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<html>
<head>
<title>QUAST 5.1.0 manual</title>
<style type="text/css">
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</style>
</head>
<body>
<h1>QUAST 5.1.0 manual</h1>
<p> QUAST stands for <u>QU</u>ality <u>AS</u>sessment <u>T</u>ool.
The tool evaluates genome assemblies by computing various metrics. This document provides instructions
for the general <b>QUAST</b> tool for genome assemblies, <b>MetaQUAST</b>, the extension for metagenomic datasets,
<b>QUAST-LG</b>, the extension for large genomes (e.g., mammalians), and <b>Icarus</b>, the interactive visualizer for these tools.
<br>
<br>
You can find key project news and the latest version of the tool
at <a href="http://quast.sf.net/">http://quast.sf.net/</a>.
We also post all our news and related stuff on <a href="https://twitter.com/quast_bioinf">Twitter</a>.
<br>
<br>
QUAST default pipeline utilizes <a href="https://github.com/lh3/minimap2">Minimap2</a>.
Functional elements prediction modules use
<a href="http://exon.gatech.edu/GeneMark/">GeneMarkS</a>,
<a href="http://exon.gatech.edu/GeneMark/gmes_instructions.html">GeneMark-ES</a>,
<a href="https://ccb.jhu.edu/software/glimmerhmm/">GlimmerHMM</a>,
<a href="https://github.com/tseemann/barrnap">Barrnap</a>,
and <a href="http://busco.ezlab.org/">BUSCO</a>.
QUAST module for finding structural variations applies <a href="https://github.com/lh3/bwa">BWA</a>,
<a href="https://github.com/lomereiter/sambamba">Sambamba</a>,
and <a href="https://github.com/PapenfussLab/gridss">GRIDSS</a>.
Also we use <a href="http://bedtools.readthedocs.io/">bedtools</a> for calculating raw and physical read coverage, which is shown in Icarus contig alignment viewer. Icarus also can use <a href="http://circos.ca">Circos</a> if it is installed in PATH.
QUAST-LG introduced modules requiring <a href="http://http://sun.aei.polsl.pl/kmc">KMC</a>
and <a href="http://toolsmith.ens.utulsa.edu">Red</a>.
In addition, MetaQUAST uses <a href="http://exon.gatech.edu/GeneMark/meta_gmhmmp_instructions.html">MetaGeneMark</a>,
<a href="http://sourceforge.net/p/krona/home/krona/">Krona tools</a>,
<a href="http://blast.ncbi.nlm.nih.gov/Blast.cgi">BLAST</a>, and
<a href="http://www.arb-silva.de/">SILVA</a> 16S rRNA database.
<br>
Almost all tools listed above are built in into the QUAST package which is ready for use by academic, non-profit institutions and U.S. Government agencies.
If you are not in one of these categories please refer to <a href="LICENSE.txt">LICENSE</a> section 'Third-party tools incorporated into QUAST'
for guidelines on how to complete the licensing process.
<br>
<br>
Version 5.1.0rc1 (release candidate) of QUAST was released under GPL v2 on April 29, 2020. Note that some of build-in third-party tools are not under GPL v2. See <a href="LICENSE.txt">LICENSE</a> for details.
</p>
<h2>Contents</h2>
<ol>
<li><a href="#sec1">Installation</a></li>
<li><a href="#sec2">Running QUAST</a>
<ol>
<li><a href="#sec2.1">For impatient people</a></li>
<li><a href="#sec2.2">Input data</a></li>
<li><a href="#sec2.3">Command line options</a></li>
<li><a href="#sec2.4">Metagenomic assemblies</a></li>
</ol>
</li>
<li><a href="#sec3">QUAST output</a>
<ol>
<li><a href="#sec3.1">Metrics description</a>
<ol>
<li><a href="#sec3.1.1">Summary report</a></li>
<li><a href="#sec3.1.2">Misassemblies report</a></li>
<li><a href="#sec3.1.3">Unaligned report</a></li>
</ol>
</li>
<li><a href="#sec3.2">Plots descriptions</a></li>
<li><a href="#sec3.3">MetaQUAST output</a></li>
<li><a href="#sec3.4">Icarus output</a></li>
</ol>
</li>
<li><a href="#sec4">Adjusting QUAST reports and plots</a></li>
<li><a href="#sec5">Citation</a></li>
<li><a href="#sec6">Feedback and bug reports</a></li>
<li><a href="#sec7">FAQ</a></li>
</ol>
<a name="sec1"></a>
<h2>1. Installation</h2>
<p>
QUAST can be run on Linux (64-bit and 32-bit with slightly limited functionality) or macOS (OS X).
</p>
<p>
Its default pipeline requires:
</p>
<ul>
<li>Python2 (2.5 or higher) or Python3 (3.3 or higher)</li>
<li>GCC 4.7 or higher</li>
<li>Perl 5.6.0 or higher</li>
<li>GNU make and ar</li>
<li>zlib development files</li>
</ul>
<p>
In addition, QUAST submodules require:
</p>
<ul>
<li>Time::HiRes perl module for GeneMark-ES (gene prediction in eukaryotes)</li>
<li>Java 1.8 or later for GRIDSS (SV detection)</li>
<li>R for GRIDSS (SV detection)</li>
</ul>
All default requirements are usually pre installed on Linux. The only missing one could be <code>zlib-dev</code>.
See <a href="https://zlib.net/">their web-site</a> for installation details for various platforms.
In particular, it is pre installed on macOS and it can be simply installed on Ubuntu as:<br>
<pre class="code">
sudo apt-get install zlib1g-dev
</pre>
MacOS, however, initially misses <code>make</code>, <code>gcc</code> and <code>ar</code>, so you will have to install
<a href='https://developer.apple.com/xcode/'>Xcode</a> to make them available.
<br>
<br>
QUAST draws plots in two formats: HTML and PDF. If you need the PDF versions, make sure that you have installed <a href="http://matplotlib.sourceforge.net/">Matplotlib</a> Python library. We recommend to use Matplotlib version 1.1 or higher. QUAST is fully tested with Matplotlib v.1.3.1.
