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AKTU R Programming for Bioinformatics (BBI503) Syllabus PDF – B.Tech 3rd Year
R Programming for Bioinformatics BBI503
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Official AKTU syllabus of R Programming for Bioinformatics (BBI503) for B.Tech 3rd Year – 4 credits, L-T-P 3-1-0. Read the unit-wise course content, course outcomes, evaluation pattern and recommended books online, or download the PDF.
Syllabus — R Programming for Bioinformatics
Download PDFBBI503 · 4 credits · L-T-P 3-1-0
Course Outcomes
At the end of the course, the student will be able to:
| CO1 | Recall and explain the basics of R programming, environment setup, and representation of biological sequences. | K2 |
| CO2 | Apply conditional and looping constructs to develop simple biological programs (e.g., nucleotide counting, GC content). | K3 |
| CO3 | Use data structures and functions to handle biological datasets and perform string operations. | K3 |
| CO4 | Apply file handling techniques to import/export biological data and analyze FASTA sequences. | K3 |
| CO5 | Apply bioinformatics packages, statistics, visualization, and basic machine learning techniques for biological data analysis. | K3 |
Course Content
L-T-P: 3-1-0
| Unit | Topic | Proposed Lecture |
|---|---|---|
| I | INTRODUCTION TO R: Introduction to R and its applications in bioinformatics, installation of R and RStudio, R environment, variables, constants, data types, operators, and basic R programs. Representation of DNA, RNA, and protein sequences. | 08 |
| II | PROGRAM FLOW CONTROL: Conditional statements (if, if-else), looping statements (for, while), break and next statements. Simple biological programs such as nucleotide counting and GC content calculation. | 08 |
| III | DATA STRUCTURES AND FUNCTIONS: Vectors, matrices, lists, data frames, factors, string operations, built-in functions, user-defined functions, and handling biological datasets. | 08 |
| IV | FILE HANDLING: Reading and writing text and CSV files, importing biological data, working with FASTA files, and simple sequence analysis. | 08 |
| V | PACKAGES, STATISTICS AND VISUALIZATION: Introduction to packages like ggplot2 and Bioconductor, basic statistics, graphs, charts, and introduction to clustering and classification Machine Learning algorithms in bioinformatics. | 08 |
Text Books
- Grolemund, G. (2014). Hands-On Programming with R. O'Reilly Media.
- Wickham, H., & Grolemund, G. (2017). R for Data Science: Import, Tidy, Transform, Visualize, and Model Data. O'Reilly Media.
- Matloff, N. (2011). The Art of R Programming: A Tour of Statistical Software Design. No Starch Press.
- Peng, R. D. (2016). R Programming for Data Science. Leanpub.
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