Version Compatibility

Reference examples tested with: BioPython 1.83+, MAGeCK 0.5+, numpy 1.26+, pandas 2.2+

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

• Python: pip show <package> then help(module.function) to check signatures

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

Library Design

"Design a custom CRISPR library for my screen" → Select optimal sgRNAs for knockout, CRISPRi/a, or base editing libraries with on-target scoring, off-target filtering, and control guide design.

• Python: CRISPOR-based scoring with BioPython for sequence handling

sgRNA Selection Criteria

Goal: Score and rank candidate sgRNAs for a target gene based on design quality metrics.

Approach: Scan the gene sequence for PAM sites, extract 20-nt protospacer sequences, score each on GC content, poly-T avoidance, 5' G preference, and length, then return the top-ranked candidates.

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

def score_sgrna(sequence, pam='NGG'):
    '''Score sgRNA based on multiple criteria.'''
    scores = {}

    gc_content = (sequence.count('G') + sequence.count('C')) / len(sequence)
    scores['gc_content'] = 1 - abs(gc_content - 0.5) * 2

    if len(sequence) >= 4:
        has_poly_t = 'TTTT' in sequence
        scores['poly_t'] = 0 if has_poly_t else 1

    starts_with_g = sequence.startswith('G')
    scores['start_g'] = 1 if starts_with_g else 0.5

    scores['length'] = 1 if len(sequence) == 20 else 0.8

    overall = np.mean(list(scores.values()))
    return overall, scores

def design_sgrnas_for_gene(gene_sequence, n_guides=4, pam='NGG'):
    '''Design sgRNAs targeting a gene.'''
    candidates = []

    pam_pattern = pam.replace('N', '[ACGT]')
    import re

    for strand in ['+', '-']:
        seq = gene_sequence if strand == '+' else str(Seq(gene_sequence).reverse_complement())

        for match in re.finditer(f'([ACGT]{{20}})({pam_pattern})', seq):
            sgrna = match.group(1)
            position = match.start()

            if strand == '-':
                position = len(seq) - position - 23

            score, details = score_sgrna(sgrna)

            candidates.append({
                'sequence': sgrna,
                'pam': match.group(2),
                'strand': strand,
                'position': position,
                'score': score,
                'gc_content': (sgrna.count('G') + sgrna.count('C')) / 20,
                **details
            })

    candidates_df = pd.DataFrame(candidates)
    candidates_df = candidates_df.sort_values('score', ascending=False)

    return candidates_df.head(n_guides)

gene_seq = 'ATGCGATCGATCGATCGATCGAATCGATCGATCGAGGCGATCGATCGATCGATCGAATCGATCGATCGAGGCGATCGATCGATCGATCGAATCGATCGATCGAGG'
guides = design_sgrnas_for_gene(gene_seq, n_guides=5)
print(guides[['sequence', 'position', 'strand', 'score', 'gc_content']])

Library Composition

Goal: Assemble a complete sgRNA library targeting a list of genes with appropriate controls.

Approach: Design top-scoring guides for each gene, append non-targeting, essential-control, and safe-harbor-control guides, and compile into an ordered library table.

def design_library(gene_list, guides_per_gene=4, include_controls=True):
    '''Design complete library for gene list.'''
    library = []

    for gene in gene_list:
        gene_data = get_gene_sequence(gene)

        guides = design_sgrnas_for_gene(gene_data['sequence'], n_guides=guides_per_gene)

        for idx, guide in guides.iterrows():
            library.append({
                'gene': gene,
                'gene_id': gene_data.get('ensembl_id', ''),
                'guide_number': idx + 1,
                'sequence': guide['sequence'],
                'pam': guide['pam'],
                'position': guide['position'],
                'strand': guide['strand'],
                'score': guide['score'],
                'type': 'targeting'
            })

    if include_controls:
        controls = design_control_guides()
        library.extend(controls)

    return pd.DataFrame(library)

def get_gene_sequence(gene_name):
    '''Fetch gene sequence (placeholder - use Ensembl API or local files).'''
    return {
        'sequence': 'ATGC' * 250,
        'ensembl_id': f'ENSG_{hash(gene_name) % 100000:05d}'
    }

genes = ['TP53', 'BRCA1', 'KRAS', 'MYC', 'CDK4']
library = design_library(genes, guides_per_gene=4)
print(f'Library size: {len(library)} guides')
print(f'Genes: {library["gene"].nunique()}')

Control Guide Design

Goal: Design control guide sets for normalization and quality assessment in CRISPR screens.

