Simulation of 69 microbial communities indicates sequencing depth and false positives are major drivers of bias in Prokaryotic metagenome-assembled genome recovery

Abstract

We hypothesize that sample evenness, sequencing depth and taxonomic relatedness influence the recovery of metagenome-assembled genomes (MAGs). To test this hypothesis, we assessed MAG recovery in three in silico microbial communities composed of 42 species with the same richness but different sample evenness, sequencing depth and taxonomic distribution profiles using three different pipelines for MAG recovery. The pipeline developed by Parks and colleagues (8K) generated the highest number of MAGs and the lowest number of true positives per community profile. The pipeline by Karst and colleagues (DT) showed the most accurate results (~ 92%), outperforming the 8K and Multi-Metagenome pipeline (MM) developed by Albertsen and collaborators. Sequencing depth influenced the accurate recovery of genomes when using the 8K and MM, even with contrasting patterns: the MM pipeline recovered more MAGs found in the original communities when employing sequencing depths up to 60 million reads, whilst the 8K recovered more true positives in communities sequenced above 60 million reads. DT showed the best species recovery from the same genus, even though close-related species have a low recovery rate in all pipelines. Our results highlight that more bins do not translate to the actual community composition and that sequencing depth plays a role in MAG recovery and increased community resolution. Even low MAG recovery error rates can significantly impact biological inferences. Our data indicates the scientific community should their findings from MAG recovery, especially when asserting novel species or metabolic traits.

MuDoGeR: Multi‐Domain Genome recovery from metagenomes made easy

“…Sequencing depth was found to influence the accurate recovery of genomes significantly. However, it is also worth mentioning that those samples have recovered bins but did not pass the requirements to be classified as MAGs (da Rocha et al, 2023).…”

“…recommendations. However, it is always relevant for the user to understand its dataset and tools limitations to adapt the workflow to their hypothesis or research question (da Rocha et al, 2023).…”