Version Compatibility

Reference examples tested with: BioPython 1.83+, numpy 1.26+, scipy 1.12+

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.

Ribosome Stalling Detection

"Find ribosome pause sites in my data" → Detect codon-level ribosome stalling and pausing events from Ribo-seq footprint density, identifying positions with abnormally high ribosome occupancy.

• Python: plastid for codon-resolution density calculation, scipy for statistical scoring

Concept

Ribosome stalling/pausing occurs when ribosomes slow or stop at specific codons:

• Rare codons (low tRNA availability)
• Specific amino acid motifs (polyproline)
• Regulatory pause sites (upstream of stress response genes)
• Nascent chain interactions

Calculate Codon-Level Occupancy

Goal: Quantify ribosome occupancy at each codon position across all transcripts.

Approach: Map reads to P-sites using a fixed offset, then bin counts into codons along each CDS.

python
from plastid import BAMGenomeArray, GTF2_TranscriptAssembler, FivePrimeMapFactory
import numpy as np
from collections import defaultdict

def get_codon_occupancy(bam_path, gtf_path, psite_offset=12):
    '''Calculate ribosome occupancy per codon'''
    # Load reads with P-site mapping
    alignments = BAMGenomeArray(
        bam_path,
        mapping=FivePrimeMapFactory(offset=psite_offset)
    )

    transcripts = list(GTF2_TranscriptAssembler(gtf_path))

    codon_counts = defaultdict(lambda: defaultdict(int))

    for tx in transcripts:
        if tx.cds_start is None:
            continue

        cds = tx.get_cds()
        cds_seq = tx.get_sequence(cds)

        # Get counts at each position
        counts = alignments.count_in_region(cds)

        # Assign to codons
        for i in range(0, len(cds_seq) - 2, 3):
            codon = cds_seq[i:i+3]
            codon_pos = i // 3
            codon_counts[tx.get_name()][codon_pos] = counts  # Simplified

    return codon_counts

Identify Pause Sites

Goal: Detect codon positions with significantly elevated ribosome occupancy indicative of translational pausing.

Approach: Z-score normalize occupancy per transcript and flag positions exceeding a threshold (default z > 3).

python
def find_pause_sites(codon_occupancy, threshold_zscore=3):
    '''Find positions with significantly elevated ribosome occupancy

    Pause sites have much higher occupancy than surrounding codons
    '''
    pause_sites = []

    for tx, occupancy in codon_occupancy.items():
        values = np.array(list(occupancy.values()))

        if len(values) < 10 or values.sum() < 100:
            continue

        # Z-score normalization
        mean_occ = values.mean()
        std_occ = values.std()

        if std_occ == 0:
            continue

        zscores = (values - mean_occ) / std_occ

        # Find positions above threshold
        for pos, zscore in enumerate(zscores):
            if zscore > threshold_zscore:
                pause_sites.append({
                    'transcript': tx,
                    'codon_position': pos,
                    'occupancy': values[pos],
                    'zscore': zscore
                })

    return pause_sites

Codon-Specific Occupancy

Goal: Calculate average ribosome occupancy for each of the 64 codon types across all genes.

Approach: Aggregate read density per codon identity across all CDS positions and compute per-codon mean occupancy.

python
from Bio.Seq import Seq
from Bio.Data import CodonTable

def codon_occupancy_table(bam_path, gtf_path, psite_offset=12):
    '''Calculate average occupancy per codon type'''
    # Count reads per codon type
    codon_reads = defaultdict(list)

    alignments = BAMGenomeArray(bam_path,
                                 mapping=FivePrimeMapFactory(offset=psite_offset))
    transcripts = list(GTF2_TranscriptAssembler(gtf_path))

    for tx in transcripts:
        if tx.cds_start is None:
            continue

        cds = tx.get_cds()
        cds_seq = str(tx.get_sequence(cds))

        # Get read density
        density = alignments.get_density(cds)

        for i in range(0, len(cds_seq) - 2, 3):
            codon = cds_seq[i:i+3]
            if len(density) > i + 2:
                codon_reads[codon].append(sum(density[i:i+3]))

    # Calculate mean occupancy per codon
    codon_means = {codon: np.mean(reads) for codon, reads in codon_reads.items()}

    return codon_means

Correlate with Codon Usage

Goal: Test whether ribosome pausing correlates with tRNA availability across codons.

Approach: Compute Spearman rank correlation between per-codon occupancy and tRNA abundance; expect a negative relationship.

python
def correlate_with_trna(codon_occupancy, trna_abundance):
    '''Test if pausing correlates with tRNA availability

    Rare codons (low tRNA) should have higher occupancy
    '''
    from scipy import stats

    codons = list(set(codon_occupancy.keys()) & set(trna_abundance.keys()))

    occ = [codon_occupancy[c] for c in codons]
    trna = [trna_abundance[c] for c in codons]

    corr, pval = stats.spearmanr(occ, trna)

    return corr, pval  # Expect negative correlation

Motif Analysis at Pause Sites

Goal: Extract amino acid sequence context around identified pause sites to discover recurrent motifs.

Approach: Translate the coding region flanking each pause site and collect fixed-width windows for motif analysis.

python
def extract_pause_motifs(pause_sites, sequences, window=10):
    '''Extract amino acid context around pause sites'''
    motifs = []

    for site in pause_sites:
        tx = site['transcript']
        pos = site['codon_position']
        seq = sequences.get(tx, '')

        if len(seq) > pos * 3 + window * 3:
            start = max(0, (pos - window) * 3)
            end = min(len(seq), (pos + window + 1) * 3)
            aa_seq = str(Seq(seq[start:end]).translate())
            motifs.append(aa_seq)

    return motifs

Known Pause Motifs

| Motif | Description | |-------|-------------| | PPP | Polyproline (ribosome tunnel interaction) | | XPX | Proline-containing | | D/E-rich | Negatively charged nascent chain | | Stop codon context | Influenced by nucleotides around stop |

Related Skills

• ribosome-periodicity - Validate data quality
• orf-detection - Context for pause sites
• translation-efficiency - Gene-level translation