Biological Sequence Analysis using R Programming

Introduction to the Course

Course Objectives

• Understand the fundamentals of biological sequences and their importance in genomics and proteomics.

• Learn R programming techniques specifically designed for handling and analyzing biological data.

• Conduct motif discovery and functional annotation of DNA, RNA, and protein sequences.

• Explore phylogenetic relationships and evolutionary analysis using R.

• Appreciate the limitations, challenges, and ethical considerations when working with biological sequence data.

What Will You Learn (Modules)

Module 1: R for Bioinformatics and Sequence Data Basics (Beginner)

• R basics for bioinformatics applications and understanding sequence data representation in computers.
• File formats (FASTA/FASTQ), sequence representation (alphabet), and quality scores (FASTQ).
• Hands-on exercise: Reading and analyzing sequence files in R and generating a simple report on sequence data.

Module 2: Sequence Cleaning, Manipulation & Basic Statistics

• Filtering sequences, dealing with ambiguous bases, and basic statistics for sequences.
• Length distribution, GC content, base composition, and quality filtering (FASTQ).
• Hands-on exercise: Generating sequence quality plots and preparing cleaned data for analysis.

Module 3: k-mers, Motifs & Pattern Searching (Core Sequence Analytics)

• Unraveling patterns in sequences and their biological significance.
• k-mer counting, motif searching, regex pattern searching, and frequency analysis.
• Hands-on exercise: Searching for motifs and k-mer patterns in sample sequence data.

Module 4: Translation, ORFs & Protein-Level Insight

• Learn about the translation of DNA/RNA into protein sequences and ORF detection.
• Examine codons, translation, peptide characteristics, and protein sequence properties derived from sequences.
• Hands-on: Predict ORFs, translate sequences, and calculate basic protein sequence attributes.

Module 5: Alignment Concepts + Similarity Interpretation (Intermediate)

• Learn the importance of alignment and its relation to similarity and evolution and function.
• Understand pairwise alignment concepts and interpreting alignment results.
• Hands-on: Perform basic alignment tasks (using R connections) and create similarity reports.

Final Project

• Import + QC + filtering
• Pattern/motif or k-mer profiling
• ORF/translation analysis (where applicable)
• Interpretation + final reproducible report (R Markdown)

Who Should Take This Course?

The following individuals might benefit greatly from this course:

• Students (UG/PG) in biotechnology, bioinformatics, genetics, microbiology, or computational biology
• Researchers working with genomic, transcriptomic, or protein datasets
• Life science professionals moving into bioinformatics or data-driven biology roles
• Data science learners entering computational biology and genomics
• Career switchers aiming for roles in genomics, sequencing labs, or bioinformatics teams

Job Opportunities

Students completing this course will be well-prepared for roles such as:

• Bioinformatics Analyst: Analyze genomic and proteomic data to identify patterns and functional elements.

• Computational Biologist: Develop algorithms and workflows for large-scale sequence analysis.

• Genomics Data Scientist: Integrate sequence data with other omics datasets for research insights.

• Research Scientist: Conduct studies in molecular biology, evolutionary biology, or personalized medicine.

• Pharmaceutical & Biotechnology Analyst: Use sequence data for drug discovery and biomarker development.

Why Learn With Nanoschool?

At Nanoschool, you gain expert-led training in R programming for biological sequence analysis with practical, hands-on experience. Key benefits include:

• Expert-Led Training: Learn from instructors with extensive backgrounds in bioinformatics and computational biology.

• Hands-On Learning: Work with real sequence datasets and R tools used in professional bioinformatics workflows.

• Industry-Relevant Skills: Stay up to date with the latest methods in sequence analysis and bioinformatics.

• Career Support: Get guidance on projects, portfolios, and job opportunities in bioinformatics and computational biology.

Key outcomes of the course

After completing the Biological Sequence Analysis using R Programming course, you will:

• Develop practical skills in sustainability-focused AI and environmental data analysis
• Gain the ability to work with environmental time-series and geospatial datasets
• Apply forecasting and anomaly detection techniques to real-world monitoring and resource management problems
• Build a portfolio-ready project aligned with climate tech and ESG requirements
• Strengthen your career prospects for roles in environmental analytics and sustainability AI

Enroll now and discover how R programming can empower you to analyze biological sequences, uncover hidden patterns, and contribute to research, diagnostics, and biotechnology!