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name bio-ribo-seq-ribosome-periodicity
description Validate Ribo-seq data quality by checking 3-nucleotide periodicity and calculating P-site offsets. Use when assessing library quality or determining read offsets for downstream analysis.
tool_type python
primary_tool Plastid

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

Metagene Plots

Goal: Visualize the metagene profile around start codons with frame-colored bars and a periodicity power spectrum.

Approach: Plot read counts by reading frame and compute FFT to confirm a dominant period of 3 nucleotides.

def plot_metagene(metagene_data, offset=12):
    '''Plot metagene profile around start codon'''
    fig, axes = plt.subplots(1, 2, figsize=(12, 5))

    # Frame 0, 1, 2 around start codon
    positions = np.arange(-50, 100)

    # Plot by frame
    for frame in range(3):
        frame_positions = positions[positions % 3 == frame]
        counts = metagene_data[positions % 3 == frame]
        axes[0].bar(frame_positions, counts, alpha=0.7, label=f'Frame {frame}')

    axes[0].set_xlabel('Position relative to start codon')
    axes[0].set_ylabel('Normalized counts')
    axes[0].legend()
    axes[0].axvline(0, color='red', linestyle='--', label='Start')

    # Periodicity
    from scipy.fft import fft
    fft_result = np.abs(fft(metagene_data))
    freq = np.fft.fftfreq(len(metagene_data))

    axes[1].plot(1/freq[1:len(freq)//2], fft_result[1:len(freq)//2])
    axes[1].set_xlabel('Period (nt)')
    axes[1].set_ylabel('Power')
    axes[1].axvline(3, color='red', linestyle='--')

    plt.tight_layout()
    plt.savefig('periodicity.pdf')

Assess by Read Length

Goal: Evaluate 3-nucleotide periodicity strength for each read length to identify the most informative footprint sizes.

Approach: Group reads by query length, compute periodicity score per group, and retain lengths with strong triplet signal.

def periodicity_by_length(bam_path, annotation_file):
    '''Calculate periodicity score for each read length'''
    import pysam

    # Group reads by length
    reads_by_length = {}
    with pysam.AlignmentFile(bam_path, 'rb') as bam:
        for read in bam:
            if not read.is_unmapped:
                length = read.query_length
                if length not in reads_by_length:
                    reads_by_length[length] = []
                reads_by_length[length].append(read)

    # Calculate periodicity for each length
    # Good lengths show strong 3-nt periodicity
    results = {}
    for length, reads in reads_by_length.items():
        if len(reads) > 1000:  # Need sufficient reads
            periodicity = calculate_periodicity(reads, annotation_file)
            results[length] = periodicity

    return results

P-site Offset Table

Common P-site offsets by read length (5' end mapping):

Read Length P-site Offset
28 nt 12
29 nt 12
30 nt 13
31 nt 13
32 nt 14

Validate with RiboCode

Goal: Run an automated periodicity and ORF detection pipeline as an independent validation of data quality.

Approach: Execute RiboCode's one-step command, which internally assesses periodicity and generates diagnostic plots.

# RiboCode includes periodicity analysis
RiboCode_onestep \
    -g annotation.gtf \
    -r riboseq.bam \
    -f genome.fa \
    -o output_dir

# Check output for periodicity plots

Related Skills

  • riboseq-preprocessing - Generate aligned BAM
  • orf-detection - Uses P-site offsets
  • translation-efficiency - Requires proper positioning