Bioinformatics Procedure for NGS Data Analysis

A step-by-step Bioinformatics procedure for Next-Generation Sequencing (NGS) are,

1. Data quality control using tools like FastQC to assess raw data.
2. Data preprocessing for adapter trimming and low-quality base removal with tools like Trimmomatic or FastP.
3. Read mapping to a reference genome using aligners such as BWA or Bowtie2.
4. Post-alignment processing including duplicate removal with Picard and variant calling with GATK or Samtools.
5. Downstream analysis and visualization for specific applications like differential gene expression or variant interpretation using tools like R packages or IGV.

A more detailed breakdown of those were given below

1. Data Quality Control (QC)

Purpose: To check the quality of the raw sequencing reads and identify any potential issues.

Tools:

• FastQC: A widely used tool to generate quality control reports for raw sequencing data.

Output: A report summarizing metrics like Phred scores, adapter contamination, and sequence quality across all bases.

2. Data Preprocessing

Purpose: To remove low-quality bases and adapter sequences from the raw reads, which can interfere with subsequent analysis.

Tools:

• Trimmomatic: A versatile tool for trimming adapter sequences and removing low-quality bases.
• FastP: An all-in-one tool that performs quality trimming and adapter removal efficiently.

Output: Cleaned FASTQ files, ready for alignment.

3. Read Mapping (Alignment)

Purpose: To align the preprocessed reads to a known reference genome or transcriptome.

Tools:

• BWA (Burrows-Wheeler Aligner): A popular tool for aligning short reads to a reference genome.
• Bowtie2: Another high-performance short-read aligner, often used in read mapping.

Output: A SAM or BAM file containing the aligned reads and their positions on the reference.

4. Post-Alignment Processing

Purpose: To refine the alignment data, identify variants, and prepare it for downstream analysis.

Tools:

• Picard Tools: A suite of tools used for manipulating SAM/BAM files, including duplicate removal.
• Samtools: A comprehensive suite for working with sequence alignment files (SAM/BAM).
• GATK (Genome Analysis Toolkit): A popular toolkit for identifying genetic variations (SNPs, insertions, deletions).

Output: Processed BAM files and variant calls (e.g., in VCF format).

5. Downstream Analysis & Interpretation

Purpose: To perform analyses specific to the research question, such as variant interpretation, differential gene expression, or metagenomic analysis.

Tools:

• R packages (e.g., DESeq2, edgeR): For differential gene expression analysis (RNA-Seq).
• IGV (Integrative Genomics Viewer): For visualizing sequence alignments and variants.
• Custom scripts and bioinformatics platforms: To perform specialized analyses and integrate results.

Output: Biologically relevant findings, such as identified disease-causing variants, gene expression patterns, or community composition.