R FOR BIOLOGISTS - Level I

Begin from Scratch

OCT 22 - 30, 2026

Biology today is fast transforming into data-science - curtsey the high-throughput technologies. Manual analysis of this data is neither feasible nor possible anymore. With the data deluge in Life Science Research, programming is fast becoming  a desirable and essential skill, even for wet lab researchers, for data wrangling, analysis,  and data visualization.  

The 'R for Biologists - Level I '  curriculum is specially designed for researchers with no coding background and outlines a structured eight-day (~10 hours) training program. 

The first half of the course focuses on foundational coding skills, covering essential topics such as data types, data structures, control flow, and file management. Students learn to manipulate biological sequences and perform statistical testing before moving on to more complex tools. The latter portion of the syllabus introduces specialized libraries like Dplyr and ggplot2 to facilitate high-level data analysis and visualization. Finally, the course covers bioinformatics-specific packages such as Bioconductor to equip learners with the ability to process Next-Generation Sequencing data. 

NGS Fundamentals and Data Analysis 

DNASeq - Variant  Calling & Variant Prediction 

Dates - November 12 - 20 , 2026 : Online

Timings - 7:00 - 8:15 pm IST or 5:45 - 7:00 am IST 

This workshop offers a comprehensive introduction to the field of Next-Generation Sequencing (NGS) data processing and genomic variant identification. 

A DNA-Seq variant calling pipeline processes raw sequencing reads into a list of genetic changes like SNPs and indels. The standard workflow moves from raw FASTQ files through quality control, alignment to a reference genome, post-processing, variant detection, and functional annotation 

The curriculum begins by exploring modern sequencing technologies - Short Read Sequencing - Illumina and Ion Torrent & Long Read - Nanopore and Sequel, and fundamental bioinformatics tools, such as the Linux command line and the Conda package manager. 

Participants will learn to navigate essential genomic data formats and perform critical quality control procedures to ensure data integrity. The latter portion of the course emphasizes practical skills in reference based mapping of short reads and the discovery of genetic variants. Finally, the program concludes with methods for genome data visualization, allowing researchers to effectively interpret and display their findings.

Be my guest on this journey most remarkable as i take you through the basics of NGS Data Analysis 

RNA-Seq Fundamentals &

Differential Gene Expression Analysis - Linux+ R (DESEQ2)

Dates - November 23 - 30, 2026 - Online 

Time - 7:00 - 8:15 pm IST or 5:45 - 7:00 am IST 

RNA-seq differential gene expression (DGE) analysis compares transcript levels between distinct biological groups (like treatment versus control) to find genes that change significantly. 

It uses raw read count matrices, applies statistical models to correct for library size and overdispersion, and outputs altered genes for functional study 

The curriculum begins by exploring modern sequencing technologies - Short Read Sequencing - Illumina and Ion Torrent & Long Read - Nanopore and Sequel, and fundamental bioinformatics tools, such as the Linux command line and the Conda package manager

Workflow Steps:

1. Read Quantization: Map sequencing reads to a reference genome or transcriptome to build a raw gene count matrix.

2. Quality Control & Filtering: Inspect sample clustering (using PCA) and remove low-count genes to reduce noise.

3. Normalization: Adjust counts for differences in sequencing depth and RNA composition across samples.

4. Statistical Modeling: Fit count values using negative binomial distributions via specialized software to handle biological variance.

5. Hypothesis Testing: Calculate fold changes and adjusted p-values to spot significant up-regulation or down-regulation

6. Downstream Visualization & Interpretation : Volcano Plots, MA Plots, Heatmaps

Python for Biologists - Level I 

           Dates - October 12 -20, 2026 - Online

Time - 7:00 - 8:15 pm IST or 5:45 - 7:00 am IST  

Begin from absolute scratch - Recordings available 

Machine Learning for Biologists 

(with Python) 

Dates - 11th to 18th September, 2026 - Online

Time - 7:00 - 8:15 pm IST or 5:45 - 7:00 am IST 

Take a deep dive into Machine learning - the terminology, statistics, visualization, model fitting, estimation and selection with Python  

