🧠 NGS Fundamentals

Q: What is Next-Generation Sequencing (NGS)?

Next-Generation Sequencing (NGS) refers to high-throughput DNA and RNA sequencing technologies that massively parallelize the sequencing process, producing millions to billions of reads simultaneously. NGS has revolutionized genomics by making sequencing fast, affordable, and scalable compared to Sanger sequencing.

Q: What is the difference between short-read and long-read sequencing?

Short-read sequencing (Illumina) produces reads of 50-300 bp with very high accuracy and throughput, ideal for variant calling, RNA-Seq, and SNP genotyping. Long-read sequencing (PacBio, Oxford Nanopore) produces reads of 10-100 kb, which are essential for resolving repeats, structural variants, full-length isoforms, and complete genome assembly.

Q: Which sequencing platform should I use?

Use Illumina for high-accuracy short reads (variant calling, RNA-Seq, ChIP-Seq). Use PacBio HiFi for highly accurate long reads (genome assembly, SV detection, full-length isoforms). Use Oxford Nanopore for ultra-long reads, portable sequencing, real-time analysis, and direct DNA/RNA methylation detection.

Q: What is a FASTQ file?

FASTQ is the standard text format for storing raw sequencing reads and their quality scores. Each read is represented by four lines: a header (starting with @), the sequence, a separator (+), and the quality string (Phred-scaled ASCII characters). It's the starting point for nearly all NGS analysis pipelines.

🎯 What is Next-Generation Sequencing?

Next-Generation Sequencing (NGS) refers to high-throughput DNA and RNA sequencing technologies that massively parallelize the sequencing process, producing millions to billions of reads simultaneously. NGS has revolutionized genomics by making sequencing fast, affordable, and scalable compared to Sanger sequencing. It's the foundation of modern genomics, transcriptomics, and epigenomics.

Why NGS Matters

Genomes: Complete sequences. Transcriptomes: Expression. Epigenomes: Regulation. Variants: Disease. Microbiomes: Communities. Clinical: Diagnostics. Single-cell: Heterogeneity.

📌 Massively Parallel📌 High Throughput📌 Cost-Effective📌 Scalable

# Next-Generation Sequencing — the big picture
# High-throughput DNA and RNA sequencing.

# CONCEPTUAL OVERVIEW
print("""
=== Next-Generation Sequencing (NGS) ===

What it is:
  - Massively parallel sequencing
  - Millions to billions of reads
  - High throughput
  - Cost-effective
  - Scalable

Why it matters:
  - Complete genomes
  - Transcriptomes
  - Epigenomes
  - Variant discovery
  - Microbiomes
  - Clinical diagnostics
  - Single-cell

Evolution:
  1977: Sanger sequencing
  2005: 454 (first NGS)
  2006: Illumina/Solexa
  2010: PacBio
  2014: Nanopore
  2019: PacBio HiFi
  2020s: Single-cell, spatial

Platforms:
  - Illumina (short reads)
  - PacBio (long reads)
  - Oxford Nanopore (long reads)
  - Ion Torrent (semiconductor)
  - MGI (short reads)

Read types:
  - Short: 50-300 bp
  - Long: 10-100 kb
  - Ultra-long: > 100 kb

Workflow:
  1. Sample collection
  2. DNA/RNA extraction
  3. Library preparation
  4. Sequencing
  5. QC (FastQC)
  6. Trimming (fastp)
  7. Alignment (BWA)
  8. Downstream analysis
  9. Visualization
  10. Interpretation

Applications:
  - Whole genome sequencing
  - Whole exome sequencing
  - RNA-Seq
  - ChIP-Seq
  - ATAC-Seq
  - Metagenomics
  - Single-cell
""")

# WHY IT MATTERS
print("=== Why It Matters ===")
print("""
Biological questions:
  - What is the genome sequence?
  - What genes are expressed?
  - What variants exist?
  - How is chromatin organized?
  - What microbes are present?

NGS answers:
  - Genome sequences
  - Expression levels
  - Variant calls
  - Chromatin maps
  - Microbial communities
""")

# HISTORICAL CONTEXT
print("=== Historical Context ===")
print("""
1977: Sanger sequencing
2001: Human Genome Project (draft)
2005: 454 (first NGS)
2006: Illumina/Solexa
2007: SOLiD
2010: PacBio
2011: Ion Torrent
2014: Oxford Nanopore
2019: PacBio HiFi (Sequel II)
2020s: Single-cell, spatial, complete genomes
""")

# CHALLENGES
print("=== Challenges ===")
print("""
1. DATA VOLUME
   - Terabytes per run
   - Storage
   - Transfer

2. COMPUTATIONAL
   - Alignment
   - Assembly
   - Variant calling
   - Memory

3. ERRORS
   - Sequencing errors
   - PCR artifacts
   - Mapping errors

4. REPEATS
   - Short reads
   - Long reads
   - Assembly

5. INTERPRETATION
   - Variants
   - Expression
   - Function
   - Clinical
""")

🎯 Why NGS Matters

🎯 The Value of NGS

NGS has transformed biology and medicine, enabling genome-scale analysis at unprecedented speed and cost.

Key Applications

Genomes: Complete. Exomes: Coding. RNA: Expression. ChIP: Regulation. Metagenome: Communities. Clinical: Diagnostics.

🎯 Genomics🎯 Transcriptomics🎯 Epigenomics🎯 Clinical

# Why NGS Matters
print("=== 1. Whole Genome Sequencing (WGS) ===")
print("""
WGS:
  - Complete genome
  - All variants
  - Structural variants

Workflow:
  1. DNA extraction
  2. Library prep
  3. Sequencing
  4. Alignment
  5. Variant calling
  6. Annotation

Coverage:
  - 30x for germline
  - 100x for somatic

Examples:
  - Human genome
  - Pathogens
  - Plants
  - Animals

Tools:
  - BWA
  - GATK
  - VEP
""")

print("\n=== 2. Whole Exome Sequencing (WES) ===")
print("""
WES:
  - Protein-coding regions
  - ~1-2% of genome
  - Cost-effective

Workflow:
  1. Exome capture
  2. Sequencing
  3. Alignment
  4. Variant calling

Coverage:
  - 50-100x

Examples:
  - Rare disease
  - Cancer
  - Clinical

Tools:
  - GATK
  - VEP
  - ClinVar
""")

print("\n=== 3. RNA-Seq ===")
print("""
RNA-Seq:
  - Gene expression
  - Splicing
  - Fusion genes

Workflow:
  1. RNA extraction
  2. Library prep
  3. Sequencing
  4. Alignment
  5. Quantification
  6. DE analysis

Examples:
  - Disease vs normal
  - Drug response
  - Development
  - Single-cell

Tools:
  - STAR
  - Salmon
  - DESeq2
""")

print("\n=== 4. ChIP-Seq ===")
print("""
ChIP-Seq:
  - Protein-DNA binding
  - Histone modifications
  - Regulatory elements

Workflow:
  1. ChIP
  2. Library prep
  3. Sequencing
  4. Peak calling
  5. Annotation

Examples:
  - TF binding
  - H3K27ac
  - Enhancers
  - Disease

Tools:
  - Bowtie2
  - MACS2
  - HOMER
""")

print("\n=== 5. ATAC-Seq ===")
print("""
ATAC-Seq:
  - Open chromatin
  - Regulatory elements
  - TF footprinting

Workflow:
  1. Tn5 transposition
  2. Library prep
  3. Sequencing
  4. Peak calling
  5. Motif analysis

Examples:
  - Cell types
  - Development
  - Cancer

Tools:
  - Bowtie2
  - MACS2
  - HOMER
""")

print("\n=== 6. Metagenomics ===")
print("""
Metagenomics:
  - Microbial communities
  - Unculturable
  - Functional potential

Workflow:
  1. Sample
  2. DNA extraction
  3. Sequencing
  4. Assembly
  5. Binning
  6. Annotation

Examples:
  - Gut microbiome
  - Soil
  - Ocean
  - Clinical infections

Tools:
  - SPAdes
  - MEGAHIT
  - MetaBAT
""")

print("\n=== 7. Single-Cell ===")
print("""
Single-cell:
  - Per-cell expression
  - Heterogeneity
  - Rare cells

Workflow:
  1. Cell isolation
  2. 10x Genomics
  3. Sequencing
  4. Cell Ranger
  5. Seurat/Scanpy

Examples:
  - Immune atlas
  - Brain atlas
  - Tumor
  - Development

Tools:
  - Cell Ranger
  - Seurat
  - Scanpy
""")

print("\n=== 8. Clinical Diagnostics ===")
print("""
Clinical:
  - Rare disease
  - Cancer
  - Pharmacogenomics
  - Prenatal

Workflow:
  1. Patient sample
  2. Sequencing
  3. Variant calling
  4. Annotation
  5. Report

Examples:
  - BRCA1/2
  - CFTR
  - CYP2D6
  - HLA

Tools:
  - GATK
  - VEP
  - ClinVar
""")

📜 History & Evolution

🎯 From Sanger to Single-Cell

Sequencing has evolved dramatically from Sanger's chain termination to modern high-throughput platforms.

