LSU-GeneLab-BioMMED - OmicsLogic Training-2

Enabling Students and Faculty to Conduct Research in the Age of Big Data

OmicsLogic Training Modules are designed to address the pressing needs of students, faculty and researchers to deal with growing amounts of high throughput data generated in life sciences. With a focus on accessibility, hands-on practice and project-driven design, these modules will help you get started and grow in bioinformatics.

To learn more and get started,

Join us for the FREE WEBINAR on Dec 06 at 12 noon CST

Designed for Tulane University

Omics Logic - A Growing Community of Students, Experts and Mentors

Asynchronous & Self-paced

Study at your own pace online completing modules designed for graduate and undergraduate levels


Don't just learn about bioinformatics, learn while working on research projects with real data

Big Data in your Browser

Go from practice to research in a matter of days by leveraging the cloud-based analytical platform

Supported by Experts

When you start learning, you are not on your own. The programs are supported by an expert team

Our community leverages publicly available data, online tools for big data analysis and a network of mentors to help students learn bioinformatics, apply their skills to meaningful research projects and work with mentors on turning their projects into publications or research posters. The programs we offer provide training, access to high quality tutorials and tools anyone can learn to use independently. The program is offered at university, high school or community college levels as well as directly to citizen scientists around the world.

Getting Started with Bioinformatics

A comprehensive collection to supplement online education and provide students and researchers with project-based training and research support.

OL Intro to bioinformatics square

An Introduction to Big Data in Life Sciences

From the basics of Bytes and Molecules to applications of Big Data in Oncology, Neuroscience, Agriculture and Infectious Diseases


Analysis, Visualization and Machine Learning

From biology to data science - commonly used tools and programming techniques to extract meaningful information from data

OL Transcriptomics Module

Logic Behind Omics Data Analysis

An overview of analysis methods for commonly used omics technologies: Genomics, Transcriptomics, Metagenomics etc.

OL Genomics Module

An introduction to the bioinformatics sub-discipline of genomics


From visualization, statistical analysis to biological interpretation

OL Metagenomics Module

From 16S rRNA data processing to visualization using packages 


Explore various types of data being used to study epigenetic variation

Multi-Omics Training and Research Programs

OmicsLogic offers training and research guidance on various multi-omics domains. Our motive is to make bioinformatics more accessible, even for non-bioinformaticians. Our training modules will help students and researchers browse data repositories for the right dataset, get hands-on with bioinformatics tools and utilize the toolkits on the T-Bioinfo server for statistical analysis & biological interpretation of biomedical data. The multi-omics fields being covered by OmicsLogic include: 

Genomics: Genomics is an essential subfield of bioinformatics, and a major force in expanding human knowledge of genetic associations with disease and other traits. This course serves as an introduction to the bioinformatics sub-discipline of genomics. 

Transcriptomics: In this course, you will learn about transcriptomic (RNA-Seq) data Analysis, followed by downstream analysis of transcriptomic (RNA-Seq) data - from basic visualization to statistical analysis of differentially expressed genes using the popular DESEQ2 package. 

Metagenomics: In this course, you will learn about processing 16s rRNA data with DADA2, visualizing OTU abundance tables for diversity and using the Phyloseq R package that can help build phylogeny-aware species richness, as well as alpha and beta diversity plots. 

Epigenomics: Epigenetics - an important research area for molecular biology and important for analysis of molecular data types like Chip-Seq, Bisulfite-Seq and specialized RNA-Seq. We will introduce important biological and analytical concepts related to the types of data used to analyze epigenetic variation. 

Covid-19 Sequencing: Variants of Concern

The SARS-COV-2 pandemic has transformed our appreciation of genomics and bioinformatics. The amount and detail of data accumulated during this time has surpassed any other outbreak of a viral pathogen. To understand how genomic data analysis tools can help identify specific viral strains, variants of concern and their role in pathogenicity, we prepared a series of tutorial sand curated datasets on the topic. These tutorials are designed to explain methods like multiple sequence alignment, mutability, phylodynamics, and the way to find variants of concern using significance of mutations in the context of viral protein structure and function. Importantly, this training is structured around publicly available data containing viral genomes of Covid-19 patients to understand the function and significance of SARS-COV-2 genes, proteins, and the pathogenesis. Such training will prepare anyone with minimal relevant background to identify a dataset, prepare it for analysis and ask important questions that researchers are investigating for basic and translational research, including:

  • Using online repositories like NCBI and GISAID
  • Phylogeny and evolution - origins and zoonotic transmission
  • Genomic analysis and Variant Analysis
  • Tracking and reporting variants of concern
  • Mapping Variants on Genes and Protein Structure
Variant analysis and detection Workshop -1
OmicsLogic Transcriptomics – single cell RNA-Seq

