Genomic Data Science Specialization - Code Exercises

This repository contains my personal solutions, scripts, and notes from the Johns Hopkins Genomic Data Science Specialization on Coursera.

The aim of this repo is to document the work I actually did during the courses - from basic sequence processing and algorithms, to command-line workflows, Bioconductor analyses, and introductory statistical genomics - using Python, R, Bash, and Jupyter Notebooks.

Tech stack: Python · R · Bash · Jupyter Notebooks · Bioconductor · Bowtie2 · BWA · HISAT2 · samtools · bedtools · DESeq2

This is a learning repository, not a polished production pipeline. It is meant to show my progression through the material and give me a reference for future work in bioinformatics.

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Courses covered

This repo includes code and notebooks corresponding to exercises from the following courses in the specialization:

1. Introduction to Genomic Technologies
2. Python for Genomic Data Science
3. Algorithms for DNA Sequencing
4. Command Line Tools for Genomic Data Science
5. Bioconductor for Genomic Data Science
6. Statistics for Genomic Data Science
7. Genomic Data Science with Galaxy
8. Genomic Data Science Capstone

Not every exercise from every course is represented, but the key coding and command-line components are included.

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Repository structure

The exact folder names may evolve, but the structure is organised around courses and shared utilities:

• 3 - Algorithms for DNA Sequencing/
  Solutions for the algorithms course: Python and notebook implementations of classic string/sequence algorithms, pattern matching, indexing, and basic read processing.

• 4 - Command line tools for Genomic Data Science/
  Shell scripts and command sequences using Unix tools (e.g. grep, awk, sed), plus genomics-specific tools such as samtools, bedtools, and related utilities for working with FASTQ/BAM/VCF files.

• 5 - Bioconductor for Genomic Data Science/
  R scripts and RMarkdown/notebook files using Bioconductor packages for tasks like differential expression, annotation, and basic genomic workflows.

• 6 - Statistics for Genomic Data Science/
  R code and notebooks covering statistical concepts applied to genomic data, including basic models, hypothesis testing, and visualisation.

• Notebooks_commands/
  General Jupyter notebooks and command summaries that span multiple courses (e.g. combined notes on tools, small experiments, or scratch work).

• R/
  Shared R helper scripts and utility functions reused across course assignments.

If your local clone has slightly different directory names, the same logic applies: each top-level directory corresponds to a course or shared code.

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Getting started

Prerequisites

You will need:

• A Unix-like environment (Linux, macOS, or WSL on Windows)
• Python (≥ 3.10) with Jupyter Notebook or JupyterLab
• R (≥ 4.x) with RStudio or another R environment
• For command-line exercises:
  • Standard Unix tools (grep, awk, sed, sort, etc.)
  • Genomics tools used in the courses, typically including:
    • samtools
    • bedtools
    • Other utilities as specified in individual notebooks/scripts

Note: the exact tool versions are those commonly used during the Coursera course; some commands may need minor adjustment if you are using newer versions.

Clone the repository

git clone https://github.com/barbavegeta/Genomic_Data_Science_Specialization.git
cd Genomic_Data_Science_Specialization

# Example - adjust package list as needed
conda create -n genomic-data-science python=3.10 jupyter numpy pandas biopython
conda activate genomic-data-science
jupyter notebook


# Bioconductor
install.packages(c("tidyverse", "data.table"))

if (!require("BiocManager")) {
  install.packages("BiocManager")
}
BiocManager::install(c(
  "GenomicRanges",
  "DESeq2",
  "edgeR"
  # add more Bioconductor packages here as used in the scripts
))