BigData 2020

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)	Coronaviruses	SARS-CoV-2
Gains & losses of host-defense genes among Lyme pathogen genomes (Qiu & Martin 2014)	p-value (y-axis) vs. fold change (x-axis)	genes significantly down or up-regulated (at p<1e-4)

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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

• Comparative genomics of worldwide Lyme disease pathogens
• Evolutionary origin of coronaviruses
• Covid-19 Genome Tracker

Bioinformatics workflow for comparative analysis of bacterial pathogen genomes

• Pathogen isolation -> DNA extraction -> Library preparation -> High-through sequencing
• De novo genome assembly (canu; velvet; etc)
• Identify reference genome from NCBI database (kraken)
• Variant call (bwa; cortex_var; samtools mpileup)
• Infer genome phylogeny (muscle; reXML)
• Annotation (PATRIC)
• Custom genome browser (JavaScript; D3 library for interactive graphics)

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)

Textbooks for genome evolution

• Graur, 2016, Molecular and Genome Evolution, First Edition, Sinauer Associates, Inc. ISBN: 978-1-60535-469-9. Publisher's Website
• Baum & Smith, 2013. Tree Thinking: an Introduction to Phylogenetic Biology, Roberts & Company Publishers, Inc.

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

• 9:00 - 9:25: Introduction; Install R & R Studio; Download fasta file & save as "ospC-pep.fasta" :
  File:OspC-pep.txt
• 9:30 - 10:00: Unix Tutorial (Part I. Unix Basics)
• 10:05 - 10:30: Unix Tutorial (Part II Advanced Unix)
• 10:35 - 11:00: Tree-thinking Quizzes: Slides
  File:Big-data-phylogeny.pptx
  & Handouts
  File:Pretest.pdf
• 11:05 - 12: Demo: identification of genomic mutations associated with antibiotic resistance
  • Slides on background
  • Demo (on wallace lab server)

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