Single Cell Transcriptomics

In this course, you will learn about single cell RNA-seq: how it is generated, how to analyze it and what specific challenges need to be considered. Single cell RNA-seq or “scRNA-seq” has been demonstrated as a powerful technique for classification of tissue-specific cells and is used to study cell differentiation using time-course experiments. However, specialized data preparation techniques and high noise-signal ratio of this type of data require specialized approaches to its analysis. In addition, resulting expression tables contain sparse data that need to be prepared for downstream analysis . In this training, participants will learn about common methods to process and analyze 10x Genomics and drop-see single cell transcriptomic data, including:

  • Cell Ranger tools and zUMI processing
  • Data Normalization with Harmony
  • Seurat clustering and cell type annotation
  • Monocl and pseudo-time trajectories
  • Differential Gene Expression by cell type


R & Python for Bioinformatics

 To help introduce biologists, clinicians, and students to cutting edge bioinformatics methods and commonly used data science concepts, we offer bioinformatics-focused coding training in Python and R methods for Data Wrangling, Visualization, Processing and Analysis, including advanced methods like Machine Learning. These include online tutorials about syntax, logic and data specific examples you can practice with right in your browser using our training console and links to Google Colab notebooks with detailed data examples. code consoleAfter completing the basic training, students will also be led to code tests and coding challenges

r coding console
Cheminformatics Feature Image-1

Structural Biology - Cheminformatics for Biomedical Drug Discovery 

This program is designed to address the challenges associated with understanding, modelling,  screening and applying Cheminformatics strategies to improve drug discovery results. The process of finding a new drug against a chosen target for a particular disease usually involves high-throughput screening (HTS), wherein large libraries of chemicals are tested for their ability to modify the target. With contributions from leading researchers in academia and the Biotechnology, pharmaceutical industry as well as experts from the software industry, this comprehensive mentor guided training program explains how cheminformatics enhances drug discovery.  Drug designing. Participants will get practical experience and in-person guidance for following topics: 

  • Intro (structure, chemical interaction, etc.) and databases
  • Virtual screening
  • Structure based drug designing
  • Hit identification
  • QSAR
  • Docking
  • Hit to lead modification
  • Chemical compound modification (Rational drug design)
  • ADMET Prediction
  • Free Energy perturbations 
  • Computer vision (image classification)
  • AI in clinical trials 



Analysis of complex Omics data requires infrastructure: large hard drive space, lots of RAM, CPUs and even accelerated GPUs. Without such resources large projects are unfeasible and any variation to your analysis becomes a huge challenge. That is why we offer every user a free account to get started with BIG DATA BIOINFORMATICS. Process and analyze BIG MULTI-OMICS data on scale with the T-Bioinfo platform. No Bioinformatics or coding skills required to start. Extensive educational and research features available.

PDX Project


T-BioInfo is a multi-omics analysis platform designed to work with genomic, transcriptomic, epigenomic, metagenomic, metabolomic, proteomic, and structural biology data . It was developed at the Tauber Bioinformatics Research Center at the University of Haifa, Israel, to bypass the technical requirements that are common to bioinformatics. The platform stands out from some of the alternatives that include both open-source solutions like Galaxy , and paid solutions like SevenBridges. A combination of highly trusted “gold standard” and proprietary bioinformatics tools enable novel approaches. Using machine learning tools, the platform is designed to allow flexibility and choice according to the user’s expertise. All analyses use a simple and intuitive interface that is consistent across all sections of the platform. The platform uses a pipeline approach; thus, it processes specific data types through a series of steps. Each step is carried out by an individual algorithm that accomplishes a specific task. These tools are arranged in a network of branching and rejoining chains. The user, however, need only enter the data and specify which “chains” (also known as pipelines) should be used, making pipeline assembly intuitive and visual. From processing to statistical analysis methods, the T-BioInfo platform offers a user-friendly interface that is ready for advanced bioinformatics analysis using a single interface to access various processing capabilities like phylogenetic tree reconstruction, multiple sequence alignment, variant calling from NGS genomic data, transcriptomic analysis of RNA-seq data, 3D structural analysis and screening of small molecules as well as specialized pipelines for oncology, virology, neuroscience and agrobiological applications. The T-BioInfo pipeline builder offers a color-coded, logical and easy to follow interface that works for multiple structured data outputs, offers intuitive visual outputs and creates reproducible workflows for FAIR data analysis.

Request More Information

A comprehensive overview of available modules, project examples, tools and skills covered in the Omics Logic Training Modules

As many options are available to offer asynchronous, synchronous and blended versions of the training modules, we recommend faculty and students to get in touch with our program coordinator to get more information.

After filling out the form on your right, you will be able to schedule a call with one of our representers to answer your questions about the training and find the right program for you and your organization.