Next Generation Sequencing
Course description:
This course sheds light on the next generation sequencing technologies that have a wide area of
applications and analyzes several properties of biological systems. NGS is a revolutionary
technology in the biological sciences. With its ultra-high throughput, scalability, and speed, NGS
enables researchers to perform a wide variety of applications and study biological systems at a
level never before possible. Today’s complex genomic research questions demand a depth of
information beyond the capacity of traditional DNA sequencing technologies. Next-generation
sequencing has filled that gap and become an everyday research tool to address these questions.
This course will deal with diverse types of tests, analysis and visualization techniques that may be
used in the analysis of diverse types of NGS techniques.
Recommended Books:
1. Next Generation Sequencing – 2018 Methods and Protocols by Dr. Steven R. Head Dr. Phillip Or- doukhanian Dr. Daniel R. Salomon. Springer
2. Next-generation Sequencing: Current Technologies and Applications. Canister Academic Press
3. Clinical Applications for Next-Generation Sequencing. Urszula Demkow Rafal Ploski.
Elsevier
Course Learning Outcomes:
After the course, the students will apply different concepts of NGS analysis on practical problems.
They would be able to grasp the idea of high throughput sequencing and their application in various
domains.
Assessment system:
Quizzes 10-15%
Assignments 5-10%
MSE 30-40%
ESE 40-50%
Lecture Plan:
S.NO. Lecture Topic Quizzes Assignments
1. Overview of Genetic Testing and Sequencing
Technologies
2. What is Genetic Testing?
The Promise of Genetic Testing and Sequencing Technologies
3. Clinical Genetic Testing
Overview of Genetic Testing Amino Acids and Proteins
4. Traditional Cytogenetics Microarray Diagnostics
1
5. Biochemical Genetic Tests Diagnostic Genetic Testing
6. Reproductive Genetic Testing 2
7. Sequencing Technologies Sequencing Approaches
8. Polymerase Chain Reaction Sanger Sequencing Sequencing by Synthesis
2
9. Raw Data Analysis
Variant Identification
10. Clinical Linkage: Diagnostic Genetic Testing 3
11. Emerging Areas in Genetic Testing and Analysis
12. Non-coding Variants DNA Methylation Testing
3
13. Improving Variant Classification Complex Trait Variants
14.Determining Risk in Complex Traits
4
15.Long Read Sequencing
16. Single Cell Sequencing
4 Artificial Intelligence in Healthcare
Course description:
The healthcare service system plays a pivotal role in the medical domain that constitutes massive
demands of human life. To evolve, healthcare providers in developing countries are using
intelligent technologies like artificial intelligence (AI) and machine learning techniques.
Regarding AI integration, advancements in healthcare have led to research on human-centered
healthcare intelligent systems. AI technologies exert influence on the development of intensive
care and supervisory activities in hospitals and clinics.
Recommended Books:
1. Ranschaert ER, Morozov S, Algra PR, editors. Artificial intelligence in medical imaging:
opportunities, applications and risks. Springer; 2019 Jan 29.
2. Agah A. Introduction to medical applications of artificial intelligence. CRC Press; 2013
Nov 6.
3. Consoli S, Recupero DR, Petkovic M. Data science for healthcare. Berlin: Springer
International Publishing; 2019.
Course Learning Outcomes:
After the course, the students will develop a holistic understanding of AI’s growing role in health
care through an immersive online experience that draws on real-world case studies. The course
will explore how AI strategies have been successfully deployed within the sector. The students
will learn to ask the right questions when evaluating an AI technique for potential use within their
own context.
Assessment system:
Quizzes 10-15%
Assignments 5-10%
MSE 30-40%
ESE 40-50%
Lecture Plan:
S.NO. Lecture Topic Quizzes Assignments
1. Introduction to clinical data and healthcare systems
2. Fundamentals of AI in healthcare 1
3. Integration of AI in medical imaging and home-care
technologies
1
4. Evaluations of AI Applications in healthcare
5. AI assisted decision making analysis and health
services
2
6. Integration of AI with advanced technologies, such as
blockchain
2
7.
