Background and Metadata

Last updated on 2023-11-27 | Edit this page

Overview

Questions

  • What data are we using?
  • Why is this experiment important?

Objectives

  • Why study E. coli?
  • Understand the data set.
  • What is hypermutability?

Background

We are going to use a long-term sequencing dataset from a population of Escherichia coli.

  • What is E. coli?
    • E. coli are rod-shaped bacteria that can survive under a wide variety of conditions including variable temperatures, nutrient availability, and oxygen levels. Most strains are harmless, but some are associated with food-poisoning.
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  • Why is E. coli important?
    • E. coli are one of the most well-studied model organisms in science. As a single-celled organism, E. coli reproduces rapidly, typically doubling its population every 20 minutes, which means it can be manipulated easily in experiments. In addition, most naturally occurring strains of E. coli are harmless. Most importantly, the genetics of E. coli are fairly well understood and can be manipulated to study adaptation and evolution.

The data

  • The data we are going to use is part of a long-term evolution experiment led by Richard Lenski.

  • The experiment was designed to assess adaptation in E. coli. A population was propagated for more than 40,000 generations in a glucose-limited minimal medium (in most conditions glucose is the best carbon source for E. coli, providing faster growth than other sugars). This medium was supplemented with citrate, which E. coli cannot metabolize in the aerobic conditions of the experiment. Sequencing of the populations at regular time points revealed that spontaneous citrate-using variant (Cit+) appeared between 31,000 and 31,500 generations, causing an increase in population size and diversity. In addition, this experiment showed hypermutability in certain regions. Hypermutability is important and can help accelerate adaptation to novel environments, but also can be selected against in well-adapted populations.

  • To see a timeline of the experiment to date, check out this figure, and this paper Blount et al. 2008: Historical contingency and the evolution of a key innovation in an experimental population of Escherichia coli.

View the metadata

We will be working with three sample events from the Ara-3 strain of this experiment, one from 5,000 generations, one from 15,000 generations, and one from 50,000 generations. The population changed substantially during the course of the experiment, and we will be exploring how (the evolution of a Cit+ mutant and hypermutability) with our variant calling workflow. The metadata file associated with this lesson can be downloaded directly here or viewed in Github. If you would like to know details of how the file was created, you can look at some notes and sources here.

This metadata describes information on the Ara-3 clones and the columns represent:

Column Description
strain strain name
generation generation when sample frozen
clade based on parsimony-based tree
reference study the samples were originally sequenced for
population ancestral population group
mutator hypermutability mutant status
facility facility samples were sequenced at
run Sequence read archive sample ID
read_type library type of reads
read_length length of reads in sample
sequencing_depth depth of sequencing
cit citrate-using mutant status

Challenge

Based on the metadata, can you answer the following questions?

  1. How many different generations exist in the data?
  2. How many rows and how many columns are in this data?
  3. How many citrate+ mutants have been recorded in Ara-3?
  4. How many hypermutable mutants have been recorded in Ara-3?
  1. 25 different generations
  2. 62 rows, 12 columns
  3. 10 citrate+ mutants
  4. 6 hypermutable mutants

Information about Illumina Sequencing

This video is a useful background on the Illumina sequencing techology that was used to generate this data. In the next episode we’ll be starting our journey on processing the reads generated from the sequencer.

Key Points

  • It is important to record and understand your experiment’s metadata.