Library design

Version Compatibility

Reference examples tested with: CRISPOR 5.01+, BioPython 1.83+, pandas 2.2+, numpy 1.26+, Azimuth 2.0+ (Doench 2016), CRISPRon 1.0+ (Xiang 2021), DeepSpCas9 1.0+ (Kim 2019).

Before using code patterns, verify installed versions match. If versions differ:

• CLI: crispor.py --help from the crisporWebsite clone
• Python: Azimuth has no console script; call azimuth.model_comparison.predict(...)

If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.

sgRNA Library Design

"Design a CRISPR library for my screen" -> Pick a chemistry (Cas9 KO, CRISPRi, CRISPRa, Cas12a, base or prime editor), score candidate guides for on-target activity and off-target liability, position them relative to gene/TSS, add appropriate controls, lay out the oligo for synthesis, and validate the cloned pool.

• Python: crispor.py (web + CLI) for batch genome-wide guide scoring with CFD+MIT off-target
• Python: azimuth (Microsoft Research) for Rule Set 2 on-target predictions (Brunello-style)
• Python: CRISPRon, DeepSpCas9 for modern deep-learning predictors
• R: crisprDesign (Bioconductor) for integrated annotation-aware design

Library Chemistry Decision Tree

Goal	Chemistry	Canonical library	Guides/gene	TSS / target window
Loss-of-function essentiality, fitness	SpCas9 KO	Brunello, TKOv3, Avana	4 (Brunello), 4 (TKOv3), 6 (Avana)	Constitutive exons, prefer aa 5-65% from N-terminus
Knockdown of non-cuttable genes, dosage-sensitive	dCas9-KRAB (CRISPRi)	Dolcetto, Horlbeck v2	6 (Dolcetto), 5 (Horlbeck)	Optimum +25 to +75 downstream of FANTOM5 TSS, searched out to -50/+300 (Dolcetto, Sanson 2018); -25 to +500 (Horlbeck v2)
Gain-of-function, gene activation	dCas9-VP64 / SAM / SunTag (CRISPRa)	Calabrese, Horlbeck-CRISPRa	6 (Calabrese), 5 (Horlbeck)	-150 to -75 from TSS (Calabrese); -550 to -25 (Horlbeck v2)
Paralog buffering, GI screens	enAsCas12a multiplex	Inzolia, in4mer	4-guide arrays	Constitutive exons
Variant function, SNV scanning	CBE / ABE	Custom tiling library	Tile editing windows	Editing window pos 4-8 from PAM-distal end
Precise edit, indel-free	Prime editor	Custom PRIDICT-designed	Tile pegRNAs	Anywhere with NGG PAM within 30 nt of edit

Fails when:

• CRISPRi/a targeting wrong TSS: any TSS without FANTOM5 CAGE evidence is suspect; guides positioned against the wrong TSS lose most of their knockdown.
• Cas9 KO of essential paralogs: single-KO buffering hides paralog-redundant essentials (42% of constitutively expressed genes never score, Dede 2020); switch to Cas12a multiplex.
• Base editor over an exon-intron boundary: editing-window bystanders create splice variants instead of the intended SNV.

On-Target Scoring: Algorithmic Taxonomy

Predictor	Year	Training set	Strengths	Fails when
Doench Rule Set 1	2014	Flow-sorted GFP+ knockouts	Simple, interpretable	Limited training data; sub-optimal at >NGG context
Doench Rule Set 2 / Azimuth 2.0	2016	1,841 flow-cytometry guides (Doench 2014) plus new guides tiling additional genes	Gold-standard for SpCas9; basis of Brunello	Trained on dropouts; under-predicts efficacy for nuclear-localized targets
DeepSpCas9	2019	12,832 synthetic targets integrated in HEK293T	Spearman ~0.77 vs measured indel frequency on held-out data	Black-box; sensitive to chromatin/context features it wasn't trained on
CRISPRon	2021	High-throughput indel sequencing	Best for therapeutic-grade target nomination	Slow per-guide; over-fits to its specific cell line
DeepHF	2019	~171k guides in HEK293T (WT 55,604; eSpCas9(1.1) 58,167; HF1 56,888)	Separate model per enzyme variant, including WT	Pick the model matching the enzyme actually used

Reconciliation: When predictors disagree, prefer the model whose training cell line matches the screen line (DeepSpCas9 was trained on synthetic targets integrated in HEK293T). For Brunello selection, Azimuth/Rule Set 2 is sufficient because the library was built with it -- introducing a different scorer creates apples-to-oranges ranking with the original library.

