Teaching

Bioinformatics Mentorship Program

Bioinformatics Mentorship Program (BMP)

Our flagship 12-week intensive research program designed for serious researchers. Master NGS data analysis lifecycle—from raw sequencing data to publication-ready manuscripts and polished figures. Includes personalized mentorship, HPC access, and proprietary research pipelines.

NGS

Cancer Genomics

Neurogenomics

Publication

No-Code & Agentic AI for Life Sciences

A 4-week live program focused on leveraging Generative and Agentic AI tools to automate biomedical research workflows. Learn to use Claude, NotebookLM, and Gemini for literature reviews, drug discovery, and Omics analysis—100% no-code.

No-Code

LLMs

AI Agents

Automation

Bulk RNA-Seq Analysis for Absolute Beginners

Unlock the secrets of the transcriptome. Designed for biologists with zero programming experience, this course teaches the tools and statistical rigor required to turn raw sequencing data into publication-quality insights—covering Linux, R, DESeq2, and Pathway Analysis.

RNA-Seq

DESeq2

R/Bioconductor

GSEA

Single-Cell RNA-Seq Analysis for Absolute Beginners

Master the resolution of individual cells. This course guides beginners through the complete single-cell workflow—from quality control and batch integration to clustering, cell-type annotation, and trajectory analysis using the scverse ecosystem.

scRNA-Seq

Scanpy

Seurat

Batch Correction

NGS Data Analysis for Absolute Beginners

A foundational course for mastering Next-Generation Sequencing (NGS) data. Learn the end-to-end workflow from raw FASTQ files to variants and expression matrices, covering quality control, read mapping, and downstream analysis using industry-standard tools.

FASTQ

BWA/Bowtie2

GATK

Quality Control

Reproducible Bioinformatics Workflows

Reproducible Bioinformatics Workflows: Ideation to Execution

A 3-day intensive workshop on building reproducible and scalable bioinformatics pipelines. Participants learn to orchestrate complex genomic workflows using industry-standard tools like Nextflow, Snakemake, and Docker, ensuring computational research is portable and transparent.

Nextflow

Snakemake

Docker

Conda

Statistical Analysis with R for Research

A comprehensive course teaching statistical analysis using R for researchers. Covers data manipulation, visualization, hypothesis testing, regression analysis, and reproducible research workflows — equipping researchers with the practical R skills needed to analyze and interpret their data.

Statistics

Data Analysis

ggplot2

Course Website

Transcriptomics Data Analysis

Transcriptomics Data Analysis (Bulk + Single Cell)

A live, instructor-led training program covering best practices in high-throughput RNA-seq data analysis. Participants learn to independently analyze gene expression datasets using R, Bioconductor, and the scverse ecosystem — from raw counts to biological interpretation.

Bioconductor

DESeq2

Seurat

Scanpy

Cohort 01

Cohort 02

Biomarker Identification using Machine Learning

Hands-on course applying advanced machine learning and bioinformatics techniques to identify novel biomarkers from high-dimensional biological data. Focuses on cancer genomics using TCGA data to discover clinically relevant biomarkers for diagnosis, prognosis, and treatment selection.

Python

scikit-learn

TCGA

Cancer Genomics

Cohort 01

Pan-Cancer Bioinformatics with R

Pan-Cancer Bioinformatics with R: Ideation to Publication

An intensive training program guiding participants through the complete journey of pan-cancer analysis — from research idea to publication. Covers TCGA data access, expression, survival, mutation, and immune analyses, multi-omics integration, publication-ready visualization, and manuscript preparation.

TCGA

Survival Analysis

Multi-Omics

AI in Drug Discovery: In Silico Toxicology Modeling

A one-day hands-on workshop introducing AI-powered toxicology modeling for drug discovery. Participants build machine learning models to predict compound toxicity, classify molecules, and analyze structure–activity relationships using real-world cheminformatics datasets.

Python

Machine Learning

Cheminformatics

RDKit

NextGen Drug Discovery with AI

A program on AI-driven drug discovery, covering how machine learning and generative AI accelerate target identification, molecule design, and virtual screening in modern pharmaceutical research.

Drug Discovery

Cheminformatics

Data Science and Machine Learning for Biologists

Taught at CBLAST, University of Dhaka, this course introduces biologists to data science and machine learning through hands-on biological case studies. Covers data processing, visualization, and analysis using Python and R, with applications of supervised and unsupervised learning in genomics, transcriptomics, and health data.

Python

scikit-learn

Genomics

End-to-end bulk RNA-seq Quantification Pipeline using Salmon

Hands-On Notebooks

Pipelines

RNA-Seq

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.

InRaw FASTQ files, reference transcriptome

OutTranscript/gene-level count matrix, quantification summary

Python Salmon tximeta DESeq2

View on GitHub →

RNA-Seq

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.

InMultiple RNA-seq datasets (FASTQ or SRA accessions), sample sheets

OutIntegrated count matrix, cross-study DE results, batch-corrected expression profiles

Nextflow nf-core Salmon DESeq2

View on GitHub →

Single-Cell

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.

InRaw FASTQ files (10x Chromium or similar)

OutFiltered cell × gene count matrix, QC metrics

Python kallisto bustools kb-python scanpy

A Practical Guide for Single-Cell Data Analysis with scverse Ecosystem

Single-Cell

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.

InCount matrix (AnnData .h5ad or 10x format)

OutAnnotated cell clusters, UMAP embeddings, marker genes, cell type labels

Python scanpy scvi-tools AnnData

Protein Structure Prediction

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.

InAmino acid sequence (FASTA format)

OutPredicted 3D structures (PDB), confidence scores (pLDDT), PAE plots

Python ColabFold AlphaFold2 MMseqs2 py3Dmol

Protein Structure Prediction

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.

InProtein sequence and ligand SMILES/SDF

OutPredicted complex structures (PDB), binding affinity scores, interaction maps

Python Boltz2 RDKit py3Dmol

AI in Drug Discovery: Molecular Property Prediction and Virtual Screening

Drug Discovery

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.

InCompound libraries (SMILES), molecular descriptors

OutToxicity predictions, ADMET profiles, ranked hit compounds

Python RDKit DeepChem scikit-learn

AI in Drug Discovery: In Silico Toxicology Modeling

Drug Discovery

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.

InChemical compounds (SMILES), toxicity endpoint labels

OutToxicity classification models, SAR insights, safety predictions

Python RDKit scikit-learn Mordred

Video Lectures

Videos

R for Research

Video lectures covering R programming fundamentals, data manipulation, statistical testing, and visualization for researchers.

Statistics R

Machine Learning for Bioinformatics

Hands-on video tutorials applying machine learning techniques to biological and genomic datasets.

Machine Learning Bioinformatics

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.

Genomics R

AI for Drug Discovery

Video series introducing AI-powered approaches to drug discovery, including toxicology modeling and structure-activity analysis.

Machine Learning Drug Discovery

Cancer Bioinformatics

Video tutorials on cancer genomics analysis, covering TCGA data access, mutation analysis, survival modeling, and multi-omics integration.

Cancer Genomics Bioinformatics

Academic Writing

Video lectures on scientific writing, manuscript preparation, and publishing in peer-reviewed journals.

Academic Writing

Python for Health Data Analytics

Video tutorials on using Python for health data analysis, covering data wrangling, visualization, and statistical methods for public health and clinical datasets.

Python Data Science