Soil Microbial Dark Matter Explored from Genome-Resolved Metagenomics

Ma, Bin; Lu, Caiyu; Wang, Yiling; Yu, Jingwen; Zhao, Kankan; Xue, Ran; Lv, Xiaofei; Pan, Ronghui; Zhang, Jiabao; Zhu, Yun; Xu, Jianming

doi:10.2139/ssrn.4309042

Cited by 1 publication

(3 citation statements)

References 62 publications

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“…In Supplementary Fig. 8 , we show that adding in new catalogues of ocean MAGs and soil MAGs [53,54] can modestly improve the average percentage of detected reads for recently available samples of plant (25.5% to 36.1%) and ocean metagenomes (17.4% to 22.8%). Nevertheless, database incompleteness for species-level profiling is still an issue for understudied microbiomes ( Discussion ).…”

Section: Resultsmentioning

confidence: 99%

“…Supplementary Figure 14: Coverage estimates output by sylph (y-axis) for the biofloc MAGs versus coverages obtained from read alignment using BWA (x-axis) with Pearson correlation coefficient shown. Supplementary Figure 15: Profiling of plant-associated metagenomes [81] (PRJNA904562) and ocean metagenomes [82] (PRJEB61010) against ocean MAGs (OceanDNA) [52] and Soil MAGs (SMAG) [53]. We checked that these metagenomes were not included in the generation of the databases.…”

Section: Software/genomementioning

confidence: 99%

“…Supplementary Figure 8: Profiling of plant-associated metagenomes [79] (PRJNA904562) and ocean metagenomes [80] (PRJEB61010) against ocean MAGs (OceanDNA) [53] and Soil MAGs (SMAG) [54]. We checked that these metagenomes were not included in the generation of the databases.…”

Section: Software/genomementioning

confidence: 99%

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Metagenome profiling and containment estimation through abundance-corrected k-mer sketching with sylph

Shaw,

2023

Preprint

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Profiling metagenomes against databases allows for the detection and quantification of microbes, even at low abundances where assembly is not possible. We introduce sylph (https://github.com/bluenote-1577/sylph), a metagenome profiler that estimates metagenome-genome average nucleotide identity (ANI) through zero-inflated Poisson k-mer statistics, enabling ANI-based taxa detection. Sylph is the most accurate method on the CAMI2 marine dataset, and compared to Kraken2 for multi-sample profiling, sylph takes 10× less CPU time and uses 30× less memory. Sylph’s ANI estimates provide an orthogonal signal to abundance, enabling an ANI-based metagenome-wide association study for Parkinson’s disease against 289,323 genomes, confirming known butyrate-PD associations at the strain level. Sylph takes < 1 minute and 16 GB of RAM to profile against 85,205 prokaryotic and 2,917,521 viral genomes, detecting 30× more viral sequences in the human gut compared to RefSeq. Sylph offers precise, efficient profiling with accurate ANI estimation for even low-coverage genomes.

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Section: Resultsmentioning

confidence: 99%