Designs and analyzes pooled prime-editor (PE) screens for installing precise genetic variants without bystander confounding. Covers pegRNA design with PRIDICT and PRIDICT2 for predicting per-pegRNA editing efficiency, pegRNA architecture (spacer + scaffold + PBS + RTT), PE2/PE3/PE3b/PEmax variants, MOSAIC in situ saturation mutagenesis, the PRIME pooled-screen methodology (Ren 2023; ~3,699 ClinVar variant screens), chromatin context as a major locus-level determinant of PE efficiency, scaffold-incorporation and indel byproduct quantification with CRISPResso2, and the cross-modal validation strategy of PE + base-editor screens for variant function. Use when designing a pegRNA library for variant installation, choosing between BE and PE for a specific edit, predicting pegRNA efficiency before library synthesis, analyzing PE screen output, distinguishing intended-edit from scaffold-incorporation, or scaling PE screens to thousands of variants.
Instalar com Codex ou Claude Copie este prompt, cole no Codex, Claude ou outro assistente e deixe que ele revise a página da skill e instale para você.
Um comando direto ignora o prompt de revisão. Verifique a origem antes de executá-lo.
Designs and analyzes pooled prime-editor (PE) screens for installing precise genetic variants without bystander confounding. Covers pegRNA design with PRIDICT and PRIDICT2 for predicting per-pegRNA editing efficiency, pegRNA architecture (spacer + scaffold + PBS + RTT), PE2/PE3/PE3b/PEmax variants, MOSAIC in situ saturation mutagenesis, the PRIME pooled-screen methodology (Ren 2023; ~3,699 ClinVar variant screens), chromatin context as a major locus-level determinant of PE efficiency, scaffold-incorporation and indel byproduct quantification with CRISPResso2, and the cross-modal validation strategy of PE + base-editor screens for variant function. Use when designing a pegRNA library for variant installation, choosing between BE and PE for a specific edit, predicting pegRNA efficiency before library synthesis, analyzing PE screen output, distinguishing intended-edit from scaffold-incorporation, or scaling PE screens to thousands of variants.
If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.
Prime-Editing Screen Analysis
"Design or analyze a pooled prime-editor screen" -> Design pegRNAs (spacer + scaffold + PBS + RTT) for intended edits, predict efficiency with PRIDICT2, filter pre-synthesis to efficient candidates, install variants in the screen, quantify intended-edit vs scaffold-incorporation vs indel via CRISPResso2, and aggregate to per-variant fitness scores.
Python: PRIDICT2 for pegRNA efficiency prediction
Python: ePRIDICT for chromatin-context prediction; pair with PRIDICT2 rather than replacing it
CLI: CRISPResso --prime_editing_pegRNA_* for amplicon-level analysis
PE3 plus MMR inhibition (MLH1dn) on the PEmax architecture
1%
Highest efficiency at favorable sites
PE6 / dual-pegRNA (2023)
2023
Engineered compact PE; twin-pegRNA systems
Variable
Specific applications
Decision rule: For pooled screens at scale, PE2 or PEmax (single-guide architecture) is preferred over PE3, whose additional nicking sgRNA complicates library architecture. For specific high-efficiency edits, PEmax + PRIDICT2-optimized pegRNA.
pegRNA Architecture
A pegRNA contains four critical elements that determine efficiency:
5' SPACER (20 nt) -- standard sgRNA spacer; defines target locus via NGG PAM
+
SCAFFOLD (~80 nt) -- canonical or recoded scaffold (Chen 2021 recodes it to cut scaffold-incorporation byproducts)
+
PBS (Primer Binding Site, 8-15 nt) -- complements protospacer downstream of cut site
+
RTT (Reverse Transcription Template, 10-30 nt) -- encodes intended edit; copied by RT
3'
Key design parameters:
PBS length: 11-13 nt typical; longer for high-GC contexts; PBS GC fraction critical (35-65% target)
RTT length: 10-20 nt typical; longer for distant edits (10+ bp away from cut)
RTT-edit position: intended edit at position 4-30 from cut site
Scaffold: standard sgRNA scaffold OR the Chen 2021 recoded scaffold, which removes homology with the genomic target and cuts scaffold-derived byproducts. The recoding does not itself raise editing efficiency; that comes from MLH1dn and the PEmax architecture.
