Biopython Course
Master Biopython and take full control of biological data analysis through hands-on Python programming. This course takes you from Python fundamentals to advanced workflows covering sequence analysis, database retrieval, BLAST searches, and phylogenetics. You will write real scripts that solve real bioinformatics problems from day one.
What you will learn:
In this course, you will learn to use Biopython to handle biological sequences, parse major file formats like FASTA, GenBank, and FASTQ, and access public databases, including NCBI and UniProt, programmatically. You will perform pairwise and multiple sequence alignments, run BLAST searches, and analyse restriction enzymes and codon usage. The course also covers phylogenetic tree construction, protein structure analysis with Bio.PDB, and data visualisation for bioinformatics. Advanced topics include NGS data handling, machine learning applied to sequences, and workflow automation with Snakemake and Docker. By the end, you will be able to design and deploy complete, reproducible bioinformatics pipelines.
How you study in practice Biopython Course
How you practise Biopython Course
For companies looking to train their teams
With Elevify for businesses, the course includes exercises and examples tailored to your company and its specific needs.
Course content
8 Chapters • 33 LessonsDuration between 4 and 360 hours (you decide)
Chapter 1HideHide detailsSee detailsPython Foundations for Bioinformatics
Python Foundations for Bioinformatics
Lesson 1 • Python Environment Setup
Covers installation of Python, conda/pip environments, and Jupyter notebooks. Prepares a reproducible workspace for all subsequent Biopython exercises.
Lesson 2 • Core Python Data Structures
Reviews strings, lists, dictionaries, and tuples with biological data examples. Builds the manipulation skills needed for sequence and record handling.
Lesson 3 • File I/O and Data Parsing
Teaches reading and writing text, CSV, and FASTA-like files using Python built-ins. Directly prepares students for parsing biological file formats.
Lesson 4 • Functions, Modules, and Scripts
Introduces reusable functions, importing modules, and organising code into scripts. Enables students to write maintainable Biopython pipelines.
Chapter 2HideHide detailsSee detailsBiopython Architecture and Sequence Objects
Biopython Architecture and Sequence Objects
Lesson 1 • The Seq Object in Depth
Explores creating, slicing, and operating on Seq objects for DNA, RNA, and protein. Establishes the core data type used throughout the course.
Lesson 2 • SeqRecord and Annotations
Teaches the SeqRecord container that wraps sequences with metadata and features. Students annotate sequences and access feature qualifiers programmatically.
Lesson 3 • Alphabets and Sequence Validation
Explains biological alphabets and how Biopython enforces sequence validity. Prevents common errors when converting between sequence types.
Lesson 4 • Biopython Package Overview
Maps the major Biopython submodules and their biological purposes. Orients students to the library before diving into specific tools.
Chapter 3HideHide detailsSee detailsBiological File Format Parsing
Biological File Format Parsing
Lesson 1 • AlignIO for Alignment Files
Covers reading and writing multiple sequence alignments in Clustal, PHYLIP, and Stockholm formats. Prepares students for downstream alignment analysis.
Lesson 2 • SeqIO for Sequence Files
Introduces Bio.SeqIO.parse and read for FASTA, FASTQ, and GenBank files. Forms the primary interface for loading biological sequence data.
Lesson 3 • Indexing and Large File Handling
Teaches SeqIO.index and SeqIO.index_db for random access to large sequence files. Addresses performance challenges with genome-scale datasets.
Lesson 4 • Writing and Converting Formats
Demonstrates SeqIO.write and format conversion pipelines between FASTA, GenBank, and FASTQ. Enables interoperability with external bioinformatics tools.
Chapter 4HideHide detailsSee detailsAccessing Biological Databases
Accessing Biological Databases
Lesson 1 • NCBI Entrez Utilities
Covers esearch, efetch, einfo, and elink for querying NCBI databases. Enables automated retrieval of nucleotide, protein, and taxonomy records.
Lesson 2 • Protein Data Bank Access
Covers Bio.PDB for downloading and parsing PDB structure files. Prepares students for structural bioinformatics workflows.
Lesson 3 • Parsing NCBI Records
Demonstrates parsing GenBank, PubMed, and taxonomy XML records returned by Entrez. Connects database retrieval to structured Python objects.