Off-Target Scoring

Score	Year	Math	Cutoff convention
MIT (Hsu)	2013	Position-weighted mismatch penalty	Specificity score 0-100, higher is better; CRISPOR treats >=50 as a good guide
CFD (Doench)	2016	Position+nucleotide-specific penalty fit on Brunello	Per-site CFD >0.2 counts a candidate off-target (Doench 2016); CRISPOR's aggregate CFD specificity score is 0-100, higher is better
Elevation	2018	ML on CFD + mismatch positions	Tighter than CFD

CFD remains the default for genome-wide library design. Critical pitfall: CFD penalizes only mismatches, not bulges; for ≤1 mismatch + 1-bp bulge off-targets, validate empirically with GUIDE-seq or CIRCLE-seq. CRISPOR reports both the MIT (Hsu) and CFD guide specificity scores in a single output.

Score and Rank sgRNAs for a Target Gene

Goal: Generate ranked sgRNA candidates for a single gene, jointly scored on on-target activity (Rule Set 2 / Azimuth) and off-target liability (CFD).

Approach: Identify all PAM-adjacent 20-nt protospacers in the target gene's coding sequence, retain only those in the first 5-65% of the protein (constitutive-exon convention from Brunello), filter on GC 30-70% and absence of poly-T (≥4 Ts terminates U6), call Azimuth for on-target and CRISPOR for off-target, and select the top N satisfying both criteria.

import re
import pandas as pd
import numpy as np
from Bio.Seq import Seq

def find_sgrna_candidates(cds_sequence, pam='NGG', guide_length=20):
    '''Return all protospacer candidates with PAM coordinates on + strand.
    Caller must filter by exon position and Azimuth/CFD score.'''
    pam_pattern = re.compile(f'(?=([ACGT]{{{guide_length}}}{pam.replace("N", "[ACGT]")}))')
    candidates = []
    for strand, seq in [('+', cds_sequence), ('-', str(Seq(cds_sequence).reverse_complement()))]:
        for m in pam_pattern.finditer(seq):
            spacer = m.group(1)[:guide_length]
            if 'TTTT' in spacer or spacer.count('G') + spacer.count('C') not in range(6, 15):
                continue
            candidates.append({'spacer': spacer, 'strand': strand,
                               'pos_in_cds': m.start() if strand == '+' else len(seq) - m.start() - 23,
                               'gc_frac': (spacer.count('G') + spacer.count('C')) / guide_length})
    return pd.DataFrame(candidates)

def annotate_exon_position(candidates_df, cds_length):
    '''Filter to protospacers within first 5-65% of CDS (Brunello convention).
    Reason: N-terminal indels truncate protein; very-N-terminal hits alt initiation;
    C-terminal hits miss functional domains (Doench 2016 Nat Biotech).'''
    lo, hi = 0.05 * cds_length, 0.65 * cds_length
    return candidates_df[(candidates_df['pos_in_cds'] >= lo) & (candidates_df['pos_in_cds'] <= hi)].copy()

CRISPRi / CRISPRa TSS Targeting

Goal: Position guides relative to the empirical TSS for maximum knockdown (CRISPRi) or activation (CRISPRa).

Approach: Resolve TSS from FANTOM5 CAGE peaks (highest-ranked peak per gene; fall back to Ensembl/RefSeq if absent), define the modality-specific window, score candidate spacers in that window with Rule Set 2 plus the Horlbeck/Sanson CRISPRi/a-tailored rules, and select 5-6 guides per gene biased toward the window center.

