCAN-ATAC-Sim	Ref	C++	Scalable and efficient method for simulating single-cell ATAC-seq data from bulk-tissue experiments
Scasat	Ref	Jupyter Notebook	Single cell ATAC-seq preprocessing and analysis pipeline
scATAC-pro	Ref	Perl	Comprehensive tool for processing, analyzing and visualizing single cell chromatin accessibility sequencing data
SCATE	Ref	R	Single-cell ATAC-seq Signal Extraction and Enhancement, adaptively integrates information from co-activated cis regulatory regions, similar cells, and publicly available regulome data
scBasset	Ref	Python	Sequence-based convolutional neural network method to model scATAC data
scBFA	Ref	R	Modeling detection patterns to mitigate technical noise in large-scale single cell genomics data (scRNA-seq and scATAC-seq)
scDEC	Ref		Computational tool for single cell ATAC-seq analysis with deep generative neural networks
scFAN	Ref	Python	Single-cell factor analysis network, predicting transcription factor binding in single cells through deep learning
SCRAT	Ref	R	Single-Cell Regulome Analysis Tool (ATAC-seq, DNase-seq, ChIP-seq)
Signac	Ref	R	Signac is an extension of Seurat for the analysis, interpretation, and exploration of single-cell chromatin datasets
simATAC	Ref	R	single-cell ATAC-seq simulation framework, providing a robust and systematic approach to generate in silico scATAC-seq samples with cell labels for a comprehensive tool assessment
SnapATAC	Ref	R, Python	Single Nucleus Analysis Pipeline for ATAC-seq
SOMatic	Ref	C++, R	Novel approach using self-organizing maps (SOM) to link scATAC-seq regions with scRNA-seq genes
STREAM	Ref	Jupyter Notebook	Single-cell Trajectories Reconstruction, Exploration And Mapping, interactive pipeline capable of disentangling and visualizing complex branching trajectories from both single-cell transcriptomic and epigenomic data
TimeReg	Ref	Matlab	Time course regulatory analysis from paired gene expression and chromatin accessibility time course data
UniPath	Ref	R	Uniform approach for pathway and gene-set based analysis of heterogeneity in single-cell epigenome and transcriptome profiles

scHiCExplorer	Ref	Python	Single cell Hi-C data analysis toolbox, set of programs to process, normalize, analyse and visualize single-cell Hi-C data
Scool	Ref	Python	New data storage format for single-cell Hi-C data

Bhmem	Ref	Java	A mapping tool for Methyl-HiC and single-cell Methyl-HiC based on bwa
deTOKI	Ref	Python	decode TAD boundaries that keep chromatin interaction insulated (deTOKI) from ultra-sparse Hi-C data using NMF, tool for identification of chromatin topologically associating domains (TAD) in single cells
DPDchrom	Ref	Python	Dissipative particle dynamics for chromosome simulations, reconstruction of 3D conformation of chromatin fiber using single cell Hi-C data
GiniQC	Ref	Python	Tool for single-cell Hi-C quality control which quantifies unevenness in the distribution of inter-chromosomal reads in the scHi-C contact matrix to measure the level of noise
hickit	Ref	C	TAD calling, phase imputation, 3D modeling and more for diploid single-cell Hi-C (Dip-C) and general Hi-C
HiCImpute	Ref	R	A Bayesian Hierarchical Model for Identifying Structural Zeros and Enhancing Single Cell Hi-C Data
HiCRep.py	Ref	Python	Fast comparison of Hi-C contact matrices in Python
NucProcess	Ref	Python	Chromatin contact paired-read single-cell Hi-C processing module for Nuc3D and NucTools
scHiCluster	Ref	Python	single-cell clustering algorithm for Hi-C contact matrices that is based on imputations using linear convolution and random walk
scHiCSRS	Ref	R	Self-Representation Smoothing Method with Gaussian Mixture Model for Imputing single cell Hi-C Data
scHiCTools	Ref	Python	Computational toolbox for analyzing single cell Hi-C data
SCL	Ref	C++	Single-Cell Lattice, lattice-based approach to infer 3D chromosome structures from single-cell Hi-C data
SnapHiC	Ref	Python	Single Nucleus Analysis Pipeline for Hi-C Data, method that can identify chromatin loops at high resolution and accuracy from scHi-C data

ASHLEYS	Ref	Python	Automated Selection of High quality Libraries for the Extensive analYsis of Strandseq data, utomated quality control for single-cell Strand-seq data
BaSiC	Ref		Tool for background and shading correction of optical microscopy images, improves continuous single-cell quantification through correction of temporal drift in time-lapse microscopy data
CellPhy	Ref	R	Maximum likelihood framework for inferring phylogenetic trees from somatic single-cell single-nucleotide variants
DeepFlow	Ref	Python	Data analysis workflow for imaging flow cytometry that combines deep convolutional neural networks with non-linear dimension reduction
SpatialDE	Ref	Jupyter Notebook	Method to identify genes which significantly depend on spatial coordinates in non-linear and non-parametric ways (spatially resolved RNA-sequencing from e.g. spatial transcriptomics, in situ gene expression measurements from e.g. SeqFISH or MERFISH)
tTt and qTfy	Ref		Enabling single- cell tracking and quantification of cellular and molecular properties in time-lapse imaging data

Awesome single cell	List of software packages and data resources for single-cell, including RNA-seq, ATAC-seq, etc.
omicX	Platform that provides real-time access to the pathways used by life science practitioners in their published works, includes
scRNA tools	Table of tools for the analysis of single-cell RNA-seq data
scRNA-seq notes	List of scRNA-seq analysis tools