History

1977: Sanger. 2005: 454. 2006: Illumina. 2010: PacBio. 2014: Nanopore. 2019: HiFi. 2020s: Complete genomes.

📜 Sanger📜 454📜 Illumina📜 Nanopore

# History & Evolution
print("=== Generations of Sequencing ===")
print(""
First generation (1977):
  - Sanger
  - Capillary
  - 1 read at a time
  - 1000 bp
  - Accurate

Second generation (2005-):
  - 454, Illumina, SOLiD
  - Massively parallel
  - Short reads (50-300 bp)
  - High throughput
  - Low cost

Third generation (2010-):
  - PacBio, Nanopore
  - Single molecule
  - Long reads (10-100 kb)
  - Real-time
  - Portable

Fourth generation (2020s):
  - Single-cell
  - Spatial
  - Multi-omics
  - Complete genomes
""")

print("\n=== Timeline ===")
print(""
1977: Sanger sequencing
1986: First automated sequencer
1990: Human Genome Project starts
1995: First bacterial genome
2001: Human genome draft
2003: Human genome complete
2005: 454 (first NGS)
2006: Illumina/Solexa
2007: SOLiD
2008: First NGS human genome
2010: PacBio
2011: Ion Torrent
2012: Oxford Nanopore
2014: Nanopore MinION
2019: PacBio HiFi
2022: Telomere-to-telomere human genome
2024: 200K+ PDB structures
""")

print("\n=== Cost of Sequencing ===")
print(""
Cost per genome:
  2001: $100M
  2008: $10M
  2010: $100K
  2015: $1K
  2020: $500
  2024: $200

Moore's law:
  - Faster than Moore's law
  - Exponential decrease
  - Democratization
""")

print("\n=== Key Milestones ===")
print(""
1. Sanger (1977):
   - First method
   - Still used
   - Accurate

2. 454 (2005):
   - First NGS
   - Pyrosequencing
   - Discontinued

3. Illumina (2006):
   - Dominant
   - Short reads
   - High throughput

4. PacBio (2010):
   - Single molecule
   - Long reads
   - Real-time

5. Nanopore (2014):
   - Portable
   - Ultra-long
   - Real-time

6. HiFi (2019):
   - Highly accurate
   - Long reads
   - Complete genomes
""")

print("\n=== Human Genome Project ===")
print(""
HGP:
  - 1990-2003
  - $2.7 billion
  - Sanger
  - Draft 2001
  - Complete 2003

T2T:
  - 2019-2022
  - Complete
  - No gaps
  - Telomeres
  - Centromeres

Impact:
  - Reference
  - Medicine
  - Biology
""")

print("\n=== Current Landscape ===")
print(""
Short reads:
  - Illumina (dominant)
  - MGI (China)
  - Ion Torrent

Long reads:
  - PacBio (HiFi)
  - Oxford Nanopore

Single-cell:
  - 10x Genomics
  - Smart-seq

Spatial:
  - 10x Visium
  - MERFISH
  - Slide-seq

Applications:
  - Clinical
  - Research
  - Agriculture
  - Environmental
""")

🖥️ Sequencing Platforms

🎯 The Major Platforms

Several sequencing platforms dominate the market, each with its own strengths and weaknesses.

Platforms

Illumina: Short reads. PacBio: Long reads. Nanopore: Ultra-long. Ion Torrent: Semiconductor. MGI: Short reads. Sanger: Classic.

🖥️ Illumina🖥️ PacBio🖥️ Nanopore🖥️ MGI

# Sequencing Platforms
print("=== Illumina ===")
print(""
Illumina:
  - Dominant
  - Short reads
  - Sequencing by synthesis

Platforms:
  - NovaSeq X
  - NovaSeq 6000
  - NextSeq
  - MiSeq
  - iSeq

Read length:
  - 50-300 bp

Throughput:
  - Up to 20B reads
  - 6 Tb per run

Accuracy:
  - Q30 > 90%
  - Q40 available

Applications:
  - WGS
  - WES
  - RNA-Seq
  - ChIP-Seq
  - Single-cell
""")

print("\n=== PacBio ===")
print(""
PacBio:
  - Long reads
  - Single molecule
  - SMRT sequencing

Platforms:
  - Sequel II
  - Sequel IIe
  - Revio

Read types:
  - CLR (Continuous Long Read)
  - HiFi (High Fidelity)

Read length:
  - CLR: 10-60 kb
  - HiFi: 10-25 kb

Accuracy:
  - CLR: ~85-90%
  - HiFi: > 99.9%

Applications:
  - Genome assembly
  - SV detection
  - Full-length isoforms
  - Methylation
  - Metagenomics
""")

print("\n=== Oxford Nanopore ===")
print(""
Oxford Nanopore:
  - Ultra-long reads
  - Portable
  - Real-time

Platforms:
  - MinION
  - GridION
  - PromethION
  - Flongle

Read length:
  - 10-100 kb
  - Up to 2 Mb

Accuracy:
  - Q20+ (R10.4)
  - Improving

Applications:
  - Genome assembly
  - SV detection
  - Real-time surveillance
  - Portable field work
  - Direct methylation
""")

print("\n=== Ion Torrent ===")
print(""
Ion Torrent:
  - Semiconductor
  - Short reads
  - Fast

Platforms:
  - Ion GeneStudio
  - Ion S5

Read length:
  - 200-600 bp

Accuracy:
  - Q20

Applications:
  - Targeted
  - Clinical
  - Microbial
""")

print("\n=== MGI ===")
print(""
MGI (BGI):
  - Short reads
  - China
  - Competitive

Platforms:
  - DNBSEQ-T7
  - DNBSEQ-G400
  - DNBSEQ-G50

Read length:
  - 50-300 bp

Throughput:
  - High

Applications:
  - WGS
  - WES
  - RNA-Seq
""")

print("\n=== Sanger ===")
print(""
Sanger:
  - Classic
  - Capillary
  - Accurate

Platforms:
  - ABI 3730
  - SeqStudio

Read length:
  - 500-1000 bp

Accuracy:
  - > 99.99%

Applications:
  - Validation
  - Small targets
  - Clinical
""")

print("\n=== Platform Comparison ===")
print(f"{'Platform':<20} {'Read':<15} {'Accuracy':<15} {'Use':<20}")
print("-" * 70)
print(f"{'Illumina':<20} {'50-300 bp':<15} {'Q30-Q40':<15} {'WGS, RNA':<20}")
print(f"{'PacBio HiFi':<20} {'10-25 kb':<15} {'Q30+':<15} {'Assembly, SV':<20}")
print(f"{'Nanopore':<20} {'10-100 kb':<15} {'Q20+':<15} {'Real-time':<20}")
print(f"{'Ion Torrent':<20} {'200-600 bp':<15} {'Q20':<15} {'Targeted':<20}")
print(f"{'MGI':<20} {'50-300 bp':<15} {'Q30':<15} {'WGS':<20}")
print(f"{'Sanger':<20} {'500-1000 bp':<15} {'Q40+':<15} {'Validation':<20}")

🔵 Illumina (Short Reads)

🎯 The Dominant Platform

Illumina is the dominant short-read sequencing platform, known for high accuracy and throughput.