AI and Telehealth 3
8. Integration of AI in the management of healthcare
setups
3
9.
AI for medical diagnosis 4
10.
AI and innovations in the Internet of Medical Things 4
Computational Vaccinology
Course description:
Computational vaccinology overlaps with Computational Immunology, reverse vaccinology,
vaccinomics, and systems vaccinology, to address questions in vaccinology. It is an
interdisciplinary field, uses computational resource and algorithm to help in design vaccine. In
this course, recent developments in computational vaccinology, highlighting work in epitope and
antigen identification, and the discovery of delivery vectors and adjuvants, etc. are highlighted.
These diverse activities all have the potential to significantly reduce the laboratory resource needed
for efficient vaccine discovery. This course offers how computational analysis of pathogenic
genomes by epitope mapping and reverse vaccinology can provide viable vaccine targets.
Recommended Books:
1. Rappuoli, R., & Bagnoli, F. (2011). Vaccine design: Innovative approaches and novel
strategies. Horizon Scientific Press.
2. Sakharkar, K. R., Sakharkar, M. K., & Chandra, R. (2015). Post-Genomic Approaches in
Drug and Vaccine Development.
3. Tong, J. C., & Ranganathan, S. (2013). Computer-aided vaccine design. Elsevier.
Course Learning Outcomes:
After the course the students will be able to apply different concepts of vaccinology in
computational domain.
Assessment system:
Quizzes 10-15%
Assignments 5-10%
MSE 30-40%
ESE 40-50%
Lecture Plan:
S.NO. Lecture Topic Quizzes Assignments
1. Introduction to Computational Vaccinology
2. Design of New Vaccines in the Genomic and Postgenomic Era
1
3.
Application of Computational Immunology to Vaccine
Design
1
5. Target Identification for Vaccines
6. Computational vaccinology workflow 2
7. Cancer vaccines: computational modeling approaches
8. Reverse Vaccinology & Vaccine screening
9. Epitope-driven approaches for vaccine design 3
10. DNA vaccines 3
11. Allergen Bioinformatics
12. Identification of vaccine targets in pathogens 4
13.
Computational Vaccinology: Quantitative Approaches
14. Structural and Computational Biology in the Design of
Immunogenic
4
15. Vaccine Antigens
Molecular Oncology
Course description:
Molecular Oncology is an integrated basic science program composed of a diverse group of
investigators with strengths in cancer stem cells, DNA damage repair and genomic instability,
tumor-host interactions, and other fundamental areas of cancer biology.
Recommended Books:
Bronchud MH, Foote MA, Giaccone G, Olopade O, Workman P, editors. Principles of molecular
oncology. New Jersey:: Humana Press; 2008.
Prerequisite:
1. Essential of Genetics
Course Learning Outcomes:
After the course the students should be able to: describe general principles of cancer diagnostics
and treatment, understand the basic processes underlying the transformation of a normal cell to its
malignant counterpart, and the consequences of malignant transformation on the cellular and
organism level, understand how the biological knowledge of cancer development is used in
modern cancer treatment, show knowledge of laboratory techniques used in experimental cancer
research, demonstrate knowledge in biostatistics and cancer epidemiology.
Assessment system:
Quizzes 10-15%
Assignments 5-10%
MSE 30-40%
ESE 40-50%
Lecture Plan:
S.NO. Lecture Topic Quizzes Assignments
1 Introduction to Molecular Oncology
2 Molecular Markers
Selecting the Right Targets for Cancer Therapy
1
3 Clinical Importance of Prognostic Factors: Moving from
Scientifically Interesting to Clinically Useful
1
4 Cellular and Tissue Markers in Solid Tumors 2
5 Growth Factor-Signaling Pathways in Cancer
6 Estrogen Action and Breast Cancer 2
7 Cyclin-Dependent Kinases and Their Regulators as
Potential Targets for Anticancer Therapeutics
8 Apoptosis Pathways: Clinical Relevance 3
10 DNA Repair Pathways: Mechanisms and Defects in the
Maintenance of Genome Stability
11 Angiogenesis Switch Pathways 3
12 Invasion and Metastasis
13 Molecular Pathways of Drug Resistance 4
14 Antitumor Immunity as Therapy for Human Cancer
15 Emerging Technologies: Molecular Targets and the Drug
Delivery Process
4
16 Emerging Molecular Therapies for Cancer
17 Emerging Molecular Therapies: Small-Molecule Drug
Pharmacoinformatic
Course description:
Pharmacoinformatic is a specialized degree that builds upon the reputation of the School of
Pharmacy. For over 175 years, the School of Pharmacy has been advancing scientific knowledge.
