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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: Computational Biology – Data Visualization with GGPLOT2

🎯 Objective:

Understand how to effectively represent biological datasets visually using the ggplot2 package in R, focusing on map aesthetics, layers, and geometric objects.

🎥 Session:

  • Session 01 – Computational Biology: Data Visualization with GGPLOT2

  • Session Link: click here to access

📝 Task:

Write Notes On:

  • The philosophy of the Grammar of Graphics in ggplot2.

  • Essential aesthetic mappings (aes) and core geometric layers (geom_point, geom_boxplot, geom_line).

  • How to customize scales, axes, labels, and themes for publication-ready biological figures.

🔬 Practice / Research:

  • Load a built-in R dataset (like iris or mtcars) and generate a scatter plot comparing two features.

  • Research how data visualization helps identify patterns or outliers in high-throughput gene expression datasets.

📂 Assignment:

  • Task: Create a customized, faceted boxplot displaying the distribution of genomic or sample features across different categories using a theme of your choice (e.g., theme_minimal). Export the plot as a high-resolution PDF/PNG and write a brief description (100–150 words) interpreting the visual trends.

📋 Internship Task Completion Status Form

After completing this task, interns must fill out the completion form and upload:

TASK 2: Computational Biology – K-means Clustering

🎯 Objective:

Master the fundamentals of unsupervised machine learning by applying K-means clustering to group unlabelled biological data based on feature similarity.

🎥 Session:
📝 Task:

Write Notes On:

  • The algorithmic mechanics of K-means clustering (centroid initialization and assignment steps).

  • Methods for determining the optimal number of clusters ($k$), such as the Elbow Method.

  • The sensitivity of K-means to data scaling and outliers.

🔬 Practice / Research:

  • Explain the statistical difference between looking at within-cluster sum of squares (WSS) versus between-cluster sum of squares.

  • Investigate how K-means clustering is utilized in classifying single-cell RNA-seq profiles or patient clinical data.

📂 Assignment:

  • Task: Write an R script that normalizes a dataset, executes a K-means clustering pipeline across a range of $k$ values ($k=1$ to $k=10$), plots the resulting Elbow curve to justify an optimal cluster count, and visualizes the final clusters.

📋 Internship Task Completion Status Form

After completing this task, interns must fill out the completion form and upload:

TASK 3: Computational Biology – K-medoids Clustering

🎯 Objective:

Explore robust partitioning techniques by evaluating K-medoids clustering as a reliable alternative to K-means when dealing with noisy biological datasets.

🎥 Session:
📝 Task:

Write Notes On:

  • The definition of a medoid and how the Partitioning Around Medoids (PAM) algorithm operates.

  • A comparative analysis of K-means vs. K-medoids regarding computational cost and outlier robustness.

  • Understanding and interpreting silhouette widths and silhouette plots.

🔬 Practice / Research:

  • Research why K-medoids is often preferred over K-means when working with biological distance matrices (e.g., sequence alignment scores).

  • Define the role of Manhattan distance vs. Euclidean distance in robust clustering workflows.

📂 Assignment:

  • Task: Using the cluster package in R, apply the pam() function to a multivariate dataset. Generate and interpret a silhouette plot to evaluate the overall clustering quality, explicitly stating the average silhouette width.

📋 Internship Task Completion Status Form

After completing this task, interns must fill out the completion form and upload:

TASK 4: Computational Biology – DBSCAN Clustering Prediction

🎯 Objective:

Understand density-based spatial clustering of applications with noise (DBSCAN) to discover clusters of arbitrary shapes and filter out data noise in complex biological matrices.

🎥 Session:

  • Session 04 – Computational Biology: DBSCAN Clustering Prediction

  • Session Link: click here to access

📝 Task:

Write Notes On:

  • The core concepts of DBSCAN: Core points, Border points, and Noise/Outliers.

  • How to define and optimize the two critical parameters: Epsilon ($\epsilon$) and Minimum Points ($MinPts$).

  • The advantages of density-based approaches over partition-based metrics when looking at irregular biological structures.

🔬 Practice / Research:

  • Research the use of the k-distance plot to systematically discover the ideal value for Epsilon.

  • Identify a real-world scenario in structural biology or spatial transcriptomics where clusters do not form spherical shapes.

📂 Assignment:

  • Task: Implement a DBSCAN model using the dbscan package in R. Use a k-distance graph to determine $\epsilon$, run the algorithm, isolate the data points flagged as noise, and create a scatter plot highlighting the density clusters against the background noise.

📋 Internship Task Completion Status Form

After completing this task, interns must fill out the completion form and upload:

TASK 5: Computational Biology – Introduction to Python in Bioinformatics

🎯 Objective:

Transition into programmatic environments using Python to read, manipulate, and process foundational molecular biology data types.

🎥 Session:
📝 Task:

Write Notes On:

  • Python basics for biological data: Strings, Lists, Dictionaries, and File handling.

  • An introduction to specialized libraries such as Biopython.

  • Parsing structured data files common in genomics (FASTA, GenBank, PDB formats).

🔬 Practice / Research:

  • Write a native Python script to calculate the GC content percentage of a raw DNA sequence string.

