Research Topics

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.

                                                 Areas of Analysis                                           For Different Specialization Tracks

Infectious Diseases-1

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

  • Identification of signatures significantly different under therapeutic (drug) treatment and untreated conditions
  • Understanding bacterial resistance to treatments from gene expression signatures.

METAGENOMICS DATA ANALYSIS

  • Analyze microbiome data & Species Diversity
  • Determine the relationship between communities.
  • Identify shared origins of viral genomes (phylogenetic analysis) and identify a connection between genomic variation and disease phenotype.

 

Project Examples on OmicsLogic Learn

OL Project SARS-COV-2
OL Project Malaria
OL Project SF9 Viral Contamination
OL Project Ebola Virus
Infectious Diseases – 1

 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.

 

 

Project Examples on OmicsLogic Learn

OL Project Cancer Macrophages
OL Project TCGA Liver Cancer
OL Project PDX Microenvironment
Precision Medicine
TRANCRIPTOMICS DATA ANALYSIS
  • Analyze various omics data types, integrate them and associate them with a phenotype (response to treatment) using sophisticated machine learning algorithms. 
  • To study the informative multi-omics features in an integrated way and give a perspective on drug screening and on precise diagnosis of patients.

CLINICAL DATA ANALYSIS 

 

EPIGENOMIC DATA ANALYSIS                                                                                          

GENOMIC DATA ANALYSIS                         

OL Project Modeling Precision Medicine (1)
Agriculture

GENOMICS DATA ANALYSIS

  • Study how distinct genetic profiles are associated with a condition (for ex: drought resistance)

METAGENOMICS DATA ANALYSIS

  • Analyze microbiome composition and species diversity. Processing 16s rRNA data, visualize OTU abundance tables for diversity, explore species richness through phylogenetic analysis

TRANCRIPTOMICS DATA ANALYSIS

  • Identifying gene expression levels and interpreting results using statistical methods such as p-values and false discovery rates
  • Explore dataset using different visualization techniques using standard tools (heatmaps, box plots, etc.) and advanced techniques like dimensionality reduction (Principal Component Analysis or PCA).

 

OL Project Potato Drought Resistance
NEURO

TRANCRIPTOMICS DATA ANALYSIS

  • Important pathways altered in advanced AD in comparison to normal samples using gene enrichment and Gene set enrichment analysis (GSEA) analysis.
  • To identify variability in differentially expressed genes across the datasets.
  • Finding Omics data to study Alzheimer's disease

EXAMPLE DATASETS AND PROJECTS

 

 

                                                                                                   

Alzheimers Disease Pateint Pathology 1
MD

TRANCRIPTOMICS DATA ANALYSIS

  • Comparison of Kidney Transcriptomic Profiles of Early and Advanced Diabetic Nephropathy Reveals Potential New Mechanisms for Disease Progression
  • Finding RNA genes differentially regulated between the two conditions in order to identify novel targets
SO

TRANCRIPTOMICS DATA ANALYSIS

  • Analysis of RNA-seq data of organisms such as mice, zebrafish, microbes, and human cell lines exposed to spaceflight or simulated microgravity/ radiation. 
  • Finding differentially expressed genes
  • Looking for significantly altered pathways.

 

 

EPIGENOMIC DATA ANALYSIS

  • Analysis of bisulfite-seq or ChIp-seq data of organisms in space. 
  • Studying the epigenetic changes in gut microbiome and other essential pathways such as wnt, p53, and DNA damage response, etc.

METAGENOMICS DATA ANALYSIS

  • Identifying the changes in gut microbiota of mice exposed to radiation and spaceflight.

Research Projects on above-mentioned Specialization Areas by OmicsLogic Research Fellows

Glimpse of our Expert Talks

Omics in Virology : How the Pandemic has Changed Our Approach

Dr.Raul Andino  Professor in the Deptof Microbiology & Immunology, University of California

Molecular & Cell Biology & Immunopathogenesis of Coronaviruses

Dr. Gus Kousoulas President of NAIPI, USA

                                                                                                   

Mathematical modeling of the single-stranded RNA virus: role of naturally occurring defective virus genomes

Dr. Leonid Brodsky Director, TBRC, Haifa University

Omics in Virology: How the pandemic has changed our approach

Prof. Shiladitya DasSarma                                    Prof. Dept. of Microbiology and  Immunology, University of Maryland School of Medicine 

Systems Medicine is a new and emerging field that leverages complex computational tools

Prof.SonaVasudevan                                        Georgetown University Medical Center                         

Interpretable Machine Learning Model for Precision Oncology                                                       

Dr. Raghavendran                                        Postdoctoral fellow at SciWhy Lab, SC&IS                  Jawaharlal NehruUniversity, Delhi                                   

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