/applied-computational-genomics

Applied Computational Genomics Course at UU: Spring 2020

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Applied Computational Genomics Course at UU: Spring 2020

  • Faculty: Aaron Quinlan (aquinlan at genetics.utah.edu)
  • Teaching assistants: Michael Cormier (cormiermichaelj @ gmail.com), Katie Owings (katie.owings @ utah.edu), Ryan Miller (miller.ryan.h @ gmail.com), and Mark Wadsworth (mew225 @ gmail.com)
  • Meets Tu and Th from 9:10-10:30 in EIHG Auditorium; January 7 - April 21
  • TA Hours: Monday and Wednesday, 10-11am, HSEB

Overview

This course will provide a comprehensive introduction to fundamental concepts and experimental approaches in the analysis and interpretation of experimental genomics data. It will be structured as a series of lectures covering key concepts and analytical strategies. A diverse range of biological questions enabled by modern DNA sequencing technologies will be explored including sequence alignment, the identification of genetic variation, structural variation, and ChIP-seq and RNA-seq analysis. Students will learn and apply the fundamental data formats and analysis strategies that underlie computational genomics research. The primary goal of the course is for students to be grounded in theory and leave the course empowered to conduct independent genomic analyses.

Grading policy

All assignments are due on the date stated in class. Ten percent of the grade will be deducted for each 24 hours that the assignment is late.

Stats course at the U

https://www.huber.embl.de/msmb/Chap-Models.html greg Stoddard's class was aimed towards basic bio and clinical research. it was fine but used stata PBHLT 7100 - biostatistics 2 There is an R-based stats class called Advanced Stastical Modeling for Biologists but it is designed for people who haven't used R and is basic ways to model stats, Don Feener

Course lecture slides

Homework

  • [Homework 1: Basic Unix analysis]
  • [Homework 2: DNA Pattern exploration in a FASTA file]
  • [Homework 3: Working with the FASTQ format]
  • [Homework 4: BAM files, samtools, IGV]
  • [Homework 5: Exploring genetic variation in VCF files]
  • [Homework 6: Bedtools analysis problems. Bottom of page]
  • Homework 7: Probability and R

Syllabus

  • Class 1 (Tu Jan 9; Layer): Course overview and Intro to UNIX

    • Class 1 Slides
    • Required Reading Prior to Lecture:
    • Topics covered
      • Brief history of computational biology
      • Course computing environment
      • Intro. to UNIX: Part 1
        • Logging in
        • The "shell"
        • "Home"
        • Navigation
        • File system
        • Files
        • Basic commands: ls, pwd, cd, mkdir, head

  • Class 2 (Th Jan 11; Layer): Intro to UNIX Part 2

  • Class 3 (Tu Jan 16; Quinlan): The human genome

    • Class 3 Slides
    • Required Reading Prior to Lecture:
    • Topics covered
      • Karyotype
      • Chromosome structure
      • Centromeres
      • Banding
      • Chromatin
      • How was the genome sequenced?
        • sequencing technology
        • assembly strategy
      • Chromosomes
        • size
        • gene content
        • centromeres
      • Haplotypes
      • Genes and transcripts
      • Repeat content
        • mobile elements
        • simple repeats
      • GC content, banding
      • CpG islands

  • Class 4 (Th Jan 18; Quinlan): Using UNIX to find patterns in a genome

    • Required Reading Prior to Lecture:
      • None.
    • Topics covered
      • The UNIX PATH
      • Environment variables
      • Basic regular expressions with grep
      • sort
      • uniq
    • Homework 2 (finding biological patterns in FASTA files with UNIX) assigned

  • Class 5 (Tu Jan 23; Quinlan): Genetic variation: mutations, polymorphisms, and haplotypes

    • Required Reading Prior to Lecture:
    • Topics covered
      • Genetic variation: what, why, etc.
      • Mutation vs. polymorhism
      • De novo mutation
        • Human mutation rates
      • Polymorphism
      • SNPs INDELs
        • abundance
        • frequency
        • examples
        • 1000 Genomes
        • Site frequency spectrum
      • Population stratification
      • Intro to haplotypes and recombination

  • Class 6 (Th Jan 25; Quinlan): Modern DNA sequencing technologies

  • Class 7 (Tu Jan 30; Quinlan): DNA sequence mapping and alignment](https://docs.google.com/presentation/d/1RskyGhXx4Lc6wSvvb_ZuCUJGUiP2RAr9X8bGh9Kz77I/edit?usp=sharing)

    • Required Reading Prior to Lecture:
    • Topics covered
      • Sequence alignment
        • Theory
        • Mapping versus alignment
        • Local versus global alignment
          • Smith waterman
          • Needleman-wunsch
        • Advanced algorithms
        • Alignment for RNA-seq
        • Alignment for SV detection.
        • Tools
          • BWA, etc.