  • Research the history and current landscape of Python libraries driving modern computational structural biology and machine learning.

📂 Assignment:

  • Task: Write a Python script that opens a multi-sequence FASTA file, calculates the length and GC content of each individual sequence, filters out any sequences below a user-defined length threshold, and writes the filtered headers out to a new file.

📋 Internship Task Completion Status Form

After completing this task, interns must fill out the completion form and upload:

TASK 6: Computational Biology – Data Frame Operations using Pandas

🎯 Objective:

Leverage the Python pandas library to clean, filter, aggregate, and transform large tabular biological matrices like gene expression counts or clinical metadata.

🎥 Session:

  • Session 06 – Computational Biology: Data Frame Operations using Pandas

  • Session Link: click here to access

📝 Task:

Write Notes On:

  • pandas Core Data Structures: Series and DataFrames.

  • Methods for importing data files (CSV, TSV, Excel) and handling missing values (dropna, fillna).

  • Indexing, slicing, conditional filtering, and calculating summary statistics across axes.

🔬 Practice / Research:

  • Explain the difference between explicit indexing via .loc and implicit integer-based indexing via .iloc.

  • Explore how pandas handles large-scale operations relative to basic Python loops when dealing with multi-gigabyte genomic variant call format (VCF) data.

📂 Assignment:

  • Task: Import a gene expression table using pandas. Clean the data by removing rows with null values, filter out low-expression items (e.g., keep rows where count $> 10$), create a new calculated column converting counts to log2 scale, and group the data by tissue type to calculate mean expression values.

📋 Internship Task Completion Status Form

After completing this task, interns must fill out the completion form and upload:

TASK 7: Computational Biology – Advanced Data Frame Merging using Pandas

🎯 Objective:

Execute complex relational database joins in Python to combine disparate biological data sources, such as linking genomic coordinates with clinical phenotypic records.

🎥 Session:

  • Session 07 – Computational Biology: Merge Two Data Frames using Pandas

  • Session Link: click here to access

📝 Task:

Write Notes On:

  • Types of relational merges: Inner, Outer, Left, and Right joins using pd.merge().

  • Concatenating and appending data frames vertically and horizontally using pd.concat().

  • Troubleshooting overlapping columns and managing suffix naming conventions during merges.

🔬 Practice / Research:

  • Describe a biological scenario where an inner join would inadvertently erase critical information compared to using a left join.

  • Investigate the performance impacts of sorting keys before performing massive merges.

📂 Assignment:

  • Task: Take two distinct mock biological datasets—one containing patient demographic profiles mapped to sample IDs, and another containing normalized sequencing metrics mapped to sample IDs. Merge these sets using various join strategies (Inner and Left), and write a short comparison report (150 words) displaying how the total sample dimensions change based on the strategy used.

📋 Internship Task Completion Status Form

After completing this task, interns must fill out the completion form and upload:

FINAL MAJOR PROJECT: End-to-End Computational Biology Pipeline

🎯 Objective:

Integrate your R data analytics, unsupervised machine learning workflows, and Python data manipulation skills to process, analyze, and interpret a multi-omic biological dataset.

🎥 Session:

  • Final Capstone – Comprehensive Project Submission

  • Project Workspace Link: [Insert Project Directory Link Here]

📝 Task:

Write a Comprehensive Project Proposal Document Outline Detailing:

  • Data Pipeline Architecture: A workflow diagram explaining how data moves from raw files through filtering, analysis, and final reporting.

  • Algorithm Selection Logic: Justification for choosing specific data structure frameworks (Python vs. R) and clustering techniques based on dataset size and distribution traits.

🔬 Practice / Research:

  • Investigate open-source biological data repositories (such as NCBI GEO or TCGA) to source two related tabular matrices (e.g., a patient metadata file and an expression profile file).

📂 Assignment:

  • Task: Complete the following multi-stage project workflow:

    1. Stage 1 (Python): Read and clean the raw expression dataset using pandas. Filter out noisy data points, handle missing features, and perform a left-join merge to cleanly map the expression matrix rows with the patient metadata keys. Export this consolidated master file as a clean TSV.

    2. Stage 2 (R): Import your clean data into R. Run exploratory visualization arrays via ggplot2 (histograms of feature ranges, sample heatmaps).

    3. Stage 3 (R – Machine Learning): Implement and evaluate three independent clustering structures on your merged dataset: K-means, K-medoids, and DBSCAN. Generate diagnostic plots for each (Elbow curves, Silhouette plots, and density outlier graphs) to objectively declare the superior grouping mechanism.

    4. Stage 4 (Scientific Synthesis): Compile a comprehensive final project report (800–1000 words) summarizing your data curation decisions, algorithmic outcomes, and biological interpretations.

📋 Internship Task Completion Status Form

After completing this final capstone project, interns must fill out the master verification form and upload:

  • [ ] Structured Pipeline Proposal Document

  • [ ] Documented Python Preprocessing Script (.ipynb or .py)

  • [ ] Documented R Machine Learning Script (.R)

  • [ ] Final High-Resolution Comparative Evaluation Figure Panel

  • [ ] Comprehensive Scientific Report Document

  • Form Submission Link: https://forms.gle/dcfdpoDgksgP9KUY8

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