Bioinformatics and Computational Biology Internship Program
in BIOTECHNOLOGYAbout this course
Bioinformatics & Computational Biology Internship Program: 6-Week Structured Learning and Experience
Introduction
Bioinformatics and Computational Biology combine the power of biology, computer science, and data analytics to interpret and visualize biological data. This program introduces participants to tools, databases, and programming techniques used in genomics, proteomics, and molecular biology. It offers hands-on experience with biological databases, sequence analysis, phylogenetics, structure prediction, and data visualization.
Designed for students and researchers in biology, biotechnology, or computer science, this internship culminates in a mini-project focused on disease gene analysis.
Program Highlights
Week 1: Foundations of Bioinformatics
· Introduction to Bioinformatics
• Task: Research and write a report on the scope and applications of bioinformatics.
• Outcome: A 500-word report summarizing the field and its relevance in modern biology.
· Understanding Biological Databases
• Task: Explore and compare NCBI, UniProt, and EMBL databases.
• Outcome: Comparative table and summary explaining key features and data types.
· DNA & Protein Sequence Formats
• Task: Study FASTA format and retrieve DNA/protein sequences.
• Outcome: Document containing 3 retrieved sequences and explanation of formats.
Week 2: Sequence Analysis and Evolutionary Biology
· Pairwise Sequence Alignment
• Task: Perform pairwise sequence alignment using BLAST.
• Outcome: Screenshot and analysis of BLAST results with biological significance.
· Multiple Sequence Alignment (MSA)
• Task: Use Clustal Omega to perform an MSA of protein sequences.
• Outcome: MSA output and short interpretation of conserved regions.
· Phylogenetic Tree Construction
• Task: Construct a phylogenetic tree from aligned sequences.
• Outcome: Phylogenetic tree diagram with interpretation of relationships.
Week 3: Genomics and Structural Bioinformatics
· Gene Prediction Tools
• Task: Use GENSCAN or GeneMark to predict genes in a genomic sequence.
• Outcome: Tool output with annotation of predicted gene regions.
· Protein Structure Prediction
• Task: Use SWISS-MODEL or Phyre2 to predict 3D structure of a protein.
• Outcome: Screenshots of predicted model and explanation of structure.
· Protein-Protein Interaction Networks
• Task: Analyze interaction networks using STRING database.
• Outcome: Network diagram with interpretation of key interactions.
Week 4: Transcriptomics and Functional Analysis
· RNA-Seq Data Analysis Introduction
• Task: Learn basics of RNA-Seq and explore example datasets.
• Outcome: Summary of pipeline and key insights from sample data.
· Introduction to R for Bioinformatics
• Task: Install R and perform basic data manipulation tasks.
• Outcome: R script with loading, filtering, and plotting operations.
· DNA Motif Discovery
• Task: Use MEME Suite to discover motifs in DNA sequences.
• Outcome: Identified motifs with biological interpretation.
Week 5: Comparative & Functional Genomics
· Comparative Genomics
• Task: Compare orthologous genes across species using Ensembl Genome Browser.
• Outcome: Report on similarities/differences in gene structures.
· Biological Pathway Analysis
• Task: Explore KEGG or Reactome pathways for a specific disease.
• Outcome: Diagram of relevant pathways with a 200-word analysis.
· Drug Target Identification
• Task: Use bioinformatics databases to identify potential drug targets.
• Outcome: Shortlist of 3 targets with justification for selection.
Week 6: Advanced Bioinformatics Applications & Final Project
· miRNA Target Prediction
• Task: Use TargetScan or miRDB to predict miRNA targets.
• Outcome: List of predicted targets with brief explanation.
· CRISPR-Cas9 Off-target Analysis
• Task: Use CRISPR design tools to analyze potential off-targets.
• Outcome: Report showing off-target sites and interpretation of scores.
· Molecular Docking Basics
• Task: Perform simple ligand-protein docking using AutoDock.
• Outcome: Visualization of docking pose with short analysis.
· Bioinformatics Data Visualization
• Task: Create visualizations (heatmap, scatter plot) using gene expression data.
• Outcome: Graphical plots and interpretation using R or Python.
· Final Mini Project: Disease Gene Analysis
• Task: Select a disease and conduct a complete analysis using bioinformatics tools.
• Outcome: 800-word mini-project report with datasets, analysis, and visualizations.
Expected Outcomes
By the end of this internship, participants will:
- Understand core concepts of bioinformatics, genomics, and computational biology.
- Retrieve and analyze biological sequences using standard formats and tools.
- Perform alignments, phylogenetics, structure prediction, and motif analysis.
- Analyze gene expression, discover drug targets, and visualize biological data.
- Gain practical experience with R, databases like NCBI and STRING, and tools like BLAST, MEME, AutoDock, and KEGG.
- Deliver a comprehensive bioinformatics mini-project showcasing learned skills.
Requirements
Laptop
Internet Connection
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To gain a foundational understanding of bioinformatics, its importance in life sciences, and its various applications in research, healthcare, and biotechnology.
To become familiar with major biological databases and understand the types of data they store and how they are used in bioinformatics research.
To understand the structure and format of biological sequences and practice retrieving and interpreting them using online databases.
To understand the concept of sequence similarity and apply pairwise sequence alignment to compare biological sequences using BLAST.
To explore how conserved regions across protein families can be identified using multiple sequence alignment techniques.
To understand evolutionary relationships among organisms or genes by constructing and interpreting a phylogenetic tree based on sequence alignment.
To learn how to identify potential gene regions in a genomic DNA sequence using computational prediction tools.
To explore computational modeling of protein tertiary structures using homology-based prediction tools.
To analyze biological interaction networks and understand how proteins function in interconnected pathways using STRING database.
To understand the RNA-Seq pipeline and gain experience exploring gene expression data using publicly available datasets.
To introduce students to the R programming environment and teach basic data handling and visualization for biological datasets.
To learn how to identify conserved motifs in DNA sequences using computational tools like MEME Suite.
To explore how gene structure and conservation vary across species using genome browsers and comparative tools.
To understand disease mechanisms by exploring molecular pathways using curated biological databases.
To explore bioinformatics resources to identify potential molecular targets for therapeutic drug development.
To understand how microRNAs regulate gene expression by predicting their target genes using bioinformatics tools.
To explore CRISPR-Cas9 design strategies and assess the risk of off-target mutations using genome editing prediction tools.
To introduce the concept of molecular docking and simulate ligand-protein interaction using AutoDock or AutoDock Vina.
To practice visualizing biological datasets such as gene expression profiles using R or Python.
To apply bioinformatics tools to analyze a specific disease, identify associated genes, and investigate relevant pathways and interactions.
