Analyzes base-editing screens for variant function. Covers library design (Hanna 2021 ClinVar-scale CBE screen benchmarked on BRCA1/2, Cuella-Martin 2021 DDR saturation), CBE vs ABE chemistry choice (BE3/BE4 vs ABE7.10/ABE8.20/ABE8e), editing-window math (positions 4-8 from PAM-distal end; 4-7 for ABE7.10), bystander-edit quantification and the variant-call ambiguity it creates, sgRNA-efficiency filtering before hit calling, indel byproduct interpretation, the substitution-vs-indel diagnostic, variant annotation against ClinVar / COSMIC, and the Broad be-validation-pipeline. Use when designing a BE variant screen, choosing CBE vs ABE for a specific edit, interpreting bystander-confounded hits, distinguishing functional signal from indel artifact, integrating CRISPResso2 output with screen scoring, or deciding BE vs PE for SNV installation.
Analyzes base-editing screens for variant function. Covers library design (Hanna 2021 ClinVar-scale CBE screen benchmarked on BRCA1/2, Cuella-Martin 2021 DDR saturation), CBE vs ABE chemistry choice (BE3/BE4 vs ABE7.10/ABE8.20/ABE8e), editing-window math (positions 4-8 from PAM-distal end; 4-7 for ABE7.10), bystander-edit quantification and the variant-call ambiguity it creates, sgRNA-efficiency filtering before hit calling, indel byproduct interpretation, the substitution-vs-indel diagnostic, variant annotation against ClinVar / COSMIC, and the Broad be-validation-pipeline. Use when designing a BE variant screen, choosing CBE vs ABE for a specific edit, interpreting bystander-confounded hits, distinguishing functional signal from indel artifact, integrating CRISPResso2 output with screen scoring, or deciding BE vs PE for SNV installation.
Before using code patterns, verify installed versions match. If versions differ:
CLI: CRISPResso --version
Python: pip show CRISPResso2; BE-Hive is a GitHub clone (maxwshen/be_predict_bystander), not a PyPI package
If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.
Base Editing Screen Analysis
"Analyze my base-editor variant-function screen" -> Quantify per-sgRNA target-base conversion, bystander rate, and indel byproducts from amplicon sequencing; filter on editing efficiency; map each sgRNA to its intended SNV (target + bystander pattern); compute per-variant fitness from the screen log-fold change; reconcile target vs bystander variant attribution; annotate against ClinVar / COSMIC.
CLI: CRISPResso --base_editor_output for per-amplicon BE quantification
CLI: Broad be-validation-pipeline for end-to-end pooled-screen analysis with editing-efficiency filtering
Python: BE-Hive (Arbab 2020) for editing-efficiency prediction; clone maxwshen/be_predict_bystander and import via sys.path
Web: BE-Designer (Hwang 2018, RGEN Tools) for variant-encoding sgRNA design
Base Editor Chemistry Selection
Editor
Reaction
Editing window
Indel byproduct rate
When to use
BE3 (Komor 2016)
C->T (also G->A on opposite strand)
Pos 4-8 from PAM-distal end
5-10%
Original; superseded
BE4 / BE4max (Koblan 2018)
C->T
Pos 4-8
<5%
CBE standard
eA3A-BE3
C->T narrow specificity
Pos 5-7
<5%
Specifically TC contexts (eA3A prefers TC)
ABE7.10 (Gaudelli 2017)
A->G (T->C opposite strand)
Pos 4-7
<2%
First ABE; slow at non-TA contexts
ABE8.20 (Gaudelli 2020)
A->G
Pos 4-8
<2%
Modern ABE; high activity
ABE8e (Richter 2020)
A->G
Pos 4-8
<2%
Highest editing activity; more processive than ABE7.10
evoCDA-BE
C->T (broader)
Pos 1-9
5-10%
Larger editing window; more bystander
CGBE1 (Kurt 2021)
C->G
Pos 5-7
5-10%
C-to-G transversion; rare use
GBE (Zhao 2021)
C->G or C->A
Pos 4-7
5-10%
Transversions; less mature
Decision rule: For a target SNV at position 4-8 of a candidate spacer with no bystander Cs/As in the same window, BE3-BE4 or ABE7.10 is sufficient. For high-throughput variant scanning where bystander tolerance must be minimized, use eA3A-BE3 (TC contexts only) for C->T, or ABE7.10 rather than ABE8e/ABE8.20 for A->G -- its 4-7 window is the narrowest ABE.