def crispri_window(tss_coord, strand='+'):
    '''Dolcetto convention: search -50 to +300 around the FANTOM5 highest-rank CAGE peak.
    Reason: Sanson 2018 found +25 to +75 nt downstream of the TSS optimal for CRISPRi,
    so rank candidates toward that band; the search is relaxed outward to fill the
    per-gene guide quota when poorly-annotated TSSs leave too few candidates.'''
    if strand == '+':
        return (tss_coord - 50, tss_coord + 300)
    return (tss_coord - 300, tss_coord + 50)

def crispra_window(tss_coord, strand='+'):
    '''Calabrese convention: -150 to -75 upstream of TSS.
    Reason: dCas9-VP64 (and SAM, SunTag) activate maximally when bound
    just upstream of Pol II loading. Horlbeck v2 CRISPRa uses -550 to -25
    (broader, lower per-guide signal). For SAM, prefer Calabrese tightness;
    for SunTag, Horlbeck width is acceptable.'''
    if strand == '+':
        return (tss_coord - 150, tss_coord - 75)
    return (tss_coord + 75, tss_coord + 150)

Critical nuance: Cell-type-specific TSSs differ from the FANTOM5 consensus in ~15% of genes. For tissue-specific screens (e.g., neuron, hepatocyte), re-derive TSSs from a matched CAGE / GRO-seq / PRO-seq dataset before locking guide positions, or knockdown efficiency drops several-fold. The single most common cause of "weak" CRISPRi hits is mis-positioned guides against an alternative TSS.

Genome-Wide Library Selection

Library	Year	Modality	Size (genes x guides)	sgRNA rules	Notable
GeCKOv2	2014	Cas9 KO	~19k x 6 (~123k)	Exon position + off-target specificity (predates Rule Set 1)	Older; legacy datasets still use it
Avana	2016	Cas9 KO	110,257 as published; DepMap screens a ~4-guide subset (Meyers 2017: 70,086 after filtering, 17,670 genes)	Rule Set 1	Still the Broad's primary Cas9 library; CERES->Chronos changed in 2021, not the library
Brunello	2016	Cas9 KO	~19k x 4 (~77k)	Rule Set 2 + CFD	Modern standard for new screens
TKOv3	2017	Cas9 KO	~18k x 4 (~71k)	Hart on/off-target	Bagel/BAGEL2-optimized
Humagne	2020	enAsCas12a	~19.8k x 1 dual-guide construct (~20k per set)	enAsCas12a rules	Compact Cas12a sets C and D
Horlbeck CRISPRi v2	2016	dCas9-KRAB	~18k x 5 (~104k)	Horlbeck CRISPRi rules	First-gen, still widely used
Dolcetto	2018	dCas9-KRAB	~19k x 3 per set (114,061 across Sets A+B)	Horlbeck + Rule Set 2	Modern CRISPRi standard
Horlbeck CRISPRa	2016	dCas9-VP64	~18k x 5 (~104k)	Horlbeck CRISPRa rules	Original CRISPRa
Calabrese	2018	dCas9-VP64	~18.9k x 3 per set (113,238 across Sets A+B)	Tight TSS window	Modern CRISPRa standard
Inzolia	2024	enAsCas12a	~49k arrays: 19,687 genes (2 arrays each) plus ~4,435 paralog pairs	enAsCas12a rules	Paralog-pair multiplex; ~30% smaller than a typical Cas9 library
in4mer	2024	Cas12a (4-guide)	Custom	enAsCas12a multiplex	Triple/quadruple KO per cassette

dAUC trajectory (essentiality benchmark): GeCKOv2 < Avana < Brunello/TKOv3 (Doench 2016 + Hart 2017). Moving from 4 to 6 sgRNAs/gene gives diminishing returns; the larger gain is moving from Rule Set 1 to Rule Set 2.

Cost-coverage tradeoff: A 77k-guide Brunello at 500x cells/sgRNA needs 38.5M cells in pool, scalable. A 117k-guide Calabrese at 500x needs 59M cells -- often the deciding factor against CRISPRa for difficult-to-grow lines.

