Welcome to Dr. Vipin's Classroom
Facilitating Biologists transition to Data Science :
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Break the MENTAL BLOCK !
YOU CAN CODE TOO
BEGIN FROM SCRATCH HERE !
For strong foundations in Bioinformatics and coding in R, Python & Linux - Next Generation Sequencing
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for live, participatory, non-mandatory assignment based learning with a personal touch
Python for Biologists - Level I
Python for Biologists - Level I
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.
This curriculum outlines a structured eight-day training program designed to teach Genomic Data Science using the Python programming language. The first half of the course focuses on foundational coding skills, covering essential topics such as 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 Pandas and Plotnine to facilitate high-level data analysis and visual representation. Finally, the course covers bioinformatics-specific packages such as BioPython to equip learners with the ability to process Next-Generation Sequencing data.
Coding is simple, intuitive, easy to learn and truly a universal skill today with vast applications in data analysis, data visualization, automation, scaleup and precession. Unleash the power of coding -
Make a confident start by writing your first few of the many codes you may potentially write ... with me! Rediscover yourself !
NGS Fundamentals and Data Analysis
DNASeq - Variant Calling
Jan 28 - Feb 6, 2026
This workshop offers a comprehensive introduction to the field of Next-Generation Sequencing (NGS) data processing and genomic variant identification. 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 - we extensively cover
Bulk RNA-Seq (Linux + R)
Bulk RNA-Seq (Linux + R)
Differential Expression Analysis
Differential Expression Analysis
RNA-seq differential expression (DE) analysis identifies genes with significant changes in expression levels between experimental conditions (e.g., treated vs. control). It involves processing raw reads, mapping to a reference, quantifying gene counts, normalizing data to account for library size, and applying statistical models like negative binomial distribution (DESeq2/edgeR).
Workflow Steps:
Quality Control & Mapping: Assess raw read quality (FastQC) and map to the genome/transcriptome.
Quantification: Count reads mapped to each gene/transcript.
Normalization: Adjust for differences in sequencing depth and composition (e.g., TPM, DESeq2's median-of-ratios).
Statistical Testing: Identify significant changes (e.g., DESeq2, edgeR, limma-voom).
Visualization & Interpretation: Volcano plots, and Heatmaps
Level II Courses - Recordings available - (Recorded in January 2026)
R FOR BIOLOGISTS - Level II
BIOCONDUCTOR
Bioconductor is an open-source, open-development project providing tools for the analysis and comprehension of high-throughput genomic data and molecular biology, built primarily on the R programming language
We start with a quick revision of Data Structures on day 1, then move on to writing user defined functions, creating a library and calling these functions - compacting our code.
We then 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.
PYTHON FOR BIOLOGISTS - Level II
Biopython
Biopython is a multiutility versatile package use for analysis of nucleic acid sequence, protein structure, sequence motifs, sequence alignment also machine learning.
Biopython has a lot of libraries for the help of biologists in their work as it is portable, easy, and clear.
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.
Coding is simple, intuitive, easy to learn and truly a universal skill today with vast applications in data analysis, data visualization, automation, scaleup and precession. Unleash the power of coding -
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 I
R for Biologists - Level I
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.
This curriculum outlines a structured eight-day training program designed to teach Genomic Data Science using the R programming language. The first half of the course focuses on foundational coding skills, covering essential topics such as 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 visual representation. Finally, the course covers bioinformatics-specific packages such as Bioconductor to equip learners with the ability to process Next-Generation Sequencing data.
Coding is simple, intuitive, easy to learn and truly a universal skill today with vast applications in data analysis, data visualization, automation, scaleup and precession. Unleash the power of coding -
Make a confident start by writing your first few of the many codes you may potentially write ... with me! Rediscover yourself !
Upcoming Courses in March
Also Coming up ...
An Introduction to AI - ML with R
An Introduction to AI - ML with Python
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