INTRODUCTION TOBIOINFORMATICS IN R (1)

Hello, visitor! As biology is saturated with complex datasets that have to be sorted, explored, and “looked into”, anyone handling data generation, analysis or decision-making based on data has to gain some level of “data science” skills. In most biological and biomedical settings, you will be expected to run or implement programs written in R, and others.

R programming offers a complete range of functionality that you can leverage to perform in-depth statistical analysis, visualization, and annotation. Getting started can be hard - programming is like learning a new language! That is why we offer easy-to-follow, structured, and simple coding tutorials designed around bioinformatics challenges. To view the full path to getting started, explore the links below or go to the learn portal link and get started!

Register for Introduction to Bioinformatics in R (Webinar and 1-month Mentor Guided Program)

Bioinformatics in R – 15

Expert-Developed Asynchronous Courses to Start with Bioinformatics in R

OL BioInfo in R

 

Getting Started with Bioinformatics in R: As the need for management and analysis of data grows, coding is becoming a must-have skill for a bioinformatician. This does not have to be an overwhelming career-change for a biologist, so our team put together elementary tutorials to understand programming basics using examples of loading, analysis and visualization of structured data. By completing this course, anyone will be able to utilize this popular coding language to create objects for analysis using pre-defined packages used by bioinformaticians in various research projects.

 

Biomedical Data Science in R: This course is designed to introduce elements of data science in R, such as data wrangling, visualization, statistical analysis, and machine learning. The methods will be reviewed in the context of biomedical and other scientific problems using -omics data. The exercises focus on importing and understanding various data types, transforming them into categorical variables, and continuous data, and extracting meaningful patterns for visualization. Then, the training continues to include statistical analysis, complex data visualization, machine learning, and an introduction to deep learning. After completing these modules, you will be able to apply your skills to various types of omics data, as described below.

OL BioML – R
What's included?

Choose a tenure that supports your needs and join a community of our program experts!

45 Days

 Coding Sessions
 Hands-on Assignments
 Group Sessions
 Session Recordings
 Omicslogic Program Access
 Example Cloud Pipelines
 Online Mentor Support
 Online Technical Support

OUTCOME:

⭐️ Certification of Completion

⭐️ Job Opportunities

60 Days

 Coding Sessions
 Hands-on Assignments
 Group Sessions
 Online Mentor Support
 Omicslogic Program Access
 Education Cloud Pipelines
 One-on-One Mentor Sessions

OUTCOME:

⭐️ Certificate of Completion

⭐️ Job Opportunity

⭐️ Research Poster

90 Days

 Coding Sessions
 Hands-on Assignments
 Group Sessions
 Online Mentor Support
 Omicslogic Program Access
 Education Cloud Pipelines
 Research Project

OUTCOME:

⭐️ Certificate of Excellence

⭐️ Job Opportunities

⭐️ Research Publication

Payment checkout links (Introduction to Bioinformatics in R)

Note: For Low-Income Countries scholarship opportunities are available, please reach out to us and our team at: marketing@omicslogic.com and we will find the best possible way for you to participate and get the best outcomes from the training program.

We also have the option for financial assistance with weekly/monthly installment options and payment options according to your country (e.g., INR, etc.)

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How is R used in Bioinformatics?

Bioinformatics can be defined as “the application of computational tools to organize, analyze, understand, visualize and store information associated with biological macromolecules”. Dealing with data efficiently to process, analyze, visualize and annotate will ultimately require some coding - even if the code launches other scripts developed by a more experienced programmer. Therefore, everyone dealing with data (and especially omics data) needs to develop an understanding of how to read, write, change or optimize code.

Coding is very important to bioinformatics. Having a bioinformatician tell you they never had a programming class is like having an MD tell you they never took anatomy. Soon you will realize that in order to “do” bioinformatics, you have to have a minimum knowledge of programming, even for running someone else's software. You can be a basic bioinformatician and perform analysis using available software but as soon as you want to modify some of the outputs, inputs, or settings you will often be limited if you can’t go deeper into the code and at least read it to change it according to what you want to do. 

Thus, you can look into practical examples of scripting languages for visualization and “making sense” of biological data. 

Introduction to Bioinformatics in R (Beginners): 1-month Training Omics Logic Training Program

Session Title

Topics Covered

Online Course work

Session 1: Getting Started with Bioinformatics in R

 

Introduction to the program, Mentor introductions, Course curriculum overview, Resource account settings. Course structure, expectations, deadlines, feedback, and intended outcomes. 


Topics To Be Covered: 

  • Introduction to Big Data in Bioinformatics 
  • Introduction to Bioinformatics languages (R)
  • Using the Code Playground (R)
  • Loading data in R 

Access to Educational portal: (https://learn.omicslogic.com/)/ Analytical server (server.t-bio.info)/ T-Bioinfo: T-BioInfo Interface Overview

Associated online lessons: 

Associated Coding Lessons: 

Session 2: Working with sequences in R

Topics To Be Covered: 

  • Types of Data: Continuous and categorical 
  • Log Normal Transformation and Heatmaps 
  • Introduction to DNA (Genomics) - Reading FASTA files and compute the length
  • DNA Replication & Reverse Complementation coding in R

Associated Coding Lessons: 

Session 3: Annotation of Genomic Variants in R

Topics to be covered:

  • Introducing Genomic Variants
  • Loading & Processing of Variant Call Format (VCF)
  • Identifying Clinical Significance of Variants
 

Session 4: Transcriptomics Data processing and Visualization in R


Topics To Be Covered: 