Installation on Ubuntu:
<pre class="code">
sudo apt-get install -y pkg-config libfreetype6-dev libpng-dev python-matplotlib
</pre>
<br>
<!--
To download the <a href="https://downloads.sourceforge.net/project/quast/quast-5.1.0.tar.gz">QUAST source code tarball</a> and extract it, type:
<pre class="code">
wget https://downloads.sourceforge.net/project/quast/quast-5.1.0.tar.gz
tar -xzf quast-5.1.0.tar.gz
cd quast-5.1.0
</pre>
-->
To download the <a href="https://github.com/ablab/quast/releases/download/quast_5.1.0rc1/quast-5.1.0rc1.tar.gz">QUAST source code tarball</a> and extract it, type:
<pre class="code">
wget https://github.com/ablab/quast/releases/download/quast_5.1.0rc1/quast-5.1.0rc1.tar.gz
tar -xzf quast-5.1.0rc1.tar.gz
cd quast-5.1.0rc1
</pre>
<br>
<p>
QUAST automatically compiles all its sub-parts when needed (on the first use).
Thus, installation is not required. However, if you want to precompile everything and add quast.py to your <code>PATH</code>, you may choose either:
<p>
Basic installation (about 120 MB):
<pre class="code">
./setup.py install
</pre>
</p>
or
<p>
Full installation (about 540 MB, additionally includes (1) tools for SV detection based on read pairs, which is used for more precise misassembly detection, and (2) tools/data for reference genome detection in metagenomic datasets):
<pre class="code">
./setup.py install_full
</pre>
</p>
The default installation location is <code>/usr/local/bin/</code> for the executable scripts, and <code>/usr/local/lib/</code> for the python modules and auxiliary files.
If you are getting a permission error during the installation, consider running <code>setup.py</code> with <code>sudo</code>,
or creating a virtual python environment and <a href="http://docs.python-guide.org/en/latest/dev/virtualenvs/">install into it</a>.
Alternatively, you may use old-style installation scripts (<code>./install.sh</code> or <code>./install_full.sh</code>), which build QUAST package inplace.
</p>
<a name="sec2"></a>
<h2 style='margin-bottom: -40px;'>2. Running QUAST</h2>
<a name="sec2.1"></a>
<h3>2.1 For impatient people</h3><br>
Running QUAST on test data from the installation tarball (reference genome, gene annotations, and two assemblies of the first 10<span class="rhs"> </span>kbp of <i>E. coli</i>):
<pre class="code">
./quast.py test_data/contigs_1.fasta \
test_data/contigs_2.fasta \
-r test_data/reference.fasta.gz \
-g test_data/genes.gff
</pre>
View the summary of the evaluation results with the <a href="http://www.greenwoodsoftware.com/less/">less</a> utility:
<pre class="code">
less quast_results/latest/report.txt
</pre>
<a name="sec2.2"></a>
<h3>2.2 Input data</h3>
<p>
The <code>test_data</code> directory contains examples of assemblies, reference genomes, gene and operon annotations, and raw reads files.<br>
<br>
<b>Sequences</b><br>
The tool accepts assemblies and reference genomes in FASTA format. Files may be compressed with zip, gzip, or bzip2.<br>
A reference genome with multiple chromosomes can be provided as a single FASTA file with separate sequence for each chromosome inside.<br>
<span style='line-height: 50%;'> </span><br>
<b>Reads</b><br>
QUAST accepts Illumina, PacBio, and Oxford Nanopore reads in FASTQ format (may be compressed) or in the aligned form in SAM/BAM formats.<br>
<span style='line-height: 50%;'> </span><br>
<b>Genes and operons</b><br>
One can also specify files with gene and operon positions in the reference genome. QUAST will count fully and partially aligned regions,
and output <a href='#genes'>total values</a> as well as <a href='#gene_plot'>cumulative plots</a>.<br>
<span style='line-height: 50%;'> </span><br>
The following file formats are supported:
<ul>
<li>GFF, versions <a href="http://www.sanger.ac.uk/resources/software/gff/spec.html">2</a> and <a href="http://www.sequenceontology.org/gff3.shtml">3</a>;
<li><a href="http://www.ensembl.org/info/website/upload/bed.html">BED</a>: sequence name, start position, end position, gene/operon id, optional fields;
<li>the <a href="http://www.ncbi.nlm.nih.gov/gene">format used by NCBI</a> for genes ("Summary (text)");
<li>four tab-separated columns: sequence name, gene/operon id, start position, end position.
</ul>
Note that the sequence name has to fully match a name in the reference file.<br>
<span style='line-height: 50%;'> </span><br>
Coordinates are 1-based, i.e. the first nucleotide in the reference genome has position 1, not 0.
If a <i>start position</i> is less than a corresponding <i>end position</i>, such gene or operon is located on the forward strand,
and on the reverse-complement strand otherwise.
</p>
<a name="sec2.3"></a>
<h3>2.3 Command line options</h3>
<br>
QUAST runs from a command line as follows:
<pre class='code'>
python quast.py [options] <contig_file(s)>
</pre>
Options:
<div class='options'>
<div class='option'>
<code><b>-o</b> <output_dir></code>
</div>
Output directory. The default value is <code>quast_results/results_<date_time></code>.<br>
Also, <code>quast_results/latest</code> symlink is created.<br>
<br>
Note: QUAST reuses existing alignments if run repeatedly with the same output directory.
Thus, you can efficiently reuse already computed results when running QUAST with different parameters, or adding more assemblies to the existing comparison.
<div class='option'>
<code><b>-r</b> <path></code>
</div>
Reference genome file. Optional. Many metrics can't be evaluated without a reference. If this is omitted, QUAST will only report the metrics that can be evaluated without a reference.
<div class='option'>
<a name='genomic_feature'></a>
<code><b>--features</b></code> (or <code>-g</code>) <code><path></code>
</div>
File with genomic feature positions in the reference genome. See details about the file format in <a href="#sec2.2">section 2.2</a>.<br>
If you use GFF format and would like to count only a specific feature from it (e.g., only "CDS" or only "gene") you can
specify this feature followed by a colon (":") as the filepath prefix (do not use spaces!). For example:<br>
<div style='margin-left: 30px; margin-top: 5px; margin-bottom: 10px;'>
<code>--features CDS:~/data/my_genome_annotation.gff</code>
</div>
or<br>
<div style='margin-left: 30px; margin-top: 5px; margin-bottom: 10px;'>
<code>--features gene:./test_data/genes.gff</code>
</div>
Otherwise, all features from the file will be considered.