PAM Variants and Alternative Cas Enzymes

Enzyme	PAM	Spacer length	Best for
SpCas9 (WT)	NGG	20 nt	Standard pooled screens; broadest library support
eSpCas9, SpCas9-HF1	NGG	20 nt	Lower off-target rate; use for therapeutic-grade nomination
SpCas9-NG	NG	20 nt	Expanded targeting (~4x coverage); accept lower activity per guide
SpRY	NRN / NYN	20 nt	Near-PAMless; coverage at every position; ~50% lower per-guide activity
SaCas9	NNGRRT	21 nt	AAV-packageable (small ORF); rarely used in pooled screens
AsCas12a, LbCas12a	TTTV	23 nt	AT-rich regions; staggered cut; lower expression noise
enAsCas12a (DeWeirdt 2021)	Expanded TTTV + several non-canonical	23 nt	Combinatorial / paralog screens

Decision rule: If the screen requires every possible TSS position (saturation tiling, dense regulatory dissection), use SpRY despite lower activity; otherwise, NGG is best because the on-target predictors were trained on it.

Control Guides

A genome-wide library should include:

Control type	Count	Purpose
Non-targeting (scrambled, no genomic match)	500-1,000 (~1% of library)	Primary null distribution for CRISPRi/a; safe baseline for normalization
Safe-harbor (AAVS1, ROSA26-equivalent)	50-100	Cas9-only: absorbs cut-toxicity baseline (matters for amplicon-correction)
Olfactory receptors (presumed non-expressed)	50-100	Second null set for orthogonal normalization
Reference essentials (CEGv2 subset: e.g. RPS3, RPL11, EIF3A, POLR2A)	50-100	Internal positive control; QC dropout signal
Reference non-essentials (NEGv1 subset)	50-100	Internal negative control; BAGEL2 calibration

Critical pitfall: Using only AAVS1 as the negative control in a Cas9 screen creates a normalization baseline biased toward "any cut is bad." Always add NTCs or non-essentials so that downstream median normalization and PR-AUC against CEGv2 work without baseline-shift artifacts.

Library Composition for Specialized Screens

Paralog buffering (Cas12a multiplex): Build 4-guide arrays where positions 1-2 target gene A and positions 3-4 target paralog gene B. Inzolia covers ~4,435 paralog pairs within ~49k arrays. Singleton controls (gene A alone, gene B alone) must be included to score genetic interaction = double_KO_LFC - sum(single_KO_LFC).

Base editor screens (tiling-library design): Tile NGG-adjacent spacers across exons; ensure editing window (positions 4-8 from PAM-distal end) lands inside coding exons; flag bystander Cs/As in the window for downstream interpretation. Restrict to 50-90% editing efficiency a priori (filter out predicted low-efficacy guides) -- see [[base-editing-analysis]].

Tiling / regulatory dissection: Dense (every 5-10 bp) CRISPRi or CRISPRa guides across the candidate region; CRISPRi has broader signal width (good for enhancer discovery) but Cas9-indel tiling has sharper resolution (good for pinpointing critical bases). Pair with CRISPR-SURF deconvolution.

Oligo Design for Pooled Synthesis

Goal: Generate the final oligo sequence ready for chip-based synthesis. Vendor limits differ: Twist oligo pools cap at ~300 nt per oligo with no fixed pool size, GenScript's 92K format spans 20-170 nt, and Agilent OLS 244K spans 30-230 nt.

Approach: Add subpool PCR primers (so multiple sublibraries can share a synthesis array), the BsmBI/Esp3I overhang for golden-gate cloning into LentiGuide-Puro (Addgene 52963) or LentiCRISPRv2, and append the tracrRNA scaffold if the array length permits.

def build_oligo(spacer, vector='lentiGuide-Puro', subpool_idx=None):
    '''Construct final oligo for pooled synthesis.

    LentiGuide-Puro / LentiCRISPRv2 use BsmBI (Esp3I) with these overhangs:
        forward: 5'-CACCG[spacer]-3'
        reverse: 5'-AAAC[revcomp(spacer)]C-3'
    For chip synthesis, the spacer is flanked by subpool-specific PCR primers.'''
    subpool_fwd = {
        1: 'GGAAAGGACGAAACACCG',   # subpool 1 forward primer + BsmBI overhang
        2: 'GAGGCACTGGGCAGGTACCG',
    }.get(subpool_idx, 'GGAAAGGACGAAACACCG')
    # First 33 nt of the Chen 2013 sgRNA(F+E) optimized scaffold. NOTE: lentiGuide-Puro (#52963)
    # and lentiCRISPRv2 (#52961) carry the ORIGINAL scaffold; F+E belongs to lentiCRISPRv2-Opti (#163126).
    scaffold_short = 'GTTTAAGAGCTATGCTGGAAACAGCATAGCAAG'
    oligo = subpool_fwd + spacer + scaffold_short
    if len(oligo) > 200:
        raise ValueError(f'Oligo length {len(oligo)} exceeds the 200 nt design budget; check the vendor limit')
    return oligo

Subpool design: A large synthesis pool can be partitioned into multiple sublibraries via subpool primers; each sub-PCR amplifies its subpool, allowing one synthesis batch to serve several screens. Typical subpool size: 10k-20k oligos.