  • Learn to load, clean, and visualize data summaries
  • Practice: basic data visualization
  • Crosstabs, Boxplot, Heatmap Scatter Plot & Histogram
  • Practice: Advanced data visualization
  • Data visualization using GGPLOT2
  • Practice: Object-oriented visualization
  • Dimensionality reduction: PCA & visualization

Review:  How to load data and check for variable data R-CODE

Associated Coding Lessons: 

Session 5: Statistical Analysis & Differential Gene Expression

Topics To Be Covered: 

  • Statistical Analysis tests
  • T-Test & ANOVA
  • Regression & Correlation
  • Differential Gene Expression

Associated Coding Lessons: 

Session 6: Machine Learning

Topics To Be Covered: 

  • Unsupervised Machine Learning Analysis
  • Introduction to Random Forest
  • Training and Test data Preparation
  • Evaluate the model's performance in terms of ROC
  • Prediction Models with Caret

Associated Coding Lessons: 

Session 7: Metagenomic Analysis in R: Working with an example project

Topics To Be Covered: 

  • Example Project
  • Q&A Session 
  • Introduce Research Fellowship Program

Associated Coding Lessons: 

 

Transcriptomic Data Analysis in R

T- R1-1

RNA Seq Data Analysis

Learn how to load RNA expression data table, summarize, normalize, transform and visualize your data through different visualization techniques. 

T- R2-1

Statistical Analysis Tests

Learn & practice how you can perform various statistical analysis tests which includes T-test, Regression, Correlation, and ANOVA in R.

T-R5-1

Machine Learning Methods

Learn R-code for different machine learning methods including unsupervised ML - clustering methods and supervised ML classification methods.

Box plot for log scale transformed data.

Getting started in R can be a challenge

There are many things to consider as you are getting started - installing packages, debugging, learning the environment and the list goes on. But to get started, you need to make sure you can get through these challenges quickly and learn how to do it before your motivation runs out!

That is why on our portal, you can practice and run the analysis right in the browser by using our R console on the Omicslogic learn portal and gain immediate feedback for your code. 

In each tutorial, you will be able to learn the syntax, run the provided code and complete challenges where sections of the code need to be completed independently - all right within the browser!

Genomic Data Analysis in R

DNA replication

DNA Replication & Reverse Complements in R

Learn about DNA & its replication process. Also learn how to compute nucleotide frequencies & create complementary strands of DNA through R scripting.

Phylogenetic tree

Learn about the generation of the phylogenetic tree from the DNA sequences of samples to understand the evolutionary relationships among them. 

Working with sequences

Working with Multiple Sequence Alignment 

Learn & practice how you can perform and visualize multiple sequence alignment and filter out the variant information. Also learn the codeblocks to analyze DNA.

Metagenomic Data Analysis in R

Visualization of 16S RNA

Metagenomic Data Analysis

Learn analysis of 16s RNA & the significance of metagenomics. Perform downstream analysis of 16S amplicon data using R to investigate Microbial Abundance.

Learn about the visualization of taxonomic (phylum) composition in the metagenomic samples in the form of stack plots & analyze high-dimensionality datasets.

Phyloseq Analysis

Phyloseq is a tool to handle & analyze high-throughput microbiome census data. Learn how to import the tables produced by the DADA2 pipeline.

Research Projects and Case Studies

There are opportunities to leverage what you know, discover a lot more and make a difference.

, you might be thinking how your skills in bioinformatics can make a difference and what can you do to make an impact. That is why it is important to learn about bioinformatics in the context of a research area you can relate to. This includes precision medicine, population health, agrobiology and astrobiology - all areas with active utilization of omics data. Learn more by exploring some of the projects and programs we offer that cover these areas in detail.

 

Register for the upcoming webinar

Getting started with Bioinformatics in R - 1 (1)
Job Listing (OmicsLogic Portal)
Achievements Feed (OMICSLOGIC Portal)
Bioinformatics in R – 12-1

Payment checkout links (Introduction to Bioinformatics in R)

Note: For Low-Income Countries scholarship opportunities are available, please reach out to us and our team at: marketing@omicslogic.com and we will find the best possible way for you to participate and get the best outcomes from the training program.

We also have the option for financial assistance with weekly/monthly installment options and payment options according to your country (e.g., INR, etc.)

WhatsApp.svg+91-9876134120

Here is what our users say about these courses:

Navya
"Explained the concepts using R for bioinformatics analysis very well and was very interesting to learn. I had the opportunity to practice and remember some important concepts that I know that in the next stages of the course it will be very helpful to have them very clear".
- Navya Wadhwa , M.Sc Bioinformatics
Natalie Chang
"The information was easy to understand, and the practice problems with pairwise alignment and R for multiple sequence alignment were useful. For the most part the lesson was good and provided a solid introduction and it leaves me interested in learning more".
- Natalie Chang, High School Student
Adetayo
"The step by step guide is reminiscent of all the educational courses on this platform making learning bioinformatics the Omicslogic way pleasantly easy, didactically rewarding and outrightly inspiring. Pinebio has encouraged me to learn both R which I was initially fluent in and Python which I now find more alluring to explore and initialize for machine learning algorithms. However, everything seems easier".
- Adetayo , Oral Maxillofacial Pathologist
sabita
"It was a valuable learning experience as I learned how R could be used to understand the gene expression that yields important insights about cellular process changes. The instructions were very well defined and the code was self explanatory and thus helped me gain a clear understanding".
- Sabita Tamang , Research Associate

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. To join, simply create an account for free on learn.omicslogic.com!