<span style='line-height: 50%;'> </span><br>
If you do not have the annotated positions, you can make QUAST predict genes with <code><a href='#gene_finding'>--gene-finding</a></code>.<br>
<div class='option'>
<a name='min_contig'></a>
<code><b>--min-contig</b></code> (or <code>-m</code>) <code><int></code>
</div>
Lower threshold for a contig length (in bp). Shorter contigs won't be taken into account
(except for specific metrics, see <a href="#sec3">section 3</a>). The default value is 500.
<div class='option'>
<a name='threads_opt'></a><code><b>--threads</b></code> (or <code>-t</code>) <code><int></code>
</div>
Maximum number of threads. The default value is 25% of all available CPUs but not less than 1. If QUAST fails to determine the number of CPUs, maximum threads number is set to 4.
</div>
<br>
Advanced options:
<div class='options'>
<div class='option'>
<a name='scaffolds'></a><code><b>--split-scaffolds</b></code> (or <code>-s</code>)
</div>
The assemblies are scaffolds (rather than contigs). QUAST will add split versions
of assemblies to the comparison (named <assembly_name>_broken).
Assemblies are split by continuous fragments of N's of length ≥ 10.
If broken version is equal to the original assembly (i.e. nothing was split) it is not included in the comparison.
<div class='option'>
<code><b>--labels</b></code> (or <code>-l</code>) <code><label,label...></code>
</div>
Human-readable assembly names. Those names will be used in reports, plots and logs. For example:<br>
<div style='margin-left: 30px; margin-top: 5px; margin-bottom: 10px;'>
<code>-l SPAdes,IDBA-UD</code>
</div>
If your labels include spaces, use quotes:<br>
<div style='margin-left: 30px; margin-top: 5px; margin-bottom: 10px;'>
<code>-l SPAdes,"Assembly 2",Assembly3</code>
</div>
<div style='margin-left: 30px; margin-top: 5px; margin-bottom: 10px;'>
<code>-l "SPAdes 2.5, SPAdes 2.4, IDBA-UD"<br></code>
</div>
<div class='option'>
<code><b>-L</b></code>
</div>
Take assembly names from their parent directory names.
<div class='option'>
<a name="eukaryote"></a>
<code><b>--eukaryote</b></code> (or <code>-e</code>)
</div>
Genome is eukaryotic. Affects gene finding, conserved orthologs finding and contig alignment:<br>
<ol class='my_ol' style='margin-top: 0;'>
<li>For prokaryotes (which is default), GeneMarkS is used. For eukaryotes, GeneMark-ES is used.
<li><a href='#rna_finding'>Barrnap</a> will use eukaryotic database to predict ribosomal RNA genes.
<li><a href='#busco'>BUSCO</a> will use eukaryotic database to find conserved orthologs.
<li>By default, QUAST assumes that a genome is circular and correctly processes its linear representation.
This options indicates that the genome is not circular.
</ol>
<div class='option'>
<a name="fungus"></a>
<code><b>--fungus</b></code>
</div>
Genome is fungal. Affects gene finding, conserved orthologs finding and contig alignment:<br>
<ol class='my_ol' style='margin-top: 0;'>
<li>For gene finding, GeneMark-ES is used with <code>--fungus</code> option.
<li><a href='#rna_finding'>Barrnap</a> will use eukaryotic database to predict ribosomal RNA genes.
<li><a href='#busco'>BUSCO</a> will use fungal database to find conserved orthologs.
<li>By default, QUAST assumes that a genome is circular and correctly processes its linear representation.
This options indicates that the genome is not circular.
</ol>
<div class='option'>
<a name="large"></a>
<code><b>--large</b></code>
</div>
Genome is large (typically > 100 Mbp). Use optimal parameters for evaluation of large genomes.
Affects speed and accuracy. In particular, imposes <code>--eukaryote --min-contig 3000 --min-alignment 500 --extensive-mis-size 7000</code>
(can be overridden manually with the corresponding options). In addition, this mode tries to identify misassemblies caused by transposable elements
and exclude them from the number of misassemblies. See <a href="http://dx.doi.org/10.1093/bioinformatics/bty266">Mikheenko et al., 2018</a> for more details.
<div class='option'>
<a name='k_mer_stats'></a><code><b>--k-mer-stats</b></code> (or <code>-k</code>)
</div>
Compute k-mer-based quality metrics, such as <span class='metric-ref'>k-mer-based completeness, # k-mer-based misjoins</span>.
Recommended for large genomes (see also <code>--large</code> option).
Affects performance, thus disabled by default.<br>
<div class='option'>
<a name='k_mer_size'></a><code><b>--k-mer-size</b></code> <code><int></code>
</div>
Size of <code>k</code> used in <code>--k-mer-stats</code> options. The default value is 101 bp. Use smaller values for genomes
with high levels of heterozygosity. However, note that very small k-mer sizes may give irrelevant results for repeat-rich genomes.
<div class='option'>
<a name='circos'></a><code><b>--circos</b></code>
</div>
Plot Circos version of Icarus contig alignment viewer.
The plot (<code>circos.png</code>), legend explaining its labels and content (<code>legend.txt</code>),
and Circos configuration file (<code>circos.conf</code>) will be saved in <code><quast_output_dir>/circos/</code>.
Feel free to edit the configuration file, e.g. update label names, and regenerate the plow with <code>circos -conf <quast_output_dir>/circos/circos.conf</code>.
Note that Circos software is not embedded in QUAST, so if
Circos is not installed on your machine (in the <code>PATH</code> environment variable) only the legend and configuration file will be created.
<div class='option'>
<a name='gene_finding'></a><code><b>--gene-finding</b></code> (or <code>-f</code>)
</div>
Enables gene finding. Affects performance, thus disabled by default.<br>
<span style='line-height: 50%;'> </span><br>
By default, we assume that the genome is prokaryotic, and apply GeneMarkS for gene finding.
