HackBio Internship – BioCoding

📌 Table of Contents

1. Introduction
2. Team Information
3. Task 0: Team Formation & Data Representation
4. Task 1: Microbial Growth Curve Analysis
5. Task 2: Advanced Bioinformatics Analyses
   • 2.1 Microbiology: Growth Curve Analysis
   • 2.3 Botany & Plant Science: Metabolic Response Analysis
   • 2.4 Biochemistry & Oncology: Protein Mutation Impact
   • 2.6 Transcriptomics: RNA-seq Data Analysis
   • 2.7 Public Health: NHANES Data Analysis
6. Upcoming Tasks
7. How to Contribute
8. Contact & Socials

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Introduction

This repository documents the HackBio BioCoding Internship, where we engage in bioinformatics problem-solving using Python and R. Our goal is to enhance our coding proficiency while applying computational techniques to biological datasets.

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Team Information

Name	Slack Username	Email	Hobby	Country	Discipline	Preferred Language
Musa Al Hassan Kromah	Musa	kromahmusa86@gmail.com	Hiking	Liberia	Biotechnology	Python, R
Fowowe Toyin	Toyin	toyintoyo05@gmail.com	Reading	Nigeria	Biochemistry	Python

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Task 0: Team Formation & Data Representation

Objective

• Organize team information in a structured data format using Python or R.
• Ensure no loops, conditionals, or functions are used.

Approach & Implementation

R Implementation:

# Load necessary library
data <- data.frame(
  Name = c("Musa Al Hassan Kromah", "Nina Julian", "Fowowe Toyin"),
  Slack_Username = c("Musa", "Julian", "Toyin"),
  Email = c("kromahmusa86@gmail.com", "anyangonina39@gmail.com", "toyintoyo05@gmail.com"),
  Hobby = c("Hiking", "Listening to Music", "Reading"),
  Country = c("Liberia", "Kenya", "Nigeria"),
  Discipline = c("Biotechnology", "Biotechnology", "Biochemistry"),
  Preferred_Language = c("Python, R", "R", "Python")
)
print(data)

👉 Outcome: A structured data representation successfully printed.

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Task 1: Microbial Growth Curve Analysis

Objective

• Analyze microbial growth curves for knockout (-) and knock-in (+) strains.
• Compute time to carrying capacity.
• Visualize data using scatter and box plots.
• Perform statistical analysis.

Approach & Implementation

Python Implementation:

import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

# Load dataset
data = pd.read_csv("microbial_growth.csv")

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Task 2: Advanced Bioinformatics Analyses

Objective

• Perform computational analyses in various biological disciplines.
• Apply data science, visualization, and statistical modeling techniques.

2.1 Microbiology: Growth Curve Analysis

🔹 Objective: Analyze microbial growth under different conditions.

🔹 Approach: Used Python to process growth curve data, visualize trends, and determine significant differences.

Python Implementation:

# Import necessary libraries
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

# Load growth curve data
data = pd.read_csv("growth_curve_data.csv")

# Plot microbial growth
g = sns.lineplot(data=data, x="Time", y="OD600", hue="Condition")
g.set(title="Microbial Growth Curve", xlabel="Time (hours)", ylabel="Optical Density (OD600)")
plt.show()

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2.3 Botany & Plant Science: Metabolic Response Analysis

🔹 Objective: Evaluate metabolic shifts in response to environmental changes.

🔹 Approach: Used R for data normalization and visualization.

R Implementation:

# Load required library
library(ggplot2)

# Read dataset
data <- read.csv("metabolic_data.csv")

# Generate boxplot
p <- ggplot(data, aes(x=Condition, y=Metabolite_Level, fill=Condition)) +
     geom_boxplot() +
     ggtitle("Metabolic Response Analysis")
print(p)

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2.4 Biochemistry & Oncology: Protein Mutation Impact

🔹 Objective: Assess functional impact of protein mutations.

🔹 Approach: Python-based structural modeling and variant impact prediction.

Python Implementation:

from Bio.PDB import *

# Load PDB file
parser = PDBParser()
structure = parser.get_structure("Protein", "protein_structure.pdb")

# Extract chain A
chain_A = structure[0]["A"]

# Print residue names
for residue in chain_A:
    print(residue.resname)

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2.6 Transcriptomics: RNA-seq Data Analysis

🔹 Objective: Perform differential expression analysis on RNA-seq data.

🔹 Approach: Used Python and R to preprocess and analyze RNA-seq datasets.

Python Implementation:

import pandas as pd
import seaborn as sns

# Load RNA-seq data
data = pd.read_csv("rna_seq_data.csv")

# Generate heatmap
sns.heatmap(data.corr(), cmap="coolwarm", annot=True)
plt.title("Gene Expression Correlation")
plt.show()

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2.7 Public Health: NHANES Data Analysis

🔹 Objective: Investigate health trends using NHANES dataset.

🔹 Approach: Statistical analysis in Python to uncover population health insights.

Python Implementation:

import pandas as pd

# Load NHANES dataset
data = pd.read_csv("nhanes_data.csv")

# Summary statistics
print(data.describe())