A carefully chosen collection
Search by topic, tool, or language. Every resource has a purpose, a starting level, and a note on what you’ll need. Choose one that fits your next step.
The official language tutorial. Use it as a reference while writing small scripts rather than reading everything before practicing.
Some general programming familiarity; complete beginners can start with a guided lesson.
A practical route through importing, transforming, visualizing, and communicating tabular data in R.
No prior R experience.
Learn project organization, the command line, sequence processing, and computing through biological examples.
No prior command-line experience for the introductory lessons.
An Arabic-language programming entry point retained from the original resource list. Pair the lessons with a small biological data task.
No prior Python experience.
Learn to navigate genome annotation, identifiers, comparative data, and Ensembl tools using worked training materials.
Basic genetics.
Find official introductions and training for NCBI databases and tools; choose a tutorial that matches your data source.
Basic molecular biology.
Understand the annotated matrix structure used in many Python single-cell workflows, including observations, variables, and layers.
Python and basic single-cell matrices.
A welcoming introduction to structure files, experimental information, assemblies, and interpretation.
Basic protein biology.
Develop statistical reasoning about data, probability, inference, and models before relying on a test by name.
Basic arithmetic and willingness to work through examples.
Connect RNA-seq processing with exploratory analysis and differential expression through a documented workflow.
Basic R, sample metadata, and introductory statistics.
A question-oriented reference for single-cell analysis across modalities, including preprocessing, statistics, and integration.
Basic Python or R and single-cell biology.
Practice tracking changes, working with remote repositories, and collaborating on a small project.
Basic command-line use.
Create and document isolated software environments, including environment files that can be shared with collaborators.
Basic command-line use.
Combine analysis, narrative, and outputs in a reproducible report rather than copying results into an unrelated document.
Basic R or Python for executable examples.
A deeper route into matrices, projections, and related concepts that support PCA and machine learning.
Algebra; consult the course's own prerequisite guidance.
Build conceptual and practical foundations in deep learning; select chapters relevant to the model you are studying.
Python, probability, linear algebra, and basic calculus for deeper chapters.
Find experimental structures and inspect entry-specific methods, sequence information, and validation evidence.
Basic protein biology.
Learn molecular visualization through worked examples; check the current software license for your intended use.
Basic structural biology.
Practical biological sequence and structure handling, including parsers used in the protein-structure starter project.
Basic Python.
Learn access routes, interpretation, strengths, limitations, and confidence assessment for predicted structures.
Basic protein structure knowledge.
Understand local confidence and relative-domain uncertainty before using a prediction to support a biological claim.
Protein structure basics.
An accessible prediction toolkit with examples and execution options. Check current compute availability before planning a run.
Structure prediction basics and familiarity with notebooks or command-line tools.
Explore diffusion-based protein backbone generation and conditional design through the authors' code and examples.
Protein design, structure assessment, and command-line experience.
Study sequence design conditioned on a protein backbone as a distinct stage in a broader design workflow.
Protein structures, sequence design, and scripting.
Learn simulation setup, execution, and analysis with explicit attention to the physical model and settings.
Molecular simulation basics and Python.
A reference for molecular dynamics methods, file formats, commands, and reproducible simulation configuration.
Thermodynamics, molecular structures, and command-line use.
Understand count-based differential expression, model design, contrasts, diagnostics, and interpretation.
R and experimental design.
Hands-on biological analysis tutorials across sequencing, proteomics, metabolomics, and other domains.
Choose an introductory pathway; many require no programming.
A production workflow to examine after a smaller tutorial; learn its inputs, profiles, QC outputs, and documented options.
RNA-seq concepts, Nextflow, and suitable compute access.
Work through Python single-cell analysis examples and inspect how preprocessing decisions affect downstream results.
Basic Python and single-cell matrices.
An R entry point to single-cell QC, normalization, dimensionality reduction, clustering, and marker exploration.
Basic R and single-cell concepts.
Choose probabilistic models by task and modality, and read their assumptions alongside the worked tutorials.
Python, single-cell analysis, and statistics.
A model guide for joint RNA–protein analysis, with links to CITE-seq examples and discussion of the observation model.
CITE-seq concepts, Python, and single-cell QC.
Study joint RNA–chromatin accessibility modeling, including the paired and unpaired data setting.
Single-cell RNA/ATAC concepts and Python.
Understand how multimodal observations and modality-specific matrices are organized and linked.
Python and AnnData.
Explore guide assignment, perturbation efficacy, response comparisons, and prediction through documented examples.
Single-cell analysis and experimental design.
Learn repertoire QC, clonotype analysis, and integration of receptor information with cell-level measurements.
Python, single-cell data, and basic immunology.
Use community terminology, data standards, and metadata guidance when handling receptor repertoire datasets.
Basic adaptive immune repertoire concepts.
An introduction to mass spectrometry for proteomics, starting with amino acids and proteins and covering ion sources, analyzers, detectors, tandem mass spectrometry, and peptide fragmentation.
Basic protein biology and chemistry; no programming required.
Find focused introductions to biological databases and analysis areas, including routes into additional omics fields.
Vary by course.
Reference processes, channels, modules, profiles, executors, and dependency handling while building a small pipeline.
Command-line use and workflow concepts.
A guided route from running a first pipeline to composing and configuring workflows.
Basic shell skills; start with the introductory material.
Learn conventions for using and developing community pipelines, including configuration and contribution guidance.
Nextflow basics.
Understand rules, file dependencies, software environments, execution, and reporting.
Basic Python and command-line use.
Build and extend an example workflow while learning dependency handling, configuration, and execution.
Basic Python and shell use.
Learn container execution and reproducibility concepts relevant to shared research computing systems.
Linux and software environment basics.
An applied introduction to model fitting, generalization, data handling, and evaluation.
Python and introductory algebra/statistics; check module guidance.
Practice AI-assisted software development through a research-computing lesson. The material is under development; check its current status and verify generated code.
Some programming experience; this is an evolving Carpentries Incubator lesson.
Understand language models and their practical ecosystem; pair technical learning with source verification and task-level checks.
Python and introductory machine learning for implementation chapters.
An introduction to agent concepts, tools, and implementation. Evaluate any example on a bounded scientific task before expanding it.
Python, APIs, and basic LLM familiarity.
A science-specific starting point for transparent, critical LLM use, with discussion of verification and appropriate applications.
A scientific task you want to support with an LLM.
An optimization-based binder-design pipeline combining AlphaFold-based design, sequence design, and filtering. Compare its strategy with diffusion-based approaches and inspect dependency terms.
Binder design, structure assessment, and suitable GPU access.
Try a broader term, another level, or clear the filters.
Collection review dates describe editorial review, not certification or a guarantee of availability. Resource versions, access terms, and compute requirements can change.