If the genome is eukaryotic, use <a href='#eukaryote'><code>--eukaryote</code></a> option to enable GeneMark-ES instead.
If the genome is fungal, use <a href='#fungus'><code>--fungus</code></a> option to run GeneMark-ES in a special mode for analyzing fungal genomes.
If it is a metagenome (you are running <a href='#sec2.4'>metaquast.py</a>), MetaGeneMark is used. You can also force
MetaGeneMark predictions with <a href='#mgm'><code>--mgm</code></a> option described below.
<span style='line-height: 50%;'> </span><br><br>
If a <a href='#sec2.2'>genomic feature file</a> is provided with <a href="#genomic_feature"><code>--features</code></a> as well, both
<span class='metric_ref'><a href='#genes'># genomic features</a></span> (according to the provided feature positions and assembly mappings to the reference), and
<span class='metric_ref'><a href='#predicted_genes'># predicted genes</a></span> (according to the gene finding software) are reported. Note that operons are not predicted,
but a file of known operon positions can be provided with <code>-O</code>.<br>
<div class='option'>
<a name="mgm"></a>
<code><b>--mgm</b></code>
</div>
Force use of MetaGeneMark for gene finding (instead of the default finders: GeneMarkS or GeneMark-ES).
<span style='line-height: 50%;'> </span><br>
Note: if you are working with metagenome assemblies, we recommend <a href='#sec2.4'>to use metaquast.py</a> instead of quast.py
(it is in the same directory as quast.py).
<div class='option'>
<code><b>--glimmer</b></code>
</div>
Use GlimmerHMM for gene finding (instead of GeneMark family of tools). Note: you may skip <code>--gene-finding</code> option if <code>--glimmer</code> is specified.
<div class='option'>
<code><b>--gene-thresholds</b> <int,int,...></code>
</div>
Comma-separated list of thresholds (in bp) for gene lengths to find with a finding tool. The default value is 0,300,1500,3000.
Note: this list is used only if <code>--gene-finding</code> or <code>--glimmer</code> option is specified.
<div class='option'>
<a name='rna_finding'></a><code><b>--rna-finding</b></code>
</div>
Enables ribosomal RNA gene finding. Disabled by default.<br>
<span style='line-height: 50%;'> </span><br>
By default, we assume that the genome is prokaryotic, and Barrnap uses the bacterial database for rRNA prediction.
If the genome is eukaryotic (fungal), use <a href='#eukaryote'><code>--eukaryote</code></a> (<a href='#fungus'><code>--fungus</code></a>)
option to force Barrnap to work with the eukaryotic (fungal) database.
<div class='option'>
<a name='busco'></a><code><b>--conserved-genes-finding</b></code> (or <code>-b</code>)
</div>
Enables search for Universal Single-Copy Orthologs using BUSCO (only on Linux, only with Python 2.7 or Python 3). Disabled by default.<br>
<span style='line-height: 50%;'> </span><br>
By default, we assume that the genome is prokaryotic, and BUSCO uses the bacterial database of orthologs.
If the genome is eukaryotic (fungal), use <a href='#eukaryote'><code>--eukaryote</code></a> (<a href='#fungus'><code>--fungus</code></a>)
option to force BUSCO to work with the eukaryotic (fungal) database.
<div class='option'>
<code><b>--operons</b> <path></code>
</div>
File with operon positions in the reference genome. See details about the file format in <a href="#sec2.2">section 2.2</a>.
<div class='option'>
<a name='est_ref_size'></a><code><b>--est-ref-size</b> <int></code>
</div>
Estimated reference genome size (in bp) for computing <span class='metric-ref'>NGx</span> statistics. This value will be used only if a reference genome file is
not specified (see <code>-r</code> option).
<div class='option'>
<code><b>--contig-thresholds</b> <int,int,...></code>
</div>
Comma-separated list of contig length thresholds (in bp). Used in <span class='metric-ref'># contigs ≥ x</span> and
<span class='metric-ref'>total length (≥ x)</span> metrics (see <a href="#sec3">section 3</a>). The default value is 0,1000,5000,10000,25000,50000.
<div class='option'>
<a name='x_for_Nx'></a><code><b>--x-for-Nx</b> <int></code>
</div>
Value of 'x' for <span class='metric-ref'>Nx</span>, <span class='metric-ref'>Lx</span>, <span class='metric-ref'>NGx</span>, <span class='metric-ref'>NGAx</span>, etc metrics reported in addition to <span class='metric-ref'>N50</span>, <span class='metric-ref'>L50</span>, <span class='metric-ref'>NG50</span>, <span class='metric-ref'>NGA50</span>, etc (see <a href="#sec3">section 3</a>).
The value should be an integer between 0 and 100. The default value is 90.
<div class='option'>
<code><b>--use-all-alignments</b></code> (or <code>-u</code>)
</div>
Compute <span class='metric-ref'>genome fraction, # genomic features, # operons</span> metrics in the manner used in QUAST v.1.*.
By default, QUAST v.2.0 and higher filters out ambiguous and redundant alignments, keeping only one alignment per contig
(or one set of non-overlapping or slightly overlapping alignments). This option makes QUAST count all alignments.
<div class='option'>
<a name='min_alignment'></a><code><b>--min-alignment</b></code> (or <code>-i</code>) <code><int></code>
</div>
Minimum length of alignment (in bp). Alignments shorter than this value will be filtered. Note that all alignments shorter than 65 bp will be filtered regardless of this threshold.
<div class='option'>
<a name='min_identity'></a><code><b>--min-identity</b></code> <code><float></code>
</div>
Minimum IDY% considered as proper alignment. Alignments with IDY% worse than this value will be filtered. Default is 95.0% for regular QUAST and 90.0% for <a href='#sec2.4'>MetaQUAST</a>.
Note that all alignments with IDY% less than 80.0% will be filtered regardless of this threshold.
