Bioinformatics guidance page

Quast

Introduction

QUAST stands for QUality ASsessment Tool (Gurevich et al. 2013). The tool evaluates genome assemblies by computing various metrics. This tool has different mpdules including 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.

For more information, visit the official website as well as the manual.

Installation

Installed on crunchomics: Yes,

Quast v5.2.0 is installed as part of the bioinformatics share. If you have access to crunchomics you can be added to the bioinformatics share by sending an email with your Uva netID to Nina Dombrowski.
Afterwards, you can add the bioinformatics share as follows (if you have already done this in the past, you don’t need to run this command):

conda config --add envs_dirs /zfs/omics/projects/bioinformatics/software/miniconda3/envs/

If you want to install it yourself, you can run:

mamba create --name quast_5.2.0 -c bioconda quast=5.2.0

#for quast to run, you need to downgreate minimap from 2.28 to 2.4
#2.28 gives an error that says that the miniconda2 version is not suited for this installation 
#chose 2.24 based on these notes for quast 5.2.0, https://quast.sourceforge.net/docs/CHANGES.txt
conda activate quast_5.2.0
mamba install -c bioconda minimap2=2.24

Usage

conda activate quast_5.2.0

#running quast with a reference genome, which was downloaded from ncbi
#and gff file, which was generated using prokka
quast.py \
  assembly.fasta \
  -r  db/ncbi_ref/GCF_016756315.1_genomic.fna \
  -g assembly.gff \
  --nanopore data/raw_sequence_data.fastq.gz \
  -o results/quast_report \
  -t 20

#running quast on several assemblies, i.e. several assemblies polished with medaka
srun --cpus-per-task 20 --mem=50G quast.py \
  -l 'r1,r2,r3,r4,r5,r6,r7,r8,r9,r10' \
  results/polishing/medaka/r1/consensus.fasta \
  results/polishing/medaka/r2/consensus.fasta \
  results/polishing/medaka/r3/consensus.fasta \
  results/polishing/medaka/r4/consensus.fasta \
  results/polishing/medaka/r5/consensus.fasta \
  results/polishing/medaka/r6/consensus.fasta \
  results/polishing/medaka/r7/consensus.fasta \
  results/polishing/medaka/r8/consensus.fasta \
  results/polishing/medaka/r9/consensus.fasta \
  results/polishing/medaka/r10/consensus.fasta \
  --nanopore data/raw_sequence_data.fastq.gz \
  -r  db/ncbi_ref/GCF_016756315.1_genomic.fna \
  -o results/polishing/quast_report \
  -t 20

conda deactivate

For more information about the different modules, please visit the manual.

Common Issues and Solutions

Issue 1: Running out of memory
- Solution 1: Especially when analysing more than one assembly, it might be good to increase the memory via slurm

References

Gurevich, Alexey, Vladislav Saveliev, Nikolay Vyahhi, and Glenn Tesler. 2013. “QUAST: Quality Assessment Tool for Genome Assemblies.” Bioinformatics 29 (8): 1072–75. https://doi.org/10.1093/bioinformatics/btt086.