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

The Research Fellowship program has been designed to help young researchers and students take advantage of the bioinformatics resources for analysis of complex life science data and become versed in bioinformatics. Research Fellows participate in cutting edge bioinformatics research led by expert mentors. The fellowship program will offer a combination of online resources and mentor guidance to prepare you and help you complete a bioinformatics project. Registration fees cover the costs of training, mentorship and big data T-BioInfo Server. The platform is a cloud-based analytical server used by research labs and independent scientists around the world. To learn more and register for the "Computational Biology Research Internship Program", please visit: https://edu.omicslogic.com/omics-logic-research-fellowship

What is Omics Logic?

A Growing Community of Students, Experts and Mentors

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.

Complete your Registration for the Program

The Omics Logic Advantage

Self-Paced Learning

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

Project-based Curriculum

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

Hands on with Big Data

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

Expert Mentors

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

OmicsLogic offers high grade training and research tools for hands-on analysis for various research fields which includes analysis of Big Data belonging to Multi-Omics fields (Transcriptomics, Genomics, Epigenomics & Metagenomics), Infectious Diseases, Precision Medicine, Precision Oncology, Neuroscience, Space-Omics, Metabolic Disorders, Agri-Bioinformatics, Immunology, Cheminformatics etc. We aim to utilize Data Science techniques, programming languages (R/Python for Bioinformatics), Machine Learning methods, Bioinformatics tools & softwares and AI-based training platform i.e., the T-Bioinfo Platform, designed by Tauber Bioinformatics Research Center, Israel, for carrying out the analysis and interpretation. Below we have listed the areas of research for various fields that OmicsLogic Mentors are working on and are open for all to carry out independent or groups research projects and be able to go forward for a publication in the respective field.

GENOMICS DATA ANALYSIS

Comparing DNA sequences using Phylogenetic Analysis, Multiple Sequence Alignment - visualizing and filtering alignment
Mutability Analysis & Interpretation, Differential Mutation Analysis, Copy Number Variation Analysis, Understanding origin of disease and comparison with other strains
Identification of particular viruses in patients samples through the identification of significant variants, Disease phenotype prediction based on genomic variations

EPIGENOMICS DATA ANALYSIS

DNA Methylation Analysis, DNA–Protein Interaction Analysis.
Investigate epigenomic landscape to categorize tumors for early diagnosis, prognosis and patient/treatment stratification.

TRANSCRIPTOMICS DATA ANALYSIS

Transcriptomic (RNA-Seq) data Analysis
Downstream analysis of transcriptomic (RNA-Seq) data
Visualization to statistical analysis of differentially expressed genes
Biological Interpretation and annotation of gene sets.
Transcriptomic data analysis using R/python.
Identification of transcriptomic signatures
Unsupervised &/or Supervised Machine Learning analysis of the transcriptomic data

GENOMICS DATA ANALYSIS

Combining genomics data analysis to predict which individuals/groups are at risk for developing certain (chronic) diseases & cancer.
Analysis of genomic variants like somatic mutation to understand the genomic landscape of cancer

MULTI OMICS INTEGRATION

Understanding association of clinical phenotypes with multi-omics data
Explore methods to integrate various omics data to investigate their complementarity
Integrate multi-omics data to maximize information content to study cancer biology

METAGENOMICS DATA ANALYSIS

Metagenomic data analysis for identifying diagnostic signatures microbial population associated with various cancer

MACHINE LEARNING MODELING

Regression & Factor Regression Analysis
Transcriptomic data analysis using Supervised and Unsupervised machine learning
Identifying feature importance and interpretable ML modeling.
Understanding Molecular mechanisms for cancer stage classification

EPIGENOMICS DATA ANALYSIS

DNA Methylation Analysis.
DNA–Protein Interaction Analysis.
Investigate epigenomic landscape to categorize tumor for early diagnosis, prognosis and patient/treatment stratification.

STRUCTURAL BIOLOGY AND CHEMINFORMATICS

Protein structural analysis to understand structure-functional implications of prominent genomic variants.
Exploring Cheminformatics strategies for modeling and screening to improve drug discovery results.
Exploring small molecules library based on structural similarity for candidate lead generation. Compare and contrast their effectiveness through molecular docking investigation.