<div class='option'>
<a name='ambiguity_usage'></a><code><b>--ambiguity-usage</b> (or <code>-a</code>) <<b>none</b>|<b>one</b>|<b>all</b>></code>
</div>
Way of processing equally good alignments of a contig (probably repeats):<br>
<table style="margin-left: 30px; font-size: 1em; vertical-align: bottom;">
<tr><td style='width: 40px;'><code>none</code></td><td>skip all such alignments;</td></tr>
<tr><td><code>one</code></td><td>take only one (the very best one);</td></tr>
<tr><td><code>all</code></td><td>use all alignments. Can cause a significant increase of <span class='metric-ref'># mismatches</span>
(repeats are almost always inexact due to accumulated SNPs, indels, etc.).</td></tr>
</table>
The default value is <code>one</code>.<br>
<div style='margin-top: 6;'>The value <code>all</code> is useful for metagenomic assemblies where ambiguous alignments might represent homologous sequences of different strains.
For that reason, <code>--ambiguity-usage</code> is set to <code>all</code> for the "combined reference" evaluation (see <a href='#sec2.4'>section 2.4</a>).
You may still modify this behaviour with <a href='#unique_mapping'><code>--unique-mapping</code></a>.</div>
<div class='option'>
<code><b>--ambiguity-score</b></code> <code><float></code>
</div>
Score S for defining equally good alignments of a single contig (see <a href='#ambiguity_usage'><code>--ambiguity-usage</code></a>). All alignments are sorted by decreasing LEN<span class='rhs'> </span>×<span class='rhs'> </span>IDY% value. All alignments with LEN<span class='rhs'> </span>×<span class='rhs'> </span>IDY% less than S<span class='rhs'> </span>×<span class='rhs'> </span>best(LEN<span class='rhs'> </span>×<span class='rhs'> </span>IDY%) are discarded. S should be between 0.8 and 1.0. The default value is 0.99.
<div class='option'>
<code><b>--strict-NA</b></code>
</div>
Break contigs at every misassembly event (including local ones) to compute <span class='metric-ref'>NAx</span> and
<span class='metric-ref'>NGAx</span> statistics. By default, QUAST breaks contigs <i>only at extensive</i> misassemblies (not local ones).
<div class='option'>
<a name='extensive_mis_size'></a><code><b>--extensive-mis-size</b></code> (or <code>-x</code>) <code><int></code>
</div>
Lower threshold for the relocation size (gap or overlap size between left and right flanking sequence, see <a href="#sec3.1.2">section 3.1.2</a> for details).
Shorter relocations are considered as local misassemblies. It does not affect other types of extensive misassemblies (inversions and translocations).
The default value is 1000 bp. Note that the threshold should be greater than maximum indel length which is 85 bp.
<div class='option'>
<a name='scaffold_gap_size'></a><code><b>--scaffold-gap-max-size</b></code> <code><int></code>
</div>
Max allowed scaffold gap length difference for detecting corresponding type of misassemblies (see <a href="#sec3.1.2">section 3.1.2</a>
for details).
Longer inconsistencies are considered as relocations and thus, counted as extensive misassemblies. The default value is 10000 bp.
Note that the threshold make sense only if it is greater than extensive misassembly size
(see <a href='#extensive_mis_size'><code>--extensive-mis-size</code></a>, its default value is 1000 bp).
<div class='option'>
<a name='unaligned_part_size'></a><code><b>--unaligned-part-size</b></code> <code><int></code>
</div>
Lower threshold for detecting partially unaligned contigs, see <a href="#sec3.1.3">section 3.1.3</a> for details.
The default value is 500 bp.
<div class='option'>
<a name='skip_unaligned_mis_contigs'></a><code><b>--skip-unaligned-mis-contigs</b></code>
</div>
Do not distinguish contigs with more than 50% unaligned bases as a separate group of contigs.
By default, QUAST does not count misassemblies in them.
See <a href='#unaligned_mis_contigs'><code># unaligned mis. contigs</code></a> metric for more details.
<div class='option'>
<a name='fragmented'></a><code><b>--fragmented</b></code>
</div>
Reference genome is fragmented (e.g. a scaffold reference). QUAST will try to detect misassemblies caused by the fragmentation and
mark them fake (will be excluded from <a href="#misassemblies"><code># misassemblies</code></a>).
Note: QUAST will not detect misassemblies caused by the linear representation of circular genome.
<div class='option'>
<a name='fragmented_indent'></a><code><b>--fragmented-max-indent</b></code> <code><int></code>
</div>
Mark translocation as fake if both alignments are located no further than N bases from the ends of the reference fragments.
The value should be less than extensive misassembly size
(see <a href='#extensive_mis_size'><code>--extensive-mis-size</code></a>, its default value is 1000 bp). Default value is 50.
Note: requires <a href='#fragmented'><code>--fragmented</code></a> option.
<div class='option'>
<a name='fragmented_indent'></a><code><b>--upper-bound-assembly</b></code>
</div>
Simulate upper bound assembly based on the reference genome and a given set reads
(mate-pairs or long reads, such as Pacbio SMRT/Oxford Nanopore, are REQUIRED).
This assembly is added to the comparison and could be useful for estimating the upper bounds of completeness and contiguity
that theoretically can be reached by assembly software from this particular set of reads.
The concept is based on the fact that the reference genome cannot be completely reconstructed
from raw reads due to long genomic repeats and low covered regions.
See <a href="http://dx.doi.org/10.1093/bioinformatics/bty266">Mikheenko et al., 2018</a> for more details.
Note: this option requires reads; if reads are provided, QUAST performs various types of time-consuming analysis
(structural variations detection and read-based metrics computation);
if you are not interested in this additional analysis and want to get only the upper bound assembly,
you may consider adding <a href='#no_sv'><code>--no-sv</code></a> and <a href='#no_read_stats'><code>--no-read-stats</code></a>
to your command line.
<div class='option'>
<a name='fragmented_indent'></a><code><b>--upper-bound-min-con</b></code> <code><int></code>
</div>
Minimal number of 'connecting reads' needed for joining upper bound contigs into a scaffold
(see also <code>--upper-bound-assembly</code>). This is important for a realistic estimation of genome assembly fragmentation due to long repeats. The default values is 2 for mate-pairs and 1 for long reads (PacBio or Nanopore libraries).