Editing Window Math
Why this matters for postdoc-level use: Base editors are tethered to dCas9 (or nCas9) and the deaminase acts on the displaced ssDNA "R-loop" formed when Cas9 binds. The deaminase has a fixed reach -- positions 4-8 from the PAM-distal end of the protospacer for canonical BE3/BE4, and 4-7 for ABE7.10. Outside this window, editing efficiency drops by 10-50x.
PAM-distal end PAM-proximal
| |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 NGG
^^^^^^^^^^^
Canonical editing window (positions 4-8)
For BE4max: positions 4-8 are 5-50x more efficient than positions 1-3 or 9-13 (ABE7.10: 4-7)
For SpABE8e: positions 4-8 (Richter 2020), matching the corresponding CBEs rather than ABE7.10's narrower 4-7
For evoCDA-BE: window 1-9 (broader; more bystander)
Critical implication for variant interpretation: If the intended edit is at position 5 and there is an additional editable C/A at position 7, both will be edited in the same molecule. The screen scores the combination of edits, not the intended one alone. This is bystander confounding.
sgRNA Library Design for BE Screens
Goal: Tile editing-window-positioned spacers across a protein region of interest to enable variant scanning.
Approach: For each amino acid in the target region, find NGG-adjacent spacers where the SNV-of-interest base falls in editing positions 4-8 with minimal bystander C/A in the same window. Annotate each spacer with the predicted amino acid changes (target + bystander).
import pandas as pd
import re
from Bio.Seq import Seq
deffind_be_spacers(cds_sequence, cds_protein_start, target_aa, target_base='C', editor='BE4max'):
'''Find sgRNAs that place target_base in editor-specific window at target_aa.
Returns spacers with bystander annotation.
Args:
cds_sequence: nucleotide CDS (translated frame 1)
cds_protein_start: amino acid number of CDS start (usually 1)
target_aa: amino acid number to install variant (e.g., 130 for residue 130)
target_base: 'C' (CBE) or 'A' (ABE)
editor: 'BE3', 'BE4max', 'eA3A-BE3', 'ABE7.10', 'ABE8.20', 'ABE8e', 'evoCDA-BE'
Returns: DataFrame with spacer, position-in-cds, target-base-position-in-spacer,
bystander_positions, predicted_aa_changes
'''# Editor-specific editing window (positions from PAM-distal end of spacer)
window_by_editor = {
'BE3': (4, 8), 'BE4max': (4, 8), 'eA3A-BE3': (5, 7),
'ABE7.10': (4, 7), 'ABE8.20': (4, 8), 'ABE8e': (4, 8), # SpABE8e matches CBE window (Richter 2020)'evoCDA-BE': (1, 9),
}
window_lo, window_hi = window_by_editor[editor]
aa_index = target_aa - cds_protein_start # 0-indexed in protein
aa_start_nt = aa_index * 3# nt offset in cds
candidates = []
spacer_len = 20
pam_pattern = re.compile(r'(?=([ACGT]GG))')
for strand, seq in [('+', cds_sequence), ('-', str(Seq(cds_sequence).reverse_complement()))]:
for pam_match in pam_pattern.finditer(seq):
pam_pos = pam_match.start()
spacer_start = pam_pos - spacer_len
if spacer_start < 0:
continue
spacer = seq[spacer_start:pam_pos]
# Editor-specific window from PAM-distal end (1-indexed)# Find all editable bases in window
edit_bases_in_window = []
for i, b inenumerate(spacer[window_lo-1:window_hi], start=window_lo):
if b == target_base:
edit_bases_in_window.append(i)
ifnot edit_bases_in_window:
continue# Annotate which edits hit the target_aa codon
target_codon_start = aa_start_nt
target_codon_end = target_codon_start + 3
target_position_in_spacer = []
for i in edit_bases_in_window:
genomic_pos = spacer_start + i - 1if target_codon_start <= genomic_pos < target_codon_end:
