The de-novo assembly of short-read metagenomic sequencing data has become the de-facto standard when studying the microbiome of complex samples. In contrast to genomes derived from cultured microbial isolates, the task of inferring which contig belongs to which genome is not trivial. Therefore, multiple approaches have been developed to use the sequence composition and the sequencing depth along the contigs to cluster similar contigs together (Alneberg et al. 2013; Wu, Simmons, and Singer 2016; Kang et al. 2019). While using the sequence composition and the sequencing depth allows to cluster all contigs in a short amount of time, this information is not always sufficient to correctly separate all contigs in the correct genome bins. To improve these clusters, workflows have been developed that infer the presence of lineage-specific, single-copy genes along the contigs and use these to revise the assignment of contigs into clusters (Sieber et al. 2018; Uritskiy, DiRuggiero, and Taylor 2018). However, the performance of the different combination of tools is dependent on the underlying set of sequencing data (Yue et al. 2020).
In the last five years, there have been made attempts to standardise the minimum information that is to be provided for new metagenome-assembled genomes (MAGs) (Bowers et al. 2017). Two of the most important criteria here are the genome completeness and its contamination, which are hard to estimate in the absence of a ground truth. CheckM (Parks et al. 2015) has evolved to be the quasi standard tool to estimate these two parameters by inferring single-copy marker genes on the contigs of each MAG and evaluating these against an expected set of genes that is determined by assigning the MAG to a microbial lineage. Based on the suggestions by Bowers et al. (2017), a MAG is considered to be of high-quality when the completeness estimate >= 90% and the contamination estimate < 5%. A medium-quality MAG is required to have a completeness >= 50% and a contamination < 10%.
However, in a recent publication, Orakov et al. (2021) could show that while checkM is able to identify contigs that do not belong to the consensus microbial lineage of the MAG, checkM fails to identify the presence of chimeric contigs in MAGs and thus overestimates the quality of MAG. To estimate the presence of chimeric contigs in a data set, Orakov et al. (2021) uses a very similar approach to checkM. The authors developed GUNC, which uses a database with genes and their known taxonomic origin, and identifies their presence on the contigs. If the authors observe a higher variety of taxonomic origins than expected from known microbial species, they will flag the MAG as likely be chimeric.
How to proceed with MAGs that were either assigned to high or medium quality using checkM but returned a higher than expected GUNC score is yet unclear. In the presence of a large number of additional genomes from the same habitat some researchers tended to discard these MAGs as chimeric (e.g. Saheb Kashaf et al. (2021)). However, for samples that are limited in quantity and underlie strong ethical considerations, such as ancient DNA samples, this is not an adequate solution. Suggestions have been put forward to manually curate the contigs of chimeric MAGs (Chen et al. 2020) in programs such like anvi’o (Eren et al. 2015) and discard the problematic contigs. This manual process ranges from time-consuming to infeasible, when a dataset consists out of many samples with each a large number of MAGs.
In the following, I present an automatic workflow that is heavily
influenced by the suggestions by Chen et al. (2020) and automatises many
steps that can be manually done in anvi’o. In brief, the pipeline
written in Snakemake (Mölder et al. 2021) expects MAGs refined by
MetaWRAP (Uritskiy, DiRuggiero, and Taylor 2018) as input and identifies
contigs that are likely chimeric by inferring the majority lineage
across all contigs using MMSeqs2 (Steinegger and Söding 2017) against
the GTDB reference database (Parks et al. 2020) using the command
mmseqs taxonomy and discard contigs that diverge either by average
sequencing depth or lineage assignment. For the revised contigs, a
standard set of assembly information including an updated estimate for
the genome completeness and the contamination is determined and
reported.
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