Teaching Videos
Access deep-dive video lectures and hands-on tutorials from our extensive bioinformatics library. Master computational research at your own pace.
R for Research
Video lectures covering R programming fundamentals, data manipulation, statistical testing, and visualization for researchers.
Machine Learning for Bioinformatics
Hands-on video tutorials applying machine learning techniques to biological and genomic datasets.
RNA-Seq Analysis with R
Step-by-step video tutorials on bulk RNA-seq analysis using R and Bioconductor, from raw counts to biological interpretation.
AI for Drug Discovery
Video series introducing AI-powered approaches to drug discovery, including toxicology modeling and structure-activity analysis.
Cancer Bioinformatics
Video tutorials on cancer genomics analysis, covering TCGA data access, mutation analysis, survival modeling, and multi-omics integration.
Academic Writing
Video lectures on scientific writing, manuscript preparation, and publishing in peer-reviewed journals.
Python for Health Data Analytics
Video tutorials on using Python for health data analysis, covering data wrangling, visualization, and statistical methods.
Bioinformatics Workflow Bootcamp
Intensive video series on building automated, reproducible bioinformatics workflows and data processing pipelines.
Single Cell Analysis with R
Comprehensive step-by-step video guide on scRNA-seq analysis using R and Seurat, from QC to cluster annotation.
Reusable Pipelines
Bioinformatics Workflows
Open-source, production-ready pipelines for high-throughput data analysis. Access our verified workflows on GitHub and Google Colab.
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 sequencing reads to transcript-level abundance estimates ready for downstream differential expression analysis.
In: Raw FASTQ files, reference transcriptome
Out: Transcript/gene-level count matrix, quantification summary
nf-core/rnaseqmeta: A Nextflow Pipeline for Meta-Analysis of RNA-seq Datasets
A Nextflow-based pipeline for reproducible meta-analysis of multiple RNA-seq datasets — automating sample retrieval, quality control, quantification, batch correction, and integrated differential expression analysis across studies.
In: Multiple RNA-seq datasets (FASTQ or SRA accessions), sample sheets
Out: Integrated count matrix, cross-study DE results, batch-corrected expression profiles
Fast and efficient preprocessing of scRNA-seq with kallisto | bustools | kb-python
End-to-end pipeline for scRNA-seq preprocessing using kallisto, bustools, and kb-python — from raw FASTQ files to filtered count matrices ready for downstream analysis with scanpy.
In: Raw FASTQ files (10x Chromium or similar)
Out: Filtered cell × gene count matrix, QC metrics
A Practical Guide for Single-Cell Data Analysis with scverse Ecosystem
Comprehensive walkthrough of the scverse single-cell analysis workflow — covering quality control, normalization, dimensionality reduction, clustering, marker gene identification, and cell type annotation using scanpy and related tools.
In: Count matrix (AnnData .h5ad or 10x format)
Out: Annotated cell clusters, UMAP embeddings, marker genes, cell type labels
Predicting Protein Structures with ColabFold and AlphaFold2 in Google Colab
Predict 3D protein structures from amino acid sequences using ColabFold's accelerated AlphaFold2 pipeline — with MSA generation via MMseqs2 and interactive structure visualization directly in the browser.
In: Amino acid sequence (FASTA format)
Out: Predicted 3D structures (PDB), confidence scores (pLDDT), PAE plots
Boltz2-Notebook: Diffusion-Based Protein–Ligand Structure Prediction & Affinity Analysis
Predict protein–ligand complex structures and binding affinities using the Boltz2 diffusion model — enabling rapid in silico docking and interaction analysis without traditional molecular dynamics simulations.
In: Protein sequence and ligand SMILES/SDF
Out: Predicted complex structures (PDB), binding affinity scores, interaction maps
AI in Drug Discovery: Molecular Property Prediction and Virtual Screening
End-to-end pipeline for AI-driven drug discovery — covering molecular featurization, toxicity prediction, ADMET property modeling, and virtual screening of compound libraries using machine learning and cheminformatics tools.
In: Compound libraries (SMILES), molecular descriptors
Out: Toxicity predictions, ADMET profiles, ranked hit compounds
AI in Drug Discovery: In Silico Toxicology Modeling
Build machine learning models to predict compound toxicity from molecular structure — covering molecular fingerprint generation, toxicity endpoint classification, structure–activity relationship analysis, and model interpretation for safety assessment in early-stage drug discovery.
In: Chemical compounds (SMILES), toxicity endpoint labels
Out: Toxicity classification models, SAR insights, safety predictions