Mini-Project – Calculate Assembly Metrics

Overview

Biological Learning Objectives

• Explore basic assembly metrics
• Examine impact of sequencing technologies

Computational Learning Objectives

• Practice git by developing a project
• Obtain datasets from the internet
• Parse through a .fasta file and calculate metrics

Instructions

Document your answers in ~/qbXX-answers/miniproject-assembly-metrics

git push after each exercise and do not wait until the end of the session e.g.

git add README.md
git commit -m "Start documentation for this assignment"
git push
# Confirm at github.com

Exercises

1. Start documenting your work with a README.md file

   • Change your working directory to your answers repository e.g. cd ~/qb25-answers
   • Make a new directory miniproject-assembly-metrics using mkdir
   • Navigate into your new directory using cd
   • Create a new README.md file using touch
   • Open the file in VS Code using `open README.md -a “visual studio code”
   • Add a title (e.g. # My Project) and a short description using your own words
   • Push your README.md to GitHub
   • Confirm that things look correct at github.com

2. List available genome assemblies for C. remanei

   • Navigate using a web browser to https://parasite.wormbase.org/species.html
   • Scroll down to Caenorhabditis remanei
   • Click on each of the four assemblies and copy the URLs for the Genomic Sequence (FASTA)
   • Add each URL to your README as a list item (e.g. - https://ftp.ebi.ac.uk/genome.fa.gz)
   • Push your README.md to GitHub and confirm that things look correct

3. Create a Bash script to download the genome assemblies

   • Create a new file named getGenomes.sh
   • Add a new line for each assembly in the format wget URL
   • Make the file executable using chmod +x
   • Run your script using ./getGenomes.sh
   • Confirm that you have four files that are 30-40 Mb using ls -lh
   • Uncompress the four files using gunzip *.gz
   • Update your README.md with the uncompressed file sizes
   • Push your script and README.md and to GitHub

4. Create a Python script that reads in a single .fasta file and reports basic metrics

   Fetch fasta module which defines FASTAReader class

   • Download fasta.py to your directory using wget
   • Open fasta.py in VS Code to skim over the FASTAReader class

   Write a Python script that expects a .fasta file as an argument (e.g. ./assembly-metrics.py sequences.fa)

   • Create a new file named assembly-metrics.py
   • Add a “hash-bang” (#!) line to declare this a python3 script
   • Import the sys and fasta module e.g. import fasta
   • Use open() to open the file specified as the “1-th” command line argument e.g. [1]
   • Create a FASTAReader object using fasta.FASTAReader( ___ )
   • Iterate through FASTAReader using for ident, sequence in ____
     • Count the number of contigs
     • Determine each sequence length using len() and sum up the total length
   • Print the “Number of contigs: “, “Total length: “, and “Average length: “

   Analyze the four .fa files using your script

   • Run your script on all four .fa files
   • Update README.md with instructions on using your script and summary of results
   • Push your script and updated README.md

5. Extend your Python script to calculate the N50 statistic

   • Create a list to store contig lengths
   • Append each contig length to your list using ___.append( ___ )
   • Sort contig lengths using ___.sort( reverse=True )
   • Iterate through the list using a for loop and at each iteration
     • Sum up the cumulative length thus far
     • Stop when the cumulative length is greater than half the total length
   • Print out the “sequence length of the shortest contig at 50% of the total assembly length”
   • Run your script on all four .fa files and
   • Update README.md with a summary of results
   • Push your script and updated README.md

Grading

• README – 2 pt for having all five details (description, URLs, file sizes, first summary, second summary)
• Bash script – 1 pt for getGenomes.sh
• Python script – 3 pt for original assembly-metrics.py
• N50 statistic – 2 pt for extending Python script
• git – 2 pt for making a commit after each of the five exercises