Jaipur, India  ·  Hybrid Online + In-Person

From raw data to publication-ready results

Build the computational skills that real bioinformatics careers demand — from day one.

🎓 Stackable Certifications 🔬 Research-Grade Workflows 🧬 Real Biological Datasets 🖥️ HPC Training
Start with Tier 0 — Free View full curriculum →
Who is this for

Built for biologists at every stage

Whether you're starting from scratch or already have data to analyze, there's a path designed for you.

🎓

UG / MSc Students

Biology and life sciences students who want to add real computational skills before graduation.

🔬

PhD Researchers

PhD students tackling omics datasets as part of their thesis work — need depth and reproducibility.

🧬

Lab Scientists

Working researchers who generate data and need analysis skills to make sense of their results.

📊

Career Switchers

Biologists entering bioinformatics or computational biology roles and need a structured learning path.

Program Structure

Two tracks. One clear path.

The Core Track builds comprehensive skills from scratch. The Express Track gets researchers to results fast.

Core Bioinformatics Track
Express Track

Core Bioinformatics Track

Five progressive tiers — join at your level, progress at your pace

5 TIERS + 2 UPCOMING
Day 1–2

Biology basics & central dogma

DNA, RNA, Protein · Transcription & translation · Why computation in biology?

Day 3

Biological databases

Major sequence, structure, and annotation databases · Searching and retrieving biological data.

Day 4

Bioinformatics file formats

Sequence files, alignment files, annotation files, variant files · What each represents and when to use it.

Day 5

Sequence alignment

Local vs global alignment concepts · Scoring matrices · Hands-on similarity search · Tool landscape overview.

Assignment

Practical Application

Retrieve a gene sequence from a public database, perform a similarity search, and interpret the results.

Week 1

Python basics

Variables, data types, loops, conditionals · Functions & modules · Reading/writing files · Biological context throughout.

Week 2

R basics

R syntax & data structures · Data frames & basic manipulation · Plotting fundamentals · Biological context throughout.

Week 3

Biological data handling

Parsing sequence files, tabular omics data · Visualization of biological data · Choosing the right language for the right task.

Project

Data Visualization

Parse a biological sequence file, compute a sequence property of interest, and produce a clean visualization — implemented in both Python and R.

Week 1

Linux fundamentals

File system navigation · Permissions & ownership · Essential commands for biological file handling.

Week 2

Text processing

Stream editors and text processors for biological data · Parsing sequence, annotation, and alignment files · Piping & regular expressions.

Week 3

Shell scripting

Writing shell scripts · Loops & conditionals · Automating repetitive bioinformatics tasks · Variables & functions.

Week 4

HPC & environment

What is HPC and why it matters · Job scheduler concepts and submission · Managing large compute jobs · Software environment management.

Project

Automated Pipeline

Build an automated shell pipeline that processes multiple raw sequencing files — quality assessment, preprocessing, and summary report generation.

Week 1-2

Tech & Preprocessing

Sequencing principles, experimental design. Assessing read quality, adapter trimming, quality filtering, and batch QC assessment.

Week 3-4

Alignment & Quantification

Reference-based alignment · SAM/BAM manipulation · Read counting approaches (TPM, RPKM) · Building count matrices.

Week 5-6

DE & Functional Meaning

Statistical frameworks for differential expression · Normalization · Multiple testing · Gene Ontology · Pathway analysis.

Week 7

De novo Assembly

Assembly strategies for plant and non-model organisms · Quality assessment of assemblies · Annotation approaches.

Capstone

End-to-End Analysis

Complete independent RNA-Seq analysis — QC, alignment, quantification, differential expression, pathway interpretation. Submitted as a written report with reproducible code.

Week 1-2

Intro & Quality Control

Droplet-based sequencing concepts · Count matrices · Cell quality metrics · Filtering low-quality cells, empty droplets, and doublets.

Week 3-4

Normalization & Dim Reduction

Single cell normalization approaches · Identifying highly variable features · PCA concepts · UMAP and tSNE interpretation.

Week 5-6

Clustering & Annotation

Graph-based clustering · Evaluating cluster stability · Using known marker genes for manual annotation · Reference-based automated annotation.

