Generating lineage-resolved, complete metagenome-assembled genomes from complex microbial communities

Microbial communities might include distinct lineages of closely related organisms that complicate metagenomic assembly and prevent the generation of complete metagenome-assembled genomes (MAGs). Here we show that deep sequencing using long (HiFi) reads combined with Hi-C binning can address this ch...

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Published inNature biotechnology Vol. 40; no. 5; pp. 711 - 719
Main Authors Bickhart, Derek M., Kolmogorov, Mikhail, Tseng, Elizabeth, Portik, Daniel M., Korobeynikov, Anton, Tolstoganov, Ivan, Uritskiy, Gherman, Liachko, Ivan, Sullivan, Shawn T., Shin, Sung Bong, Zorea, Alvah, Andreu, Victòria Pascal, Panke-Buisse, Kevin, Medema, Marnix H., Mizrahi, Itzhak, Pevzner, Pavel A., Smith, Timothy P. L.
Format Journal Article
LanguageEnglish
Published New York Nature Publishing Group US 01.05.2022
Nature Publishing Group
Subjects
631/114/2785
631/208/728
631/326/325/2482
Agriculture
Bioinformatics
Biomedical and Life Sciences
Biomedical Engineering/Biotechnology
Biomedicine
biosynthesis
Biotechnology
data collection
Gene clusters
Genomes
Haplotypes
Life Sciences
Metagenomics
Microbial activity
Microbiomes
Microorganisms
sheep
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Abstract Microbial communities might include distinct lineages of closely related organisms that complicate metagenomic assembly and prevent the generation of complete metagenome-assembled genomes (MAGs). Here we show that deep sequencing using long (HiFi) reads combined with Hi-C binning can address this challenge even for complex microbial communities. Using existing methods, we sequenced the sheep fecal metagenome and identified 428 MAGs with more than 90% completeness, including 44 MAGs in single circular contigs. To resolve closely related strains (lineages), we developed MAGPhase, which separates lineages of related organisms by discriminating variant haplotypes across hundreds of kilobases of genomic sequence. MAGPhase identified 220 lineage-resolved MAGs in our dataset. The ability to resolve closely related microbes in complex microbial communities improves the identification of biosynthetic gene clusters and the precision of assigning mobile genetic elements to host genomes. We identified 1,400 complete and 350 partial biosynthetic gene clusters, most of which are novel, as well as 424 (298) potential host–viral (host–plasmid) associations using Hi-C data. Metagenome sequencing can now distinguish closely related microbes using long reads and haplotype phasing.
AbstractList Microbial communities might include distinct lineages of closely related organisms that complicate metagenomic assembly and prevent the generation of complete metagenome-assembled genomes (MAGs). Here we show that deep sequencing using long (HiFi) reads combined with Hi-C binning can address this challenge even for complex microbial communities. Using existing methods, we sequenced the sheep fecal metagenome and identified 428 MAGs with more than 90% completeness, including 44 MAGs in single circular contigs. To resolve closely related strains (lineages), we developed MAGPhase, which separates lineages of related organisms by discriminating variant haplotypes across hundreds of kilobases of genomic sequence. MAGPhase identified 220 lineage-resolved MAGs in our dataset. The ability to resolve closely related microbes in complex microbial communities improves the identification of biosynthetic gene clusters and the precision of assigning mobile genetic elements to host genomes. We identified 1,400 complete and 350 partial biosynthetic gene clusters, most of which are novel, as well as 424 (298) potential host-viral (host-plasmid) associations using Hi-C data.
Microbial communities might include distinct lineages of closely related organisms that complicate metagenomic assembly and prevent the generation of complete metagenome-assembled genomes (MAGs). Here we show that deep sequencing using long (HiFi) reads combined with Hi-C binning can address this challenge even for complex microbial communities. Using existing methods, we sequenced the sheep fecal metagenome and identified 428 MAGs with more than 90% completeness, including 44 MAGs in single circular contigs. To resolve closely related strains (lineages), we developed MAGPhase, which separates lineages of related organisms by discriminating variant haplotypes across hundreds of kilobases of genomic sequence. MAGPhase identified 220 lineage-resolved MAGs in our dataset. The ability to resolve closely related microbes in complex microbial communities improves the identification of biosynthetic gene clusters and the precision of assigning mobile genetic elements to host genomes. We identified 1,400 complete and 350 partial biosynthetic gene clusters, most of which are novel, as well as 424 (298) potential host–viral (host–plasmid) associations using Hi-C data.Metagenome sequencing can now distinguish closely related microbes using long reads and haplotype phasing.