Library QC After Cloning

Metric	Target	Failure mode if missed
sgRNA detection (>25 reads/guide in plasmid pool)	≥99%	Founder effect: missing guides cannot be screened; dropout impossible to distinguish from missing
Gini coefficient of plasmid pool	<0.1	Synthesis defects or PCR bias; pool unfit for screening at standard 500x coverage
Skew ratio (top 10% / bottom 10%)	<2 (good), <5 (acceptable)	Skew >5 means underrepresented guides cannot generate statistical signal even at 1000x
% zero-count sgRNAs in plasmid pool	<0.5%	Plasmid bottleneck during cloning; re-amplify or re-clone
Replicate Pearson on plasmid pool (between sequencing technical replicates)	>0.99	Sequencing artifact, not biology

Plasmid pool sequencing convention: 200-500 reads per sgRNA before any biology (i.e. 15-40M reads for a 77k Brunello). This is the baseline against which all downstream depletion is computed; sequencing the plasmid is non-negotiable.

Failure Modes

Wrong TSS in CRISPRi/a library

Trigger: Using Ensembl/RefSeq TSS instead of empirical CAGE peak for genes with broad or non-canonical promoters. Mechanism: dCas9-KRAB knockdown is maximal within ±100 bp of the actual Pol II loading site; canonical annotation can be off by 1-10 kb. Symptom: "Easy" essentials (RPS, RPL, EIF) show normal dropout but newer genes don't; library validates poorly against CEGv2. Fix: Re-derive TSS from FANTOM5 CAGE highest-rank peak; for tissue-specific lines, use matched CAGE or GRO-seq.

Library skew from PCR bias during amplification

Trigger: Amplifying the cloned plasmid pool with too many PCR cycles (>20) or with high-GC-bias polymerase. Mechanism: GC-extreme guides amplify nonlinearly; high-GC guides dominate, low-GC guides drop out. Symptom: Gini >0.2 on plasmid pool; sgRNAs with GC <30% systematically depleted. Fix: Cap PCR at 15 cycles; use Q5 or NEBNext Ultra II (low-bias); sequence at 500x post-amp to confirm Gini.

Oligo-synthesis dropouts in low-complexity guides

Trigger: Chip-synthesis errors at homopolymer runs or guides starting with GGGG. Mechanism: Synthesis chemistry has higher error rate at low-complexity regions; missing oligos cannot be cloned. Symptom: Specific guides absent from plasmid pool despite no design-rule violation. Fix: Re-design replacement guides; for production runs, request 2-3x synthesis depth so dropouts are buffered.

Polyclonality from high MOI

Trigger: Infection at MOI >0.5 to "save cells." Mechanism: Poisson math: at MOI 0.3, 26% of all cells are infected and 4% carry >=2 sgRNAs (14% of the infected fraction); at MOI 0.5, 39% are infected and 9% carry >=2. Symptom: Hits include neutral genes that co-infect with true essentials. Fix: MOI 0.3 strict; titer Cas9-positive cells specifically; re-check by qPCR of integration.

Wrong control proportion

Trigger: <100 non-targeting controls in a 70k library. Mechanism: Null distribution for normalization and FDR rests on the NTC variance; too few NTCs yields unstable median and inflated FDR. Symptom: Erratic gene-level p-values; MAGeCK FDR fluctuates wildly between runs. Fix: ~1% of library (500-1,000) NTCs; supplement with non-essential-gene controls.

