Computational Biology Session
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Computational Biology Internship – Program Update
The Computational Biology Internship is designed to help students build strong foundations in modern biological sciences by integrating biology, computer science, mathematics, and data analysis. This program focuses on developing theoretical knowledge and practical skills required to analyze biological data and solve real-world challenges in biotechnology, genomics, healthcare, and biomedical research.
Throughout the internship, participants will:
- Learn the fundamentals of computational biology and bioinformatics
- Understand biological databases, genomic data, and computational tools
- Explore DNA, RNA, and protein sequence analysis techniques
- Study biological data modeling, machine learning, and systems biology concepts
- Gain hands-on experience with programming and biological data analysis
- Analyze real-world biological datasets and research case studies
- Work with computational methods used in genomics, drug discovery, and precision medicine
- Build a mini-project based on computational biology applications
Each Task Includes:
- 💻 Session-based learning
- 📝 Notes preparation
- 🔬 Biological data analysis exercises
- 📊 Practical computational assignments
- 📚 Research-based case studies
- 📤 Submission via Google Forms
By the end of the internship, students will gain practical exposure to computational biology, bioinformatics, genomics, biological data analysis, programming for life sciences, and modern research methodologies, preparing them for opportunities in computational biology, bioinformatics, biotechnology, genomics research, pharmaceutical industries, healthcare analytics, precision medicine, and biomedical research organizations.
TASK 1: R Bioinformatics – Introduction
🎯 Objective:
Understand the fundamentals of R programming and its importance in Bioinformatics, Computational Biology, and Biological Data Analysis.
🎥 Session:
Session 01 – R Bioinformatics: Introduction
Session Link: click here to access
📝 Task:
Write Notes On:
- What is Computational Biology?
- Introduction to Bioinformatics
- Why R is used in Biological Research
- Applications of R in Genomics and Healthcare
- Features of R Programming Language
- R Environment and RStudio
🔬 Practice / Research:
- Research 3 real-world applications of Bioinformatics.
- List 5 biological problems that can be solved using computational methods.
- Explain the role of R in biological data analysis.
📘 Assignment:
Write a short report (200–300 words):
“How Computational Biology and Bioinformatics are Transforming Modern Healthcare.”
📂 Internship Task Completion Status Form
After completing this task, interns must fill out the Internship Task Completion Status Form and upload:
✅ Session Notes
✅ Assignment Report
✅ Research Work (if applicable)
✅ Screenshots of Practical Work (if applicable)
This submission will be used to verify task completion and monitor internship progress.
📤 Form Link: https://forms.gle/dcfdpoDgksgP9KUY8
TASK 2: R Bioinformatics – Tidyverse
🎯 Objective:
Learn the fundamentals of the Tidyverse ecosystem and its role in biological data analysis.
🎥 Session:
Session 02 – R Bioinformatics: Tidyverse
Session Link: click here to access
📝 Task:
Write Notes On:
- Introduction to Tidyverse
- Components of Tidyverse
- Data Frames in R
- Data Import and Export
- Data Cleaning Basics
- Applications in Bioinformatics
🔬 Practice / Research:
- Research the major packages included in Tidyverse.
- Explain why data cleaning is important before biological analysis.
- List three examples of biological datasets.
📘 Assignment:
Prepare a comparison table explaining the purpose of:
- dplyr
- ggplot2
- tidyr
- readr
- stringr
Also write a short explanation of how these packages help biological researchers.
📂 Internship Task Completion Status Form
After completing this task, interns must fill out the Internship Task Completion Status Form and upload:
✅ Session Notes
✅ Assignment Work
✅ Research Activity
✅ Screenshots (if applicable)
📤 Form Link: https://forms.gle/dcfdpoDgksgP9KUY8
TASK 3: R Bioinformatics – Tidyverse (Continue)
🎯 Objective:
Understand advanced data manipulation techniques used in computational biology.
🎥 Session:
Session 03 – R Bioinformatics: Tidyverse (Continue)
Session Link: click here to access
📝 Task:
Write Notes On:
- Data Transformation
- Data Filtering
- Data Selection
- Data Arrangement
- Grouping and Summarizing Data
- Biological Data Processing
🔬 Practice / Research:
- Research common challenges in biological datasets.
- Explain why preprocessing is important before data analysis.
- Study how scientists clean genomic datasets.
📘 Assignment:
Write a report (250–300 words):
“The Importance of Data Cleaning and Data Transformation in Bioinformatics.”
📂 Internship Task Completion Status Form
After completing this task, interns must fill out the Internship Task Completion Status Form and upload:
✅ Session Notes
✅ Assignment Report
✅ Research Findings
✅ Practical Screenshots (if applicable)
📤 Form Link: https://forms.gle/dcfdpoDgksgP9KUY8
TASK 4: Data Visualization with GGPlot2 (Part 1)
🎯 Objective:
Learn the fundamentals of data visualization and create informative plots using GGPlot2.
