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Article ; Online: Maast: genotyping thousands of microbial strains efficiently.

Shi, Zhou Jason / Nayfach, Stephen / Pollard, Katherine S

2023 Volume 24, Issue 1, Page(s) 186

Abstract: Existing single nucleotide polymorphism (SNP) genotyping algorithms do not scale for species with thousands of sequenced strains, nor do they account for conspecific redundancy. Here we present a bioinformatics tool, Maast, which empowers population ... ...

Abstract	Existing single nucleotide polymorphism (SNP) genotyping algorithms do not scale for species with thousands of sequenced strains, nor do they account for conspecific redundancy. Here we present a bioinformatics tool, Maast, which empowers population genetic meta-analysis of microbes at an unrivaled scale. Maast implements a novel algorithm to heuristically identify a minimal set of diverse conspecific genomes, then constructs a reliable SNP panel for each species, and enables rapid and accurate genotyping using a hybrid of whole-genome alignment and k-mer exact matching. We demonstrate Maast's utility by genotyping thousands of Helicobacter pylori strains and tracking SARS-CoV-2 diversification.
MeSH term(s)	Humans ; Genotype ; SARS-CoV-2/genetics ; COVID-19 ; Genome ; Algorithms ; Polymorphism, Single Nucleotide ; Genotyping Techniques
Language	English
Publishing date	2023-08-10
Publishing country	England
Document type	Meta-Analysis ; Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't ; Research Support, U.S. Gov't, Non-P.H.S.
ZDB-ID	2040529-7
ISSN	1474-760X ; 1474-760X
ISSN (online)	1474-760X
ISSN	1474-760X
DOI	10.1186/s13059-023-03030-8
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Identifying species-specific k-mers for fast and accurate metagenotyping with Maast and GT-Pro.

Shi, Zhou Jason / Nayfach, Stephen / Pollard, Katherine S

STAR protocols

2023 Volume 4, Issue 1, Page(s) 101964

Abstract: Genotyping single-nucleotide polymorphisms (SNPs) in microbiomes enables strain-level quantification. In this protocol, we describe a computational pipeline that performs fast and accurate SNP genotyping using metagenomic data. We first demonstrate how ... ...

Abstract	Genotyping single-nucleotide polymorphisms (SNPs) in microbiomes enables strain-level quantification. In this protocol, we describe a computational pipeline that performs fast and accurate SNP genotyping using metagenomic data. We first demonstrate how to use Maast to catalog SNPs from microbial genomes. Then we use GT-Pro to extract unique SNP-covering k-mers, optimize a data structure for storing these k-mers, and finally perform metagenotyping. For proof of concept, the protocol leverages public whole-genome sequences to metagenotype a synthetic community. For complete details on the use and execution of this protocol, please refer to Shi et al. (2022a)
MeSH term(s)	Genome ; Microbiota/genetics ; Polymorphism, Single Nucleotide/genetics
Language	English
Publishing date	2023-01-20
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't ; Research Support, U.S. Gov't, Non-P.H.S.
ISSN	2666-1667
ISSN (online)	2666-1667
DOI	10.1016/j.xpro.2022.101964
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Article ; Online: Illuminating the Virosphere Through Global Metagenomics.

Call, Lee / Nayfach, Stephen / Kyrpides, Nikos C

Annual review of biomedical data science

2021 Volume 4, Page(s) 369–391

Abstract: Viruses are the most abundant biological entity on Earth, infect cellular organisms from all domains of life, and are central players in the global biosphere. Over the last century, the discovery and characterization of viruses have progressed steadily ... ...

Abstract	Viruses are the most abundant biological entity on Earth, infect cellular organisms from all domains of life, and are central players in the global biosphere. Over the last century, the discovery and characterization of viruses have progressed steadily alongside much of modern biology. In terms of outright numbers of novel viruses discovered, however, the last few years have been by far the most transformative for the field. Advances in methods for identifying viral sequences in genomic and metagenomic datasets, coupled to the exponential growth of environmental sequencing, have greatly expanded the catalog of known viruses and fueled the tremendous growth of viral sequence databases. Development and implementation of new standards, along with careful study of the newly discovered viruses, have transformed and will continue to transform our understanding of microbial evolution, ecology, and biogeochemical cycles, leading to new biotechnological innovations across many diverse fields, including environmental, agricultural, and biomedical sciences.
MeSH term(s)	Ecology ; Genome, Viral ; Metagenome ; Metagenomics ; Viruses/genetics
Language	English
Publishing date	2021-05-13
Publishing country	United States
Document type	Journal Article ; Research Support, U.S. Gov't, Non-P.H.S.
ISSN	2574-3414
ISSN (online)	2574-3414
DOI	10.1146/annurev-biodatasci-012221-095114
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Article ; Online: MIDAS2: Metagenomic Intra-species Diversity Analysis System.