Quantitative Thresholds

Threshold	Value	Source / Rationale
GC content	30-70%	Doench 2016 Nat Biotech: guides outside this range have low activity
Poly-T avoidance	≤3 consecutive T	U6 Pol III terminator; ≥4 Ts terminates sgRNA transcription
Guides per gene (Cas9)	4 (Brunello/TKOv3 standard); up to 6 (Avana, older)	Doench 2016 reports diminishing gene recovery below 4 sgRNAs/gene; returns flatten above 6
CRISPRi window	-50 to +300 search; +25 to +75 optimum	Sanson 2018 (Dolcetto); Horlbeck v2 uses -25 to +500
CRISPRa window	-150 to -75 from TSS	Sanson 2018 (Calabrese); narrower than Horlbeck v2 (-550 to -25)
NTCs in library	~1% (500-1,000 in a 70k library)	DepMap library design notes; rule-of-thumb for stable null
MOI	0.3	Poisson: P(>=2 sgRNAs/cell) = 4% at MOI 0.3
Coverage at infection	500 cells/sgRNA	DepMap convention; 200x minimum, 1000x for noisy / in-vivo
CRISPOR MIT specificity score	>=50 (higher = more specific)	CRISPOR convention (Haeussler 2016)
Library skew (top 10% / bottom 10%)	<2 ideal, <5 acceptable	Joung 2017 Nat Protoc

Common Errors

Error / symptom	Cause	Solution
sgRNA fails to express	Poly-T in spacer terminates U6	Filter TTTT in design; this is the #1 silent failure
CRISPRi guide gives no knockdown	Wrong TSS used	Re-derive TSS from FANTOM5 / matched CAGE
Library Gini >0.3 in plasmid pool	PCR over-amplification or synthesis defect	Cap at 15 cycles; re-sequence plasmid; consider re-synthesis
Hits include amplified loci (e.g. ERBB2 in HER2+)	Copy-number amplicon false-essentiality	See [[copy-number-correction]]
Paralog gene absent from hit list despite expression	Cas9 single-KO buffering	Switch to Cas12a multiplex; see [[combinatorial-screens]]
Cas12a oligo doesn't cut	Forgot Cas12a's TTTV PAM is 5' of spacer, not 3'	Re-orient: PAM-then-spacer for Cas12a, opposite of Cas9

References

• Doench JG et al. 2014. Nat Biotechnol 32:1262. Rule Set 1.
• Doench JG et al. 2016. Nat Biotechnol 34:184. Rule Set 2, CFD, Brunello/Avana libraries.
• Sanjana NE et al. 2014. Nat Methods 11:783. GeCKOv2.
• Hart T et al. 2017. G3 7:2719. TKOv3 library; CEGv2/NEGv1 reference essentiality gene sets.
• Sanson KR et al. 2018. Nat Commun 9:5416. Dolcetto + Calabrese libraries; CRISPRi/a TSS rules.
• Horlbeck MA et al. 2016. eLife 5:e19760. CRISPRi/a design rules; Horlbeck v2 library.
• Kim HK et al. 2019. Sci Adv 5:eaax9249. DeepSpCas9.
• Xiang X et al. 2021. Nat Commun 12:3238. CRISPRon.
• Tycko J et al. 2019. Nat Commun 10:4063. Off-target toxicity mitigation in CRISPR screens.
• DeWeirdt PC et al. 2021. Nat Biotechnol 39:94. enAsCas12a optimization.
• Esmaeili Anvar N et al. 2024. Nat Commun 15:3577. Inzolia / in4mer paralog library.
• Dede M et al. 2020. Genome Biol 21:262. Paralog buffering invisible to Cas9 single-KO.
• Joung J et al. 2017. Nat Protoc 12:828. Genome-wide library screen protocol.
• Shalem O et al. 2014. Science 343:84. Original GeCKO genome-scale knockout library design.

Related Skills

• crispr-screens/screen-qc - Validate library skew, Gini, replicate correlation
• crispr-screens/mageck-analysis - Analyze screens run with the designed library
• crispr-screens/combinatorial-screens - Cas12a multiplex / paralog-pair library design
• crispr-screens/base-editing-analysis - base-editor library design
• crispr-screens/prime-editing-screens - PRIDICT2-optimized pegRNA libraries
• crispr-screens/copy-number-correction - Filter amplicon-driven artifacts in cancer-cell-line screens