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Course Detail

Course Name Bioinformatics and Data Analysis
Course Code 25CLG513
Program M. Sc. Clinical Genomics
Semester 2
Credits 2
Campus Kochi

Syllabus

Unit 1

(Lectures 5)

NGS Data Formats and Quality Control: Overview of FASTQ, BAM, SAM, VCF file formats; Tools for data quality assessment (FastQC, MultiQC); Quality control (QC) criteria and preprocessing (e.g., trimming, filtering).

Unit 2

(Lectures 5)

Read Mapping Algorithms: Key algorithms for read alignment (e.g., BWA, Bowtie2); Challenges in read mapping (e.g., mismatches, gaps, and repetitive sequences).

Unit 3

(Lectures 5)

Variant Detection and CNV Analysis: Methods for identifying SNPs and small insertions/deletions (GATK, bcf tools); Tools for Copy Number Variation (CNV) analysis (e.g., CNVkit); Interpretation of VCF files.

Unit 4

(Lectures 5)

RNA Sequencing (RNA-seq): Experimental design and data processing for RNA-seq; Transcript quantification methods (e.g., featureCounts, StringTie); Quality assessment for RNA-seq data. Differential Expression Analysis: Statistical methods for differential expression (DESeq2, edgeR), Multiple hypothesis testing corrections (e.g., Bonferroni, Benjamini-Hochberg FDR)

Unit 6

(Lectures 5)

Gene Ontology (GO) and Pathway Enrichment Analysis: Basics of GO terms and pathway databases (KEGG, Reactome); Functional enrichment tools (e.g., DAVID, GSEA).

Unit 7

(Lectures 3)

Genome Assembly Algorithms: De novo assembly vs. reference-guided assembly, Assembly tools and methods (SPAdes, Velvet, Canu), Evaluation of assembly quality (e.g., QUAST).

Unit 8

(Lectures 2)

Application of NGS in Epigenomic Studies: Methods for studying epigenomic modifications (e.g., ChIP-seq, ATAC-seq), Processing and analysis of epigenomic data, Role of epigenomics in understanding gene regulation and disease.

Introduction

Pre-requisites: Understanding the basic tools and techniques for management and processing of big genomic data set

Total number of classes: 30

Objectives and Outcomes

Course Outcome

CO1 Students will develop a comprehensive understanding of the data formats and quality control on various bioinformatics tools used.

CO2 Students will develop an understanding about read map algorithms and the challenges associated with it while using these programs.

CO3 Students will gain an understanding about variant detection and CNV Analysis and understand about mutations due to various abnormalities in the DNA.

CO4: Students will demonstrate competence in experimental design and data processing for RNA sequencing..

CO5: Students will develop an understanding on gene ontology pathways and genome assembly algorithms analysis and how NGS is applied for epigenetic studies. 

 

Programme Outcomes (PO) (As given by NBA and ABET)

PO1: Bioscience Knowledge

PO2: Problem Analysis

PO3: Design/Development of Solutions

PO4: Conduct Investigations of complex problems

PO5: Modern tools usage

PO6: Bioscientist and Society

PO7: Environment and Sustainability

PO8: Ethics

PO9: Individual & Team work

PO10: Communication

PO11: Project management & Finance

PO12: Lifelong learning

CO–PO Mapping Table:

COs

PO1

PO2

PO3

PO4

PO5

PO6

PO7

PO8

PO9

PO10

PO11

PO12

CO1

3

2

–

2

3

–

–

–

–

–

–

2

CO2

3

3

2

3

3

–

–

–

–

–

–

2

CO3

3

3

2

3

3

–

–

–

–

–

–

2

CO4

3

3

3

3

3

–

–

–

–

2

2

2

CO5

3

3

2

3

3

–

–

–

–

2

–

2

Program Specific Outcomes (PSO):

PSO1. Apply fundamental molecular biology principles to interpret clinical genomic data.

PSO2. Use molecular techniques (e.g., PCR, RT-PCR, sequencing) to detect genetic mutations and biomarkers.

PSO3. Analyze genotype-phenotype correlations in inherited and acquired disorders.

PSO4. Identify pathogenic variants from NGS data and interpret their clinical relevance.

PSO5. Correlate molecular pathways with disease mechanisms and therapeutic targets.

PSO6. Develop and validate diagnostic assays based on molecular biology principles.

PSO7. Utilize molecular biology to support pharmacogenomic profiling and therapy optimization.

PSO8. Integrate multi-omic data (genomic, transcriptomic, epigenomic) for personalized health solutions.

PSO9. Apply molecular knowledge to cancer genomics, infectious diseases, and rare genetic disorders.

PSO10. Translate molecular discoveries into clinical interventions through evidence-based practice.

CO–PSO Mapping Table:

COs

PSO1

PSO2

PSO3

PSO4

PSO5

PSO6

PSO7

PSO8

PSO9

PSO10

CO1

3

–

–

3

–

–

–

–

–

–

CO2

3

–

2

3

–

–

–

–

–

–

CO3

3

–

3

3

3

–

–

–

2

–

CO4

3

–

2

3

3

–

–

2

2

–

CO5

3

–

2

3

3

–

–

3

3

2

 

Evaluation Pattern

Evaluation Pattern: 50+50 = 100

Internal Assessment – 50% 
Periodical 1  Exam  20% 
Periodical 2  Exam  20% 
Continuous Assessment  Assignment/Test/Quiz 10% 
  50%
End Semester Examination- 50% 
Theory Exam  50%   
  50%
Total 100%

 

Textbooks/ References

  1. High-Throughput Next Generation Sequencing, Methods and Applications. (Springer). Editors: Kwon, Young Min, Ricke, Steven C. (Eds.)
  2. Next Generation Sequencing, Methods and Protocols, 2018, Volume 1712, Steven R. Head, Phillip Ordoukhanian, Daniel R. Salomon (Eds), Humana Press. ISBN : 978-1-4939-7512-9
  3. Next Generation Sequencing and Data Analysis 2021, Melanie Kappelmann-Fenzl, Springer.ISBN : 978-3-030-62489-7.

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