Many analytical tasks performed on data from genome-wide CRISPR-Cas9 screens are either optionally or necessarily performed in a supervised manner. In this scenario, the screening outcomes observed for large sets of positive/negative controls, i.e. genes that are prior known to be essential/nonessential for cell survival, are adopted as benchmark or template classifiers. The analytical tasks accomplished by supervised methods range from quality control [1-5], to fold-change scaling for inter-screen comparisons and interpretability [6-11], to calling statistical significant essential genes [1,9,11-14].
However, the size of positive/negative control genes has a significant impact on the scale of the experiment to perform and it proves quite prohibitive for scale-limiteds CRISPR-Cas9 screens (e.g, CRISPR-Cas9 screens using focused libraries, primary cultures, organoids or patient-derived xenografts), where the number of reference genes would become comparable or even larger than that of the genes under investigation.
Minimal Template Estimator (MinTEs) [15] is a computational framework for assembling gene templates of reduced size allowing supervised analyses of data from scale-limited CRISPR-Cas9 screens, while having a limited, user-defined impact on the overall library size. MinTEs is trained on two large genome-wide pooled CRISPR-Cas9 datasets from Project Score [16] and Project Achilles [10], respectively.
This repository contains the snakemake pipeline to derive reduced gene templates from [15].
Zenodo DOI 
Clone code repository and install dependencies:
git clone https://github.com/AleVin1995/MinTEs.git
cd MinTEs
conda env create -f envs/MinTEs.yaml
Furthermore, figures are reproducible via Jupyter notebooks (available in the notebooks subfolder) and also executable via browser
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