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Biological Sequence Analysis using R Programming
USD $59.00 USD $249.00Price range: USD $59.00 through USD $249.00
The aim of this workshop is to provide participants with hands-on experience in utilizing R Studio for computational biology and bioinformatics tasks. Participants will gain practical skills in analyzing DNA and protein sequences, constructing phylogenetic trees, conducting differential gene expression analysis, and performing functional annotation using R.
Aim
This course trains participants to perform practical biological sequence analysis using R (with Bioconductor), covering DNA/RNA/protein sequence handling, quality checks, alignment concepts, annotation basics, and reproducible reporting. You’ll learn how to turn raw sequence data into interpretable biological insights using standard workflows and R-driven visualization.
Program Objectives
- Master Sequence Data Basics: Understand FASTA/FASTQ, GenBank, GFF/GTF, and core biological sequence concepts.
- Build an R + Bioconductor Workflow: Set up packages, manage objects, and create reproducible pipelines.
- Handle DNA/RNA/Protein Sequences: Read, clean, transform, and summarize sequences with Biostrings and related tools.
- Apply Core Analyses: Compute composition/GC, k-mers, ORFs, motifs, and simple similarity searches/interpretation.
- Alignment & Interpretation: Understand pairwise/multiple alignment outputs and how to interpret scoring and gaps.
- Communicate Results: Generate clean plots, tables, and reports (RMarkdown/Quarto) suitable for papers and audits.
- Hands-on Application: Complete a mini capstone that analyzes a real gene/protein dataset and produces a report.
Program Structure
Module 1: Foundations of Biological Sequence Analysis
- What sequences tell us: genes, transcripts, proteins, variants (high-level).
- Common file formats: FASTA, FASTQ, GenBank, GFF/GTF (what each contains).
- Key metrics: length, GC%, ambiguous bases, complexity, coverage (conceptual).
- Workflow overview: import → QC → analyze → annotate → visualize → report.
Module 2: R Setup for Bioinformatics (Bioconductor Essentials)
- R project setup: folders, scripts, sessions, package management.
- Bioconductor ecosystem: Biostrings, GenomicRanges, rtracklayer, and friends.
- Reproducibility: set.seed(), session info, renv (conceptual), versioning.
- Working with biological objects safely (strings vs ranges vs annotations).
Module 3: Importing, Cleaning & Summarizing Sequences
- Reading FASTA/FASTQ and building sequence collections.
- Basic QC checks: length distributions, N-content, GC distribution.
- Sequence transformations: reverse complement, translation, filtering rules.
- Creating summary tables for batches (multiple samples / multiple genes).
Module 4: k-mers, Composition, ORFs & Simple Feature Discovery
- k-mer counting and what it indicates (bias, repeats, complexity).
- Nucleotide/amino acid composition and enrichment comparisons.
- ORF basics: start/stop, frames, and interpreting ORF results.
- Primer/amplicon sanity checks: melting/GC screening (intro-level).
Module 5: Sequence Similarity & Alignment (Practical Interpretation)
- Pairwise similarity: identity, mismatch, gaps—reading alignment output.
- Multiple sequence alignment concepts (MSA): conserved regions and variability.
- Alignment-to-insight: consensus, variant positions, conserved motifs.
- Common pitfalls: low complexity, short sequences, over-interpreting matches.
Module 6: Motifs, Domains & Functional Clues
- Motif scanning basics and interpreting motif “hits”.
- From sequence to function: conserved motifs, active sites (conceptual).
- Annotating sequences with external references (IDs, accessions, metadata).
- Building a simple annotation table (gene/protein → features → notes).
Module 7: Genomic Coordinates & Annotation Tables (Intro)
- Coordinate thinking: ranges, exons, transcripts, CDS (intro).
- Working with GFF/GTF-style annotations in R (conceptual + practical parsing).
- Mapping sequence features to coordinates (basic overlap and summaries).
- Creating “publication-ready” tables and export formats (CSV/TSV).
Module 8: Visualization, Reporting & Avoiding Overclaims
- Plots that matter: length/GC, motif hit maps, alignment summaries.
- Reproducible reporting with RMarkdown/Quarto: code + outputs + narrative.
- What you can/can’t claim from sequence analysis without experiments.
- Checklist for transparent assumptions and limitations.
Final Project
- Analyze a curated dataset (DNA or protein sequences; single gene family or small panel).
- Define objective, dataset description, and QC/filters.
- Perform composition + k-mer analysis + motif/feature discovery + alignment interpretation.
- Deliverables: summary report + figures + results table + reproducible R script/notebook.
Participant Eligibility
- UG/PG students and researchers in Biotechnology, Genetics, Microbiology, Bioinformatics, Life Sciences
- PhD scholars working with genes/proteins, molecular biology, omics, or microbial genomics
- Professionals in genomics labs, diagnostics R&D, or computational biology teams
- Anyone with basic biology knowledge who wants to learn sequence analysis workflows in R
Program Outcomes
- Sequence Workflow Skill: Ability to import, clean, summarize, and analyze FASTA/FASTQ datasets in R.
- Interpretation Confidence: Read and explain key outputs (alignment, composition, motifs) responsibly.
- Bioinformatics Readiness: Familiarity with Bioconductor tools and object types used in real pipelines.
- Reproducible Reporting: Generate a clean, shareable report with code, figures, and tables.
- Portfolio Deliverable: A capstone report + script/notebook suitable for academic or job applications.
Program Deliverables
- Access to e-LMS: Full access to course content, templates, and practice datasets.
- R + Bioconductor Starter Pack: Setup guide, package list, import/QC templates, plotting snippets.
- Analysis Worksheets: QC checklist, motif/feature worksheet, alignment interpretation guide.
- Hands-on Project Support: Guided capstone planning, debugging, and interpretation support.
- Final Assessment: Certification after assignments + capstone submission.
- e-Certification and e-Marksheet: Digital credentials provided upon successful completion.
Future Career Prospects
- Bioinformatics Analyst (Entry-level) / Junior Computational Biologist
- Genomics Data Analyst / Sequence Data Associate
- Research Assistant (Genomics / Proteomics / Molecular Biology + Data)
- Bioconductor/R Analyst for Life Sciences Teams
- Omics Support Specialist (Academia / Core Facilities)
Job Opportunities
- Genomics & Diagnostics Labs: Sequence QC, reporting, targeted sequencing interpretation support.
- Biotech & Pharma R&D: Gene/protein sequence analytics, pipeline support, documentation.
- Academic & Research Institutes: Omics projects, gene family studies, microbial genomics support.
- Core Facilities & Service Providers: Data processing, visualization, reproducible reporting.
- Health/Agri-Bio Startups: Rapid sequence analysis prototyping and analytics dashboards.
| Category | E-LMS, E-LMS+Recordings, E-LMS+Recordings+Live Lectures |
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