target_position_in_spacer.append(i)
bystander_positions = [i for i in edit_bases_in_window if i notin target_position_in_spacer]
candidates.append({
'spacer': spacer,
'strand': strand,
'spacer_start': spacer_start,
'target_positions': target_position_in_spacer,
'bystander_positions': bystander_positions,
'n_bystanders': len(bystander_positions),
})
return pd.DataFrame(candidates).sort_values('n_bystanders')
Decision rule: Select spacers with target_positions != empty AND n_bystanders minimized. For variant-by-variant scanning, accept up to 1-2 bystanders if biology of those positions is interpretable; flag for downstream variant attribution.
Goal: Drop sgRNAs that do not edit efficiently, since unedited reads represent no biological perturbation.
Approach: From CRISPResso2 output, compute target-base-conversion percentage per sgRNA; filter library to sgRNAs with >50% target editing in a pilot or co-screened control.
deffilter_by_editing_efficiency(crispresso_outputs_dir, target_pos, target_base, efficiency_threshold=0.5):
'''Drop sgRNAs that edit <efficiency_threshold of reads at target position.
crispresso_outputs_dir: directory containing CRISPResso per-sample outputs.'''from pathlib import Path
results = []
for sample_dir in Path(crispresso_outputs_dir).glob('CRISPResso_on_*'):
sgrna_id = sample_dir.name.replace('CRISPResso_on_', '')
quant_file = sample_dir / 'Quantification_window_nucleotide_percentage_table.txt'ifnot quant_file.exists():
continue
df = pd.read_csv(quant_file, sep='\t')
# Find target position in the quantification window
target_row = df[df['Position'] == target_pos]
if target_row.empty:
continue# Editing = sum of non-original bases at target position
original_pct = target_row[target_base].values[0]
editing_pct = (100 - original_pct) / 100
results.append({'sgrna_id': sgrna_id, 'editing_pct': editing_pct,
'pass_filter': editing_pct >= efficiency_threshold})
return pd.DataFrame(results)
Convention: Drop sgRNAs below 50% editing for variant-function screens. A common working split is a 30% editing floor for primary screening and a 50% floor for confirmed hits. Below 30%, the screen has insufficient power; above 70%, results approach saturation editing.
Bystander Edit Attribution
Why this matters: When a sgRNA's editing window contains the target base AND a bystander base, the screen scores the combination. To attribute screen signal to the target variant alone, either (a) include sgRNAs that edit only the target (no bystander) -- often impossible -- or (b) deconvolute via parallel measurements.
Strategies for variant-by-variant attribution:
Tile multiple sgRNAs with different bystander patterns: If 5 different sgRNAs all hit the target base but have different bystanders, common signal across them is target-attributable (Hanna 2021 approach).
Use orthogonal chemistry: Run the same variant scan with prime editor (no bystanders); cross-validate. See [[prime-editing-screens]].
Bystander stratification: From CRISPResso2 allele table, partition reads by exact edit pattern (target only, target+bystander_1, target+bystander_2, etc.); separately score each pattern's contribution to the phenotype.
Restrict library: Use only sgRNAs with zero bystanders in the editing window (rare; may exclude most candidate spacers).
defdeconvolute_bystander(allele_table_path, target_pos, bystander_pos_list):
'''From CRISPResso2 allele table, partition reads by edit pattern at target + bystanders.
