Interactive Tutorials & Research Pipelines
Access free, reproducible computational biology walkthroughs, interactive Google Colab notebooks, and video masterclasses covering transcriptomics, single-cell analysis, AlphaFold, and AI drug discovery.
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Interactive Guides
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Colab Pipelines
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Video Playlists
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Free & Open-Source
Step-by-Step Walkthroughs
Featured Interactive Guides
Read comprehensive, in-depth documentation and code examples built directly into our learning platform.
Bulk RNA-Seq Analysis Pipeline
The gold-standard workflow for measuring differential gene expression across cell populations — covering QC, pseudo-alignment, DESeq2, and pathway enrichment.
Single-Cell RNA-Seq Analysis
Dissect cellular heterogeneity at single-cell resolution. Step-by-step guidance on Cell Ranger output parsing, Seurat/Scanpy normalization, UMAP clustering, and marker annotation.
Spatial Transcriptomics Workflows
Integrate molecular gene expression with intact tissue histopathology using 10x Genomics Visium and spatial analysis frameworks.
Ready-to-Run Code
Production-Grade Computational Pipelines
Verified bioinformatics and cheminformatics workflows ready for your research projects.
End-to-End Bulk RNA-seq Quantification Pipeline using Salmon
A complete pipeline for pseudo-alignment and quantification of bulk RNA-seq data using Salmon — from raw FASTQ reads to transcript-level abundance estimates ready for downstream DESeq2 analysis.
In: Raw FASTQ files, reference transcriptome
Out: Transcript/gene-level count matrix, quantification summary
nf-core/rnaseqmeta: Nextflow Pipeline for RNA-seq Meta-Analysis
A Nextflow pipeline for reproducible meta-analysis of multiple RNA-seq cohorts — automating sample retrieval, quality control, batch effect correction, and cross-study differential expression.
In: Multiple RNA-seq datasets (FASTQ or SRA accessions), sample sheets
Out: Integrated count matrix, cross-study DE results, batch-corrected expression
Fast Preprocessing of scRNA-seq with kallisto | bustools | kb-python
End-to-end pipeline for scRNA-seq preprocessing using kallisto, bustools, and kb-python — transforming raw sequencing data into filtered count matrices for Scanpy and Seurat.
In: Raw FASTQ files (10x Chromium)
Out: Filtered cell × gene count matrix, QC metrics
Practical Guide for Single-Cell Data Analysis with scverse Ecosystem
Comprehensive walkthrough of the scverse workflow — covering quality control filtering, highly variable genes, dimensionality reduction, Leiden clustering, and marker gene annotation.
In: Count matrix (AnnData .h5ad or 10x format)
Out: Annotated cell clusters, UMAP embeddings, cell type labels
Predicting Protein Structures with ColabFold & AlphaFold2
Predict 3D protein structures from amino acid sequences using ColabFold's accelerated AlphaFold2 pipeline with MMseqs2 MSA generation and interactive in-browser 3D structure visualization.
In: Amino acid sequence (FASTA format)
Out: Predicted 3D structures (PDB), pLDDT confidence scores, PAE plots
Boltz2-Notebook: Diffusion-Based Protein-Ligand Structure Prediction
Predict protein-ligand complex conformations and binding affinities using the state-of-the-art Boltz2 diffusion model — enabling rapid in silico docking and interaction mapping without heavy MD setups.
In: Protein sequence and ligand SMILES/SDF
Out: Predicted complex structures (PDB), binding affinity scores
AI in Drug Discovery: Molecular Property Prediction & Virtual Screening
End-to-end pipeline for AI-driven small molecule screening — covering Morgan fingerprint featurization, toxicity prediction, ADMET property modeling, and virtual screening of compound libraries.
In: Compound libraries (SMILES), molecular descriptors
Out: Toxicity predictions, ADMET profiles, ranked hit compounds
In Silico Toxicology & Safety Modeling with Machine Learning
Build machine learning models to predict compound toxicity from 2D molecular structures — covering molecular fingerprint generation, endpoint classification, and structure-activity relationships (SAR).
In: Chemical compounds (SMILES), toxicity endpoint labels
Out: Toxicity classification models, SAR insights, safety predictions
Video Masterclasses
Curated YouTube Video Courses
Structured video series covering programming, computational genomics, and academic publication.
R for Research & Bioinformatics
Video lectures covering R programming fundamentals, tidyverse data wrangling, statistical testing, and publication-ready visualization.
Machine Learning for Bioinformatics
Hands-on video tutorials applying machine learning techniques (Random Forest, XGBoost, Neural Nets) to biological and genomic datasets.
RNA-Seq Analysis with R & Bioconductor
Step-by-step video tutorials on bulk RNA-seq analysis using R, DESeq2, and Bioconductor, from raw counts to biological pathway interpretation.
AI for Drug Discovery & Cheminformatics
Video series introducing AI-powered approaches to drug discovery, including molecular dynamics, toxicology modeling, and virtual screening.
Cancer Bioinformatics & Multi-Omics
Video tutorials on cancer genomics analysis, covering TCGA data mining, mutation profiles, Kaplan-Meier survival modeling, and immune infiltration.
Academic Writing & Manuscript Preparation
Video lectures on scientific writing, publication ethics, structured methodology descriptions, and peer-review preparation.
Python for Health Data Analytics
Video tutorials on using Python, pandas, and seaborn for clinical data wrangling, epidemiologic trends, and biostatistical models.
Bioinformatics Workflow Automation Bootcamp
Intensive video series on building automated, reproducible bioinformatics workflows using Linux, Conda, Nextflow, and Docker.
Single-Cell Analysis with R & Seurat
Comprehensive step-by-step video guide on scRNA-seq analysis using R and Seurat, from QC filtering to t-SNE/UMAP cluster annotation.
Accelerate Your Research with 1-on-1 Mentorship
Take your skills further with direct weekly guidance, live debugging, real-world multi-omics datasets, and personalized career roadmaps.