Harpreet Kaur has recently joined the Pine Biotech team as a mentor. In this blog she share the RNA-Seq Course Review for the Transcriptomics series.
Harpreet completed her Ph.D (Thesis Submitted) in Bioinformatics from the Bioinformatics Centre, CSIR-Institute of Microbial Technology, Chandigarh, India and JNU, New Delhi, India. Her Doctoral Research is focused in the Domain of Cancer Genomics; specifically, “Computer aided identification of genetic biomarkers for the predicting liver cancer and its prognosis”. She has nearly 7 years of Research Experience and 1 year of teaching experience. She received her Master’s Degree in Molecular Biology & Biochemistry from the G.N.D.U. Amritsar, Punjab, India. She has expertise in the application of statistical, machine learning techniques on biomedical data, and the development of prediction tools and databases. Her current research interests are in the areas of artificial intelligence, machine learning, cancer genomics, biomarker discovery, and database development on different aspects of human health.
Harpreet has published 13 Research Articles in peer-reviewed International Journals, 2 Research Abstracts in International Journals and presented some of her Research papers in International Conferences. She has 200+ citations on Google Scholar and received the EASL-Young Investigator full Bursary Award from the European Association for the Study of the Liver (EASL) to present her Research in The Digital-International Liver Congress 2020. She has also received the “INSc-Young Researcher/Achiever Award-2020” from the Institute of Scholars (InSc), Bengaluru, India. She is a member of various scientific societies like EASL, BioClues, APBioNet, InSc. She received CSIR-Fellowship from the Council of Scientific and Industrial Research, India for her Doctoral Research. She has qualified various national level exams like CSIR-JRF-NET, CSIR-NET, GATE-2012, GATE-2013.
She shared her review of the four courses she completed on the Edu Platform. Please find the review in her own words.
I have completed four courses at Pine Biotech, i.e., Introduction to Bioinformatics, Transcriptomics 1, Transcriptomics 2, and Transcriptomics 3. I have gone through lessons, videos, completed Quizzes, Assignments for analyzing data and developing different pipelines. On the completion of these courses, I would like to discuss my experiences and views on what valuable assets these courses and PineBiotech offer to the potential learner, or researcher.
This course provides a concise overview on Bioinformatics. Further, it introduces the real world applications of bioinformatics.
- It demonstrated how Bioinformatics is applicable in the field of Healthcare:
○ To design personalized medicine.
○ Discovery and importance of diagnostic, prognostic and predictive Biomarker.
- It demonstrated how Bioinformatics applicable is in the field of Agriculture and Environmental concern with examples.
- It also introduces regarding the user-friendly analysis platform, i.e. T-Bioinfo Server.
If you are not much familiar with bioinformatics, this course introduces you with one of the most exciting research fields and provides you with relevant, real world applications of Bioinformatics through the interesting lesson content, video, etc. It makes you understand where and what kind of opportunities are available in the bioinformatics to explore and analyze the Big Data to elucidate key information.
This course provides the holistic view, importance and analysis on one of the most vital omics layers in Biology, i.e., Transcriptomics. First, it introduces different types of transcripts, their importance and process of their generation. Further, it provides description and comparison on the different techniques employed for the quantification of RNA, i.e. northern blot, reverse transcription PCR, microarray, NGS-RNA-seq.
● This course primarily dedicated to NGS/RNA-seq data analysis from raw data:
○ It emphasizes the type of data, files format, analysis, interpretation and tools/pipeline required for RNA-seq analysis.
○ It provides the details regarding the importance, logics and analysis tools for each step of RNA-seq that include Pre-processing, mapping to reference genome, calculation/quantification of raw reads and biological interpretation of data in the simplest way.
○ It explains RNA-seq analysis in step by step manner through lessons, video and hands-on-assignments with real life data.
○ It provides a platform (T-Bio Info Server) that offers you to generate your own pipeline by yourself for processing and analysis of RNA-seq data.
● This course further makes you familiarize with the Dataset matrix of RNA-expression data or table of RNA-seq expression data. It provides an explanation on handling this data. Subsequently, it provides the importance of normalization of data, with an example of Quantile normalization.
● It also provides details regarding how one can analyze, interpret data and derive biological information from this big data.
Conclusively, this course provides you with a holistic view and approach to analyze RNA-seq raw data in the simplest manner through lessons, videos, hand-on-assignments for different problems. It lays a foundation on understanding of NGS-RNA-seq technique. If you are interested in learning the RNA-seq analysis in detail, one must take this course.
This course goes one step further for the understanding of RNA-seq Data analysis. It provides details regarding the Big Data analysis; it provides explanation on the importance and techniques for normalization, statistical t-tests, differential gene expression analysis and factor regression analysis.