Approach: Generate random non-targeting sequences with acceptable GC content, add validated guides against known essential genes (positive controls) and safe-harbor loci (negative controls).

def design_control_guides(n_nontargeting=100, n_essential=20, n_nonessential=20):
    '''Design control guides for library.'''
    controls = []

    for i in range(n_nontargeting):
        sequence = generate_nontargeting_sequence()
        controls.append({
            'gene': f'NonTargeting_{i+1}',
            'gene_id': '',
            'guide_number': 1,
            'sequence': sequence,
            'pam': 'NGG',
            'position': -1,
            'strand': '',
            'score': 0,
            'type': 'non-targeting'
        })

    essential_genes = ['RPS3', 'RPL11', 'EIF3A', 'POLR2A', 'CDK1']
    for gene in essential_genes[:n_essential]:
        controls.append({
            'gene': gene,
            'gene_id': '',
            'guide_number': 1,
            'sequence': get_validated_guide(gene),
            'pam': 'NGG',
            'position': 0,
            'strand': '+',
            'score': 1,
            'type': 'essential-control'
        })

    nonessential_genes = ['AAVS1', 'ROSA26']
    for gene in nonessential_genes[:n_nonessential]:
        controls.append({
            'gene': gene,
            'gene_id': '',
            'guide_number': 1,
            'sequence': get_validated_guide(gene),
            'pam': 'NGG',
            'position': 0,
            'strand': '+',
            'score': 1,
            'type': 'safe-harbor-control'
        })

    return controls

def generate_nontargeting_sequence(length=20):
    '''Generate random non-targeting sequence.'''
    while True:
        seq = ''.join(np.random.choice(['A', 'C', 'G', 'T'], length))
        gc = (seq.count('G') + seq.count('C')) / length
        if 0.4 <= gc <= 0.6 and 'TTTT' not in seq:
            return seq

def get_validated_guide(gene):
    '''Get validated guide sequence for control gene.'''
    validated = {
        'RPS3': 'GAGCTTCTTCAGCAGCATGG',
        'RPL11': 'GAAACAGGGCATCATCTACG',
        'EIF3A': 'GTGCAAGAGGATGATGACAA',
        'AAVS1': 'GGGGCCACTAGGGACAGGAT',
        'ROSA26': 'GAAGATGGGCGGGAGTCTTC'
    }
    return validated.get(gene, generate_nontargeting_sequence())

Off-Target Analysis

Goal: Filter library guides to remove those with excessive off-target genomic matches.

Approach: Align each guide sequence against the genome with Bowtie allowing mismatches, count off-target hits within a mismatch threshold, and remove guides exceeding the maximum.

def check_offtargets(guide_sequence, genome_index, max_mismatches=3):
    '''Check for potential off-target sites.'''
    from subprocess import run
    import tempfile

    with tempfile.NamedTemporaryFile(mode='w', suffix='.fa', delete=False) as f:
        f.write(f'>guide\n{guide_sequence}\n')
        query_file = f.name

    result = run(
        ['bowtie', '-a', '-n', str(max_mismatches), '-l', '20', genome_index, '-f', query_file],
        capture_output=True, text=True
    )

    offtargets = []
    for line in result.stdout.strip().split('\n'):
        if line:
            fields = line.split('\t')
            offtargets.append({
                'chromosome': fields[2],
                'position': int(fields[3]),
                'strand': fields[1],
                'mismatches': int(fields[7]) if len(fields) > 7 else 0
            })

    return offtargets

def filter_by_offtargets(library_df, genome_index, max_offtargets=10):
    '''Filter library to remove guides with too many off-targets.'''
    filtered = []

    for _, guide in library_df.iterrows():
        offtargets = check_offtargets(guide['sequence'], genome_index)
        n_offtargets = len([ot for ot in offtargets if ot['mismatches'] <= 2])

        if n_offtargets <= max_offtargets:
            guide_dict = guide.to_dict()
            guide_dict['n_offtargets'] = n_offtargets
            filtered.append(guide_dict)

    return pd.DataFrame(filtered)

Oligo Design for Cloning

Goal: Generate forward and reverse oligo sequences ready for ordering and cloning into a lentiviral vector.

