Large-scale quality assessment of prokaryotic genomes with metashot/prok-quality

Introduction

Genome-resolved metagenomics is one of the most promising approaches to identify and characterize novel microbial species. Large-scale environmental and host-associated studies demonstrated how metagenomics can expand our knowledge of uncultivated prokaryotes, recovering thousands of metagenome-assembled genomes (MAGs) of new archaeal and bacterial species.^1,2 For this reason, automated and reproducible methods for assessing the quality of MAGs play a critical role.

To recover MAGs, metagenomic sequence reads are first assembled into contigs using specific algorithms.³ Contigs are then processed by tools like MetaBAT 2⁴ or VAMB⁵ that use tetra-nucleotide frequency (TNF) profiles and abundance patterns to group sequences that are likely to belong to the same organism (binning). Binning improves the interpretability of metagenomic data, but at the same time represents (together with assembly) a significant source of error.⁶ Manual refinement⁷ can increase the quality of resulting MAGs, but undermines the reproducibility of the analysis and is unfeasible for large-scale studies.

The recently introduced Minimum Information about a Metagenome-Assembled Genome (MIMAG) standard⁸ recommends a set of measures for assessing the quality of MAGs. This comprises basic assembly statistics (e.g. N50), genome completeness, contamination and the presence of ribosomal RNA (rRNA) and transfer RNA (tRNA) genes.

Recovering this information involves computational pipelines composed of a series of specialized tools that are often difficult to use and install. Moreover, each task can require parameters and custom scripts that are often poorly documented, making reproducibility of results challenging. Tools and standards such as Galaxy,⁹ Nextflow¹⁰ and the Common Workflow Language,¹¹ coupled with container technologies like Docker, allows researchers to circumvent these issues, providing a way to build, run and share reproducible computational workflows.¹²

We present metashot/prok-quality, a comprehensive and easy-to-use Nextflow pipeline for assessing the quality of draft prokaryotic genomes. Metashot/prok-quality reports the quality statistics and estimates recommended by the MIMAG standard. Basic assembly statistics, completeness, both redundant and non-redundant contamination, rRNA and tRNA genes are reported in a single, comprehensive table.

Methods

Implementation

Metashot/prok-quality is written using the Nextflow domain-specific language. Nextflow is a framework for building scalable scientific workflows using containers, allowing implicit parallelism on a wide range of computing systems. Reproducibility is guaranteed by versioned Docker images, which enclose software applications together with their dependencies, allowing isolation from the host environment and portability across platforms. metashot/prok-quality v1.2.0 is composed of five main modules (Figure 1) and includes several custom scripts, designed to manipulate the output of the different tasks.

Figure 1. Metashot/prok-quality workflow.

The workflow takes a series of genomes (input bins) in FASTA format and returns: i) a tab-separated values (TSV) file including, for each input genome, the quality information recommended by the MIMAG standard (genome info table); ii) a directory containing the bins filtered according the completeness and contamination thresholds; iii) a TSV file listing the cluster membership of each genome after the dereplication (optional) and iv) a directory containing the cluster representatives. The original outputs of each task (e.g. Barrnap’s GFF output) are also reported in dedicated folders.

Software included in version 1.2.0:

CheckM v1.1.2. Several tools have been developed for the assessment of completeness and contamination of MAGs. The proposed workflow includes the widely used CheckM¹³ which estimate these metrics using ubiquitous and lineage-specific, single-copy core genes (SCGs) catalogs. CheckM is also used to recover the basic assembly statistics.

GUNC v1.0.1. SCG-based tools like CheckM can have very low sensitivity towards contamination by fragments from unrelated organisms (non-redundant contamination).⁶ In order to circumvent this problem, the recent GUNC¹⁴ tool was added to the pipeline. GUNC quantifies the lineage homogeneity of contigs with respect to the full gene complement, accurately detecting chimerism induced by both redundant and non-redundant contamination.

Barrnap v0.9. The presence of 5S, 23S and 16S rRNA genes is predicted by the BAsic Rapid Ribosomal RNA Predictor (Barrnap) using Hidden Markov models (HMM). Both bacteria and archaea databases are used.

tRNAscan-SE v2.0.6. tRNA genes are searched using tRNAscan-SE,¹⁵ using bacteria and archaea covariance models. The number of tRNAs and tRNA isotypes found is reported.

dRep v2.6.2. Dereplication is a procedure that groups the input genomes according to their whole-genome similarity, using metrics such as the Average Nucleotide Identity¹⁶ (ANI). Dereplication dramatically simplifies downstream analysis when the input genomes come from different sources.¹⁷ In the proposed workflow, filtered genomes (genomes that pass completeness, contamination and GUNC filters) are optionally dereplicated using dRep.¹⁸ For each cluster, dRep reports, as the cluster representative, the best-scoring MAG using the CheckM’s quality estimates. The score is computed using the following formula:

score = completeness − 5 × contamination + 0.5 × log(N50)

Python3 custom scripts. The workflow includes three Python3 custom scripts, designed to manipulate the output of the different steps. The scripts make use of NumPy,¹⁷ Pandas and scikit-learn libraries.

Use case

As mentioned above, metagenome assembly tools combine the sequence reads into larger regions called contigs. Recently, many metagenomic assembly tools have been proposed. Amongst these, metaSPAdes³ and MEGAHIT¹⁹ have been shown to be able to efficiently handle large-scale short read sequencing data, producing high-quality contigs. Metagenomics contigs are then processed by tools like MetaBAT 2⁴ in order to group sequences that are likely to belong to the same organism (binning). After binning, it is essential to assess the quality of the resulting candidate draft genomes.

In this section, we will show how to assess the quality of draft prokaryotic genomes using metashot/prok-quality. Given a series of candidate genomes in FASTA format stored in the “bins” directory, the version 1.2.0 of the workflow can be run with the following command line:

nextflow run metashot/prok-quality -r 1.2.0
\--genomes 'bins/*.fa'
\--outdir results

A series of files and directories are created in the output directory results. The main output file is “genome_info.tsv”. This TSV file contains, for each input genome, a set of quality statistics, including completeness, contamination, GUNC filter, N50, rRNA genes found, number of tRNA and tRNA types. The columns included in this file are:

The directory “filtered” contains the genomes (in FASTA format) filtered according to --min_completeness, --max_contamination and --gunc_filter options (see below). The TSV file “genome_info_filtered.tsv” includes the same information as “genome_info.tsv”, but for the filtered genomes only. Representative (dereplicated) genomes (default ANI threshold 0.95) are reported in the “filtered_repr” folder. The companion file “derep_info.tsv” contains the summary of the dereplication procedure, including the genome filename, the cluster ID and the representativeness. A set of secondary directories contains the original output of each tool included in the pipeline:

The command options are:

Input and output

Software availability

Source code available from: https://github.com/metashot/prok-quality

Archived source code at time of publication: http://doi.org/10.5281/zenodo.4475355.²⁰

License: GPL-3.0

Docker image definitions available from: https://github.com/metashot/docker

Data availability