QUAST
QUAST is a state-of-the-art tool for (meta)genome assembly evaluation, computing over 50 quality metrics and presenting results in plain text, static plots, and interactive HTML reports.
Cite this software
Description
The current QUAST toolkit includes the general QUAST tool for genome assemblies, MetaQUAST, the extension for metagenomic datasets, QUAST-LG, the extension for large genomes (e.g., mammalians), and Icarus, the interactive visualizer for these tools.
The QUAST package works with and without reference genomes. However, it is much more informative if at least a close reference genome is provided along with the assemblies. The tool accepts multiple assemblies and is thus suitable for comparison.
This description gives a snapshot of the QUAST running instructions, output interpretation, and reported quality metrics. The online manual provides a much more detailed description of these and many other topics.
Usage
QUAST requires a 64-bit Linux or macOS machine with Python 3. The basic running command is below.
./quast.py test_data/contigs_1.fasta \
test_data/contigs_2.fasta \
-r test_data/reference.fasta.gz \
-g test_data/genes.txt \
-1 test_data/reads1.fastq.gz -2 test_data/reads2.fastq.gz \
-o quast_test_output
Output
report.txt summary table
report.tsv tab-separated version, for parsing, or for spreadsheets (Google Docs, Excel, etc)
report.tex Latex version
report.pdf PDF version, includes all tables and plots for some statistics
report.html everything in an interactive HTML file
icarus.html Icarus main menu with links to interactive viewers
contigs_reports/ [only if a reference genome is provided]
misassemblies_report detailed report on misassemblies
unaligned_report detailed report on unaligned and partially unaligned contigs
k_mer_stats/ [only if --k-mer-stats is specified]
kmers_report detailed report on k-mer-based metrics
reads_stats/ [only if reads are provided]
reads_report detailed report on mapped reads statistics
Reference-independent quality metrics
- Number of large contigs (e.g., longer than 500 bp) and total length of them.
- Length of the largest contig.
- N50 (length of a contig, such that all the contigs of at least the same length together cover at least 50% of the assembly).
- Number of predicted genes, discovered either by GeneMark.hmm (for prokaryotes), GeneMark-ES or GlimmerHMM (for eukaryotes), or MetaGeneMark (for metagenomes).
Reference-based quality metrics
- Numbers of misassemblies of different kinds (inversions, relocations, translocations, interspecies translocations (metaQUAST only) or local).
- Number and total length of unaligned contigs.
- Numbers of mismatches and indels, over the assembly and per 100 kb.
- Genome fraction %, assembled part of the reference.
- Duplication ratio, the total number of aligned bases in the assembly divided by the total number of those in the reference. If the assembly contains many contigs that cover the same regions, its duplication ratio will significantly exceed 1. This occurs due to multiple reasons, including overestimating repeat multiplicities and overlaps between contigs.
- Number of genes in the assembly, completely or partially covered, based on a user-provided list of gene positions in the reference.
- NGA50, a reference-aware version of N50 metric. It is calculated using aligned blocks instead of contigs.
Such blocks are obtained after removing unaligned regions, and then splitting contigs at misassembly breakpoints.
Thus, NGA50 is the length of a block, such that all the blocks of at least the same length together cover at least 50% of the reference.
Participating organisations
Reference papers
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- 2.Author(s): Alla Mikheenko, Andrey Prjibelski, Vladislav Saveliev, Dmitry Antipov, Alexey GurevichPublished in Bioinformatics by Oxford University Press (OUP) in 2018, page: i142-i15010.1093/bioinformatics/bty266
- 3.Author(s): Alla Mikheenko, Gleb Valin, Andrey Prjibelski, Vladislav Saveliev, Alexey GurevichPublished in Bioinformatics by Oxford University Press (OUP) in 2016, page: 3321-332310.1093/bioinformatics/btw379
- 4.Author(s): Alla Mikheenko, Vladislav Saveliev, Alexey GurevichPublished in Bioinformatics by Oxford University Press (OUP) in 2015, page: 1088-109010.1093/bioinformatics/btv697
- 5.Author(s): Alexey Gurevich, Vladislav Saveliev, Nikolay Vyahhi, Glenn TeslerPublished in Bioinformatics by Oxford University Press (OUP) in 2013, page: 1072-107510.1093/bioinformatics/btt086
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