Illumina

SBS: Sequencing by synthesis. Clusters: Bridge amplification. Reversible: Terminators. Short: 50-300 bp. Accurate: Q30+.

🔵 SBS🔵 Clusters🔵 Reversible terminators🔵 Short reads

# Illumina (Short Reads)
print("=== Sequencing by Synthesis ===")
print(""
Steps:
  1. Library prep:
     - Fragmentation
     - Adapters
     - Amplification

  2. Cluster generation:
     - Bridge amplification
     - Clonal clusters

  3. Sequencing:
     - Reversible terminators
     - Fluorescent nucleotides
     - Camera capture

  4. Base calling:
     - Images → bases
     - Quality scores

  5. Output:
     - FASTQ files
""")

print("\n=== Cluster Generation ===")
print(""
Bridge amplification:
  - Flow cell
  - Oligos on surface
  - DNA binds
  - Bridge forms
  - Amplification
  - Clusters

Clusters:
  - ~1000 copies
  - Clonal
  - Detectable signal
""")

print("\n=== Sequencing by Synthesis ===")
print(""
SBS:
  1. Add nucleotides:
     - Reversible terminators
     - Fluorescent labels

  2. Incorporate:
     - One base per cycle
     - Complementary

  3. Image:
     - Camera
     - Fluorescence

  4. Cleave:
     - Terminator
     - Fluorophore

  5. Repeat:
     - Up to 300 cycles
""")

print("\n=== Read Types ===")
print(""
Single-end:
  - One read per fragment
  - Cheaper
  - Less info

Paired-end:
  - Two reads per fragment
  - Known distance
  - Better alignment
  - SV detection

Read length:
  - 50 bp: miRNA
  - 75 bp: ChIP-Seq
  - 100 bp: RNA-Seq
  - 150 bp: WGS
  - 250-300 bp: 16S, amplicon
""")

print("\n=== Instruments ===")
print(""
1. iSeq 100:
   - Small
   - 1.2 Gb
   - 150 bp

2. MiniSeq:
   - Small
   - 7.5 Gb
   - 150 bp

3. MiSeq:
   - Benchtop
   - 15 Gb
   - 300 bp
   - Clinical

4. NextSeq:
   - Benchtop
   - 120 Gb
   - 150 bp

5. NovaSeq 6000:
   - High throughput
   - 6 Tb
   - 250 bp

6. NovaSeq X:
   - Ultra high
   - 16 Tb
   - 150 bp
""")

print("\n=== Advantages & Disadvantages ===")
print(""
ADVANTAGES:
  ✓ High accuracy
  ✓ High throughput
  ✓ Low cost per base
  ✓ Mature ecosystem
  ✓ Many applications
  ✓ Paired-end

DISADVANTAGES:
  ✗ Short reads
  ✗ Repeats
  ✗ SVs
  ✗ PCR bias
  ✗ GC bias
""")

print("\n=== Applications ===")
print(""
Illumina:
  - WGS
  - WES
  - RNA-Seq
  - ChIP-Seq
  - ATAC-Seq
  - Single-cell
  - Metagenomics
  - Targeted panels
  - Clinical
""")

🟣 Long-Read (PacBio, Nanopore)

🎯 Resolving Complexity

Long-read sequencing (PacBio, Nanopore) produces reads of 10-100 kb, essential for resolving repeats, structural variants, and complete genomes.

Long Reads

PacBio: SMRT. Nanopore: Current. Length: 10-100 kb. HiFi: Accurate. Real-time: Nanopore. Assembly: Complete.

🟣 PacBio🟣 Nanopore🟣 Ultra-long🟣 HiFi

# Long-Read Sequencing
print("=== PacBio SMRT ===")
print(""
PacBio:
  - Single Molecule Real-Time
  - Zero-mode waveguide
  - Fluorescent nucleotides
  - Real-time

Steps:
  1. DNA → SMRTbell
  2. Diffusion into ZMW
  3. Polymerase
  4. Fluorescence
  5. Base calling

Read types:
  - CLR: ~85-90% accuracy
  - HiFi: > 99.9% accuracy

Platforms:
  - Sequel II
  - Sequel IIe
  - Revio
""")

print("\n=== PacBio HiFi ===")
print(""
HiFi:
  - Highly accurate
  - Long reads
  - Circular consensus

Steps:
  1. Circular template
  2. Multiple passes
  3. Consensus
  4. HiFi read

Read length:
  - 10-25 kb

Accuracy:
  - > 99.9% (Q30+)

Applications:
  - Genome assembly
  - SV detection
  - Full-length isoforms
  - Methylation
  - Metagenomics
""")

print("\n=== Oxford Nanopore ===")
print(""
Nanopore:
  - Current disruption
  - Protein pore
  - Real-time

Steps:
  1. DNA in solution
  2. Protein pore
  3. Current changes
  4. Base calling
  5. Real-time output

Read length:
  - 10-100 kb
  - Up to 2 Mb

Accuracy:
  - Q20+ (R10.4)
  - Improving

Platforms:
  - MinION
  - GridION
  - PromethION
  - Flongle
""")

print("\n=== Advantages of Long Reads ===")
print(""
Advantages:
  ✓ Resolve repeats
  ✓ Detect SVs
  ✓ Full-length isoforms
  ✓ Phase variants
  ✓ Complete genomes
  ✓ Methylation
  ✓ Real-time
  ✓ Portable

Disadvantages:
  ✗ Higher cost
  ✗ Lower throughput
  ✗ Higher error (CLR)
  ✗ More DNA needed
""")

print("\n=== Applications ===")
print(""
Long reads:
  - Genome assembly
  - Structural variants
  - Full-length RNA
  - Methylation
  - Metagenomics
  - Clinical
  - Outbreak surveillance
  - T2T genomes
""")

print("\n=== Hybrid Assembly ===")
print(""
Hybrid:
  - Long reads (contigs)
  - Short reads (polish)
  - Best of both

Steps:
  1. Long reads → assembly
  2. Short reads → polish
  3. Long reads → polish
  4. Validate

Tools:
  - Unicycler
  - SPAdes (hybrid)
  - MaSuRCA
""")

print("\n=== Comparison ===")
print(f"{'Feature':<20} {'PacBio HiFi':<20} {'Nanopore':<20}")
print("-" * 60)
print(f"{'Read length':<20} {'10-25 kb':<20} {'10-100 kb':<20}")
print(f"{'Accuracy':<20} {'> 99.9%':<20} {'Q20+':<20}")
print(f"{'Throughput':<20} {'High':<20} {'High':<20}")
print(f"{'Portable':<20} {'No':<20} {'Yes':<20}")
print(f"{'Real-time':<20} {'No':<20} {'Yes':<20}")
print(f"{'Methylation':<20} {'Yes':<20} {'Yes':<20}")
print(f"{'Cost':<20} {'High':<20} {'Medium':<20}")

🧪 Library Preparation

🎯 Preparing DNA for Sequencing

Library preparation converts DNA or RNA into a form compatible with the sequencing platform, adding adapters and barcodes.

Library Prep

Fragmentation: Size selection. End repair: Blunt ends. Adapters: Ligation. Amplification: PCR. Barcodes: Multiplexing. QC: Validation.

