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

IF 3.8 2区 生物学 Q1 BIOCHEMICAL RESEARCH METHODS PLoS Computational Biology Pub Date : 2024-10-22 eCollection Date: 2024-10-01 DOI:10.1371/journal.pcbi.1012530
Ulisses Rocha, Jonas Coelho Kasmanas, Rodolfo Toscan, Danilo S Sanches, Stefania Magnusdottir, Joao Pedro Saraiva
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Abstract

We hypothesize that sample species abundance, 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 species abundance, 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, while 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 that the scientific community should curate their findings from MAG recovery, especially when asserting novel species or metabolic traits.

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对 69 个微生物群落的模拟表明,测序深度和假阳性是原核生物元基因组组装基因组恢复偏差的主要驱动因素。
我们假设样本物种丰度、测序深度和分类相关性会影响元基因组组装基因组(MAG)的恢复。为了验证这一假设,我们使用三种不同的 MAG 恢复管道,评估了由 42 个物种组成的三个硅学微生物群落的 MAG 恢复情况,这三个群落的物种丰富度相同,但样本物种丰富度、测序深度和分类学分布情况不同。Parks 及其同事开发的管道(8K)产生的 MAG 数量最多,每个群落特征的真阳性数量最少。Karst 及其同事的管道(DT)显示了最准确的结果(约 92%),优于 8K 和 Albertsen 及其合作者开发的多元组管道(MM)。在使用 8K 和 MM 时,测序深度影响了基因组的准确恢复,甚至出现了截然不同的模式:MM 管道在使用测序深度达 6000 万读数的原始群落中恢复了更多的 MAG,而 8K 在测序深度超过 6000 万读数的群落中恢复了更多的真阳性。尽管所有管道对近缘物种的恢复率都很低,但 DT 对同属物种的恢复率最高。我们的研究结果突出表明,更多的分区并不能转化为实际的群落组成,测序深度在 MAG 恢复和提高群落分辨率方面发挥着作用。即使较低的 MAG 恢复错误率也会对生物推断产生重大影响。我们的数据表明,科学界应该对他们从 MAG 恢复中得出的结果进行整理,尤其是在断言新物种或新陈代谢特征时。
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来源期刊
PLoS Computational Biology
PLoS Computational Biology BIOCHEMICAL RESEARCH METHODS-MATHEMATICAL & COMPUTATIONAL BIOLOGY
CiteScore
7.10
自引率
4.70%
发文量
820
审稿时长
2.5 months
期刊介绍: PLOS Computational Biology features works of exceptional significance that further our understanding of living systems at all scales—from molecules and cells, to patient populations and ecosystems—through the application of computational methods. Readers include life and computational scientists, who can take the important findings presented here to the next level of discovery. Research articles must be declared as belonging to a relevant section. More information about the sections can be found in the submission guidelines. Research articles should model aspects of biological systems, demonstrate both methodological and scientific novelty, and provide profound new biological insights. Generally, reliability and significance of biological discovery through computation should be validated and enriched by experimental studies. Inclusion of experimental validation is not required for publication, but should be referenced where possible. Inclusion of experimental validation of a modest biological discovery through computation does not render a manuscript suitable for PLOS Computational Biology. Research articles specifically designated as Methods papers should describe outstanding methods of exceptional importance that have been shown, or have the promise to provide new biological insights. The method must already be widely adopted, or have the promise of wide adoption by a broad community of users. Enhancements to existing published methods will only be considered if those enhancements bring exceptional new capabilities.
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