Course

Unit 1

Overview of omics technologies (transcriptomics, proteomics, metabolomics) – history and evolution of omics technologies – the importance of application of AI omics research – the role of omics research in biological research.

Unit 2

Gene expression profiling using microarrays and RNA sequencing – transcriptomics data analysis using bioinformatics toll and AI-based methods – functional annotation of transcripts and pathways – alternative splicing analysis – single-cell transcriptomics and spatial transcriptomics.

Unit 3

Protein separation and identification using mass spectrometry – quantitative proteomics and label-free quantification – proteome analysis using bioinformatics tools and AI-based algorithm – proteins and pathways – protein-protein interactions and network analysis – structural proteomics and protein modifications.

Unit 4

Metabolite identification and quantification using mass spectrometry and NMR – metabolite profiling and metabolic pathway analysis – metabolomics data analysis using bioinformatics tools and AI-based algorithms – metabolic flux analysis and isotopic labelling – metabolomics in systems biology and synthetic biology – metabolomics in disease diagnosis and drug discovery.

Unit 5

Multi-omics data integration and analysis – correlation and causation analysis – AI-based predictive models – biomarker discovery and validation – A-based method to analyse biomarker data – the future direction of omics research with AI.

Course Objectives

• Introduce students to using artificial intelligence (AI) in transcriptomics, proteomics, and metabolomics research, including machine learning algorithms, data mining techniques, and other computational approaches.
• Teach students how to apply AI techniques to analyse and interpret omics data, including genomic, transcriptomic, proteomic, and metabolomic data, and integrate data from multiple omics platforms.
• Foster students’ ability to critically evaluate the strengths and limitations of AI-based approaches in omics research and understand the importance of validation and reproducibility in scientific research.

Course Outcomes

After completing this course, students will be able to

 CO-PO Mapping

Evaluation Pattern

Text Books / References

“Transcriptomics and Gene Regulation” by Simon Tavaré and Nancy R. Zhang

“Proteomics: Methods and Protocols” edited by Daniel J. Liebler

“Metabolomics: From Fundamentals to Clinical Applications”, edited by Ute Roessner and J. Cameron Thrash

“Integrated Omics Analysis: Methods and Applications” edited by Andrew J. Percy and David S. Wishart

“Transcriptome Analysis: Methods and Applications” edited by Vinita Chauhan, Rakesh Sharma, and Debmalya Barh

“Proteomics in Systems Biology: Methods and Protocols” edited by Daniel J. Kliebenstein

“Metabolomics in Practice: Successful Strategies to Generate and Analyze Metabolic Data” by Michael Lammerhofer and Wolfram Weckwerth Recent literature