● First it emphasis on the preparation and processing of Data table or dataset matrix for downstream analysis:
○ It provides details regarding how a data table should be prepared for analysis.
○ It explains why and how a data normalization should be performed.
○ It offers an hands on assignment on quantile normalization of data.
● Further, this course focuses on the understanding and application of statical analysis of RNA-seq Data:
○ It provides details regarding the need and condition of t-test in analysis.
○ It explains the methods and approaches for the implementation of t-test .
● This course provide the detailed regarding the differential gene expression analysis:
○ First it introduces the differential gene expression analysis pipeline step by step.
○ Further, it provides overview and practical application of various differential gene expression analysis algorithms, i.e., EdgeR, DeSeq, CutDiff, etc.
○ At last, it provides biological interpretation from differential gene expression analysis.
○ Here, it throws light on the concept of regression.
○ It explains the details regarding the importance and steps for the implementation of Factor Regression analysis. its emphasis on data analysis using Factor Regression analysis.
○ Further, it provides an explanation on the understanding of the results of factor regression analysis.
○ It also introduces the one of machine learning algorithm, i.e., Principal Component Analysis (PCA)
Conclusively, this course provides you with the understanding and practical implementation regarding the pre-processing, normalization, statistical analysis of the gene expression Data in the simplest way via lessons, videos, hand-on-assignments. It will make you understand regarding the analysis of RNA-expression processed Data to derive any biological interpretation from the big data. If you are interested in learning the approach for data analysis in detail, one must take this course.
This course mainly focuses on the practical applications of Bioinformatics techniques in Precision medicine or Biomedical Data. It provides the details regarding Machine learning algorithms and their implementation. It covers both the branches of machine learning, i.e. Unsupervised and Supervised Machine learning algorithms. For the practical implementation of algorithms, this course, considered one case study of Breast Cancer, takes the dataset from the publication.
- First it provides details regarding one of the unsupervised machine learning algorithms, i.e., Principal Component Analysis (PCA).
- Then, it provides details regarding various unsupervised machine learning algorithms including clustering algorithms:
○ It describes the Hierarchical Clustering (H-Clust) and how one can perform H-clust using the T-Bioinfo platform.
- ○ It explains the K-mean clustering approach in detail and its implementation using the T-Bioinfo platform.
- Further, it describes different supervised machine learning algorithms:
○ Here, first, it provides the details in context to the implementation of classification algorithms.
○ It provides the explanation and logics behind the training and test dataset during the implementation of machine learning algorithms.
○ It explains the concepts of the supervised machine learning algorithms, i.e.,
Decision tree, Random Forest, LDA, Support vector machine (SVM), etc.
○ Further, it describes the concept and logics of feature selection and feature extraction techniques.
○ It demonstrated the implementation of feature selection techniques, like step-wise LDA and Random forest using the T-bioinfo platform.
○ At last, it demonstrated the interpretation of biological annotation of the data using the T-bioinfo platform.
Conclusively, this course provides you with the comprehensive understanding and implementation of machine learning algorithms on transcriptomics data to derive biological significance from the big data in the simplest manner though well structured lessons, videos, hand-on-assignments. If someone is interested in understanding the application of machine learning techniques on biomedical data in detail, one must take this course.
Overall Valuable Takeaways from Pine Biotech Courses for Students:
● Very well-structured courses.
● Well organization of biology, defining terms and techniques for RNA-seq analysis.
● Easily accessible content in forms of Video and graphical presentation to make it more convenient in understanding.
● Workflow to represent a process of analysis to create better understanding of the concept.
● Step by step explanation and implementation make it convenient for practical implementation.
● Assignment on Data analysis by creating the pipelines (on T-Bioinfo platform) provides a deep understanding and practical implementation.
● Gaining exposure and experience in Data analysis that is not offered in books or classrooms (i.e. exposure to implement NGS-RNA-Seq algorithms, feature selection, Machine learning, etc.)
In conclusion, Pine Biotech offers a learner/scholar convenient, easily understandable content that is unlike reading a book or any experience in a classroom. The comprehensive information with media content and hands-on-assignments (using T-bioinfo platform) in each course provides the students with the most valuable assets to understand the application of bioinformatics in the biomedical data. Whether you are a beginner or familiar, these courses of Pine Biotech provide you with a better understanding of Bioinformatics techniques in real life data. If you are willing to learn and implement Bioinformatics, you must take these courses.
We hoped you enjoyed reading through this RNA-Seq Course Review and learning more about our new mentor. To help you learn more about related topics, we have a few other blogs you can check out via the links below.