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Learning Objectives

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Learning Objectives

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Reading compass

Now · 1. Genome Annotation

Learning Objectives

Analyze genomic data for functional elements
Understand variant calling pipeline
Apply comparative genomics

1. Genome Annotation

Identifying functional elements: gene prediction (ab initio with HMM or evidence-based), repeat annotation, ncRNA identification, regulatory region annotation.

2. Variant Calling

Types: SNPs (single base change), indels (small insertions/deletions), CNVs (large duplications/deletions), SVs (inversions, translocations). Pipeline: Align reads (BWA, Bowtie) -> Sort/Mark duplicates (SAMtools) -> Call variants (GATK, FreeBayes) -> Filter -> Annotate (SnpEff, VEP).

3. Comparative Genomics

Comparing genomes across species reveals conserved regions (functionally important), lineage-specific changes, evolutionary rates (dN/dS ratio).

Q1: How are genes predicted in a genome?

Ab initio: train HMM on known genes, predict new ones based on sequence patterns (start/stop codons, splice sites, codon usage). Evidence-based: align RNA-seq transcripts. Combined is most accurate. Q2: What is a SNP and how is it called?

Single Nucleotide Polymorphism: position where individuals differ by one base. Called by aligning reads to reference and finding positions where many reads disagree at high quality. Q3: What is the dN/dS ratio?

dN = non-synonymous substitutions (change amino acid), dS = synonymous (silent). dN/dS > 1 = positive selection, < 1 = purifying selection (conservation), = 1 = neutral. Q4: What is the GATK best practices pipeline?

Preprocess reads (align, dedup, BQSR) -> Call variants (HaplotypeCaller) -> Filter (VQSR) -> Annotate and analyze. Industry standard for variant calling. Q5: Homozygous vs heterozygous variants?

Homozygous: both chromosomes have variant (all reads show variant). Heterozygous: one variant, one reference (~50% reads show variant). Detected by allele balance statistics. Join Discord PreviousGenome AssemblyNextProtein Structure & Prediction