PRIDICT and PRIDICT2 pegRNA Efficiency Prediction
Mathis N et al 2023 Nat Biotechnol 41:1151 (PRIDICT v1) / 2025 Nat Biotechnol 43(5):712 (PRIDICT2; published online June 2024) developed deep-learning predictors of per-pegRNA editing efficiency. PRIDICT2 is the current state of the art.
import pandas as pd
from pathlib import Path
defload_pridict2_predictions(prediction_dir):
'''Load PRIDICT2 batch outputs from prediction_dir/'''
summary = pd.read_csv(Path(prediction_dir) / '<timestamp>_summary_K562_batch_summary.csv')
# summary has columns: sequence_name, PBS, RTT, predicted_efficiency, predicted_indel, etc.return summary
Key determinants of PE efficiency (Mathis 2025 PRIDICT2):
Feature
Effect on efficiency
PBS GC content
40-55% optimal; high GC slows annealing
PBS length
11-13 nt optimal; longer for high-GC PBS
RTT length
10-20 nt typical; trade-off between coverage and processivity
Edit position in RTT
Closest to PBS = highest efficiency
Chromatin context
Dominant locus effect; H3K9me3 heterochromatin ~0.8% vs ~2.2% elsewhere
Cell line / Cas9 expression
Variable; piloting required
Cell cycle phase
S/G2 = higher efficiency
Critical insight from Mathis 2025: Chromatin context is a major locus-level determinant that sequence-only predictors miss, which is why ePRIDICT is designed to be combined with PRIDICT2.0 rather than replace it -- the pairing helps most in regions of lower chromatin accessibility. For genome-scale screens, validate predictions empirically at representative loci.
PRIME Pooled Screen Methodology
Ren X et al 2023 Mol Cell 83:4633 established the PRIME pooled prime-editing screen methodology (earlier 2023 bioRxiv preprint):
pegRNA library covering thousands of intended variants, screened for specificity at design time (Ren 2023 used GuideScan2; add PRIDICT2 efficiency prediction for new designs)
Lentiviral delivery in a PE-expressing cell line (Ren 2023: MOI 0.3 for the MYC-enhancer screen, MOI 0.5 for the variant screens)
Selection on integration marker
Time-course screen for variant function (e.g., drug sensitivity)
Endpoint amplicon sequencing of each pegRNA target locus
CRISPResso2 quantification of intended-edit %
MAGeCK / drugZ-style hit calling on edit-efficient pegRNAs
Quantified scale: ~3,699 ClinVar variants installed in a single PRIME screen, alongside 1,304 breast-cancer GWAS variants.
MOSAIC In Situ Saturation Mutagenesis
MOSAIC (Hsu 2024, bioRxiv) is a high-throughput in-situ saturation-mutagenesis prime-editing method with multiplexed read-out:
Tile pegRNAs across protein domains for systematic mutagenesis
Saturation: every possible amino acid change in a region
Use case: Cancer-drug-resistance variant scanning; protein-domain function mapping.
Run PRIDICT2 on a Custom pegRNA Library
Goal: Predict editing efficiency for thousands of pegRNAs before library synthesis.
Approach: Build a CSV with one row per intended edit (sequence + edit notation), run PRIDICT2 in batch mode, parse the per-pegRNA efficiency summary, and filter to candidates above the chosen efficiency threshold.
# Step 3: parse and filterimport pandas as pd
predictions = pd.read_csv('predictions/<timestamp>_summary_K562_batch_summary.csv')
# Filter to pegRNAs with predicted efficiency > 50% (library-inclusion convention)
filtered = predictions[predictions['predicted_editing_efficiency'] > 50]
print(f'pegRNAs passing PRIDICT2 >50%: {len(filtered)} / {len(predictions)}')
# Pick top 3 per intended edit
top3 = (filtered.sort_values(['sequence_name', 'predicted_editing_efficiency'],
ascending=[True, False])
.groupby('sequence_name').head(3))
top3.to_csv('peg_library_filtered.csv', index=False)
Cross-Validate PE with Base Editor Screens
Goal: Confirm variant-function calls from PE with orthogonal BE screens.
Approach: Design parallel BE library for the same variants; run both screens; intersect hits.
# BE screen output (target conversion + bystander)
be_hits = pd.read_csv('be_screen_hits.tsv', sep='\t')
# PE screen output (intended edit + scaffold-incorp + indel)
pe_hits = pd.read_csv('pe_screen_hits.tsv', sep='\t')
# Intersect on intended variant
concordant = be_hits.merge(pe_hits, on='variant_id', suffixes=('_be', '_pe'))
# Filter to high-confidence: both methods call variant + same direction
concordant['high_confidence'] = (concordant['be_fdr'] < 0.05) & (concordant['pe_fdr'] < 0.05) & \
(np.sign(concordant['be_lfc']) == np.sign(concordant['pe_lfc']))
Critical: PE-only hits in BE-coverable variants are suspect (BE should detect them). PE-only hits in non-BE-coverable variants (e.g., transversions) are genuinely PE-unique.