Basic understanding of python will be helpful

Basic terminology – ML, model, feature, response/target variable

Basic statistics behind ML – Linear and Logistic regression

Python libraries for ML – Scikit Learn, Pandas, Numpy , Plotnine, Seaborn

ML paradigms – Supervised and Unsupervised Learning 

Supervised Learning – Steps in Supervised learning – Data Partition – Training set, Test Set, Cross Validation, Model evaluation, Model selection - Examples of Linear and Logistic Regression

Unsupervised Learning – Scaling,  K means clustering, determining optimum clusters, Elbow plot, AUC,  Within-Cluster Sum of Squares (WSS), model evaluation

Generics – Feature extraction and selection methods 

Dataset – Iris, Penguins  and Cancer Dataset – Kaggle database   

Conclusions and comments 

Machine Learning with R

(Recordings available) 

Take a deep dive into Machine learning - the terminology, statistics, visualization, model fitting, estimation and selection with R

Basic understanding of R will be helpful

Basic terminology – ML, model, feature, response/target variable

Basic statistics behind ML – Linear and Logistic regression

Python libraries for ML – Carte, MLbench

ML paradigms – Supervised and Unsupervised Learning 

Supervised Learning – Steps in Supervised learning – Data Partition – Training set, Test Set, Cross Validation, Model evaluation, Model selection - Examples of Linear and Logistic Regression

Unsupervised Learning – Scaling,  K means clustering, determining optimum clusters, Elbow plot, AUC,  Within-Cluster Sum of Squares (WSS), model evaluation

Generics – Feature extraction and selection methods 

Dataset – Iris, Penguins  and Cancer Dataset – Kaggle database   

Conclusions and comments 

Biopython :Python for Biologists - Level II

Functions, OOPS, Automation and Scaleup

With the data deluge in Life Science Research, programming is fast becoming  a desirable and essential skill, even for wet lab researchers, for data wrangling, analysis,  and data visualization. 

Biopython is a multiutility versatile package use for analysis of nucleic acid sequence,  protein structure, sequence motifs, sequence alignment also machine learning.

While Biopython is fundamentally designed using an OOP architecture (representing biological data like sequences, motifs, and structures as objects), you can manipulate it using Functional Programming paradigms to build highly efficient, clean, and testable bioinformatics pipelines 

The most robust Python code blends both concepts 

• Use OOP for data representation (e.g., standardizing data using Biopython's - SeqRecord or PDB classes).

• Use FP for data processing (e.g., passing sequence streams through transformation functions without mutating the original source files)

This helps in creating memory efficient compacted code - since most of the common biological processes can be analyzed by commands in Biopython 

The first half of the workshop deals with functional programming and OOPs - followed by genome scale examples of how Biopython can be used.

In the seond half I discuss usecases of how Biopython can be used for data analy

Make a confident start by writing your first few of the many codes you may potentially write ... with me! Rediscover yourself !

Break the mental block ... you can code too !

R FOR BIOLOGISTS - Level II

BIOCONDUCTOR

(Recordings available)

Bioconductor is an open-source, open-development software project and a specialized repository of over 2,000 R packages specifically designed for the analysis and comprehension of high-throughput biological and genomic data. It leverages R's powerful statistical and graphical capabilities to provide tools for bioinformatics and computational biology

 
We start with a quick revision of R Data types and Data Structures on day 1. We then then move on to Functional Programming paradigm, writing user defined functions, creating a library of functions and calling these functions - compacting our code. On Day 4 , we look at the key features of OOPs - Object Oriented Programming in R - creating a class and defining its Objects and methods 

 
In the second half of the workshop we look at specialized Bioconductor modules - Biostrings  -  for sequence Analysis,  RBLAST for  automated homology search,  RMSA and Decipher for Multiple  Sequence Alignments,  and  RSubreads for NGS analytics
In the end we also look at making customized advanced plots such as HeatMaps, Upset plots, Volcano plots etc.