🧪 Fragmentation🧪 Adapters🧪 Barcodes🧪 PCR

# Library Preparation
print("=== General Steps ===")
print(""
1. DNA fragmentation:
   - Sonication
   - Enzymatic
   - Size selection

2. End repair:
   - Blunt ends
   - 5' phosphorylation
   - 3' adenylation

3. Adapter ligation:
   - Platform-specific
   - Index/barcode
   - Sequencing primer

4. Amplification:
   - PCR
   - Enrichment
   - Optional

5. QC:
   - Bioanalyzer
   - Qubit
   - qPCR
""")

print("\n=== DNA Library Types ===")
print(""
1. WGS:
   - Whole genome
   - 350-500 bp inserts
   - 30x coverage

2. WES:
   - Exome capture
   - 50-100x coverage
   - Protein-coding

3. Targeted:
   - Panel
   - Specific genes
   - Deep coverage

4. Amplicon:
   - PCR products
   - 16S, ITS
   - Microbial

5. ChIP-Seq:
   - Immunoprecipitation
   - Protein-DNA
   - 20-40M reads

6. ATAC-Seq:
   - Tn5 transposase
   - Open chromatin
   - 20-40M reads
""")

print("\n=== RNA Library Types ===")
print(""
1. mRNA-Seq:
   - Poly-A selection
   - Eukaryotic
   - Expression

2. Total RNA:
   - rRNA depletion
   - Prokaryotic
   - Non-coding

3. Small RNA:
   - miRNA
   - Size selection
   - 20-30 nt

4. Single-cell:
   - 10x Genomics
   - Droplet
   - Cell barcodes
""")

print("\n=== Adapters ===")
print(""
Adapters:
  - Platform-specific
  - Sequencing primer
  - Index/barcode
  - Flow cell binding

Illumina:
  - P5, P7
  - Read 1, Read 2
  - Index 1, Index 2

Indexes:
  - i7 (single)
  - i5 + i7 (dual)
  - Unique dual (UDI)
""")

print("\n=== Multiplexing ===")
print(""
Multiplexing:
  - Multiple samples
  - One run
  - Barcodes

Barcodes:
  - 6-8 bp
  - Unique per sample
  - Demultiplexing

Advantages:
  - Cost-effective
  - Higher throughput
  - Less bias

Considerations:
  - Index hopping
  - Balanced
  - Unique dual indexes
""")

print("\n=== QC ===")
print(""
QC:
  - Qubit (concentration)
  - Bioanalyzer (size)
  - qPCR (quantification)
  - TapeStation

Metrics:
  - Concentration
  - Size distribution
  - Adapter dimers
  - Quality
""")

print("\n=== Kits ===")
print(""
Illumina:
  - TruSeq
  - Nextera
  - NovaSeq X

PacBio:
  - SMRTbell
  - Express Template

Nanopore:
  - Ligation
  - Rapid
  - Ultra-long

10x:
  - Chromium
  - Single-cell
""")

print("\n=== Best Practices ===")
print(""
1. High-quality DNA/RNA
2. Appropriate insert size
3. Unique dual indexes
4. QC before sequencing
5. Avoid over-amplification
6. Balance multiplexing
7. Document everything
8. Use kits appropriately
""")

🧹 QC & Trimming

🎯 Clean Before You Analyze

Quality control and trimming are essential first steps in any NGS pipeline. Garbage in, garbage out.

QC

FastQC: Raw reads. MultiQC: Aggregate. fastp: Trim. Trimmomatic: Trim. Adapters: Remove. Quality: Filter.

🧹 FastQC🧹 MultiQC🧹 fastp🧹 Trimmomatic

# QC & Trimming
print("=== FastQC ===")
print(""
FastQC:
  - Per-base quality
  - GC content
  - Adapter contamination
  - Duplication
  - Overrepresented sequences

Command:
  fastqc reads_1.fastq.gz reads_2.fastq.gz -o qc/

Output:
  reads_1_fastqc.html
  reads_1_fastqc.zip

Metrics:
  - Per base sequence quality
  - Per sequence quality
  - Per base GC content
  - Per sequence GC content
  - N content
  - Sequence duplication
  - Overrepresented
""")

print("\n=== MultiQC ===")
print(""
MultiQC:
  - Aggregate all QC
  - One report
  - Compare samples

Command:
  multiqc qc/ -o multiqc_report/

Output:
  multiqc_report.html

Advantages:
  - One report
  - Compare samples
  - Summary
  - Publication
""")

print("\n=== fastp ===")
print(""
fastp:
  - Fast
  - All-in-one
  - Adapter detection
  - Quality filtering

Command:
  fastp \
    -i reads_1.fastq.gz \
    -I reads_2.fastq.gz \
    -o clean_1.fastq.gz \
    -O clean_2.fastq.gz \
    --detect_adapter_for_pe \
    --qualified_quality_phred 20 \
    --length_required 36 \
    --json report.json \
    --html report.html

Key options:
  -q 20 (quality)
  -l 36 (min length)
  -u 40 (max uncalled)
  --cut_front, --cut_tail
""")

print("\n=== Trimmomatic ===")
print(""
Trimmomatic:
  - Java
  - Flexible
  - Popular

Command:
  trimmomatic PE \
    reads_1.fastq.gz reads_2.fastq.gz \
    clean_1.fq.gz unpaired_1.fq.gz \
    clean_2.fq.gz unpaired_2.fq.gz \
    ILLUMINACLIP:adapters.fa:2:30:10 \
    LEADING:3 TRAILING:3 \
    SLIDINGWINDOW:4:20 MINLEN:36

Options:
  - ILLUMINACLIP
  - LEADING
  - TRAILING
  - SLIDINGWINDOW
  - MINLEN
""")

print("\n=== Adapter Trimming ===")
print(""
Adapters:
  - Sequencing adapters
  - Index sequences
  - Contamination

Tools:
  - fastp
  - Trimmomatic
  - cutadapt
  - BBDuk

cutadapt:
  cutadapt -a ADAPTER -o output.fastq input.fastq
""")

print("\n=== Quality Filtering ===")
print(""
Filter:
  - Phred score
  - Length
  - N content
  - Complexity

Phred:
  - Q20: 99%
  - Q30: 99.9%
  - Q40: 99.99%

Filtering:
  - Q < 20: filter
  - Length < 36: filter
  - N > 10%: filter
""")

print("\n=== Long-Read QC ===")
print(""
Long reads:
  - Higher error
  - Different QC

Tools:
  - NanoPlot
  - NanoStat
  - pycoQC
  - LongQC

Metrics:
  - Read length
  - Quality
  - N50
  - Throughput
""")

print("\n=== Best Practices ===")
print(""
1. Always run FastQC
2. Aggregate with MultiQC
3. Trim adapters
4. Filter low quality
5. Check for contamination
6. Remove duplicates (optional)
7. Document everything
8. Keep raw data
""")

📄 File Formats (FASTQ, BAM, VCF)

🎯 The Language of NGS

NGS data flows through standard file formats: FASTQ (raw), BAM/SAM (aligned), VCF (variants), and more.

Formats

FASTQ: Raw reads. FASTA: Sequences. SAM: Alignments. BAM: Binary SAM. CRAM: Compressed. VCF: Variants. BED: Regions.

📄 FASTQ📄 SAM/BAM📄 VCF📄 BED

# File Formats
print("=== FASTQ ===")
print(""
FASTQ:
  - Raw reads
  - Sequence + quality
  - 4 lines per read

Format:
  @read_id
  SEQUENCE
  +
  QUALITY

Example:
  @SRR123456.1
  ACGTACGTACGTACGT
  +
  IIIIIIIIIIIIIIII

Quality:
  - Phred+33
  - ASCII characters
  - Q = ord(char) - 33
""")

print("\n=== FASTA ===")
print(""
FASTA:
  - Sequences
  - No quality
  - Reference

Format:
  >sequence_id
  SEQUENCE

Example:
  >chr1
  ACGTACGTACGTACGT
""")

print("\n=== SAM ===")
print(""
SAM:
  - Sequence Alignment/Map
  - Text
  - Alignments

Header:
  @HD, @SQ, @RG, @PG

Columns:
  QNAME, FLAG, RNAME, POS, MAPQ, CIGAR,
  RNEXT, PNEXT, TLEN, SEQ, QUAL

Example:
  read1  0  chr1  100  60  100M  *  0  0  ACGT...  IIII...
""")

print("\n=== BAM ===")
print(""
BAM:
  - Binary SAM
  - Compressed
  - Indexed

Advantages:
  - Smaller
  - Faster
  - Standard

Tools:
  - samtools
  - pysam
  - IGV

Commands:
  samtools view -b sample.sam > sample.bam
  samtools sort sample.bam -o sample.sorted.bam
  samtools index sample.sorted.bam
""")

print("\n=== CRAM ===")
print(""
CRAM:
  - Compressed BAM
  - Reference-based
  - Smaller

Advantages:
  - 30-60% smaller
  - Reference-based
  - EBI standard

Tools:
  - samtools
  - Scramble

Commands:
  samtools view -C -T reference.fa sample.bam > sample.cram
""")

print("\n=== VCF ===")
print(""
VCF:
  - Variant Call Format
  - Variants
  - Standard

