Bioinformatics (bif) Codes

Group Bioinformatics Assignments

Q2: Assignment: RNA-Seq Data Analysis. Analyze a provided RNA-Seq dataset and perform differential gene expression analysis. Present the differentially expressed genes, their functional annotations, and potential biological interpretations. The answer calculates average counts, fold change, and log2 fold change from a small sample CSV; use replicate-aware tools and adjusted p-values for a full study.

RNA-Seq Data Analysis

Solution and implementation for Q2 from Bioinformatics (bif).

2_rnaseq_differential_expression.py Download 
"""Compare control and treatment gene counts using fold change."""

import csv
import math
from pathlib import Path


with Path(__file__).with_name("rna_counts.csv").open(newline="", encoding="utf-8") as file:
    genes = list(csv.DictReader(file))

print(f"{'Gene':<10}{'Control':<12}{'Treatment':<12}{'Fold change':<14}{'Log2FC':<10}Status")
for gene in genes:
    control = (float(gene["Control1"]) + float(gene["Control2"])) / 2
    treatment = (float(gene["Treatment1"]) + float(gene["Treatment2"])) / 2
    fold_change = treatment / control if control else 0
    log2fc = math.log2(fold_change) if fold_change else 0
    if log2fc >= 1:
        status = "Upregulated"
    elif log2fc <= -1:
        status = "Downregulated"
    else:
        status = "Not significant"
    print(f"{gene['Gene']:<10}{control:<12.2f}{treatment:<12.2f}{fold_change:<14.2f}{log2fc:<10.2f}{status}")

print("A full study should use replicate-aware tools such as DESeq2 or edgeR and adjust for multiple testing.")

Other Questions in Bioinformatics

See All Available Questions 


        

        

            

                Download