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.

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