Science Score: 33.0%
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Python module to analyse CRISPR-based libraries
Basic Info
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README.md
Module with utility functions to process CRISPR-based screens and method to correct gene independent copy-number effects.
Description
Crispy uses Sklearn implementation of Gaussian Process Regression, fitting each sample independently.
Install
Install pybedtools and then install Crispy
``` conda install -c bioconda pybedtools
pip install cy ```
Examples
Support to library imports: ```python from crispy.CRISPRData import Library
Master Library, standardised assembly of KosukeYusa V1.1, Avana, Brunello and TKOv3
CRISPR-Cas9 libraries.
masterlib = Library.loadlibrary("MasterLib_v1.csv.gz")
Genome-wide minimal CRISPR-Cas9 library.
minimallib = Library.loadlibrary("MinLibCas9.csv.gz")
Some of the most broadly adopted CRISPR-Cas9 libraries:
'Avanav1.csv.gz', 'Brunellov1.csv.gz', 'GeCKOv2.csv.gz', 'ManjunathWu_v1.csv.gz',
'TKOv3.csv.gz', 'Yusa_v1.1.csv.gz'
brunellolib = Library.loadlibrary("Brunello_v1.csv.gz") ```
Select sgRNAs (across multiple CRISPR-Cas9 libraries) for a given gene: ```python from crispy.GuideSelection import GuideSelection
sgRNA selection class
gselection = GuideSelection()
Select 5 optimal sgRNAs for MCL1 across multiple libraries
geneguides = gselection.selectsgrnas( "MCL1", nguides=5, offtarget=[1, 0], jacksthres=1, ruleset2_thres=.4 )
Perform different rounds of sgRNA selection with increasingly relaxed efficiency thresholds
geneguides = gselection.selectionrounds("TRIM49", nguides=5, doamberround=True, dored_round=True) ```
Copy-number correction: ```python import crispy as cy import matplotlib.pyplot as plt from crispy.CRISPRData import ReadCounts, Library
""" Import sample data """ rawcounts, copynumber = cy.Utils.getexampledata()
""" Import CRISPR-Cas9 library
Important: Library has to have the following columns: "Chr", "Start", "End", "ApprovedSymbol" Library and segments have to have consistent "Chr" formating: "Chr1" or "chr1" or "1" Gurantee that "Start" and "End" columns are int """ lib = Library.loadlibrary("Yusa_v1.1.csv.gz")
lib = lib.rename( columns=dict(start="Start", end="End", chr="Chr", Gene="Approved_Symbol") ).dropna(subset=["Chr", "Start", "End"])
lib["Chr"] = "chr" + lib["Chr"]
lib["Start"] = lib["Start"].astype(int) lib["End"] = lib["End"].astype(int)
""" Calculate fold-change """ plasmids = ["ERS717283"] rawcounts = ReadCounts(rawcounts).removelowcounts(plasmids) sgrnafc = rawcounts.normrpm().foldchange(plasmids)
""" Correct CRISPR-Cas9 sgRNA fold changes """ crispy = cy.Crispy( sgrnafc=sgrnafc.mean(1), copynumber=copynumber, library=lib.loc[sgrnafc.index] )
Fold-changes and correction integrated funciton.
Output is a modified/expanded BED formated data-frame with sgRNA and segments information
n_sgrna: represents the minimum number of sgRNAs required per segment to consider in the fit.
Recomended default values range between 4-10.
beddf = crispy.correct(nsgrna=10) print(bed_df.head())
Gaussian Process Regression is stored
crispy.gpr.plot(xfeature="ratio", yfeature="fold_change")
plt.show()
```
Credits and License
Developed at the Wellcome Sanger Institue (2017-2020).
For citation please refer to:
Owner
- Name: Emanuel
- Login: EmanuelGoncalves
- Kind: user
- Location: Lisbon
- Company: Instituto Superior Técnico (IST)
- Website: https://emanuelgoncalves.github.io/
- Twitter: emanuelvgo
- Repositories: 7
- Profile: https://github.com/EmanuelGoncalves
Assistant Professor
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| eg14 | e****s@g****m | 139 |
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- Total packages: 1
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Total downloads:
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- Total versions: 42
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pypi.org: cy
Modelling CRISPR dropout data
- Homepage: https://github.com/EmanuelGoncalves/crispy
- Documentation: https://cy.readthedocs.io/
- License: BSD License
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Latest release: 0.5.8
published almost 5 years ago