Lesson 4 • UniProt and ExPASy Access
Uses Bio.ExPASy and Bio.SwissProt to retrieve and parse UniProt protein records. Extends database skills to proteomics data sources.
Chapter 5HideHide detailsSee detailsSequence Analysis and Manipulation
Sequence Analysis and Manipulation
Lesson 1 • Restriction Enzyme Analysis
Applies Bio.Restriction to find cut sites, simulate digestion, and select enzymes. Directly supports cloning design and gel electrophoresis prediction.
Lesson 2 • Codon Usage and Translation
Analyses codon usage tables and translates sequences using alternative genetic codes. Supports gene expression and heterologous expression studies.
Lesson 3 • Motif Searching and Pattern Matching
Uses Bio.motifs and regex-based approaches to find sequence motifs and binding sites. Connects pattern detection to regulatory and functional annotation.
Lesson 4 • Sequence Statistics and Properties
Calculates GC content, molecular weight, and isoelectric point using Biopython utilities. Provides quantitative descriptors for sequence comparison tasks.
Chapter 6HideHide detailsSee detailsPairwise and Multiple Sequence Alignment
Pairwise and Multiple Sequence Alignment
Lesson 1 • Alignment Statistics and Visualisation
Computes percent identity, conservation scores, and gap profiles from alignments. Connects alignment metrics to biological interpretation.
Lesson 2 • Pairwise Alignment Fundamentals
Introduces global and local alignment algorithms using Bio.Align.PairwiseAligner. Builds conceptual understanding of scoring matrices and gap penalties.
Lesson 3 • Multiple Sequence Alignment Tools
Wraps ClustalW, MUSCLE, and MAFFT via Biopython command-line wrappers. Produces MSAs for phylogenetic and conservation analysis.
Lesson 4 • Substitution Matrices
Loads and applies BLOSUM and PAM matrices for protein alignment scoring. Explains how matrix choice affects alignment sensitivity and specificity.
Chapter 7HideHide detailsSee detailsBLAST and Sequence Similarity Search
BLAST and Sequence Similarity Search
Lesson 1 • Parsing BLAST XML Results
Applies Bio.Blast.NCBIXML to parse hits, HSPs, and alignment strings from BLAST output. Enables automated filtering and ranking of search results.
Lesson 2 • Running BLAST via NCBI
Uses Bio.Blast.NCBIWWW to submit remote BLAST searches and retrieve XML results. Automates database searches without local BLAST installation.
Lesson 3 • BLAST Concepts and Parameters
Reviews BLAST algorithm fundamentals, E-value statistics, and HSP structure. Provides the conceptual basis for interpreting programmatic BLAST results.
Lesson 4 • Local BLAST and Automation
Configures local BLAST databases and runs searches using NcbiblastnCommandline. Scales similarity searches to large in-house sequence collections.
Chapter 8HideHide detailsSee detailsPhylogenetics and Structural Bioinformatics
Phylogenetics and Structural Bioinformatics
Lesson 1 • Protein Structure Analysis with Bio.PDB
Calculates distances, dihedral angles, and RMSD between protein structures. Applies structural metrics to compare and classify protein conformations.
Lesson 2 • Building Phylogenetic Trees
Constructs distance-based and parsimony trees using Biopython and external tools. Connects MSA output to evolutionary relationship inference.
Lesson 3 • Integrated Analysis Pipeline
Combines sequence retrieval, alignment, BLAST, phylogeny, and structure into one workflow. Demonstrates professional pipeline design using all course skills.
Lesson 4 • Parsing and Traversing Trees
Reads Newick, Nexus, and PhyloXML formats with Bio.Phylo and traverses tree topology. Enables programmatic extraction of clades and branch lengths.
Lesson 5 • Tree Visualisation and Annotation
Draws phylogenetic trees with Bio.Phylo.draw and matplotlib, adding clade labels. Produces publication-ready tree figures from Python scripts.
Your valid completion certificate
This course is for you:
Molecular biologist: wants to automate repetitive lab data tasks using Python scripts.
Graduate student: needs computational skills to strengthen thesis research and publications.
Bioinformatics newcomer: has biology knowledge but lacks confidence writing analysis code.
Computational biology career changer: seeks structured training to enter the life sciences field.
Research technician: aims to reduce manual data handling and increase reproducibility at work.
Genomics hobbyist: explores sequencing data independently and wants professional-grade programming tools.
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