Cellular Genetics Software

Computational tools developed by the research groups and scientific teams of the Cellular Genetics Programme

List of Computational Tools from the Cellular Genetics Programme

Single Cell Computational Tools for Antigen Receptor Reconstruction

Name	Website
TraCeR	https://github.com/Teichlab/tracer
TraCeR – reconstruction of T cell receptor sequences from single-cell RNAseq data
BraCeR	https://github.com/Teichlab/bracer
BraCeR – reconstruction of B cell receptor sequences from single-cell RNAseq data
KirID	https://github.com/Teichlab/KIRid
KIRquant – quantification of KIR genes using personalised references

Single Cell and Spatial Analysis Computational Tools

Name	Website
CellPhoneDB	https://www.cellphonedb.org
Publicly available repository of curated receptors, ligands and their interactions. Subunit architecture is included for both ligands and receptors, representing heteromeric complexes accurately.
SC3	http://bioconductor.org/packages/SC3 https://www.sanger.ac.uk/tool/single-cell-consensus-clustering-sc3
SC3 is a method for unsupervised clustering of single-cell RNA-seq data. In addition to a graphical user-interface, SC3 provides additional information about potential outliers and marker genes for each cluster.
D3E	https://sanger.ac.uk/tool/discrete-distributional-differential-expression-d3e
D3E is a method for identifying differentially expressed genes from single-cell RNA-seq experiments. D3E compares the full distribution between two sample to identify a set of differentially expressed genes.
MPRAnator	https://sanger.ac.uk/tool/mpranator/
A tool for the design of high-throughput massively parallel reporter assays (MPRAs).
scRNAseq course	https://scrnaseq-course.cog.sanger.ac.uk/website/index.html
Teaching material the Hemberg Group’s course on computational analysis of single-cell RNA-seq data.
scRNAseq datasets	https://hemberg-lab.github.io/scRNA.seq.datasets
A collection of publicly available datasets used by the Hemberg Group at the Sanger Institute.
cardelino	https://github.com/PMBio/cardelino
Clone identification from single-cell data. This R package contains a Bayesian method to infer clonal structure for a population of cells using single-cell RNA-seq data (and possibly other data modalities).
scLVM	https://github.com/PMBio/scLVM
scLVM is a modelling framework for single-cell RNA-seq data that can be used to dissect the observed heterogeneity into different sources, thereby allowing for the correction of confounding sources of variation.
MNN	https://github.com/MarioniLab/MNN2017
Code for the paper Correcting batch effects in single-cell RNA sequencing data by matching mutual nearest neighbours by Haghverdi et al. (2018).
bbknn	https://github.com/Teichlab/bbknn
BBKNN is a fast and intuitive batch effect removal tool that can be directly used in the scanpy workflow.
kBET	https://github.com/theislab/kBET
An R package to test for batch effects in high-dimensional single-cell RNA sequencing data.
SCCAF	https://github.com/SCCAF/sccaf.github.io
Machine learning based self-projection framework to assess the clustering quality of single-cell RNA-seq data, find similar cell clusters that encode identical signature, detect potential doublets and find marker genes.
scfind	https://scfind.sanger.ac.uk/
Search engine for genes in large single-cell sequencing collections.
scmap	https://scmap.sanger.ac.uk/
A method for projecting cells from a scRNA-seq experiment onto the cell-types or individual cells identified in other experiments

CRISPR/Cas9

Name	Website
FORECasT	https://partslab.sanger.ac.uk/FORECasT
FORECasT is a tool for predicting the mutational outcomes resulting from double stranded breaks induced by CRISPR/Cas9.
JACKS	https://partslab.sanger.ac.uk/JACKS
JACKS: joint analysis of CRISPR/Cas9 knockout screens.

Other tools

Name	Website
spatialDE	https://github.com/Teichlab/SpatialDE
SpatialDE is a method to identify genes which significantly depend on spatial coordinates in non-linear and non-parametric ways.