Header:
  ##fileformat=VCFv4.2
  #CHROM POS ID REF ALT QUAL FILTER INFO FORMAT

Columns:
  CHROM, POS, ID, REF, ALT, QUAL, FILTER,
  INFO, FORMAT, SAMPLE

Example:
  chr1  1000  rs123  A  G  50  PASS  DP=30  GT:DP  0/1:30
""")

print("\n=== BED ===")
print(""
BED:
  - Browser Extensible Data
  - Regions
  - 0-based

Columns:
  chrom, start, end, name, score, strand

Example:
  chr1  999  1010  peak1  100  +

Tools:
  - bedtools
  - IGV
  - UCSC
""")

print("\n=== GFF/GTF ===")
print(""
GFF/GTF:
  - Gene annotation
  - Features
  - 1-based

Columns:
  seqid, source, type, start, end,
  score, strand, phase, attributes

Example:
  chr1  ENSEMBL  gene  1000  2000  .  +  .  ID=gene1
""")

print("\n=== Format Comparison ===")
print(f"{'Format':<15} {'Type':<20} {'Use':<20}")
print("-" * 60)
print(f"{'FASTQ':<15} {'Text':<20} {'Raw reads':<20}")
print(f"{'FASTA':<15} {'Text':<20} {'Sequences':<20}")
print(f"{'SAM':<15} {'Text':<20} {'Alignments':<20}")
print(f"{'BAM':<15} {'Binary':<20} {'Alignments':<20}")
print(f"{'CRAM':<15} {'Binary':<20} {'Compressed':<20}")
print(f"{'VCF':<15} {'Text':<20} {'Variants':<20}")
print(f"{'BED':<15} {'Text':<20} {'Regions':<20}")
print(f"{'GFF/GTF':<15} {'Text':<20} {'Annotation':<20}")

⚙️ Analysis Workflow

🎯 From Reads to Insight

The NGS analysis workflow has well-defined steps: QC, alignment/assembly, downstream analysis, and interpretation.

Workflow

QC: FastQC. Trim: fastp. Align: BWA. Sort: samtools. Variant: GATK. Annotate: VEP. Visualize: IGV.

⚙️ QC⚙️ Align⚙️ Variant⚙️ Annotate

# NGS Analysis Workflow
print("=== Complete Pipeline ===")
print(""
1. QUALITY CONTROL
   - FastQC
   - MultiQC

2. TRIMMING
   - fastp
   - Trimmomatic

3. ALIGNMENT
   - BWA-MEM (DNA)
   - STAR (RNA)
   - Bowtie2 (ChIP)
   - minimap2 (long)

4. POST-ALIGNMENT
   - Sort (samtools)
   - Mark duplicates (Picard)
   - Index (samtools)

5. DOWNSTREAM
   - Variant calling (GATK)
   - RNA-Seq (featureCounts)
   - ChIP-Seq (MACS2)
   - Assembly (SPAdes)

6. ANNOTATION
   - VEP
   - SnpEff
   - ANNOVAR

7. VISUALIZATION
   - IGV
   - UCSC
   - Integrative

8. INTERPRETATION
   - Clinical
   - Biological
   - Report
""")

print("\n=== Alignment ===")
print(""
DNA:
  - BWA-MEM
  - Bowtie2
  - minimap2

RNA:
  - STAR
  - HISAT2
  - Salmon (alignment-free)

ChIP:
  - Bowtie2
  - BWA

Long reads:
  - minimap2
  - NGMLR
  - GraphMap

Example:
  bwa mem -t 8 reference.fa reads_1.fq reads_2.fq | \
    samtools sort -@ 8 -o sample.bam
""")

print("\n=== Post-alignment ===")
print(""
1. Sort:
   samtools sort -@ 8 -o sample.sorted.bam sample.bam

2. Mark duplicates:
   picard MarkDuplicates I=sample.sorted.bam \
     O=sample.dedup.bam M=metrics.txt

3. Index:
   samtools index sample.dedup.bam

4. QC:
   samtools flagstat sample.dedup.bam
""")

print("\n=== Variant Calling ===")
print(""
Germline:
  gatk HaplotypeCaller -R ref.fa -I sample.bam \
    -O sample.g.vcf.gz -ERC GVCF

Joint:
  gatk GenomicsDBImport ...
  gatk GenotypeGVCFs ...

Somatic:
  gatk Mutect2 -R ref.fa -I tumor.bam -I normal.bam \
    -O somatic.vcf.gz

Filtering:
  gatk VariantFiltration ...
  gatk ApplyVQSR ...
""")

print("\n=== RNA-Seq ===")
print(""
Alignment:
  STAR --genomeDir index --readFilesIn r1.fq r2.fq \
    --outSAMtype BAM SortedByCoordinate

Quantification:
  featureCounts -T 8 -a genes.gtf -o counts.txt *.bam

DE:
  DESeq2
  edgeR
  limma-voom
""")

print("\n=== Assembly ===")
print(""
Short reads:
  spades.py -1 r1.fq -2 r2.fq -o output

Long reads:
  flye --pacbio-raw reads.fq --out-dir output
  canu -p assembly -d output genomeSize=5m -pacbio reads.fq

Hybrid:
  unicycler -1 short_1.fq -2 short_2.fq -l long.fq -o output
""")

print("\n=== Annotation ===")
print(""
VEP:
  vep -i variants.vcf --cache --assembly GRCh38 \
    --vcf -o annotated.vcf

SnpEff:
  snpEff ann GRCh38.105 variants.vcf > annotated.vcf

ANNOVAR:
  annotate_variation.pl -geneanno -buildver hg38 \
    variants.vcf humandb/
""")

print("\n=== Visualization ===")
print(""
IGV:
  - BAM
  - VCF
  - BED
  - Coverage

UCSC:
  - Custom tracks
  - Public data

Tools:
  - IGV
  - UCSC
  - JBrowse
  - Circos
""")

print("\n=== Best Practices ===")
print(""
1. Document pipeline
2. Version control
3. Reproducible
4. QC at each step
5. Validate
6. Report parameters
7. Share data
8. Cite tools
""")

💼 Applications

🎯 What NGS Can Do

NGS enables a wide range of applications, from whole-genome sequencing to single-cell transcriptomics.

Applications

WGS: Genome. WES: Exome. RNA-Seq: Expression. ChIP-Seq: Regulation. ATAC: Chromatin. Metagenome: Microbes. Single-cell: Cells.

💼 WGS💼 RNA-Seq💼 ChIP-Seq💼 Single-cell

# Applications
print("=== 1. Whole Genome Sequencing ===")
print(""
WGS:
  - Complete genome
  - All variants
  - Structural variants

Workflow:
  1. DNA extraction
  2. Library prep
  3. Sequencing (30x)
  4. Alignment (BWA)
  5. Variant calling (GATK)
  6. Annotation (VEP)

Examples:
  - Human
  - Pathogens
  - Plants
  - Animals

Tools:
  - BWA
  - GATK
  - VEP
""")

print("\n=== 2. Whole Exome Sequencing ===")
print(""
WES:
  - Protein-coding
  - ~1-2% genome
  - Cost-effective

Workflow:
  1. Exome capture
  2. Sequencing (50-100x)
  3. Alignment
  4. Variant calling
  5. Annotation

Examples:
  - Rare disease
  - Cancer
  - Clinical

Tools:
  - GATK
  - VEP
  - ClinVar
""")

print("\n=== 3. RNA-Seq ===")
print(""
RNA-Seq:
  - Gene expression
  - Splicing
  - Fusion genes

Workflow:
  1. RNA extraction
  2. Library prep
  3. Sequencing
  4. Alignment (STAR)
  5. Quantification
  6. DE (DESeq2)

Examples:
  - Disease
  - Drug
  - Development
  - Single-cell

Tools:
  - STAR
  - Salmon
  - DESeq2
""")

print("\n=== 4. ChIP-Seq ===")
print(""
ChIP-Seq:
  - Protein-DNA
  - Histone marks
  - Regulatory

Workflow:
  1. ChIP
  2. Library
  3. Sequencing
  4. Peak calling (MACS2)
  5. Motif (HOMER)