Approach: Add vector-specific adapter sequences (overhangs for BsmBI or BbsI restriction sites) to each guide and its reverse complement, formatted for the target vector backbone.

def design_oligos(library_df, vector='lentiGuide-Puro'):
    '''Design oligos for library cloning.'''
    vector_specs = {
        'lentiGuide-Puro': {
            'forward_prefix': 'CACCG',
            'forward_suffix': '',
            'reverse_prefix': 'AAAC',
            'reverse_suffix': 'C'
        },
        'pLKO': {
            'forward_prefix': 'CCGG',
            'forward_suffix': 'CTCGAG',
            'reverse_prefix': 'AATTCTCGAG',
            'reverse_suffix': ''
        }
    }

    spec = vector_specs.get(vector, vector_specs['lentiGuide-Puro'])

    oligos = []
    for _, guide in library_df.iterrows():
        seq = guide['sequence']

        forward = spec['forward_prefix'] + seq + spec['forward_suffix']
        reverse = spec['reverse_prefix'] + str(Seq(seq).reverse_complement()) + spec['reverse_suffix']

        oligos.append({
            'guide_id': f"{guide['gene']}_{guide['guide_number']}",
            'gene': guide['gene'],
            'guide_sequence': seq,
            'forward_oligo': forward,
            'reverse_oligo': reverse,
            'type': guide.get('type', 'targeting')
        })

    return pd.DataFrame(oligos)

oligos = design_oligos(library)
oligos.to_csv('library_oligos.csv', index=False)
print(f'Designed {len(oligos)} oligo pairs')

Pool Design for Synthesis

def design_array_oligos(library_df, array_format='12K'):
    '''Design array oligos for pooled synthesis.'''
    formats = {
        '12K': {'capacity': 12000, 'length': 200},
        '92K': {'capacity': 92000, 'length': 150},
        '244K': {'capacity': 244000, 'length': 60}
    }

    spec = formats[array_format]

    primer_5 = 'AGGCTTGGATTTCTATAACTTCGTATAGCATACATTATACGAAGTTAT'
    primer_3 = 'ATAACTTCGTATAATGTATGCTATACGAAGTTATCTTGGATTTCTAGA'
    scaffold = 'GTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGCTAGTCCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGC'

    array_oligos = []
    for _, guide in library_df.iterrows():
        full_oligo = primer_5 + guide['sequence'] + scaffold + primer_3

        if len(full_oligo) > spec['length']:
            print(f"Warning: {guide['gene']} oligo too long for {array_format}")
            continue

        array_oligos.append({
            'id': f"{guide['gene']}_{guide['guide_number']}",
            'sequence': full_oligo,
            'length': len(full_oligo)
        })

    if len(array_oligos) > spec['capacity']:
        print(f"Warning: Library ({len(array_oligos)}) exceeds {array_format} capacity ({spec['capacity']})")

    return pd.DataFrame(array_oligos)

array_oligos = design_array_oligos(library, '92K')
array_oligos.to_csv('array_synthesis.csv', index=False)

Library QC

def qc_library(library_df):
    '''Quality control checks for library design.'''
    qc = {}

    qc['total_guides'] = len(library_df)
    qc['unique_genes'] = library_df[library_df['type'] == 'targeting']['gene'].nunique()
    qc['guides_per_gene'] = library_df[library_df['type'] == 'targeting'].groupby('gene').size().describe()

    gc_contents = library_df['sequence'].apply(lambda x: (x.count('G') + x.count('C')) / len(x))
    qc['gc_mean'] = gc_contents.mean()
    qc['gc_std'] = gc_contents.std()
    qc['gc_range'] = (gc_contents.min(), gc_contents.max())

    has_poly_t = library_df['sequence'].apply(lambda x: 'TTTT' in x)
    qc['poly_t_count'] = has_poly_t.sum()

    type_counts = library_df['type'].value_counts()
    qc['control_ratio'] = type_counts.get('non-targeting', 0) / len(library_df)

    return qc

qc = qc_library(library)
print('Library QC:')
for key, value in qc.items():
    print(f'  {key}: {value}')

Alternative PAM Systems

The examples above use SpCas9 with NGG PAM. Alternative systems expand targeting range:

System	PAM	Use Case
SpCas9	NGG	Standard, most validated
SpCas9-NG	NG	Relaxed PAM requirement
SpRY	NRN/NYN	Near-PAMless, broadest targeting
Cas12a (Cpf1)	TTTV	AT-rich regions, staggered cuts
SaCas9	NNGRRT	AAV delivery (smaller gene)

For alternative PAMs, modify the design_sgrnas_for_gene() function:

# Cas12a example (TTTV PAM, 23nt guide)
def design_cas12a_guides(gene_sequence, n_guides=4):
    pam_pattern = 'TTT[ACG]'  # TTTV
    guide_length = 23

    for match in re.finditer(f'({pam_pattern})([ACGT]{{{guide_length}}})', gene_sequence):
        pam = match.group(1)
        guide = match.group(2)
        # Cas12a cuts downstream of guide
        # ...

Related Skills

• mageck-analysis - Analyze screen results
• crispresso-editing - Validate editing efficiency
• screen-qc - QC sequencing data
• hit-calling - Identify screen hits