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NGS Analysis Program

The NGS Analysis program is designed to provide students with a comprehensive understanding of next-generation sequencing analysis. The program covers various topics, including:

• Next-Generation Sequencing Analysis Resources
• Pre-Requisites, such as Intro to R and Introduction to Linux
• NGS Sequencing Technology and File Formats, including FastA, FastQ, and SAM/BAM/CRAM formats
• Alignment, including Trimming with Trimmomatic
• Visualization
• Variant Calling, including Pre-Processing and Variant Discovery
• RNA-seq Analysis, including Aligning RNA-seq data and Introduction to R
• HPC, including Resources for editing files on the HPC and SLURM
• ChipSeq analysis
• De novo genome assembly
• Single cell RNA sequencing
• Metagenomics
• Deep Learning using Keras

Program Structure

The program is structured into several modules, each covering a specific topic in NGS analysis. The modules include:

1. Introduction to NGS Analysis

This module introduces students to the basics of NGS analysis, including the different types of sequencing technologies and file formats.

2. Pre-Requisites

This module covers the pre-requisites for the program, including Intro to R and Introduction to Linux.

3. NGS Sequencing Technology and File Formats

This module delves into the details of NGS sequencing technology and file formats, including FastA, FastQ, and SAM/BAM/CRAM formats.

4. Alignment

This module covers the alignment of sequencing data, including Trimming with Trimmomatic.

5. Visualization

This module introduces students to the visualization of sequencing data.

6. Variant Calling

This module covers the variant calling process, including Pre-Processing and Variant Discovery.

7. RNA-seq Analysis

This module covers the analysis of RNA-seq data, including Aligning RNA-seq data and Introduction to R.

8. HPC

This module introduces students to the use of High-Performance Computing (HPC) in NGS analysis, including Resources for editing files on the HPC and SLURM.

9. ChipSeq Analysis

This module covers the analysis of ChipSeq data.

10. De Novo Genome Assembly

This module introduces students to the de novo genome assembly process.

11. Single Cell RNA Sequencing

This module covers the analysis of single cell RNA sequencing data.

12. Metagenomics

This module introduces students to the field of metagenomics.

13. Deep Learning using Keras

This module covers the use of deep learning in NGS analysis using Keras.

Variant Discovery

The variant discovery process involves several steps, including:

1. Call Variants: This step uses the GATK HaplotypeCaller to perform variant calling.
2. Filter Variants: This step applies filters to the raw variant calls to reduce false positives.
3. Annotation: This step uses the program SnpEff to annotate and predict the effects of variants on genes.
4. Visualization: This step uses the Integrative Genomics Viewer (IGV) to visualize the variant calls.

Annotation

The annotation step uses the program SnpEff to annotate and predict the effects of variants on genes. This involves:

• Locating and downloading the SnpEff database: This step involves locating and downloading the SnpEff database for the organism of interest.
• Running SnpEff: This step involves running SnpEff on the filtered variant calls to produce an annotated VCF file.

Visualization

The visualization step uses the Integrative Genomics Viewer (IGV) to visualize the variant calls. This involves:

• Loading the genome: This step involves loading the genome of interest into IGV.
• Loading the variant calls: This step involves loading the variant calls into IGV.
• Visualizing the variant calls: This step involves visualizing the variant calls in IGV.