Examples:
  - TF binding
  - H3K27ac
  - Enhancers

Tools:
  - Bowtie2
  - MACS2
  - HOMER
""")

print("\n=== 5. ATAC-Seq ===")
print(""
ATAC-Seq:
  - Open chromatin
  - Regulatory elements
  - TF footprinting

Workflow:
  1. Tn5
  2. Library
  3. Sequencing
  4. Peak calling
  5. Motif

Examples:
  - Cell types
  - Development
  - Cancer

Tools:
  - Bowtie2
  - MACS2
  - HOMER
""")

print("\n=== 6. Metagenomics ===")
print(""
Metagenomics:
  - Microbial communities
  - Unculturable
  - Functional

Workflow:
  1. Sample
  2. DNA
  3. Sequencing
  4. Assembly (SPAdes)
  5. Binning (MetaBAT)
  6. Annotation

Examples:
  - Gut
  - Soil
  - Ocean
  - Clinical

Tools:
  - SPAdes
  - MEGAHIT
  - MetaBAT
""")

print("\n=== 7. Single-Cell ===")
print(""
Single-cell:
  - Per-cell expression
  - Heterogeneity
  - Rare cells

Workflow:
  1. Cells
  2. 10x
  3. Sequencing
  4. Cell Ranger
  5. Seurat/Scanpy

Examples:
  - Immune
  - Brain
  - Tumor
  - Development

Tools:
  - Cell Ranger
  - Seurat
  - Scanpy
""")

print("\n=== 8. Clinical ===")
print(""
Clinical:
  - Rare disease
  - Cancer
  - Pharmacogenomics

Workflow:
  1. Patient
  2. Sequencing
  3. Variant calling
  4. Annotation
  5. Report

Examples:
  - BRCA1/2
  - CFTR
  - CYP2D6
  - HLA

Tools:
  - GATK
  - VEP
  - ClinVar
""")

print("\n=== Comparison ===")
print(f"{'Application':<25} {'Reads':<15} {'Coverage':<15} {'Tool':<20}")
print("-" * 75)
print(f"{'WGS':<25} {'Short/long':<15} {'30x':<15} {'GATK':<20}")
print(f"{'WES':<25} {'Short':<15} {'50-100x':<15} {'GATK':<20}")
print(f"{'RNA-Seq':<25} {'Short':<15} {'20-30M':<15} {'STAR':<20}")
print(f"{'ChIP-Seq':<25} {'Short':<15} {'20-40M':<15} {'MACS2':<20}")
print(f"{'ATAC-Seq':<25} {'Short':<15} {'20-40M':<15} {'MACS2':<20}")
print(f"{'Metagenome':<25} {'Short/long':<15} {'Variable':<15} {'SPAdes':<20}")
print(f"{'Single-cell':<25} {'Short':<15} {'50K reads':<15} {'Cell Ranger':<20}")
print(f"{'Clinical':<25} {'Short':<15} {'100x':<15} {'GATK':<20}")

🐍 Python Integration

🎯 NGS from Python

You can run NGS tools from Python using subprocess and parse outputs with pysam, Biopython, and pandas.

Python

subprocess: Run CLI. pysam: BAM. Biopython: FASTQ. pandas: VCF. matplotlib: Plot. Pipeline: Automate.

🐍 subprocess🐍 pysam🐍 Biopython🐍 pandas

# Python Integration
print("=== Parse FASTQ ===")
print(""
from Bio import SeqIO

def parse_fastq(fastq_file):
    \"\"\"Parse FASTQ file.\"\"\"
    for record in SeqIO.parse(fastq_file, "fastq"):
        print(f"ID: {record.id}")
        print(f"Sequence: {record.seq}")
        print(f"Quality: {record.letter_annotations['phred_quality'][:10]}")

# Use
# parse_fastq("reads.fastq")
""")

print("\n=== Run BWA ===")
print(""
import subprocess

def run_bwa(reference, reads_1, reads_2, output_bam,
            threads=8):
    \"\"\"Run BWA-MEM alignment.\"\"\"
    cmd = (
        f"bwa mem -t {threads} {reference} "
        f"{reads_1} {reads_2} | "
        f"samtools sort -@ {threads} -o {output_bam} -"
    )
    
    result = subprocess.run(cmd, shell=True, capture_output=True,
                           text=True)
    
    if result.returncode != 0:
        raise RuntimeError(f"BWA failed: {result.stderr}")
    
    # Index
    subprocess.run(['samtools', 'index', output_bam], check=True)
    
    return output_bam

# Use
# bam = run_bwa("ref.fa", "r1.fq", "r2.fq", "sample.bam")
""")

print("\n=== Parse BAM with pysam ===")
print(""
import pysam

def parse_bam(bam_file):
    \"\"\"Parse BAM file.\"\"\"
    bam = pysam.AlignmentFile(bam_file, "rb")
    
    count = 0
    for read in bam:
        print(f"Read: {read.query_name}")
        print(f"  Flag: {read.flag}")
        print(f"  Ref: {read.reference_name}:{read.reference_start}")
        print(f"  MapQ: {read.mapping_quality}")
        print(f"  CIGAR: {read.cigarstring}")
        
        count += 1
        if count >= 5:
            break
    
    bam.close()

# Use
# parse_bam("sample.bam")
""")

print("\n=== Run GATK ===")
print(""
def run_gatk(reference, bam, output_vcf, threads=4):
    \"\"\"Run GATK HaplotypeCaller.\"\"\"
    cmd = [
        'gatk', 'HaplotypeCaller',
        '-R', reference,
        '-I', bam,
        '-O', output_vcf,
        '-ERC', 'GVCF',
        '--native-pair-hmm-threads', str(threads)
    ]
    
    result = subprocess.run(cmd, capture_output=True, text=True)
    
    if result.returncode != 0:
        raise RuntimeError(f"GATK failed: {result.stderr}")
    
    return output_vcf

# Use
# vcf = run_gatk("ref.fa", "sample.bam", "sample.g.vcf.gz")
""")

print("\n=== Parse VCF ===")
print(""
import pysam
from cyvcf2 import VCF

def parse_vcf(vcf_file):
    \"\"\"Parse VCF with CyVCF2.\"\"\"
    vcf = VCF(vcf_file)
    
    for variant in vcf:
        print(f"{variant.CHROM}:{variant.POS} "
              f"{variant.REF}>{variant.ALT[0]} "
              f"QUAL={variant.QUAL}")
        
        for sample in variant.genotypes:
            print(f"  GT: {sample}")

# Use
# parse_vcf("variants.vcf.gz")
""")

print("\n=== FASTQ Stats ===")
print(""
from Bio import SeqIO
import numpy as np

def fastq_stats(fastq_file):
    \"\"\"Compute FASTQ statistics.\"\"\"
    lengths = []
    qualities = []
    
    for record in SeqIO.parse(fastq_file, "fastq"):
        lengths.append(len(record.seq))
        qualities.extend(
            record.letter_annotations['phred_quality']
        )
    
    print(f"Reads: {len(lengths)}")
    print(f"Mean length: {np.mean(lengths):.0f}")
    print(f"Mean quality: {np.mean(qualities):.1f}")
    print(f"Q30: {np.mean(np.array(qualities) >= 30)*100:.1f}%")
    
    return lengths, qualities

# Use
# fastq_stats("reads.fastq")
""")

print("\n=== Complete Pipeline ===")
print(""
import subprocess
import os

def ngs_pipeline(reads_1, reads_2, reference,
                 output_dir="ngs_output", threads=8):
    \"\"\"Complete NGS pipeline.\"\"\"
    os.makedirs(output_dir, exist_ok=True)
    
    # 1. QC
    print("QC...")
    subprocess.run([
        'fastqc', reads_1, reads_2,
        '-o', f'{output_dir}/qc'
    ], check=True)
    
    # 2. Trim
    print("Trimming...")
    subprocess.run([
        'fastp', '-i', reads_1, '-I', reads_2,
        '-o', f'{output_dir}/clean_1.fq.gz',
        '-O', f'{output_dir}/clean_2.fq.gz',
        '--detect_adapter_for_pe'
    ], check=True)
    
    # 3. Align
    print("Aligning...")
    bam = run_bwa(
        reference,
        f'{output_dir}/clean_1.fq.gz',
        f'{output_dir}/clean_2.fq.gz',
        f'{output_dir}/sample.bam',
        threads
    )
    
    # 4. Variant calling
    print("Calling variants...")
    vcf = run_gatk(reference, bam, f'{output_dir}/sample.g.vcf.gz')
    
    print("Pipeline complete!")
    return vcf

# Use
# vcf = ngs_pipeline("r1.fq", "r2.fq", "ref.fa")
""")

⚖️ Platform Comparison

🎯 Which Platform?

