QUAST 4.0 Using Atmosphere

Rationale and background:

QUAST: QUality ASsesment Tool for Genome Assemblies

Gurevich, A., Saveliev, V., Vyahhi, N., and Tesler, G. (2013) QUAST: quality assessment tool for genome assemblies. Bioinformatics 29, 1072-1075

QUAST is a tool for evaluating genome assemblies by computing various metrics, including 

  • N50, length for which the collection of all contigs of that length or longer covers at least 50% of assembly length,
  • NG50, where length of the reference genome is being covered,
  • NA50 and NGA50, where aligned blocks instead of contigs are taken,
  • misassemblies, misassembled and unaligned contigs or contigs bases,
  • genes and operons covered

QUAST Builds convenient plots for different metrics

  • cumulative contigs length,
  • all kinds of N-metrics,
  • genes and operons covered,
  • GC content.


This tutorial will orient you to using the QUAST (version 4.0) installed on Atmosphere. This tutorial provides instructions for the general QUAST tool for genome assemblies, MetaQUAST, the extension for metagenomic datasets, and Icarus, interactive visualizer for these tools. 

This tutorial will take users through steps of:

  1. Launching the QUAST-4.0 Atmosphere image
  2. Running QUAST-4.0 on an test data 

Please work through the tutorial and add your comments on the bottom of this page. Or send comments per email to upendra@cyverse.org. Thank you.

Learn about allocations

Learn about CyVerse's allocation policies here. 

Part 1: Connect to an instance of an Atmosphere Image (Virtual Machine)

Step 1. Go to https://atmo.iplantcollaborative.org and log in with your CyVerse credentials.

Step 2. Click on the Launch New Instance button and search for QUAST-4.0

Step 3. Select the image QUAST 4.0 and click Launch Instance. It will take 10-15 minutes for the cloud instance to be launched. 


Note: Instances can be configured for different amounts of CPU, memory, and storage depending on user needs.  This tutorial can be accomplished with the medium instance size, small1 (2 CPUs, 8 GB memory, 60 GB root)

Part 2: Set up a Quast-4.0 run using the Terminal window

Step 1. Open the Terminal.  Add the ssh details along with your IP address to connect the instance through the terminal. Remember to put your actually iPlant username in place of the text 'username' and 'IPaddress' in this next line of code:

$ ssh <username>@<IPaddress>

Step 2. You will find test data in "/opt/quast-4.0/test_data" folder. List its contents with the ls command. 

$ ls /opt/quast-4.0/test_data/
contigs_1.fasta  genes.gff   genes.txt             meta_contigs_2.fasta  meta_ref_2.fasta  operons.gff  reads1.fastq.gz  reference.fasta.gz
contigs_2.fasta  genes.ncbi  meta_contigs_1.fasta  meta_ref_1.fasta      meta_ref_3.fasta  operons.txt  reads2.fastq.gz

We'll change to the test_data directory for the remaining steps.

$ cd /opt/quast-4.0/

Part 3: Run Quast-4.0

1. Basic testing 


$ python quast.py -o ~/quast_test_output -R test_data/reference.fasta.gz -G test_data/genes.gff test_data/contigs_1.fasta test_data/contigs_2.fasta

2. SV calling

$ python quast.py -o ~/quast_test_output_sv -R /opt/quast-4.0/test_data/reference.fasta.gz -O /opt/quast-4.0/test_data/operons.gff -G /opt/quast-4.0/test_data/genes.gff --gage  --gene-finding  --eukaryote  --glimmer  -1 /opt/quast-4.0/test_data/reads1.fastq.gz -2 /opt/quast-4.0/test_data/reads2.fastq.gz /opt/quast-4.0/test_data/contigs_1.fasta /opt/quast-4.0/test_data/contigs_2.fasta

3. MetaQuast with reference

$ python metaquast.py -o ~/metaquast_test_output -R /opt/quast-4.0/test_data/meta_ref_1.fasta,/opt/quast-4.0/test_data/meta_ref_2.fasta,/opt/quast-4.0/test_data/meta_ref_3.fasta /opt/quast-4.0/test_data/meta_contigs_1.fasta /opt/quast-4.0/test_data/meta_contigs_2.fasta

4. MetaQuast with no reference

$ sudo python metaquast.py -o ~/metaquast_test_output_no_ref /opt/quast-4.0/test_data/meta_contigs_1.fasta /opt/quast-4.0/test_data/meta_contigs_2.fasta


Successful execution of the QUAST assessment pipeline will create the following ouput

QUAST output contains:

report.txtassessment summary in plain text format,
report.tsvtab-separated version of the summary, suitable for spreadsheets (Google Docs, Excel, etc),
report.texLaTeX version of the summary,
alignment.svgcontig alignment plot (file is created if matplotlib python library is installed),
report.pdfall other plots combined with all tables (file is created if matplotlib python library is installed),
report.htmlHTML version of the report with interactive plots inside,
misassemblies_reportdetailed report on misassemblies
unaligned_reportdetailed report on unaligned and partially unaligned contigs


  • metrics based on a reference genome are computed only if a reference is provided
  • metrics based on genes and operons are computed only if proper annotations are provided

 More detailed explanation of the above ouput is provided in QUAST manual


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