Microbial communities might include distinct lineages of closely related organisms that complicate metagenomic assembly and prevent the generation of complete metagenome-assembled genomes (MAGs). Here we show that deep sequencing using long (HiFi) reads combined with Hi-C binning can address this challenge even for complex microbial communities. Using existing methods, we sequenced the sheep fecal metagenome and identified 428 MAGs with more than 90% completeness, including 44 MAGs in single circular contigs. To resolve closely related strains (lineages), we developed MAGPhase, which separates lineages of related organisms by discriminating variant haplotypes across hundreds of kilobases of genomic sequence. MAGPhase identified 220 lineage-resolved MAGs in our dataset. The ability to resolve closely related microbes in complex microbial communities improves the identification of biosynthetic gene clusters and the precision of assigning mobile genetic elements to host genomes. We identified 1,400 complete and 350 partial biosynthetic gene clusters, most of which are novel, as well as 424 (298) potential host–viral (host–plasmid) associations using Hi-C data. Metagenome sequencing can now distinguish closely related microbes using long reads and haplotype phasing.
Microbial communities might include distinct lineages of closely related organisms that complicate metagenomic assembly and prevent the generation of complete metagenome-assembled genomes (MAGs). Here we show that deep sequencing using long (HiFi) reads combined with Hi-C binning can address this challenge even for complex microbial communities. Using existing methods, we sequenced the sheep fecal metagenome and identified 428 MAGs with more than 90% completeness, including 44 MAGs in single circular contigs. To resolve closely related strains (lineages), we developed MAGPhase, which separates lineages of related organisms by discriminating variant haplotypes across hundreds of kilobases of genomic sequence. MAGPhase identified 220 lineage-resolved MAGs in our dataset. The ability to resolve closely related microbes in complex microbial communities improves the identification of biosynthetic gene clusters and the precision of assigning mobile genetic elements to host genomes. We identified 1,400 complete and 350 partial biosynthetic gene clusters, most of which are novel, as well as 424 (298) potential host-viral (host-plasmid) associations using Hi-C data.Microbial communities might include distinct lineages of closely related organisms that complicate metagenomic assembly and prevent the generation of complete metagenome-assembled genomes (MAGs). Here we show that deep sequencing using long (HiFi) reads combined with Hi-C binning can address this challenge even for complex microbial communities. Using existing methods, we sequenced the sheep fecal metagenome and identified 428 MAGs with more than 90% completeness, including 44 MAGs in single circular contigs. To resolve closely related strains (lineages), we developed MAGPhase, which separates lineages of related organisms by discriminating variant haplotypes across hundreds of kilobases of genomic sequence. MAGPhase identified 220 lineage-resolved MAGs in our dataset. The ability to resolve closely related microbes in complex microbial communities improves the identification of biosynthetic gene clusters and the precision of assigning mobile genetic elements to host genomes. We identified 1,400 complete and 350 partial biosynthetic gene clusters, most of which are novel, as well as 424 (298) potential host-viral (host-plasmid) associations using Hi-C data.
Author Pevzner, Pavel A.
Shin, Sung Bong
Smith, Timothy P. L.
Tseng, Elizabeth
Korobeynikov, Anton
Uritskiy, Gherman
Liachko, Ivan
Sullivan, Shawn T.
Zorea, Alvah
Medema, Marnix H.
Portik, Daniel M.
Panke-Buisse, Kevin
Bickhart, Derek M.
Kolmogorov, Mikhail
Andreu, Victòria Pascal
Tolstoganov, Ivan
Mizrahi, Itzhak
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Copyright This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply 2022
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This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply 2022.
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PublicationDateYYYYMMDD 2022-05-01
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  day: 01
PublicationDecade 2020
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PublicationSubtitle The Science and Business of Biotechnology
PublicationTitle Nature biotechnology
PublicationTitleAbbrev Nat Biotechnol
PublicationTitleAlternate Nat Biotechnol
PublicationYear 2022
Publisher Nature Publishing Group US
Nature Publishing Group
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– reference: 34980920 - Nat Microbiol. 2022 Feb;7(2):193-194
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Snippet Microbial communities might include distinct lineages of closely related organisms that complicate metagenomic assembly and prevent the generation of complete...
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SubjectTerms 631/114/2785
631/208/728
631/326/325/2482
Agriculture
Bioinformatics
Biomedical and Life Sciences
Biomedical Engineering/Biotechnology
Biomedicine
biosynthesis
Biotechnology
data collection
Gene clusters
Genomes
Haplotypes
Life Sciences
Metagenomics
Microbial activity
Microbiomes
Microorganisms
sheep
Title Generating lineage-resolved, complete metagenome-assembled genomes from complex microbial communities
URI https://link.springer.com/article/10.1038/s41587-021-01130-z
https://www.ncbi.nlm.nih.gov/pubmed/34980911
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Volume 40
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