Choosing the right sequencing platform depends on your application, budget, and required read length.

Comparison

Illumina: Short. PacBio: HiFi. Nanopore: Ultra-long. Ion: Targeted. MGI: Short. Sanger: Validation.

⚖️ Illumina⚖️ PacBio⚖️ Nanopore⚖️ Sanger

Platform	Read Length	Accuracy	Throughput	Best For
Illumina	50-300 bp	Q30-Q40	Very high	WGS, RNA-Seq, ChIP
PacBio HiFi	10-25 kb	Q30+	High	Assembly, SV, isoforms
PacBio CLR	10-60 kb	Q20	Medium	Assembly, SV
Nanopore	10-100 kb	Q20+	High	Ultra-long, real-time
Ion Torrent	200-600 bp	Q20	Medium	Targeted, clinical
MGI	50-300 bp	Q30	Very high	WGS, WES
Sanger	500-1000 bp	Q40+	Very low	Validation

# Decision Guide
print("=== Choose Platform by Application ===")
print(""
WGS (germline):
  → Illumina (30x)
  → MGI

WGS (complete):
  → PacBio HiFi
  → Nanopore

WES:
  → Illumina
  → MGI

RNA-Seq:
  → Illumina
  → Nanopore (isoforms)

ChIP-Seq:
  → Illumina

ATAC-Seq:
  → Illumina

Metagenomics:
  → Illumina + Nanopore
  → PacBio HiFi

Single-cell:
  → Illumina
  → 10x Genomics

Clinical:
  → Illumina
  → Sanger (validation)

Field work:
  → Nanopore (portable)
""")

print("\n=== Speed Comparison ===")
print(f"{'Platform':<20} {'Run time':<15} {'Output':<15}")
print("-" * 50)
print(f"{'iSeq 100':<20} {'~1 day':<15} {'1.2 Gb':<15}")
print(f"{'MiSeq':<20} {'~1 day':<15} {'15 Gb':<15}")
print(f"{'NextSeq':<20} {'~1 day':<15} {'120 Gb':<15}")
print(f"{'NovaSeq 6000':<20} {'~2 days':<15} {'6 Tb':<15}")
print(f"{'NovaSeq X':<20} {'~2 days':<15} {'16 Tb':<15}")
print(f"{'PacBio Revio':<20} {'~1 day':<15} {'25M reads':<15}")
print(f"{'Nanopore PromethION':<20} {'~1 day':<15} {'100+ Gb':<15}")

print("\n=== Cost Comparison ===")
print(f"{'Platform':<20} {'Cost/Gb':<15} {'Cost/Genome':<15}")
print("-" * 50)
print(f"{'Illumina':<20} {'$5-15':<15} {'$200-500':<15}")
print(f"{'MGI':<20} {'$3-10':<15} {'$150-400':<15}")
print(f"{'PacBio HiFi':<20} {'$50-100':<15} {'$1000-2000':<15}")
print(f"{'Nanopore':<20} {'$30-80':<15} {'$500-1500':<15}")
print(f"{'Sanger':<20} {'$1000+':<15} {'N/A':<15}")

print("\n=== When to Use Which ===")
print(""
USE ILLUMINA:
  ✓ WGS
  ✓ WES
  ✓ RNA-Seq
  ✓ ChIP-Seq
  ✓ ATAC-Seq
  ✓ Single-cell
  ✓ High accuracy
  ✓ Low cost

USE PACBIO HIFI:
  ✓ Complete genomes
  ✓ SVs
  ✓ Full-length isoforms
  ✓ Methylation
  ✓ Assembly

USE NANOPORE:
  ✓ Ultra-long reads
  ✓ Real-time
  ✓ Portable
  ✓ Field work
  ✓ Methylation

USE SANGER:
  ✓ Validation
  ✓ Small targets
  ✓ Clinical
""")

💼 Use Cases

🎯 Where NGS Is Used

NGS is used in virtually every area of biology and medicine: genomics, transcriptomics, epigenomics, and more.

Use Cases

Genomics: Genomes. Transcriptomics: Expression. Epigenomics: Regulation. Microbiome: Communities. Clinical: Diagnostics. Single-cell: Heterogeneity.

💼 Genomics💼 Transcriptomics💼 Epigenomics💼 Clinical

# Use Cases
print("=== 1. Human Genomics ===")
print(""
Use case:
  - Reference genome
  - Variants
  - Disease

Workflow:
  1. WGS/WES
  2. Alignment
  3. Variant calling
  4. Annotation
  5. Report

Examples:
  - 1000 Genomes
  - UK Biobank
  - gnomAD
  - Clinical

Tools:
  - BWA
  - GATK
  - VEP
""")

print("\n=== 2. Cancer Genomics ===")
print(""
Use case:
  - Somatic mutations
  - Driver mutations
  - Treatment

Workflow:
  1. Tumor + normal
  2. WGS/WES
  3. Somatic calling (Mutect2)
  4. Annotation
  5. Report

Examples:
  - TCGA
  - ICGC
  - Clinical trials

Tools:
  - Mutect2
  - Strelka2
  - VEP
""")

print("\n=== 3. Rare Disease ===")
print(""
Use case:
  - Diagnosis
  - Novel genes
  - Clinical

Workflow:
  1. Patient + parents
  2. WES/WGS
  3. Variant calling
  4. Trio analysis
  5. Report

Examples:
  - Undiagnosed
  - Mendelian
  - De novo

Tools:
  - GATK
  - VEP
  - Exomiser
""")

print("\n=== 4. Infectious Disease ===")
print(""
Use case:
  - Outbreak
  - Resistance
  - Surveillance

Workflow:
  1. Pathogen
  2. Sequencing
  3. Assembly
  4. Phylogenetics
  5. Report

Examples:
  - SARS-CoV-2
  - TB
  - Influenza
  - HIV

Tools:
  - SPAdes
  - IQ-TREE
  - Nextstrain
""")

print("\n=== 5. Microbiome ===")
print(""
Use case:
  - Gut
  - Environment
  - Health

Workflow:
  1. Sample
  2. 16S or WGS
  3. Assembly
  4. Binning
  5. Annotation

Examples:
  - Human Microbiome Project
  - Earth Microbiome
  - Clinical

Tools:
  - QIIME2
  - SPAdes
  - MetaBAT
""")

print("\n=== 6. Agriculture ===")
print(""
Use case:
  - Crop genomics
  - Breeding
  - Traits

Workflow:
  1. Plant DNA
  2. WGS
  3. Variant calling
  4. GWAS
  5. Breeding

Examples:
  - Rice
  - Maize
  - Wheat
  - Tomato

Tools:
  - GATK
  - TASSEL
  - PLINK
""")

print("\n=== 7. Single-Cell ===")
print(""
Use case:
  - Heterogeneity
  - Cell types
  - Trajectories

Workflow:
  1. Cells
  2. 10x
  3. Sequencing
  4. Cell Ranger
  5. Seurat

Examples:
  - Human Cell Atlas
  - Tumor
  - Brain
  - Immune

Tools:
  - Cell Ranger
  - Seurat
  - Scanpy
""")

print("\n=== 8. Epigenomics ===")
print(""
Use case:
  - Chromatin
  - Regulation
  - Disease

Workflow:
  1. ChIP/ATAC
  2. Sequencing
  3. Peak calling
  4. Motif
  5. Networks

Examples:
  - ENCODE
  - Roadmap
  - Cancer

Tools:
  - MACS2
  - HOMER
  - deepTools
""")

print("\n=== Comparison ===")
print(f"{'Use Case':<25} {'Platform':<20} {'Output':<20}")
print("-" * 65)
print(f"{'Human genomics':<25} {'Illumina':<20} {'Variants':<20}")
print(f"{'Cancer':<25} {'Illumina':<20} {'Somatic':<20}")
print(f"{'Rare disease':<25} {'Illumina':<20} {'Diagnosis':<20}")
print(f"{'Infectious':<25} {'Nanopore':<20} {'Outbreak':<20}")
print(f"{'Microbiome':<25} {'Illumina':<20} {'Taxa':<20}")
print(f"{'Agriculture':<25} {'Illumina':<20} {'Traits':<20}")
print(f"{'Single-cell':<25} {'Illumina':<20} {'Clusters':<20}")
print(f"{'Epigenomics':<25} {'Illumina':<20} {'Peaks':<20}")

❓ FAQ

🎯 Frequently Asked Questions

Answers to common questions about Next-Generation Sequencing.