<div class='option'>
<a name='fragmented_indent'></a><code><b>--est-insert-size</b></code> <code><int></code>
</div>
Paired-reads insert size used in the upper bound assembly construction (see also <code>--upper-bound-assembly</code>).
It is used for detecting minimal repeat size that spans the upper bound assembly into contigs.
By default, the value is automatically detected as the median insert size of provided paired-end reads.
If no paired-end reads are provided, 255 is used as the default value.
<div class='option'>
<code><b>--plots-format</b></code> <code><format></code>
</div>
File format for plots. Supported formats: emf, eps, pdf, png, ps, raw, rgba, svg, svgz.
The default format is PDF.
<div class='option'>
<a name='memory_eff'></a><code><b>--memory-efficient</b></code>
</div>
Use one thread, separately per each assembly and each chromosome. This may significantly reduce memory consumption for large genomes.
Note: this option will significantly slow down the processing as well.
So, it makes sense to first just try to reduce the default number of threads using <a href='#threads_opt'><code>-t</code></a> option and
only then use <code>--memory-efficient</code> (or <code>-t 1</code>).
Another possible way to reduce RAM usage is to skip gene/operon annotation step (do not specify the corresponding files with <code>-g</code> and <code>-O</code>).
Of course the latter advice is applicable only if you are fine to get the report without such annotations.
<div class='option'>
<a name='space_eff'></a><code><b>--space-efficient</b></code>
</div>
Create only primary output items (reports, plots, quast.log, etc). All auxiliary files (.stdout, .stderr, etc) will not be created.
This may significantly reduce disk space usage on large assemblies (more than 100k contigs).
Note: Icarus viewers also will not be built because they became enormously large and slow in case of
zillions of contigs, thus not applicable. Circos plot needs detailed information about all alignments, so it also will not be created.
</div>
<br>
<a name='reads_opt'></a>Reads options:
<p>Note: among other applications, reads are used for SV detection (<b>experimental</b>,
please use it carefully until we finalize the feature; you can skip SV processing with <a href='#no_sv'><code>--no-sv</code></a>).
The reads are aligned to reference genome using BWA, then GRIDSS SV calling tool is run on BWA output.
Found SVs are used for classifying QUAST misassemblies into true ones and fake ones (caused by structural differences between reference sequence and
sequenced organism). Fake misassemblies are excluded from <a href="#misassemblies"><code># misassemblies</code></a> and reported as <a href="#sv"><code># structural variations</code></a>.
</p>
<p>
For specifying multiple read files of the same format just use the corresponding option multiple times. For example:
<div style='margin-left: 30px; margin-top: 5px; margin-bottom: 10px;'>
<code>--pe1 lib1/R1.fq --pe2 lib1/R2.fq --pe1 lib2/R1.fq.gz --pe2 lib2/R2.fq.gz</code>
</div>
or<br>
<div style='margin-left: 30px; margin-top: 5px; margin-bottom: 10px;'>
<code>--single lib1/unpaired_a.fq --single lib1/unpaired_b.fq --single lib2/unpaired.fastq</code>
</div>
</p>
<p>
Currently, the supported read types are Illumina unpaired, paired-end and mate-pair reads, PacBio SMRT, and Oxford Nanopore long reads.
If paired reads are specified in separate files (e.g. using <code>--mp1/--mp2</code>), the number of reads in both files should be exactly the same.
Moreover, the names of forward and reverse reads of the same pair should be exactly the same except trailing /1 and /2, respectively.
</p>
<div class='options'>
<div class='option'>
<code><b>--pe1</b></code> (or <code>-1</code>) <code><path></code>
</div>
File with forward paired-end reads in FASTQ format (files compressed with gzip are allowed).
<div class='option'>
<code><b>--pe2</b></code> (or <code>-2</code>) <code><path></code>
</div>
File with reverse paired-end reads in FASTQ format (files compressed with gzip are allowed).
<div class='option'>
<code><b>--pe12</b> (or <code>--12</code>) <path></code>
</div>
File with interlaced forward and reverse paired-end reads in FASTQ format (files compressed with gzip are allowed).
<div class='option'>
<code><b>--mp1</b> <path></code>
</div>
File with forward mate-pair reads in FASTQ format (files compressed with gzip are allowed).
<div class='option'>
<code><b>--mp2</b> <path></code>
</div>
File with reverse mate-pair reads in FASTQ format (files compressed with gzip are allowed).
<div class='option'>
<code><b>--mp12</b> <path></code>
</div>
File with interlaced forward and reverse mate-pair reads in FASTQ format (files compressed with gzip are allowed).
<div class='option'>
<code><b>--single</b> <path></code>
</div>
File with unpaired reads in FASTQ format (files compressed with gzip are allowed).
<div class='option'>
<code><b>--pacbio</b> <path></code>
</div>
File with PacBio SMRT reads in FASTQ format (files compressed with gzip are allowed).
<div class='option'>
<code><b>--nanopore</b> <path></code>
</div>
File with Oxford Nanopore reads in FASTQ format (files compressed with gzip are allowed).
<div class='option'>
<code><b>--ref-bam</b> <path></code>
</div>
File with alignments of both forward and reverse reads against the reference genome (in BAM format).
<div class='option'>
<code><b>--ref-sam</b> <path></code>
</div>
File with alignments of both forward and reverse reads against the reference genome (in SAM format).
<div class='option'>
<code><b>--bam</b> <path1,path2,...></code>
</div>
Comma-separated list of BAM alignment files obtained by aligning reads against the assemblies (use the same order as for files with assemblies; do not use spaces)
<div class='option'>
<code><b>--sam</b> <path1,path2,...></code>
</div>
Comma-separated list of SAM alignment files obtained by aligning reads against the assemblies (use the same order as for files with assemblies; do not use spaces)
<div class='option'>
<a name='sv_bedpe'></a><code><b>--sv-bedpe</b> <path></code>
</div>
Use specified file in <a href="http://bedtools.readthedocs.org/en/latest/content/general-usage.html#bedpe-format">BEDPE format</a> as a list of structural variations (SV). This option disables SV detection based on reads.
Examples of BEDPE files for various types of SV are in FAQ section, <a href="#faq_q8">question Q8</a>.