CRISPResso2 for PE Quantification
CRISPResso \
--fastq_r1 pe_sample.fq.gz \
--amplicon_seq <amplicon_seq> \
--guide_seq <20nt_spacer> \
--prime_editing_pegRNA_spacer_seq <spacer> \
--prime_editing_pegRNA_extension_seq <RTT+PBS> \
--prime_editing_pegRNA_scaffold_seq <scaffold> \
--quantification_window_size 25 \ # widen to cover edit
--output_folder pe_results \
--name sample_id
# Output: CRISPResso_quantification_of_editing_frequency.txt# Prime-editing outcomes appear as extra amplicon ROWS (Reference / Prime-edited /# Scaffold-incorporated), each with Unmodified%, Modified% and read counts.
Failure Modes
Low pegRNA efficiency despite high PRIDICT prediction
Trigger: Sequence-only prediction missed chromatin context.
Mechanism: Closed chromatin reduces Cas9 binding and RT activity; PRIDICT2 only sees sequence.
Symptom: PRIDICT2 predicts 60% efficiency; observed is 5%.
Fix: Cross-reference target with chromatin accessibility data (ATAC-seq) in the cell line; flag pegRNAs at silenced loci; pilot before screen.
High scaffold incorporation
Trigger: RTT too short relative to PBS, or RT processivity issue.
Mechanism: RT reads past edit into scaffold; resulting product is detectable but undesired.
Symptom: Scaffold incorporation >5%; intended edit efficiency low.
Fix: Re-design pegRNA with longer RTT; verify with PRIDICT2 score for scaffold_incorp; pilot at representative loci.
PE2 cell line lacks RT expression
Trigger: PE2 construct expressed at low level; insufficient RT for productive editing.
Mechanism: PE2 requires high RT expression; some cell lines down-regulate.
Symptom: Library-wide editing <10%; not locus-specific.
Fix: Verify PE2 expression by Western blot; consider PEmax (higher activity); use better-validated cell lines (K562, HEK293T, U2OS).
Multi-base intended edit but only one base installed
Trigger: Long RTT designed for multi-base edit; RT prematurely terminates.
Mechanism: RT processivity drops with longer RTT; multi-base edits often incomplete.
Symptom: Allele table shows partial-edit alleles (some bases installed, not all).
Fix: Re-design with shorter RTT covering only the closest edits; or use PE3 to nick opposite strand and force longer RT processivity.
Library missing intended variant
Trigger: No suitable PAM/PBS/RTT combination for the intended edit.
Mechanism: PE requires NGG PAM within 30 nt of edit; rare edits cannot be installed.
Symptom: Specific variants absent from library.
Fix: Use SpRY-PE for relaxed PAM; accept that some variants cannot be PE-installed; consider BE if applicable.
Cas9 vs BE vs PE for Variant Installation
Approach
Bystander
Indels
Coverage
When to use
Cas9 + HDR
None
High
Variable (depends on template integration)
Precise edits at scale; high indel byproduct
Base editor
YES
Low (<5%)
Limited by editing window
C->T or A->G at editable position
Prime editor
NONE
Low (<3%)
NGG-PAM within 30 nt of edit
Precise variants; multi-base; transversions
Cas9 (no template)
NONE
70%+
Anywhere with NGG
LoF only; not variant-specific
Decision tree:
C->T or A->G at editing-window position: BE (higher efficiency than PE)
Multi-base / transversion / out-of-window: PE
LoF without specifying variant: Cas9
Random insertions: HDR (lower throughput than PE)
Quantitative Thresholds
Threshold
Value
Source / Rationale
PRIDICT2 efficiency for library inclusion
>50%
Project-chosen cutoff; PRIDICT2 prescribes none
Intended edit % for screen power
>5%; >20% at favorable sites
Field convention
Scaffold incorporation
<2% (clean PE); <5% acceptable
Empirical
Indel byproduct
<3% (PE2); <5% (PE3)
Anzalone 2019; Chen 2021
PBS GC content
40-55%
PRIDICT2
PBS length
11-13 nt
PRIDICT2
RTT length
10-20 nt
PRIDICT2
Edit position from cut
1-30 nt
Anzalone 2019
Cell line for PE
K562, HEK293T, U2OS validated
High RT expression
Common Errors
Error / symptom
Cause
Solution
Low editing across library
Cell-line RT inactivity
Verify PE2 expression; switch to validated line
Scaffold incorporation >10%
RTT too short
Re-design with longer RTT
Partial multi-base edits
RT processivity limit
Shorter RTT or PE3
PRIDICT predicts but observes much lower
Chromatin context
Pilot at chromatin-aware sites
Library missing variants
No NGG PAM
SpRY-PE; BE alternative
PE concordant with BE on transitions, disagrees on transversions
PE handles transversions BE doesn't
Expected; trust PE
References
Anzalone AV et al. 2019. Nature 576:149. Original PE2/PE3 (foundational prime editing paper).
Mathis N et al. 2023. Nat Biotechnol 41:1151. PRIDICT v1 deep-learning pegRNA prediction.
Mathis N et al. 2025. Nat Biotechnol 43(5):712 (published online June 2024). PRIDICT2 + chromatin context (current state-of-the-art).