🎥 Session:
Session 04 – Data Visualization with GGPlot2 (Part 1)
Session Link: click here to access
📝 Task:
Write Notes On:
- Introduction to Data Visualization
- Importance of Visual Analytics
- Basics of GGPlot2
- Scatter Plots
- Bar Charts
- Plot Components
🔬 Practice / Research:
- Research how visualizations are used in genomics.
- Find examples of biological graphs used in scientific publications.
📘 Assignment:
Create a table explaining:
| Plot Type | Application in Biology |
|---|---|
| Scatter Plot | |
| Bar Plot | |
| Histogram | |
| Box Plot |
Write a brief explanation of each.
📂 Internship Task Completion Status Form
After completing this task, interns must fill out the Internship Task Completion Status Form and upload:
✅ Session Notes
✅ Assignment Table
✅ Research Work
✅ Screenshots (if applicable)
📤 Form Link: https://forms.gle/dcfdpoDgksgP9KUY8
TASK 5: Data Visualization with GGPlot2 (Part 2)
🎯 Objective:
Understand advanced visualization customization techniques in GGPlot2.
🎥 Session:
Session 05 – Data Visualization with GGPlot2 (Part 2)
Session Link: click here to access
📝 Task:
Write Notes On:
- Plot Themes
- Labels and Titles
- Color Mapping
- Legends
- Scientific Visualization Standards
- Publication-Ready Figures
🔬 Practice / Research:
- Research visualization guidelines used in scientific journals.
- Identify key characteristics of a professional scientific graph.
📘 Assignment:
Write a report (250–350 words):
“Why Effective Data Visualization is Essential in Computational Biology Research.”
📂 Internship Task Completion Status Form
After completing this task, interns must fill out the Internship Task Completion Status Form and upload:
✅ Session Notes
✅ Assignment Report
✅ Research Findings
✅ Practical Work (if applicable)
📤 Form Link: https://forms.gle/dcfdpoDgksgP9KUY8
TASK 6: Tidyverse – Mutate Function
🎯 Objective:
Learn how to create and transform variables using the mutate() function.
🎥 Session:
Session 06 – Tidyverse: Mutate
Session Link: click here to access
📝 Task:
Write Notes On:
- Introduction to mutate()
- Creating New Columns
- Data Transformation
- Feature Engineering
- Derived Variables
- Applications in Bioinformatics
🔬 Practice / Research:
- Research how scientists generate derived biological metrics.
- Explain the importance of creating new variables during analysis.
📘 Assignment:
Consider a gene expression dataset.
Write a short explanation describing how mutate() can be used to:
- Normalize expression values
- Create expression categories
- Calculate fold changes
(200–300 words)
📂 Internship Task Completion Status Form
After completing this task, interns must fill out the Internship Task Completion Status Form and upload:
✅ Session Notes
✅ Assignment Work
✅ Research Activity
✅ Screenshots of Practice Work (if applicable)
📤 Form Link: https://forms.gle/dcfdpoDgksgP9KUY8
TASK 7: Data Visualization with GGPlot2 (Part 3)
🎯 Objective:
Develop advanced visualization skills for analyzing large biological datasets.
🎥 Session:
Session 07 – Data Visualization with GGPlot2 (Part 3)
Session Link: click here to access
📝 Task:
Write Notes On:
- Advanced GGPlot2 Features
- Multi-Layer Visualizations
- Faceting
- Statistical Visualizations
- Gene Expression Visualization
- Biological Interpretation of Graphs
🔬 Practice / Research:
- Research how sequencing data is visualized.
- Study examples of gene expression visualizations.
- Identify biological datasets that benefit from advanced plotting techniques.
📘 Assignment:
Prepare a report (300–400 words):
“The Role of Data Visualization in Computational Biology and Genomics Research.”
Include:
- Importance of Visualization
- Types of Biological Data
- Benefits for Researchers
- Real-World Applications
📂 Internship Task Completion Status Form
After completing this task, interns must fill out the Internship Task Completion Status Form and upload:
✅ Session Notes
✅ Assignment Report
✅ Research Findings
✅ Practical Work or Screenshots
📤 Form Link: https://forms.gle/dcfdpoDgksgP9KUY8
🎓 FINAL MINI PROJECT
Project Title:
Biological Data Analysis and Visualization Using R
Project Objective:
Apply the concepts learned throughout the internship to analyze and visualize a biological dataset using R and Tidyverse.
Project Tasks:
- Import a biological dataset
- Clean and preprocess the data
- Perform exploratory analysis
- Create at least 3 visualizations using GGPlot2
- Interpret the findings
Final Report (500–800 Words)
Include:
- Introduction
- Dataset Description
- Methodology
- Analysis
- Visualizations
- Findings
- Conclusion
📂 Final Project Submission Form
After completing the project, upload:
✅ Final Project Report
✅ Dataset Used
✅ Visualizations Created
✅ R Scripts or Code Files
✅ Screenshots of Results