</div>
<br>
Speedup options:
<div class='options'>
<div class='option'>
<code><b>--no-check</b></code>
</div>
<div style='margin-bottom: 6;'>Do not check and correct input FASTA files (both reference genome and assemblies).
By default, QUAST corrects sequence names by replacing special characters
(all symbols except latin letters, numbers, underscores, dots, and minus signs) with underscore ("_").
QUAST also checks and corrects sequences itself. Lowercase letters are changed to uppercase.
Alternative nucleotide symbols (M, K, R, etc) are replaced with N. If non-ACGTN characters
are present after this modifications the whole FASTA file is skipped from further processing. </div>
<i>Caution</i>: use this option at your own risk. Incorrect FASTA files may cause failing of
third-party tools incorporated to QUAST, e.g. Minimap2, GeneMark, GlimmerHMM.
This option is useful for running QUAST without <code>-r</code> and <code>--gene-finding</code>
(no third-party tools will be run) or if you are absolutely sure that your FASTA files are correct.
<div class='option'>
<code><a name='no_plots'></a><b>--no-plots</b></code>
</div>
Do not draw plots.
<div class='option'>
<code><b>--no-html</b></code>
</div>
Do not build HTML reports and <a href="#sec3.4">Icarus viewers</a>.
<div class='option'>
<code><b>--no-icarus</b></code>
</div>
Do not build <a href="#sec3.4">Icarus viewers</a>.
<div class='option'>
<a name='no_snps'></a><code><b>--no-snps</b></code>
</div>
Do not report SNPs statistics. This may significantly reduce memory consumption on large genomes and speed up computation. However, all SNP-related metrics will not be reported (e.g. <code># mismatches per 100 kbp</code>).
<div class='option'>
<code><b>--no-gc</b></code>
</div>
Do not compute GC% and do not produce GC-distribution plots (both in HTML report and in PDF).
<div class='option'>
<a name='no_sv'></a><code><b>--no-sv</b></code>
</div>
Do not run structural variant calling and processing (make sense only if reads are specified).
<div class='option'>
<code><b>--no-gzip</b></code>
</div>
Do not compress large output files (files containing SNP information and predicted genes).
This may speed up computation, but more disk space is required.
<div class='option'>
<a name='no_read_stats'></a><code><b>--no-read-stats</b></code>
</div>
Do not align reads against assemblies and do not report the corresponding metrics.
Reads still will be aligned against the reference genome and used for coverage analysis,
upper bound assembly simulation, and structural variation detection.
<div class='option'>
<a name='fast'></a><code><b>--fast</b></code>
</div>
A shortcut for using all of speedup options except --no-check.
</div>
<br>
Other:
<div class='options'>
<div class='option'>
<code><b>--silent</b></code>
</div>
Do not print detailed information about each step in standard output. This option does not affect <code>quast.log</code> file.
<div class='option'>
<code><b>--test</b></code>
</div>
Run the tool on a data from the <code>test_data</code> folder and check correctness of the evaluation process. Output is saved in <code>quast_test_output</code>.
<div class='option'>
<code><b>--test-sv</b></code>
</div>
Run the tool on a data from the <code>test_data</code> folder using the reads for SV detection.
The tool will compile or download the required programs (BEDtools, BWA, and GRIDSS Structural Variant Caller).
<div class='option'>
<code><b>--help</b></code> (or <code>-h</code>)
</div>
Print help and exit.
<div class='option'>
<code><b>--version</b></code> (or <code>-v</code>)<br>
</div>
Print version and exit.
</div>
<a name="sec2.4"></a>
<h3>2.4 Metagenomic assemblies</h3>
<p>
The <code>metaquast.py</code> script accepts multiple reference genomes.
One can provide several files or directories with multiple reference files inside with <code>-r</code> option.
Option <code>-r</code> may be specified multiple times or all references may be specified as a comma-separated list (<b>without spaces!</b>)
with a single <code>-r</code> option beforehand. Another way is to use <a href='#references_list'><code>--references-list</code></a> option.
<p>General usage:
<pre class="code">
python metaquast.py contigs_1 contigs_2 ... -r reference_1,reference_2,reference_3,...
</pre>
<p>
The tool partitions all contigs into groups aligned to each reference genome.
Note that a contig may belong to several groups simultaneously if it aligns to several references.
<br>
MetaQUAST runs quast.py for each of the following:<br>
<ol>
<li>for all reference genomes in combination (simple concatenation of the FASTA files, we refer to it as "combined reference"),
<li>for each reference genome separately, by using corresponding group of contigs ("runs per reference"),
<li>for the rest of the contigs that were not aligned to any reference genome.
</ol>
<p>
In contrast to regular QUAST, MetaQUAST uses a relaxed minimum identity threshold of 90% for both stages #1 and #2.
This value reflects the fact that metagenomic references are usually inaccurate and represent strains slightly different from the ones in the real environmental sample.
However, this default threshold can be easily changed with <a href='#min_identity'><code>--min-identity</code></a> option,
e.g. <code>--min-identity 95</code> corresponds to 95% IDY threshold as in regular QUAST.
Also, MetaQUAST uses <a href='#ambiguity_usage'><code>--ambiguity-usage</code></a> 'all' when running <code>quast.py</code> on
the combined reference (stage #1) until <a href='#unique_mapping'><code>--unique-mapping</code></a> is specified. Note that even with <code>--unique-mapping</code>,
some ambiguous contigs may produce several distinct mappings to different references (stage #2), e.g. in the case of closely-related strains.
To exclude this effect, one may use <a href='#reuse_combined_alignments'><code>--reuse-combined-alignments</code></a>.
Finally, for gene prediction (<a href='#gene_finding'><code>--gene-finding</code></a>), MetaQUAST uses MetaGeneMark software unlike GeneMarkS/GeneMark-ES in QUAST.
</p>
<p>If you run MetaQUAST without providing reference genomes, the tool will try to identify genome content of the metagenome.
MetaQUAST uses BLASTN for aligning contigs to SILVA 16S rRNA database, i.e. FASTA file containing small subunit ribosomal RNA sequences.
For each assembly, 50 reference genomes with top scores are chosen.