Students who complete this degree can enter a few of exciting and rewarding careers, including
research, operations, and management. Here are the benefits of this program. You’ll have a better
understanding of drug development and biopharmaceuticals. This course is to provide basic
training of bioinformatics tools application in drug discovery and development process. It presents
the student with emerging strategies and tools of computer aided drug design.
Recommended Books:
1. Benfenati, E. (Ed.). (2016). In silico methods for predicting drug toxicity. Humana Press.
2. Kenakin, T. (2016). Pharmacology in Drug Discovery and Development: Understanding
Drug Re- sponse. Academic Press.
3. Kenakin, T. P. (2017). Pharmacology in drug discovery and development.
Course Learning Outcomes:
After completing this course, students will understand the application of bioinformatics tools in
the drug discovery and development process. They will acquire additional skills in
Pharmacoinformatics for workforce required in pharma industries, vaccine development, clinical
research projects, research, etc. This course will add skills to the students for designing novel drugs
for the treatment of untreatable diseases.
Assessment system:
Quizzes 10-15%
Assignments 5-10%
MSE 30-40%
ESE 40-50%
Lecture Plan:
Week Lecture Topic Quizzes Assignments
1.
General Pharmacology
2. Overview of Bioinformatics and Information Technology
Pharmacophore Kinetics
1 1
3.
Drug Discovery and Development
4.
Bioinformatics in Pharmacology 2 2
5.
Structure representation systems 3
6.
Chemical Databases 3
7.
Modeling of small molecules 4
8. Principles of Chemo-informatics
4
Methods in Protein Modeling
Course description:
Detailed understanding of protein structure and function is a prerequisite for unravelling biological
systems and molecular networks, and for development of new drugs for treatment of diseases. The
course aims to enable the student to understand protein structure and related computational
techniques to investigate protein structure. This is important regarding protein function in
biological systems, understanding protein interactions, design of protein-targeting drugs and
protein-based drugs, and optimization of enzyme activities.
Recommended Books:
1. Na´ray-Szabo´, G. (Ed.). (2014). Protein modelling. Springer International Publishing.
2. Kukol, A. (Ed.). (2008). Molecular modeling of proteins (Vol. 443). Totowa, NJ:: Humana
Press.
3. Maia, R. T., de Moraes Filho, R. M., and Campos, M. D. A. (Eds.). (2021). Homology
Molecular Modeling: Perspectives and Applications. BoD–Books on Demand.
Course Learning Outcomes:
After completing this course, students will understand the various levels of protein structure and
their graphical representation. They will predict and interpret the structural information and the
structure-function relationships of biological macromolecules.
Assessment system:
Quizzes 10-15%
Assignments 5-10%
MSE 30-40%
ESE 40-50%
Lecture Plan:
S.NO. Lecture Topic Quizzes Assignments
1.
Fundamentals of protein biochemistry and protein structure 1
2. Protein structure databases
4. Data visualization methods 1
5. Bioanalytical techniques for protein structure analysis 2
6. Methods of protein Modeling
7. Protein interactions 2
8. Fundamentals of protein biochemistry and protein structure
9. Aspects of High-performance computing 3
10. Molecular mechanics methods for protein modelling 3
11. Analysis of molecular mechanics trajectories. Constraint
based protein modelling
12.
Docking 4
13.