What is Next-Generation Sequencing (NGS)?

What is the difference between short-read and long-read sequencing?

Short-read sequencing (Illumina) produces reads of 50-300 bp with very high accuracy and throughput, ideal for variant calling, RNA-Seq, and SNP genotyping. Long-read sequencing (PacBio, Oxford Nanopore) produces reads of 10-100 kb, which are essential for resolving repeats, structural variants, full-length isoforms, and complete genome assembly.

Which sequencing platform should I use?

Use Illumina for high-accuracy short reads (variant calling, RNA-Seq, ChIP-Seq). Use PacBio HiFi for highly accurate long reads (genome assembly, SV detection, full-length isoforms). Use Oxford Nanopore for ultra-long reads, portable sequencing, real-time analysis, and direct DNA/RNA methylation detection.

What is a FASTQ file?

FASTQ is the standard text format for storing raw sequencing reads and their quality scores. Each read is represented by four lines: a header (starting with @), the sequence, a separator (+), and the quality string (Phred-scaled ASCII characters). It's the starting point for nearly all NGS analysis pipelines.

What is the typical NGS analysis workflow?

A typical NGS workflow: (1) Library preparation, (2) Sequencing, (3) Quality control (FastQC, MultiQC), (4) Trimming (fastp, Trimmomatic), (5) Alignment (BWA, Bowtie2, minimap2) or assembly (SPAdes, Flye), (6) Downstream analysis (variant calling, RNA-Seq, ChIP-Seq), (7) Visualization and interpretation.

What coverage do I need for WGS?

For germline WGS: 30x is standard. For somatic/cancer: 100x+ tumor, 30x normal. For clinical: 100x+. For long-read assembly: 20-40x HiFi or 30-50x Nanopore. Higher coverage improves sensitivity, especially for low allele fraction variants and structural variants.

What is the difference between WGS, WES, and targeted sequencing?

WGS (Whole Genome Sequencing) sequences the entire genome (~3 Gb for human). WES (Whole Exome Sequencing) captures and sequences only protein-coding regions (~1-2% of genome). Targeted sequencing focuses on specific genes or regions. WGS is comprehensive; WES and targeted are more cost-effective for specific questions.

What is the difference between PacBio and Nanopore?

PacBio uses SMRT sequencing with zero-mode waveguides and fluorescent nucleotides; HiFi reads are highly accurate (> 99.9%) but shorter (10-25 kb). Nanopore uses protein pores and current disruption; reads can be ultra-long (> 100 kb) and it's portable and real-time, with accuracy improving (Q20+). Both detect methylation.

What is multiplexing and why is it used?

Multiplexing combines multiple samples into a single sequencing run using unique barcodes (indexes) ligated to each sample's DNA. It reduces cost per sample, increases throughput utilization, and minimizes batch effects. Use unique dual indexes (UDI) to avoid index hopping.

What is the difference between BAM and CRAM?

BAM is the binary version of SAM (Sequence Alignment/Map) and stores aligned reads. CRAM is a more compressed, reference-based format that can reduce file size by 30-60%. Both are supported by samtools, pysam, and all major genomics tools. CRAM is increasingly preferred for archival storage.

# Quick Reference
print("=== Quick Answers ===")
print(""
Q: What is NGS?
A: Massively parallel sequencing.

Q: Short vs long reads?
A: Illumina (short); PacBio/Nanopore (long).

Q: Best platform?
A: Illumina (short); PacBio HiFi (long).

Q: Coverage for WGS?
A: 30x germline, 100x somatic.

Q: FASTQ?
A: Raw reads + quality.

Q: BAM?
A: Binary alignment format.

Q: VCF?
A: Variant Call Format.

Q: Multiplexing?
A: Barcodes for multiple samples.

Q: Best long-read tool?
A: Flye, Canu, hifiasm.

Q: Free tools?
A: Yes — BWA, GATK, samtools, SPAdes.
""")

🚀 Future

🎯 Where NGS Is Going

The future is long-read first, single-cell, spatial, real-time, multi-omics, and clinical.

Future Directions

Long-read: T2T. Single-cell: Atlases. Spatial: Location. Real-time: Surveillance. Multi-omics: Integration. Clinical: Routine.

🚀 Long-read🚀 Single-cell🚀 Spatial🚀 Clinical

# Future of NGS
print("=== 1. Long-Read First ===")
print(""
Trend: Long reads

Current:
  - Short reads standard
  - Long reads specialized

Future:
  - Long reads routine
  - Complete genomes
  - T2T
  - SVs
  - Isoforms

Examples:
  - Human T2T
  - Complete genomes
  - Clinical

Tools:
  - PacBio HiFi
  - Nanopore
  - Flye
  - hifiasm
""")

print("\n=== 2. Single-Cell ===")
print(""
Trend: Single-cell

Current:
  - Bulk
  - Average

Future:
  - Millions of cells
  - All tissues
  - Atlases
  - Clinical

Examples:
  - Human Cell Atlas
  - Brain
  - Tumor
  - Development

Tools:
  - 10x
  - Seurat
  - Scanpy
  - CellxGene
""")

print("\n=== 3. Spatial ===")
print(""
Trend: Spatial

Current:
  - Dissociated
  - Lose location

Future:
  - Tissue context
  - Spatial multi-omics
  - 3D
  - Clinical

Platforms:
  - 10x Visium
  - MERFISH
  - Slide-seq
  - Stereo-seq

Tools:
  - Seurat
  - Squidpy
  - Giotto
""")

print("\n=== 4. Real-Time ===")
print(""
Trend: Real-time

Current:
  - Batch
  - Slow

Future:
  - Real-time
  - Portable
  - Field
  - Surveillance

Examples:
  - Nanopore
  - Outbreak
  - Clinical
  - Environmental

Tools:
  - Nanopore
  - Nextstrain
  - EPI2ME
""")

print("\n=== 5. Multi-Omics ===")
print(""
Trend: Integrate everything

Current:
  - Single modality
  - Separate

Future:
  - Multi-omics
  - Single-cell
  - Spatial
  - Integrated

Examples:
  - Genomics + transcriptomics
  - Proteomics + metabolomics
  - Single-cell multi-omics

Tools:
  - Seurat
  - MOFA
  - Scanpy
""")

print("\n=== 6. Clinical ===")
print(""
Trend: Clinical

Current:
  - Research
  - Specialized

Future:
  - Clinical routine
  - Rare disease
  - Cancer
  - Pharmacogenomics
  - Newborn screening

Examples:
  - BRCA1/2
  - CFTR
  - CYP2D6
  - HLA

Tools:
  - GATK
  - VEP
  - ClinVar
""")

# TIMELINE
print("\n=== Timeline ===")
print(""
Past:
  1977: Sanger
  2005: 454
  2006: Illumina
  2010: PacBio
  2014: Nanopore
  2019: HiFi
  2022: T2T

Present:
  2025: Long-read first
  2026: Single-cell atlases

Future:
  2030: Routine T2T?
  2035: Spatial multi-omics?
  2040: Clinical NGS?
""")

print("\n=== Final Thoughts ===")
print(""
NGS is:
  - Fundamental to genomics
  - Rapidly improving
  - Becoming cheaper
  - Democratizing biology

The future is:
  - Long-read first
  - Single-cell
  - Spatial
  - Real-time
  - Multi-omics
  - Clinical

"Sequencing is the microscope
 of modern biology."

Keep exploring. Keep building.
""")