Notes on Dragonstar2019 @PKU (to be finished...

@PKU Beijing

2019 07.29-08.02

龙星计划笔记，主要是对各种概念的和定义的扩充学习

• Day 1 Part Ⅰ Genomic technologies in disease studies
  • 1 Types of genetic variation
  • 2 Revolution: single-molecule long-read sequencing
    • 2.1 Oxford Nanopore Sequencing
    • 2.2 PacBio Single-molecule real-time (SMRT) sequencing
    • 2.3 Linked-read Sequencing
    • 2.4 Single-molecule optical mapping (Bionano Genomics)
• Day 1 Part Ⅱ NGS data formats and variant calling
  • 1 Basic Concepts in NGS
  • 2 Data formats
    • 2.1 The Rawset of Raw Data
    • 2.2 NGS data formats
    • 2.3 Formats use different coordinate systems, which adds confusion
  • 3 Visualization of genomic data: IGV
  • 4 Coverage
  • 4.1 What is coverage?
    • 4.2 Coverage: how many reads we need to cover the genome?
    • 4.3 Overdispersion
    • 4.4 Question on coverage
  • 5 General strategy for variant calling
    • 5.1 Possible Genotypes
    • 5.2 From Sequence to Genotype: Individual Based Prior
    • 5.3 From Sequence to Genotype: Population Based Prior
    • 5.4 Sequence Based Genotype Calls
• Day 2 Part Ⅰ Alignment of short/long-read sequencing data
  • 1 Sequence similarity
  • 2 Sequence alignment
    • 2.1 Pairwise alignment
  • 3 Dynamic programming
    • 3.1 Global alignment: Needleman-Wunsch
      • 3.1.1 Working of Needleman -Wunsch Algorithm
    • 3.2 Local alignment: Smith-Waterman
      • 3.2.1 Working of Smith-Waterman Algorithm
  • 4 BLAST: The seed-index-map-extend-merge strategy
    • 4.1 BLAST Basics
    • 4.2 Process: The seed-index-map-extend-merge strategy
      • 4.2.1 The Process
      • 4.2.2 Statistical significance of alignment
  • 5 BWT
    • 5.1 Bowtie/BWA
    • 5.2 BWT procedures
    • 5.3 BWT reverse transformation
    • 5.4 BWT search
• Day 2 Part Ⅱ Genome assembly by short/long-read sequencing
  • 1 genome assembly
    • 1.1 What is genome assembly
    • 1.2 Why genome assembly
    • 1.3 What types of genome assembly?
      • 1.3.1 de novo assembly
      • 1.3.2 Comparative assembly
      • 1.3.3 De Novo Assembly paradigms
  • 2 Graph theory
    • 2.1 Directed graph
    • 2.2 Overlap-based approach (OLC)
  • 3 Comparison of two approaches for large genomes such as human genomes
    • 3.1 U.S. Human Genome Project
    • 3.2 Draft human genome
    • 3.3 How Perl Saved the Human Genome Project
    • 3.4 Back to today
  • 4 Lander-Waterman statistics in genome assembly
    • 4.1 Coverage at a position can be modelled by Poisson distribution
    • 4.2 What’s the fraction of genome that are covered by reads?
    • 4.3 How many contigs are generated?
    • 4.4 Assembling large genomes today
    • 4.5 Canu assembler for long-read assembly
    • 4.6 Wtdbg2 assembler for long-read assembly
  • 5 Seven Bridges of Königsberg Problem
    • 5.1 Euler’s Theorem on directed graphs
    • 5.2 Euler’s Theorem on undirected graphs
    • 5.3 The Königsberg graph is not Eulerian
    • 5.4 Hamiltonian vs Eulerian path
  • 6 Constructing the de Bruijn Graph
    • 6.1 Error correction in de Bruijn graph
    • 6.2 A fun hypothetical case study: Frederick Sanger’s insulin sequencing study
• Day 3Part Ⅰ Detection of structural variants in human
  • 1 Human genetic variation
  • 2 Mechanisms underlying structural variant formation
    • 2.1 Recurrent structural variants
    • 2.2 Nonrecurrent rearrangements
  • 3 Technologies for CNV Detection
    • 3.1 Karyotyping and cytogenetic analysis
  • 4 SNP genotyping arrays
  • 5 CNV Detection
    • 5.1 Log R Ratio (LRR) and B Allele Frequency (BAF)
    • 5.2 Detection of CNVs from SNP arrays using PennCNV
      • 5.2.1 PennCNV Flowchart
      • 5.2.2 SNP Signal Intensities
      • 5.2.3 Visualization of CNVs
      • 5.2.4 Hidden Markov Model in PennCNV
      • 5.2.5 Copy Number States
      • 5.2.6 Hidden states, copy numbers, CNV genotypes, and their descriptions
  • 6 CNV Calling
    • 6.1 Viterbi algorithm for calling
    • 6.2 Other Types of Signal Data
    • 6.3 PennCNV-Affy Pipeline
    • 6.4 Joint Modeling on Family Data
    • 6.5 Joint modeling of the CNVs in a trio
    • 6.6 Likelihood of Signal Intensities
  • 7 NGS-based SV detection
    • 7.1 Read count-based methods for SV detection
    • 7.2 Detection of SVs from discordant read pairs
    • 7.5 Detection of SVs using assembly-based methods
    • 7.6 SV detection from long-read sequencing
    • 7.7 Bionano optical mapping for SV detection