Week 7

Differential Expression

Identifying marker genes per cluster · Statistical considerations specific to scRNA-Seq · Visualizing gene expression across cell types.

Capstone

End-to-End scRNA-Seq

Complete independent analysis — QC, normalization, dimensionality reduction, clustering, annotation, and marker gene identification. Submitted as a written report with reproducible code.

Express Track

Quick, practical skills for researchers who already have data

2 MODULES
Week 1

Getting started with R

Setting up the environment · Loading and inspecting omics datasets · Basic data cleaning and formatting.

Week 2

Statistical testing

Choosing the right test (t-test, ANOVA, chi-square) · Multiple testing correction · Interpreting p-values correctly.

Week 3

Omics visualization

Heatmaps · Volcano plots · PCA plots · Boxplots and violin plots · Bar charts · All figures styled for publication.

Project

Data to Results

Take a provided omics dataset, apply appropriate statistical tests, generate a set of publication-ready figures, and write a short results section.

Week 1

Loading & Quality Control

Loading count matrix data into an analysis environment · Understanding QC metrics · Filtering decisions · Normalization.

Week 2

Dim Reduction & Clustering

Running and interpreting PCA · Generating UMAP representations · Clustering and understanding resolution.

Week 3

Annotation & Figures

Identifying cell types using known marker genes · Visualizing gene expression across clusters · Interpreting results.

Project

scRNA-Seq Practical

Take a provided single cell count matrix through QC, normalization, UMAP generation, clustering, and basic annotation to produce a set of publication-ready figures.

Express Track is designed for researchers — no coding background needed for the stats module

Why OmicsWare

Not another generic online course

Every design decision serves one goal — students who can actually do the work.

🧪

Research-grade workflows

Curriculum is built on published research pipelines, not textbook examples. Real data, real problems.

📜

Stackable certifications

Independent certificate per tier with duration. Join at your level and progress at your own pace.

🇮🇳

India-first design

Directly addresses the practical bioinformatics gap in Indian universities. Built for Indian students and researchers.

💻

Hybrid delivery

Online and in-person (Jaipur). Capstone projects with reproducible code submissions — portfolio-ready outcomes.

🔗

Tool-agnostic

You learn concepts and workflows, not just one tool. Skills that transfer as the field evolves.

🌱

Tier 0 is free

Start with the foundations week at no cost. Get a real taste before committing to paid tiers.

Dr. Nitesh Kumar Sharma Dr. Sharma presenting at MetaSub 2022
18+
peer-reviewed publications
About the Founder

Dr. Nitesh Kumar Sharma

PhD in Bioinformatics from CSIR-IHBT, Palampur and former Postdoctoral Researcher at the University of Southern California (Mangul Lab, School of Pharmacy), Dr. Sharma brings genuine research experience to every course module.

His published work spans metagenomics benchmarking (MetaWiz), RNA-binding protein prediction, genome assembly of endangered Himalayan plants, wastewater SARS-CoV-2 surveillance, and machine learning in clinical data. Publications in Nature Reviews Methods Primers, iScience, Scientific Reports, PLoS ONE and more.

OmicsWare was born from a simple observation: most Indian biology graduates can describe bioinformatics workflows but cannot actually run them. Every tier is designed to close that gap.

340+ Citations
h10 H-index
USC Postdoc
11 i10-index
LinkedIn Google Scholar
"

Every topic is grounded in real biological data and real workflows — from day one. Our students don't just learn concepts; they build the computational toolkit that research careers actually demand.

— Dr. Nitesh Kumar Sharma, PhD  ·  Founder, OmicsWare Innovations  ·  Former Postdoc, University of Southern California
Get in touch

Ready to start?

Tier 0 is free — the best way to experience OmicsWare before committing to a paid tier. Reach out with any questions about the program.

📧

Email

omicsware@gmail.com

📍

Location

Jaipur, Rajasthan, India
Hybrid delivery — online & in-person

🎓

Program format

Independent certifications · Stackable tiers
Targeted at UG, MSc and PhD students

Register your interest

Send an email to get details about the next cohort, pricing, and how to enroll in Tier 0.

Send Enrollment Email →

Tier 0 — Foundations of Bioinformatics — is free to join