  • Class 8 (Th Feb 1; Quinlan): SAM/BAM format, samtools, and IGV](https://docs.google.com/presentation/d/1_iT3btOZqjPmVb8Ryk5ssMBCMxoQ0MVmasZ6G0luA-c/edit?usp=sharing)

    • The SAM/BAM format
    • Samtools
    • IGV
    • Homework 4 (creating and working with SAM/BAM files with samtools and IGV) assigned

  • Class 9 (Tu Feb 6; Quinlan): SNP and INDEL discovery (part 1)](https://docs.google.com/presentation/d/1D4XY9XxQiyYcwwhomRRONxCPr_bJvcC0WM4sb8vouZM/edit?usp=sharing)

  • Class 10 (Th Feb 8; Quinlan): SNP and INDEL discovery (part 2)](https://docs.google.com/presentation/d/12jeJQPbntPPPGYszIH1l9u83mXFVU1XdJw-bNgbFu28/edit?usp=sharing)

    • Required Reading Prior to Lecture:
    • Topics covered
      • VCF format
        • Attributes
        • Genotypes
      • Population calling
      • Basic annotations
    • Landscape of human genetic variation
      • Alleles and genotypes
      • Allele frequency spectrum
      • Hardy weinberg equilibrium
      • More on haplotypes and recombination
    • Exploring the format
      • examples
      • IGV
    • Manipulating VCF with bcftools
    • Homework 5 (variant calling and working with VCF files with bcftools and UNIX) assigned

  • Class 11 (Tu Feb 13; Quinlan): VCF format, Hardy Weinberg Equilibrium, VCF toolkits

    • Topics covered
      • VCF Format
      • Allele frequencies
      • Genotype frequencies
      • Hardy Weinberg Equilibrium

  • Class 12 (Th Feb 15; Quinlan): VCF annotation and interpetation

  • Class 13 (Tu Feb 20; Quinlan): Variation in genome structure

  • Class 14 (Th Feb 22; Quinlan): Somatic mutation in cancer

  • Class 15 (Tu Feb 27; Quinlan): Genome annotation

    • Required Reading Prior to Lecture:
      • None
    • Topics covered
      • How and why do we annotate a genome?
      • Conservation
      • CpG islands
      • Repeatmasker
      • Chromatin modifications
      • DNA methylations
      • Linkage blocks

  • Class 16 (Th Mar 1; Quinlan): Genome data formats and genome arithmetic

    • Required Reading Prior to Lecture:
      • None
    • Topics covered
      • The genome as a coordinate system
      • BED format
      • GFF format
      • VCF format
      • UCSC and Biomart to retrieve genome annotations
      • UCSC and IGV to visualize
      • a bit of awk

  • Class 17 (Tu Mar 8; Quinlan): Applied genome arithmetic with bedtools; part 1

  • Class 18 (Th Mar 8; Quinlan): Applied genome arithmetic with bedtools; part 2

  • Class 19 (Tu Mar 13; Quinlan): Digging deeper into UNIX, part 1

    • awk
    • sed
    • tr
    • PATH
    • .bashrc

  • Class 20 (Th Mar 15; Quinlan): ChIP-seq analysis

    • experimental design
    • protocols
    • examples

  • Spring Break March 18-25

  • Class 21 (Tu Mar 27; Quinlan): RNA-seq analysis

    • analyses
    • toolsets
    • Class project assignment

  • Class 22 (Th Mar 29; Quinlan): Basic probability

    • Probability with coins and dice
    • Probability with DNA
    • Conditional probabilities
    • Use R for examples

  • Class 23 (Tu Apr 3; Quinlan): Statistical tests

    • Gaussian
      • Z scores
    • Chi-squared
    • Fisher
    • KS test
    • Rank tests
    • Applications

  • Class 24 (Th Apr 5; Quinlan): How do I know if my observation is significant?

    • Models
    • Expectation
    • Tests for significance

  • Class 25 (Tu Apr 10; Quinlan): Data visualization, part 1

    • Why
    • Pattern recognition
    • Detect problems
    • Ansombe’s quartet
    • Introduce class projects

  • Class 26 (Tu Apr 12; Quinlan): Data visualization, part 2

  • Class 27 (Tu Apr 17; Quinlan): Advanced topics

    • loops
    • shuffling
    • randomization
    • advanced commands
    • basic scripts and pipelines

  • Class 28 (Th Apr 19; Quinlan): Group Presentations, part 1

  • Class 29 (Tu Apr 24; Quinlan): Group Presentations, part 2