Course

NGS Data Quality Check

• Practical session on assessing NGS data quality
• Trimming and filtering low-quality reads using tools (e.g., Trimmomatic, Cutadapt)

Read Mapping and SAM/BAM Files

• Mapping reads to reference genomes using BWA/Bowtie2
• Introduction to SAM/BAM file formats and data extraction (using SAMtools)

RNA-seq Data Processing and Differential Expression Analysis

• Processing RNA-seq data with tools like STAR/Hisat2 for alignment
• Performing differential expression analysis using DESeq2/edgeR
• GO and pathway enrichment analysis of differentially expressed genes

• Clinical genome interpretation (variant pathogenicity, ACMG guidelines)
• Use of clinical genomics tools (e.g., IGV, VarSome, Alamut)
• Design of genetic testing panels
• Laboratory techniques: PCR, MLPA, NGS, etc.
• Data handling, pipelines, and reporting

Pre-requisites: Basic understanding of Sequence data management

Total number of classes: 30

Course Outcome

CO1: Evaluate, filter, and trim NGS data.

CO2: Map reads to genomes using BWA/Bowtie2; understand SAM/BAM files.

CO3: Process RNA-seq data, perform differential expression analysis (DESeq2/edgeR), and conduct GO/pathway enrichment.

CO4: Assess variant pathogenicity, apply ACMG guidelines, and use clinical genomics tools (IGV, VarSome, Alamut).

CO5: Design genetic tests, understand lab techniques (PCR, MLPA, NGS), and manage/report genomics data.

*Program 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:

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:

Evaluation Pattern: 30+70 = 100

Textbook

1. Bioinformatics: Sequence and Genome Analysis by David W. Mount
2. Bioinformatics for High Throughput Sequencing by Naiara Rodríguez-Ezpeleta, Michael Hackenberg, Ana M. Aransay.

Reference

1. Next Generation Sequencing Methods and Protocols, Steven R. Head, Phillip Ordoukhanian, Daniel R. Salomon.