Returns: per-pattern frequency for each combination of target/bystander edits.'''
alleles = pd.read_csv(allele_table_path, sep='\t', compression='zip')
# Mark target_edited and per-bystander_edited
alleles['target_edited'] = alleles['Aligned_Sequence'].str[target_pos-1] != alleles['Reference_Sequence'].str[target_pos-1]
for bp in bystander_pos_list:
alleles[f'bystander_{bp}_edited'] = alleles['Aligned_Sequence'].str[bp-1] != alleles['Reference_Sequence'].str[bp-1]
return alleles.groupby(['target_edited'] + [f'bystander_{bp}_edited'for bp in bystander_pos_list])['Reference_pct'].sum().reset_index()
Hit Calling for Variant-Function Screens
Goal: Score per-variant fitness from a base-editor screen.
Approach: Filter library to efficiency-passing sgRNAs (>50% editing), then run MAGeCK MLE or drugZ on the sgRNA-level counts; map each significant sgRNA to its predicted variant + bystander pattern; aggregate to per-variant scores.
Hanna 2021 BRCA1/2 Variant-Function Screen Methodology
Hanna et al 2021 Cell 184:1064 benchmarked CBE variant scanning at scale, screening 68,526 sgRNAs covering 52,034 ClinVar variants across 3,584 genes, with BRCA1 and BRCA2 as the positive/negative-selection benchmark:
Design the CBE library from predicted variant impact (ClinVar annotation), covering each variant with the sgRNAs that install it
Run drug-modifier screens (PARPi sensitivity) with vehicle vs drug
Score per variant by aggregating over all sgRNAs that install it; cross-check against bystander-controlled sgRNAs
Standard surrounding practice: verify editing efficiency at a control timepoint via amplicon sequencing, drop low-efficiency sgRNAs (see the editing-efficiency convention above), and call sensitizers with a bidirectional method such as drugZ.
Quantified result: Recovered known loss-of-function variants in BRCA1 and BRCA2 with high precision, and identified PARP1 variants conferring resistance to PARP inhibitors.
Cuella-Martin 2021 DDR-Gene Variant Screening
Cuella-Martin et al 2021 Cell 184:1081-1097 screened ~86 DNA-damage-response (DDR) genes (including BRCA1/2) with CBE saturation mutagenesis:
Saturation CBE design across 86 DDR genes (not BRCA1/2 alone)
Identified pathogenic/likely-pathogenic variants in critical protein domains
Combined with biochemical and genetic validation (for example the 53BP1-USP28 interaction surface)
Demonstrated saturation mutagenesis is feasible at protein-domain scale
Relationship to Hanna 2021: the two studies appeared back-to-back in the same Cell issue and apply the same CBE variant-scanning strategy to complementary targets -- Hanna benchmarks against ClinVar-annotated variants genome-wide, Cuella-Martin saturates 86 DDR genes. Treat them as complementary methodology references, not as cross-validations of each other.
Cas9 vs Base Editor vs Prime Editor for Variant Installation
Approach
What it does
Bystander
Indels
When to use
Cas9 + HDR template
Installs precise edit + template
None
High (NHEJ competition)
When precise edit needed; high indel byproduct
Cas9 (no template)
Random indels at cut site
None
70%+
Loss-of-function; not variant-specific
CBE (BE3/BE4)
C->T at editing window
Yes (multiple Cs)
<5%
C->T variants with manageable bystanders
ABE (ABE7.10/ABE8e)
A->G at editing window
Yes (multiple As)
<2%
A->G variants; clean for single-A spacers
CGBE / GBE
C->G or C->A
Yes
5-10%
Transversions; rare use cases
Prime editor (PE2/PE3)
Templated edit; any base change
None
1-3%
Precise variants; lower efficiency
Decision: For C->T or A->G with available editing window: base editor is preferred (higher efficiency than PE). For other transitions/transversions, multi-base edits, or zero-bystander requirements: prime editor.
