BigData 2020

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City Tech/Cornell BioMedical Big Data Week 2020: Pathogen Evolutionary Genomics
Wed, July 22, 2020, 9 am - 12 noon
Instructor: Dr Weigang Qiu, Professor, Department of Biological Sciences
Office: B402 Belfer Research Building
Email: weigang@genectr.hunter.cuny.edu
Lab Website: http://diverge.hunter.cuny.edu/labwiki/
Lyme Disease (Borreliella) CoV Genome Tracker Coronavirus evolutuon
Gains & losses of host-defense genes among Lyme pathogen genomes (Qiu & Martin 2014)
Spike protein alignment

What is evolutionary genomics?

Genomes differ among individuals and species. Evolutionary genomics studies genome variability and genome changes using evolutionary principles. Typical applications in pathogen research include molecular epidemiology (e.g., wildlife origin of SARS-CoV-2 & tracking Covid-19 spread), molecular evolution (e.g., identify key genes and protein sequences contributing to virulence and immune escape), and vaccine design (e.g., influenza vaccine based on latest circulating strains).

Genome changes are studied at two distinct levels: (1) within-species/within-population variations (e.g., genomic changes during Covid-19 pandemic), and (2) between-species divergence (e.g., difference between SARS-CoV-1 and SARS-CoV-2).

The key for analyzing genome variations within species is "population-thinking", the idea that there is no one individual genome that is standard, normal, or "wildtype".

The key for comparing genomes across species is "tree-thinking", the idea that evolution happens by diversification (like a branching tree), not by climbing a ladder. There is no such thing as "advanced" or "primitive" species. All living species have the exact same evolutionary distances/time of divergence since the origin of life.

Case studies from Qiu Lab

Essential bioinformatics skills

  • Linux command-line interface (e.g., BASH shell)
  • Familiarity with a programming language (e.g., Python or Perl)
  • Data visualization & statistical analysis (e.g., JavaScript; the R statistical computing environment)

Learning Goals

  • Be able to compare evolutionary relationships using phylogenetic trees
  • Be able to use command-line tools for batch-processing of genome files
  • Be able to perform genome-wide association analysis on the R platform

Schedule

Exercises & Challenges

  • Finish Tree Thinking Quizzes
  • Unix exercises:
    • count the number of sequences using "grep -v" or "wc"
    • display the first 5 lines of a file
    • display the last 5 lines of a file
    • change upper-cases to lower-cases
    • change "|" to "_"
    • replace strings