Version Compatibility

Reference examples tested with: matplotlib 3.8+, numpy 1.26+, pysam 0.22+, 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
• CLI: <tool> --version then <tool> --help to confirm flags

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

Ribosome Periodicity Analysis

"Check if my Ribo-seq data shows triplet periodicity" → Validate Ribo-seq library quality by verifying 3-nucleotide translocation patterns and calculating P-site offsets from metagene profiles.

• Python: plastid for P-site offset calculation and metagene analysis

3-Nucleotide Periodicity

Goal: Verify that Ribo-seq reads exhibit the expected 3-nucleotide translocation pattern characteristic of active translation.

Approach: Load P-site mapped reads and compute metagene profiles around start codons to check for triplet periodicity.

Ribosomes move 3 nucleotides per codon. Good Ribo-seq data shows strong periodicity:

from plastid import BAMGenomeArray, FivePrimeMapFactory, GenomicSegment
import numpy as np
import matplotlib.pyplot as plt

# Load aligned reads
alignments = BAMGenomeArray('riboseq.bam', mapping=FivePrimeMapFactory())

# Get metagene around start codons
# Expect strong 3-nt periodicity

Calculate P-site Offset

Goal: Determine the optimal P-site offset from the 5' end of ribosome footprints for accurate codon-level positioning.

Approach: Run metagene analysis around annotated start codons and identify the offset that aligns the signal peak with the AUG position.

from plastid import metagene_analysis

# The P-site offset varies by read length
# Typically 12-15 nt from 5' end for 28-30 nt reads

def determine_psite_offset(bam_path, annotation_file):
    '''Determine optimal P-site offset from metagene analysis'''
    from plastid import GTF2_TranscriptAssembler, BAMGenomeArray

    # Load annotations
    transcripts = list(GTF2_TranscriptAssembler(annotation_file))

    # Load reads
    alignments = BAMGenomeArray(bam_path, mapping=FivePrimeMapFactory())

    # Metagene around start codons
    # Peak should align with start codon position
    metagene_data = metagene_analysis(
        transcripts,
        alignments,
        upstream=50,
        downstream=100
    )

    return metagene_data