Genome Analysis & Next-Generation Sequencing

Course Overview

This course provides comprehensive training in genome sequencing, assembly, annotation, and variant analysis. Students process real NGS data through a complete pipeline — from raw FASTQ files to annotated variants — using industry-standard tools. The course covers both prokaryotic and eukaryotic genomes, as well as comparative genomics approaches.

Learning Outcomes

1.     Explain sequencing-by-synthesis (Illumina), long-read technologies (PacBio, Oxford Nanopore), and their data characteristics.

2.     Perform quality control on FASTQ files and apply adapter trimming with Trimmomatic.

3.     Align reads to a reference genome using BWA-MEM2 and process alignments with Samtools.

4.     Call SNPs and indels using GATK HaplotypeCaller following GATK best practices.

5.     Assemble a prokaryotic genome de novo with SPAdes and annotate it with Prokka.

Curriculum Content

Week 1: Sequencing Technologies & Data Formats

•      Sequencing-by-synthesis: cluster generation, bridge amplification, reversible terminators, base calling.

•      Long-read technologies: PacBio SMRT (zero-mode waveguides), Oxford Nanopore (ionic current through pores), error characteristics.

•      FASTQ format: sequence + quality per base (Phred score, ASCII encoding); paired-end vs. single-end.

•      SAM/BAM format: header, read name, FLAG bits, CIGAR string, mapping quality; sorted/indexed BAM.

•      VCF format: CHROM, POS, ID, REF, ALT, QUAL, FILTER, INFO, FORMAT, genotype fields.

Week 2: Quality Control & Alignment

•      FastQC metrics: per-base quality, GC content distribution, adapter contamination, duplicate rate.

•      Trimmomatic: ILLUMINACLIP, SLIDINGWINDOW, LEADING, TRAILING, MINLEN parameters.

•      BWA-MEM2: Burrows-Wheeler Aligner algorithm; read groups; alignment statistics.

•      Samtools: view, sort, index, flagstat, depth, mpileup operations.

•      Picard: MarkDuplicates; CollectAlignmentSummaryMetrics.

Week 3: Variant Calling — GATK Best Practices

•      GATK HaplotypeCaller: local de novo assembly of haplotypes in active regions; PairHMM likelihood model.

•      GVCF mode for joint calling: multiple samples; CombineGVCFs → GenotypeGVCFs.

•      Variant filtering: hard filtering vs. VQSR (Variant Quality Score Recalibration); FILTER field meanings.

•      Variant annotation: ANNOVAR, SnpEff — functional consequence, population frequency (gnomAD), clinical significance (ClinVar).

Week 4: Genome Assembly

•      De novo assembly strategies: de Bruijn graph (SPAdes, Velvet), overlap-layout-consensus (Canu), hybrid assembly (Unicycler).

•      Assembly QC: QUAST metrics (N50, L50, number of contigs, misassemblies); BUSCO completeness.

•      Prokaryotic genome annotation: Prokka pipeline — tRNA (Aragorn), rRNA (RNAmmer), CDS (Prodigal), gene naming (BLAST against UniProt).

•      Comparative genomics: Roary (pangenome), Mauve (whole-genome alignment), OrthoFinder (orthologue detection).

Week 5: Comparative Genomics & Visualisation

•      IGV (Integrative Genomics Viewer): loading BAM, VCF, BED, GTF tracks; navigating variants; snapshot export.

•      Genome browsers: UCSC, Ensembl — track hubs, custom annotations.

•      Synteny analysis: MCScan, SynMap — visualising collinear blocks between genomes.

•      Pangenome concepts: core genome, accessory genome, unique genes; Roary pan_genome_results interpretation.

Hands-On Practicals

🔬 Hands-On Lab: Complete NGS Variant Calling Pipeline

Step 1: Download sample Illumina paired-end FASTQ files (use subset of NA12878 from GIAB): wget -O sample_R1.fastq.gz [URL]

Step 2: Run FastQC: fastqc sample_R1.fastq.gz sample_R2.fastq.gz -o qc_results/

Step 3: Trim adapters: trimmomatic PE sample_R1.fastq.gz sample_R2.fastq.gz R1_trimmed.fastq.gz R1_unpaired.fastq.gz R2_trimmed.fastq.gz R2_unpaired.fastq.gz ILLUMINACLIP:TruSeq3-PE.fa:2:30:10 SLIDINGWINDOW:4:20 MINLEN:36

Step 4: Align to hg38: bwa mem -R "@RG\tID:sample\tSM:sample\tPL:ILLUMINA" hg38.fa R1_trimmed.fastq.gz R2_trimmed.fastq.gz | samtools sort -o sample.sorted.bam; samtools index sample.sorted.bam

Step 5: Mark duplicates: gatk MarkDuplicates -I sample.sorted.bam -O sample.md.bam -M metrics.txt

Step 6: Call variants: gatk HaplotypeCaller -R hg38.fa -I sample.md.bam -O variants.g.vcf.gz -ERC GVCF

Step 7: Genotype: gatk GenotypeGVCFs -R hg38.fa -V variants.g.vcf.gz -O final.vcf.gz

Step 8: Visualise in IGV — find 3 SNPs and 1 indel in the region, explain each CIGAR string