Broad be-validation-pipeline
The Broad Institute's be-validation-pipeline (https://broadinstitute.github.io/be-validation-pipeline/) is a CRISPResso2 post-processing and validation toolkit for BE amplicon data -- a set of Jupyter notebooks, not a workflow-engine pipeline. Run CRISPResso2 first, then execute the notebooks in order:
git clone https://github.com/broadinstitute/be-validation-pipeline
cd be-validation-pipeline
pip install -r requirements.txt
# Step 1: run CRISPResso2 in batch mode (or use the BEV tool on GPP LIMS).# The batch file is tab-delimited with columns: name, fastq_r1, amplicon_seq, guide_seq# (plus optional -w, -wc, --exclude_bp_from_left/right).
docker run -v ${PWD}:/DATA -w /DATA -i pinellolab/crispresso2 \
CRISPRessoBatch --batch_settings batch_file.txt --skip_failed --base_edit
# Step 2: run the notebooks in order against the CRISPResso2 output# notebooks/01_BEV_allele_frequencies.ipynb# notebooks/02_BEV_nucleotide_percentage_plots.ipynb# notebooks/03_BEV_editing_efficiency.ipynb# Outputs: allele-frequency tables, nucleotide-percentage plots, editing-efficiency heat maps
The notebooks cover allele-frequency tabulation, nucleotide-level editing quantification and editing-efficiency summaries. Hit calling is NOT part of this toolkit -- score the screen separately with drugZ or MAGeCK.
Failure Modes
Mostly indels in BE sample
Trigger: Cas9 contamination, wrong vector (e.g., used pCas9-BE3 plasmid but selected on Cas9 line), or evoCDA-BE / broader-window chemistry.
Mechanism: Cas9 cuts dsDNA; BE relies on nicked-ssDNA deamination. Cas9 expression in the same cell creates indels.
Symptom: Substitution-vs-indel ratio <3 in CRISPResso output.
Fix: Verify vector (nCas9-BE3 not Cas9-BE3); confirm cell line lacks Cas9 background; restrict to specifically engineered BE-cell lines.
High editing but no biological signal
Trigger: Bystander C/A is dominating; intended variant is not the perturbation driving phenotype.
Mechanism: When target is at position 5 and bystander is at position 7, the molecule carries both; phenotype is from the bystander.
Symptom: Strong screen signal but variant attribution unclear.
Fix: Run orthogonal prime-editor scan of the same intended variants; restrict library to bystander-free spacers when possible; deconvolute via allele-frequency table.
sgRNA shows perfect editing but no fitness signal
Trigger: Intended variant is silent or compensatory; the protein function is unchanged.
Mechanism: Variants can be tolerated; not all variants are LoF or GoF.
Symptom: High editing efficiency (>70%) but per-sgRNA LFC near zero.
Fix: Expected outcome for many variants; flag silent / compensatory variants in the report.
Low editing across all guides
Trigger: Wrong cell line for the BE; cell line has poor BE activity (some lines lack APOBEC or have low expression).
Mechanism: BE efficiency depends on cell-line expression of TadA or APOBEC components.
Symptom: Median editing <30% across library.
Fix: Test in a BE-validated cell line (HEK293T, U2OS, K562 generally work); pilot before full screen.
Library missing intended-variant sgRNAs
Trigger: No NGG-adjacent spacer places target base in editing window for that codon.
Mechanism: Editor window is fixed; some codons cannot be targeted with given chemistry.
Symptom: Specific variants absent from screen.
Fix: Use PAM-relaxed BE variants (SpRY-CBE, SpRY-ABE); use prime editor for variants outside BE accessibility; accept that some variants cannot be installed.
Quantitative Thresholds
Threshold
Value
Source / Rationale
Editing window
Positions 4-8 from PAM-distal end (BE3/BE4); 4-7 (ABE7.10); 4-8 (SpABE8e)