Homology modelling 4
Immuno-Informatics
Course description:
Immunoinformatic, otherwise known as computational immunology, is the interface between
computer science and experimental immunology. This course introduces you to the world of
reverse vaccinology and computational vaccine design. Throughout the course, we will cover
various immunoinformatic tools used in the vaccine design pipeline. We will start with the retrieval
of the sequence of the antigenic protein. Then in the functional analysis of antigenic protein
session, we will look more into antigenicity, allergic nature, and physicochemical properties of
antigenic proteins. We will also look for structural features of the antigenic proteins like secondary
structures, domains, and motifs. Then, we start analyzing the several types of epitopes in antigenic
proteins. Finally, we model the structure of antigen and antibodies and then do docking to analyze
their interaction.
Recommended Books:
1. Scho¨nbach, C., Ranganathan, S., Brusic, V. (Eds.). (2007). Immunoinformatics (Vol. 1).
Springer Science Business Media.
2. Bock, G. R., Goode, J. A. (Eds.). (2004). Immunoinformatics: bioinformatic strategies
for better understanding of immune function. John Wiley Sons.
Course Learning Outcomes:
After completing this course, students will be trained to predict the pathological mechanisms and
the counter responses generated by the human body. The students will discuss mechanisms with
clinicians to cure immunology issues.
Assessment system:
Quizzes 10-15%
Assignments 5-10%
MSE 30-40%
ESE 40-50%
Lecture Plan:
S.NO. Lecture Topic Quizzes Assignments
1. Introduction to Immunology
2. Introduction to Vaccine design and Reverse
vaccinology
1
3. Primary protein structure prediction of antigenic
protein
1
4. Prediction of allergic nature of antigenic
proteins
5. Prediction of physiochemical properties of
antigenic proteins
6. Prediction of antigenicity of antigenic proteins 2
7. Prediction of the secondary structure of the
antigenic protein
2
8. Prediction of domains and important sites in
antigenic protein
9. Continuous B-cell epitope prediction
10. Discontinuous B-cell epitope prediction
11. Prediction of immunogenic regions in
antigenic protein
3 3
12. Prediction of glycoprotein antigen epitopes
13. Cytotoxic T cell epitope prediction
14. MHC class I and II prediction 4
15. Automated antigen modelling 4
16. Alignment based antigen modelling
Deep Learning in the Life Sciences
Course Description
This course introduces foundations and state-of-the-art machine learning challenges in genomics
and the life sciences. We introduce deep learning approaches to key problems, comparing and
contrasting their power and limitations. We seek to enable students to evaluate various solutions
to crucial problems we face in this rapidly developing field and to execute new enabling solutions
that can have a large impact.
Recommended Books
1. Dive Into Deep Learning, A. Zhang, Z.C. Lipton, M. Li, A. Smola.
2. Deep Learning” by Goodfellow, Bengio, and Courville
Course Learning Outcomes:
After completing this course, a student will be able to:
1. Knowledgeable about deep learning methods in life sciences, especially in tasks like
sequence and structure analysis and evolution, biological networks
2. able to understand the key algorithms for the main tasks
3. able to implement and apply the techniques to real-world datasets
Assessment System
Quizzes 10-15%
Assignments 5-10%
Midterms 30-40%
ESE 40-50%
Lecture Plan
S.NO. Description Quizzes Assignment
1. Overview of the Course/ ML Review
2. Neural Networks (CNN (review) 1
3. Neural Networks (RNN & GNN)
2
4. Neural Networks (Deconvolutional Networks)
5. Interpretability, Dimensionality Reduction
1
6. Relevance Propagation, Convolution Arithmetic
7. Maximum entropy methods
2
8. Discriminative Localisation
9.
Interpreting ML Models: visualise Filters, Measure
Gradients, Perturb inputs.
3
10. Tensor Flow Introduction 3
11.
Deep Learning Problems and compute solutions;
Genomic regulatory codes
12.
Deep Learning Problems and compute solutions;
Gene regulation – Single Cell RNA-seq
4
13.
Deep Learning Problems and compute solutions;
Genetic Variations & Diseases
14.
Graphs & Proteins
• Protein structure prediction
• Functional classifications
4
15.
Biomedical imaging
• Video Processing, structure determination
5
