bean improves CRISPR pooled screen analysis by 1) unconfounding variable per-guide editing outcome by considering genotypic outcome from reporter sequence and 2) through accurate modeling of screen procedure.
bean supports end-to-end analysis of pooled sorting screens, with or without reporter.
bean subcommands include the following: Click on the links to see the full documentation.
count,count-samples: Base-editing-aware mapping of guide, optionally with reporter from.fastqfiles.create-screencreates minimal ReporterScreen object from flat gRNA count file. Note that this way, allele counts are not included and many functionalities involving allele and edit counts are not supported.
profile: Profile editing preferences of your editor.qc: Quality control report and filtering out / masking of aberrant sample and guidesfilter: Filter reporter alleles; essential fortilingmode that allows for all alleles generated from gRNA.run: Quantify targeted variants' effect sizes from screen data.
- Screen data is saved as
ReporterScreenobject in the pipeline. BEAN stores mapped gRNA and allele counts inReporterScreenobject which is compatible with AnnData.
First install PyTorch. Then download from PyPI:
pip install crispr-beanFor the latest version of bean (and for the test files in tests/data), install from Github:
git clone https://github.com/pinellolab/crispr-bean.git
cd crispr-bean
pip install -e .See the documentation for tutorials and API references.
| Library design | Selection | Reporter | Tutorial link |
|---|---|---|---|
| GWAS variant library | FACS sorting | Yes/No | GWAS variant screen |
| Coding sequence tiling libarary | FACS sorting | Yes/No | Coding sequence tiling screen |
| GWAS variant library | Survival / Proliferation | Yes/No | GWAS variant screen |
| Coding sequence tiling libarary | Survival / Proliferation | Yes/No | Coding sequence tiling screen |
| Perturbation library without reporter | FACS sorting | No | No reporter screen |
The bean filter and bean run steps depend on the type of gRNA library design, where BEAN supports two modes of running.
-
variantlibrary: Several gRNAs tile each of the targeted variants. Only the editing rate of the target variant is considered and the bystander effects are ignored.- ➕ Increase power for your target variant, as the signal is not distributed across likely no-effect bystanders.
- ➖ Ignores potential bystander effect
- ✔️ Suitable for noncoding GWAS variant screens.
-
tilinglibrary: gRNA densely tiles a long region (e.g. gene(s), exon(s), coding sequence(s)). Bystander edits are considered to obtain alleles with significant fractions. Edited alleles can be "translated" to output coding variants.- ➕ Considers bystander effect
- ➖ If the library results in alleles that are not diverse enough across gRNAs, signal will likely be diluted to all variants in that alleles. (ex. Allele "GGGGG" with a single gRNA score will distribute scores across 5 G's.)
- ✔️ Suitable for coding variant screens with tiling design.
import bean as be
cdata = be.read_h5ad("bean_counts_sample.h5ad")
Python package bean supports multiple data wrangling functionalities for ReporterScreen objects. See the ReporterScreen API tutorial for more detail.
- Installation takes 14.4 mins after pytorch installation with pytorch in Dell XPS 13 Ubuntu WSL.
bean runtakes 4.6 mins with--scale-by-acctag in Dell XPS 13 Ubuntu WSL for variant screen dataset with 3455 guides and 6 replicates with 4 sorting bins.- Full pipeline takes 90.1s in GitHub Action for toy dataset of 2 replicates and 30 guides.
See CHANGELOG for recent updates. If you have questions or feature request, please open an issue. Please feel free to send a pull request.
If you have used BEAN for your analysis, please cite:
Ryu, J., Barkal, S., Yu, T. et al. Joint genotypic and phenotypic outcome modeling improves base editing variant effect quantification. Nat Genet (2024). https://doi.org/10.1038/s41588-024-01726-6
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