Maximum number of references to download can be specified with <a href='#max_ref_num'><code>--max-ref-number</code></a>.
<p>Reference genomes for the chosen genomes are downloaded from the NCBI database to <code><quast_output_dir>/quast_downloaded_references/</code>.
After that, MetaQUAST runs <code>quast.py</code> on all of them and removes reference genomes with low genome fraction (less than 10%) and
proceeds the usual MetaQUAST analysis with the remaining references.
</p>
<p>
In addition to standard <a href='#sec2.3'>QUAST options</a>, <code>metaquast.py</code> also accepts:
</p>
<div class='options'>
<div class='option'>
<code><b>--use-input-ref-order</b></code>
</div>
Use provided order of references in MetaQUAST summary plots (X-axis). By default,
the ordering is based on the best average value of the metric among all assemblies.
Note: this option affects only static PDF/PNG/etc plots under <code><metaquast_output_dir>/summary/</code>.
Interactive HTML report has radio button to control the order of references.
<div class='option'>
<a name='references_list'></a><code><b>--references-list</b> <path></code>
</div>
Text file with the list of reference genomes (each one on a separate line).
MetaQUAST will search for these references in the NCBI database and will download the found ones.
Example of such file is in FAQ section, <a href="#faq_q10">question Q10</a>.
<div class='option'>
<code><b>--test-no-ref</b></code>
</div>
Run MetaQUAST on a data from the <code>test_data</code> folder, but without reference genomes. The tool will download
SILVA 16S rRNA gene database (170 Mb) and BLAST binaries (55-75 Mb depending on your OS),
which will be required if you plan to use MetaQUAST without references.
See <a href="#sec2.4">section 2.4</a> for details about reference search algorithm.
<div class='option'>
<a href='blast_db'></a><code><b>--blast-db</b></code> <code><path></code>
</div>
Use custom BLAST database instead of embedded SILVA 16S rRNA database. The path should point either to directory
containing .nsq file or to .nsq file itself.
See FAQ section, <a href="#faq_q12">question Q12</a> for details about creating custom BLAST databases.
<div class='option'>
<a href='max_ref_num'></a><code><b>--max-ref-num</b></code> <code><int></code>
</div>
Maximum number of reference genomes (per each assembly) to download after searching in the SILVA database. Default value is 50.
<div class='option'>
<a name='unique_mapping'></a><code><b>--unique-mapping</b></code>
</div>
Force <a href='#ambiguity_usage'><code>--ambiguity-usage</code></a>='one' for the combined reference genome
('all' is used by default).
<div class='option'>
<a name='reuse_combined_alignments'></a><code><b>--reuse-combined-alignments</b></code>
</div>
Reuse the alignments on the combined reference (stage #1) in the subsequent runs per separate references (stage #2).
That is, the alignment procedure is performed only once (for all assemblies against the combined reference)
and does NOT executed for each subgroups of contigs against the corresponding separate reference genomes.
In each separate reference run, all precomputed assembly alignments for other references are simply ignored.
The use of this option (1) speeds up the overall computation,
(2) may change the stage #2 results, especially when used together with <a href='#unique_mapping'><code>--unique-mapping</code></a>.
</div>
<a name="sec3"></a>
<h2>3. QUAST output</h2>
<p>If an output path is not specified manually (with <code>-o</code>), QUAST generates its output into <code>quast_results/result_<DATE></code> directory and
creates <code>latest</code> symlink to it under <code>quast_results/</code> directory.
<br>
<br>
QUAST output contains:
<table style="margin-left: 20px; font-size: 1em;">
<tr>
<td style="padding-right: 20px;">report.txt</td>
<td>assessment summary in plain text format,</td>
</tr>
<tr>
<td style="padding-right: 20px;">report.tsv</td>
<td>tab-separated version of the summary, suitable for spreadsheets (Google Docs, Excel, etc),</td>
</tr>
<tr>
<td style="padding-right: 20px;">report.tex</td>
<td>LaTeX version of the summary,</td>
</tr>
<tr>
<td style="padding-right: 20px;">icarus.html</td>
<td>Icarus main menu with links to interactive viewers. See <a href="#sec3.4">section 3.4</a> for details,</td>
</tr>
<tr>
<td style="padding-right: 20px;">report.pdf</td>
<td>all other plots combined with all tables (file is created if matplotlib python library is installed),</td>
</tr>
<tr>
<td style="padding-right: 20px;">report.html</td>
<td>HTML version of the report with interactive plots inside,</td>
</tr>
<tr>
<td style="padding-right: 20px;">contigs_reports/</td>
<td>(only if a reference genome is provided)</td>
</tr>
<tr>
<td style="padding-right: 20px; padding-left: 20px;">misassemblies_report</td>
<td>detailed report on misassemblies. See <a href="#sec3.1.2">section 3.1.2</a> for details,</td>
</tr>
<tr>
<td style="padding-right: 20px; padding-left: 20px;">unaligned_report</td>
<td>detailed report on unaligned and partially unaligned contigs. See <a href="#sec3.1.3">section 3.1.3</a> for details,</td>
</tr>
<tr>
<td style="padding-right: 20px;">k_mer_stats/</td>
<td>(only if <a href='#k_mer_stats'><code>--k-mer-stats</code><a> option is specified)</td>
</tr>
<tr>
<td style="padding-right: 20px; padding-left: 20px;">kmers_report</td>
<td>detailed report on k-mer-based metrics,</td>
</tr>
<tr>
<td style="padding-right: 20px;">reads_stats/</td>
<td>(only if <a href='#reads_opt'>reads</a> are provided)</td>
</tr>
<tr>
<td style="padding-right: 20px; padding-left: 20px;">reads_report</td>
<td>detailed report on mapped reads statistics.</td>
</tr>
</table>
<br>
Note:
<ul style="margin-top: -12px;">
<li>metrics based on a reference genome are computed only if a reference is provided (see <a href="#sec2.3">section 2.3</a>), </li>
<li>metrics based on genes and operons are computed only if proper annotations are provided (see <a href="#sec2.3">section 2.3</a>). </li>
</ul>
</p>
<div class='metrics_description'>
<a name="sec3.1"></a>