Transcriptomic studies offer important insights on gene expression and regulation and have been widely studied in many organisms. As a result, the last couple of decades have witnessed an explosive growth in transcriptomic data.
To help students and researchers from diverse backgrounds manage and analyze this data, we have designed an online course on Transcriptomic Data Analysis. The course covers theoretical as well as practical concepts of transcriptomic data analysis (RNA-Seq) ranging from basic visualization to statistical analysis of differentially expressed genes using the popular DESEQ2 package and advanced machine learning techniques designed for large-scale studies. Finally, you will also learn about the principles of biological interpretation with examples.
To offer updated and relevant learning for our community, we have revised and improved our course this month. Here is a glimpse into the various updates that we have made to the course:
Separation of Logical & Technical Aspects of Transcriptomics (Logic, R & Python)
For learners who wish to learn logical and practical aspects of transcriptomics data analysis separately, we have divided the courses into three, CHECK THEM OUT!
|User-friendly Transcriptomics on T-BioInfo||Transcriptomic Data Analysis in R Programming Language||Transcriptomic Data Analysis in Python|
|Basic processing, visualization, and statistical analysis of transcriptomics data using the T-BioInfo Platform||Learn to analyze and visualize transcriptomic data using popular libraries and packages in R and R studio||Learn to analyze and visualize transcriptomic data using Python code, libraries and packages|
Updated Transcriptomics Course – Video Tutorials, Demo Pipelines, Interactive Dashboards & Improved Content
We also wanted to make sure the coursework is more interactive, so we improved lessons with step-by-step breakdowns of bioinformatics pipeline set-up and resulting outputs. We also added video tutorials for all the pipelines.
In addition, for learners who already have an educational license to the T-BioInfo Platform, we have added links to demo pipelines where input data has already been uploaded. When steps are followed correctly, learners will be able to get the pipeline outputs within seconds.
Finally, we also incorporated interactive data visualization options, such as volcano plots, heat maps, and full interactive dashboards in plotly dash. Data visualization options such as these empower biologists and researchers to visually explore and analyze large numeric datasets such as tables of gene expression in a short and intuitive manner.
Source: T-BioInfo Platform
Course Completion – Next Step: Transcriptomics Example Projects
After the completion of Course 5: Transcriptomics, one can apply the theoretical and practical knowledge gained to analyze real datasets generated by expert researchers from the public domain!
To help find relevant examples, at the end of the course, we have provided a curated set of example projects that students can complete. Here is a glimpse into some of the example projects:
|PRECISION MEDICINE||ONCOLOGY||MACHINE LEARNING|
Seek Inspiration: Check Out Completed Student Projects!
In addition to example projects, here are some of our student projects that one can look into after completing coursework:
|SPACE OMICS||INFECTIOUS DISEASES|
Registration: Transcriptomic Data Analysis (Asynchronous)
If you wish to learn transcriptomics data analysis, consider enrolling for the Transcriptomic Data Analysis (Asynchronous). To know more join us for a free webinar on December 13th, 2022.
To get instant details, reach out to:
Ms. Sparsh Dhar
Marketing Manager, Pine Biotech
Email ID: email@example.com
Contact Number: +919876134120 (Active on WhatsApp)
Program Registration: https://edu.omicslogic.com/transcriptomics
- Transcriptomics 1: https://learn.omicslogic.com/courses/course/course-5-transcriptomics
- Transcriptomics 2: https://learn.omicslogic.com/courses/course/course-52-transcriptomics-in-r
- Transcriptomics 3: https://learn.omicslogic.com/courses/course/course-53-transcriptomic-analysis-in-python