Module I: Reference-Based RNASeq

Introduction to RNA Seq
1. Necessary Tools installation
2. Learn how Data Retrieval is done
3. Quality Check of reads
4. Trimming and cleaning of data
5. Understanding mapping of reads on reference genome & File formats (SAM, BAM)
6. Visualization techniques
7. Gene Expression Quantification & Analysation
8. Pathway & Gene ontology enrichment analysis
9. Pathway Network analysis
10. R programming Basics
11. Learn Different plots (e.g. Heatmap, volcano plot etc)

Module II: Denovo-Based

1. RNA Sequencing
2. Generation of transcriptomic assembly
3. Statistical study of assembly
4. Mapping and abundance calculation
5. Visualization of mapped reads
6. Generate the count matrices for differential expression analysis

Module III: scRNA Seq

1. Introduction to Single cell RNA Sequencing
2. Data Retrieval (Cell-Ranger and SRA)
3. Data preprocessing and quality check
4. Processing of filtered data (normalization, scaling, dimension reduction)
5. Clustering
6. Identification of Differentially expressed features
7. Cluster annotation and Gene Annotation
8. Data visualization (Violon plot, Dot plot, Dim Plot)

Module IV: mi-RNASeq

1.  Data Downloading (NCBI SRA/EBI SRA)
2.  Quality control using Fastqc
3.  Trimming (cutadapt/Fastp/Trimmomatic)
4. Mapping of Reads to Reference genome using mapper.pl
5.  Generation of Known, novel and abundance miRNA reports using MirDeep2
6. Differential expression of genes using EdgeR/DESeq2 miRNA Target prediction using miRDB
7.   Annotation of Target gene Using DAVID/Uniprot
8.  Functional and Pathway Enrichment Analysis using Panther/ShinyGO miRNA-mRNA network using Cytoscape