List of Experiments / Activities
- 1Retrieve DNA sequences from NCBI and visualize basic sequence features.
- 2Primer designing.
- 3Perform gene prediction using AUGUSTUS (web server demo).
- 4Explore mutation detection using DeepVariant demo dataset.
- 5Load RNA-Seq sample data in Galaxy and generate expression plots.
- 6Cluster transcriptomic data using Orange Data Mining (drag-and-drop).
- 7Retrieve protein sequences from UniProt and analyze domains using InterPro.
- 8Predict protein secondary structure using SOPMA/PSIPRED online tools.
- 9Phosphorylation site prediction using NetPhos.
- 10Peptide structural characterization by Bioinformatics tool.
- 11Identify motifs in protein sequences using HMMER web server.
- 12Map genes/proteins to pathways using KEGG Mapper.
- 13Run a simple neural network in Google Colab to classify DNA vs. protein sequences.
- 14Apply K-means clustering on demo gene expression data (Orange/Colab).
- 15Case study: Use Orange’s built-in healthcare dataset to train a decision tree classifier.
- Note: The instructor may add / delete / modify / tune experiments as required. Dr. A.P.J. Abdul Kalam Technical University, Lucknow, Uttar Pradesh
Course Outcomes (COs)
After completion of the course student will be able to:
- CO1Demonstrate knowledge of open biological databases and AI based tools.K2
- CO2Retrieve and process genomic, transcriptomic, and proteomic data using web servers.K3
- CO3Apply alignment, clustering, and prediction methods with AI support.K3
- CO4Apply computational tools to identify protein structures, motifs, and gene expression patterns.K3
- CO5Explain AI applications in healthcare and multi-omics integration.K2
Official AKTU syllabus
This subject is on page 27, 28 of the original AKTU PDF.