The discovery of the role of mRNA as a link between the genome and proteome has led to the development of effective gene expression identification and the quantification technologies, ranging from rtPCR to Western Blots, Microarrays and Next Generation Sequencing. The most detailed of these is NGS RNA-seq, which combines the discovery of transcripts and quantification of mRNA expression levels. Today, High Throughput Sequencing (also known as Next generation Sequencing, or NGS) and RNA-seq transcriptomics studies are used regularly to understand the molecular mechanisms behind human health and disease, ecological variation and even response of crops to the changing climate conditions. It is a powerful method that is increasingly affordable and accurate.
However, there is a growing gap between the traditional biology curriculum and the growing importance of data and analytical thinking needed to extract biological meaning from it. For faculties at schools and universities, teaching these important concepts to students is often challenging due to the lack of appropriate tools and resources. Not only is there a lot to learn about the intricate biology of RNA transcription and sample preparation for Next generation Sequencing (NGS or HTSeq) technologies, but the amount of statistical and computer-science lingo and mathematical methods for analysis and interpretation can be daunting. That is why most faculty and students end up having limited understanding and experience with RNA-seq transcriptomics until much later in their studies and careers.
That’s why our team has been working hard to prepare a comprehensive package of RNA-seq transcriptomics training materials, projects and tutorials, complete with project examples, interactive assignments and in-depth technical tutorials that would be useful for undergraduate, graduate or post-graduate students from any life-science background.
T-BioInfo Portal: Modular Resources on RNA-seq Data Analysis
On the T-BioInfo portal, you can learn bioinformatics by analyzing data from research projects, guided by simple to follow tutorials and joining a community of bioinformaticians in the making. Starting from free online courses, affordable advanced courses and projects or even by joining a training program, you can go from a basic understanding of commonly used methods of analysis to an in-depth understanding of complex machine learning methods. All along, you will be assisted by a user-friendly, guided, fool-proof analytical platform that explain even to a novice how to run complex analysis on big datasets, unrestricted by computational hardware you might have and unlimited by not knowing anything about coding or statistics.
The goal of this course is to prepare a student for real-world applications of RNA-seq. We will review methods of both quantitative and qualitative analysis of RNA in a sample, how data is generated using Next Generation Sequencing, and use a sample project “Patient Derived Xenograft: Transcriptomic Biomarkers of Breast Cancer” to practice generating a table of expression from raw FASTq files. We will also learn to preview and visualize the resulting data using Principal Component Analysis (PCA). To perform the example analysis detailed in the course, students will need access to the T-BioInfo platform. Account information can be requested by filling out the form on server.t-bio.info site. Microsoft Excel is also used for plotting results. Learn more: https://edu.t-bio.info/course/transcriptomics-1/
Link to video: https://youtu.be/WbJ9OA2vevk
Transcriptomics 2 is a continuation of the Transcriptomics 1, and it focuses on finding differences in gene expression. We will start with differential gene expression and continue to look at typical challenges that can be resolved using other methods. We will look at t-test, then use DESeq2 to run a differential gene expression pipeline. Next, we will use Factor Regression Analysis. As a result, you will learn to detect obvious differences between pre-set groups as well as expand that idea to more subtle differences represented by factors that might interact with each other. The goal of this course is to further expose interested individuals to the field of big-data analysis – biologists and clinicians who currently lack the ability to work with this large scale of data, as well as further develop the analytical skills gained from Transcriptomics 1. Learn more: https://edu.t-bio.info/course/transcriptomics-2/
Link to video: https://youtu.be/FvXZNMFe1r8
Transcriptomics 3 is a continuation of Transcriptomics 1 and 2. In Transcriptomics 1, we learned how to convert raw reads from a Next Generation Sequencer into a table of expression and visualize the table of expression using PCA, which we then used to develop hypotheses. In Transcriptomics 2, we looked at statistical methods for determining differentially expressed elements in known groups of samples. We explored Student’s t-test, Bayesian methods such as Deseq and EdgeR, and Factor Regression Analysis to dissect the influence of multiple influential factors. Here, we will explore different methods for identifying groups of samples without prior knowledge (clustering) and then examine methods for developing classifiers from known samples to classify unknown samples. We will be inquiring into the clustering and the classification methods using a biological example from the publication Daemen et al. (Modeling precision treatment of breast cancer, Genome Biol. 2013. We will not repeat the analysis presented in the paper; rather we will re-analyze data from the paper and consider the differences between our own analysis and those of the authors. Learn More: https://edu.t-bio.info/course/transcriptomics-3/
Transcriptomics 4 is dedicated to the analysis of Single Cell Transcriptomics. In this course, we will talk about how single cell RNA-seq data is generated, how to analyze it and what specific challenges need to be considered. Single cell RNA-seq or “scRNA-seq” has been demonstrated as a powerful technique for classification of tissue-specific cells and is used to study cell differentiation using time-course experiments. However, specialized data preparation techniques and high noise-signal ratio of this type of data require specialized approaches to its analysis. In addition, resulting expression tables contain sparse data that need to be prepared for downstream analysis with various normalization and imputation techniques.
Learn more: https://edu.t-bio.info/course/transcriptomics-4/
Here is what several recent participants had to say about these courses:
“This is a great course. It not only introduces the basics, but builds upon the concepts by actually engaging the participant into practically performing the analysis.” – Akhil Rajput, Ph.D., Data Scientist at Insight Data Science, San Diego, CA
“Comprehensive overview of transcriptomics. Hands on experience is essential to be able to completely master the process. Appreciate the external links for further understanding.” – Ankita Dutta, Ph.D., Senior Research Fellow at National Institute of Immunology, New Delhi, India
“This course was great, exactly what I was looking for. You have clearly put a great deal of work into the materials and the result is a grade-A introduction to the topic.” – Dan Neal, MS, Statistician at University of Florida, Gainesville, FL
“Good and informative content. Explained concepts thoroughly and used pictures effectively.” – Aaron Sugimoto, Graduate Student at California State University, Long Beach
OmicsLogic Training Programs:
But often times, learning with online, self-paced materials can be difficult and to adapt such materials without becoming an expert can be prohibitively complicated. That’s why we developed plug-and-play modular programs that can be adapted to existing curricula or delivered in 1, 2 or 3 credit courses on Transcriptomic Data Analysis. Recently, we launched several OmicsLogic Transcriptomics programs at Louisiana State University, at Amity University and online, available for participants around the world. The programs are organized with online materials and regular review sessions. Theoretical material is reinforced by practical assignments and reinforced by in-class practical activities.
As a result, any faculty teaching biology can adapt the resources we developed to incorporate the complex topics of NGS data analysis, and specifically, RNA-seq data to teach their students. To learn more about these resources and schedule a time to speak to one of our curricular specialists, you can reach out to firstname.lastname@example.org or email@example.com.