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COURSES

PROJECTS | TUTORIALS | PROGRAMS

Asynchronous Resourses

OMICSLOGIC COURSES

Course 1: Introduction to Bioinformatics

Course 1: Introduction to Bioinformatics

This is an introductory course that covers the topics of big data bioinformatics and its uses in basic research, healthcare, and the biotech and pharmaceutical industries.

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Project 02: Ebolavirus: Deadly Mutations

Project 02: Ebolavirus: Deadly Mutations

Ebola viruses have affected thousands of people across the world, particularly in Africa. You will learn to employ bioinformatic approaches to analyze genomic sequences, identify particular viruses in patients samples.

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Python Course 1: Introduction to Python for Omics Data Analysis

Python Course 1: Introduction to Python for Omics Data Analysis

Introduction to analysis of biological data using Python: analysis of genomic sequences, conversion of DNA to RNA and protein code (NT to AA).

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Course 3: Genomics

Course 3: Genomics

This course serves as an introduction to the bioinformatics sub-discipline of genomics. Students will be familiarized with the biology of genetics and genetic variation while considering practical applications using publicly available datasets.

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Project 01: COVID-19 Origin & Pathogenesis of SARS-COV2

Project 01: COVID-19 Origin & Pathogenesis of SARS-COV2

Bioinformatics approaches to delineate SARS-COV2 infection, including its origin, relation with other coronavirus strains, protein components crucial for pathogenicity and transcriptomics signatures of treatment.

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Course 9: Designing a Bioinformatics Research Project

Course 9: Designing a Bioinformatics Research Project

Learn how to identify a research topic, perform literature review to refine it and find a dataset for your project. Then translate your ideas or questions into an analysis plan.

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R-Coding Course 2: Introduction to Data Science (BioML)

R-Coding Course 2: Introduction to Data Science (BioML)

Learn data wrangling, visualization, statistical analysis, and machine learning.

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R-Coding Course 1: Introduction To R for Biotechnologists

R-Coding Course 1: Introduction To R for Biotechnologists

Get started with bioinformatics by analyzing genomic sequences and finding patterns.

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INSIGHTS FROM MOLECULAR DATA

The bioinformatics experience will help students understand the relationship between biological phenomena and the underlying molecular data. Mastering various omics data types, we can uncover the complex sub-cellular regulation that drives phenotype such as disease progression, cell morphology or tissue variation.

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Developing Analytical Thinking and Data-driven Research

LEARN BIOINFORMATICS AND DATA SCIENCE

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USING REAL RESEARCH PROJECT EXAMPLES

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Relying on public-domain data generated in research labs with peer-reviewed articles as our guideline, we develop practical exercises that you can leverage to learn about the impact of bioinformatics. In these examples, we take data, analysis methods and results to make a research experience for a beginner or an expert to follow. As the logic of analysis becomes clear, you can also develop a research project of your own and put your knowledge to the test.

User-friendly Solution for Non-Technical Users

Based on Visual and Logical Learning

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USER-FRIENDLY BIG DATA ANALYSIS

Enhance, update or start from scratch with short term training or degree programs that will make a difference for your student achievement.

We live in the age of data. To harness it’s potential in life sciences, effective management, analysis and visualization skills are critical for the technician, researcher, clinician and the average, curious citizen. In the 21st century, data science skills are central to our ability to answer questions about our past, present and future. Omics Logic was designed to develop critical thinking and research skills that are based on the solid foundation of data.

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Example of RNA processing

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Informative pop-ups for each method

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Visual outputs help understand and explore analysis results

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Data Science, Machine Learning and Biostatistics

Enhanced by Practical Coding Exercises in R and Python

Relying on public-domain data generated in research labs with peer-reviewed articles as our guideline, we develop practical exercises that you can leverage to learn about the impact of bioinformatics. In these examples, we take data, analysis methods and results to make a research experience for a beginner or an expert to follow. As the logic of analysis becomes clear, you can also develop a research project of your own and put your knowledge to the test.

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#Perform main computations - train classifiersdata[,1] <- factor(data, [,1])rforest <- randomForest(class ~ ./ data=data, ntree=numtrees, importance=TRUE#Apply classifiers to the test setprediction <- predict(rforest, testset, type="response"prediction <- data.frame(prediction, check.names=FALSErownames(prediction) <- rownames(testset)
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Resources to Get Started and Grow

Use the Right Tool for the Right Task

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Understand the various OMICS data types, formats and associated challenges

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Learn about Exploratory Data Analysis and the steps of processing data and preparation for analysis

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Try BIG DATA analysis tools for large-scale project data and try machine learning

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Learn about the important link between data and biology: master biological interpretation

OmicsLogic Specialization Tracks

Specialized Programs Combine Data and Biology