Version Compatibility

Reference examples tested with: DESeq2 1.42+, 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
• R: packageVersion('<pkg>') then ?function_name to verify parameters

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

Translation Efficiency

"Calculate translation efficiency from my Ribo-seq and RNA-seq" → Compute the ratio of ribosome occupancy to mRNA abundance per gene to identify translational regulation independent of transcription changes.

• R: riborex for differential TE with DESeq2 backend
• Python: Ribo-seq/RNA-seq count ratio with statistical testing

Concept

Translation Efficiency (TE) = Ribo-seq reads / RNA-seq reads

• TE > 1: Efficiently translated (more ribosomes per mRNA)
• TE < 1: Poorly translated
• Changes in TE indicate translational regulation

Calculate TE with Plastid

from plastid import BAMGenomeArray, GTF2_TranscriptAssembler
import pandas as pd
import numpy as np

def calculate_te(riboseq_bam, rnaseq_bam, gtf_path):
    '''Calculate translation efficiency per gene'''
    # Load transcripts
    transcripts = list(GTF2_TranscriptAssembler(gtf_path))

    # Load alignments
    ribo = BAMGenomeArray(riboseq_bam)
    rna = BAMGenomeArray(rnaseq_bam)

    results = []
    for tx in transcripts:
        if tx.cds_start is None:
            continue

        # Get CDS region
        cds = tx.get_cds()

        # Count reads
        ribo_counts = ribo.count_in_region(cds)
        rna_counts = rna.count_in_region(tx)  # Full transcript for RNA-seq

        # Normalize by length
        cds_length = sum(len(seg) for seg in cds)
        tx_length = tx.length

        ribo_rpk = ribo_counts / (cds_length / 1000)
        rna_rpk = rna_counts / (tx_length / 1000)

        if rna_rpk > 0:
            te = ribo_rpk / rna_rpk
        else:
            te = np.nan

        results.append({
            'gene': tx.get_gene(),
            'transcript': tx.get_name(),
            'ribo_counts': ribo_counts,
            'rna_counts': rna_counts,
            'te': te
        })

    return pd.DataFrame(results)