Python for Bioinformatics
by Sebastian Bassi · Sebastian Bassi
Teaches Python to biologists from scratch, then applies it to sequence parsing with Biopython, regular expressions, database access, and web services. Includes chapters on graphics, XML handling, and building small tools for laboratory data.
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More resources on Bioinformatics
ensembl.org
Ensembl is a comprehensive genome browser and annotation resource offering high-quality genome assemblies, gene models, transcripts, regulatory features, and genetic variation across many species. It also provides comparative genomics tools, data downloads, and access via BioMart and APIs for querying and mining genomic data.
Galaxy Project
Open web-based workbench where you run genomics tools such as alignment, variant calling, and RNA-seq through a browser, without command-line experience. Public servers supply the compute, and every analysis is recorded as a reproducible, shareable workflow history.
Biostars
Question-and-answer site where working bioinformaticians troubleshoot tools, file formats, and pipeline errors. Searching the archive of answered threads is often faster than documentation for practical problems with BLAST, samtools, aligners, and genome annotation.
Rosalind
Problem-based platform for practising bioinformatics by writing code. Exercises begin with string manipulation on DNA sequences and build toward alignment, assembly, and phylogeny algorithms, each graded automatically against a generated dataset in whichever language you choose.
Introduction to Genomic Technologies
This course introduces you to the basic biology of modern genomics and the experimental tools that we use to measure it. We'll introduce the Central Dogma of Molecular Biology and cover how next-generation sequencing can be used to measure DNA, RNA, and epigenetic patterns. You'll also get an introduction to the key concepts in computing and data science that you'll need to understand how data from next-generation sequencing experiments are generated and analyzed. This is the first course in the Genomic Data Science Specialization.
Foundations of Computational and Systems Biology (MIT 7.91J)
Computational biology fundamentals: nucleic acid and protein sequence alignment, motif finding, structural modeling and prediction, and network models of biological systems. Includes 22 lecture videos, slides, programming assignments and projects, preparing learners to apply algorithms to genomic and protein data.