Zhao, Chunyu / Dimitrov, Boris / Goldman, Miriam / Nayfach, Stephen / Pollard, Katherine S

Bioinformatics (Oxford, England)

2022 Volume 39, Issue 1

Abstract: Summary: The Metagenomic Intra-Species Diversity Analysis System (MIDAS) is a scalable metagenomic pipeline that identifies single nucleotide variants (SNVs) and gene copy number variants in microbial populations. Here, we present MIDAS2, which ... ...

Abstract	Summary: The Metagenomic Intra-Species Diversity Analysis System (MIDAS) is a scalable metagenomic pipeline that identifies single nucleotide variants (SNVs) and gene copy number variants in microbial populations. Here, we present MIDAS2, which addresses the computational challenges presented by increasingly large reference genome databases, while adding functionality for building custom databases and leveraging paired-end reads to improve SNV accuracy. This fast and scalable reengineering of the MIDAS pipeline enables thousands of metagenomic samples to be efficiently genotyped. Availability and implementation: The source code is available at https://github.com/czbiohub/MIDAS2. Supplementary information: Supplementary data are available at Bioinformatics online.
MeSH term(s)	Metagenome ; Software ; Metagenomics ; Genotype ; Databases, Factual
Language	English
Publishing date	2022-11-02
Publishing country	England
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't ; Research Support, U.S. Gov't, Non-P.H.S.
ZDB-ID	1422668-6
ISSN	1367-4811 ; 1367-4803
ISSN (online)	1367-4811
ISSN	1367-4803
DOI	10.1093/bioinformatics/btac713
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Article ; Online: iPHoP: An integrated machine learning framework to maximize host prediction for metagenome-derived viruses of archaea and bacteria.

Roux, Simon / Camargo, Antonio Pedro / Coutinho, Felipe H / Dabdoub, Shareef M / Dutilh, Bas E / Nayfach, Stephen / Tritt, Andrew

PLoS biology

2023 Volume 21, Issue 4, Page(s) e3002083

Abstract: The extraordinary diversity of viruses infecting bacteria and archaea is now primarily studied through metagenomics. While metagenomes enable high-throughput exploration of the viral sequence space, metagenome-derived sequences lack key information ... ...

Abstract	The extraordinary diversity of viruses infecting bacteria and archaea is now primarily studied through metagenomics. While metagenomes enable high-throughput exploration of the viral sequence space, metagenome-derived sequences lack key information compared to isolated viruses, in particular host association. Different computational approaches are available to predict the host(s) of uncultivated viruses based on their genome sequences, but thus far individual approaches are limited either in precision or in recall, i.e., for a number of viruses they yield erroneous predictions or no prediction at all. Here, we describe iPHoP, a two-step framework that integrates multiple methods to reliably predict host taxonomy at the genus rank for a broad range of viruses infecting bacteria and archaea, while retaining a low false discovery rate. Based on a large dataset of metagenome-derived virus genomes from the IMG/VR database, we illustrate how iPHoP can provide extensive host prediction and guide further characterization of uncultivated viruses.
MeSH term(s)	Archaea/genetics ; Metagenome/genetics ; Viruses/genetics ; Bacteria/genetics ; Metagenomics/methods ; Machine Learning ; Genome, Viral/genetics
Language	English
Publishing date	2023-04-21
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't ; Research Support, U.S. Gov't, Non-P.H.S.
ZDB-ID	2126776-5
ISSN	1545-7885 ; 1544-9173
ISSN (online)	1545-7885
ISSN	1544-9173
DOI	10.1371/journal.pbio.3002083
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Article ; Online: Identification of mobile genetic elements with geNomad.

Camargo, Antonio Pedro / Roux, Simon / Schulz, Frederik / Babinski, Michal / Xu, Yan / Hu, Bin / Chain, Patrick S G / Nayfach, Stephen / Kyrpides, Nikos C

Nature biotechnology

2023

Abstract: Identifying and characterizing mobile genetic elements in sequencing data is essential for understanding their diversity, ecology, biotechnological applications and impact on public health. Here we introduce geNomad, a classification and annotation ... ...

Abstract	Identifying and characterizing mobile genetic elements in sequencing data is essential for understanding their diversity, ecology, biotechnological applications and impact on public health. Here we introduce geNomad, a classification and annotation framework that combines information from gene content and a deep neural network to identify sequences of plasmids and viruses. geNomad uses a dataset of more than 200,000 marker protein profiles to provide functional gene annotation and taxonomic assignment of viral genomes. Using a conditional random field model, geNomad also detects proviruses integrated into host genomes with high precision. In benchmarks, geNomad achieved high classification performance for diverse plasmids and viruses (Matthews correlation coefficient of 77.8% and 95.3%, respectively), substantially outperforming other tools. Leveraging geNomad's speed and scalability, we processed over 2.7 trillion base pairs of sequencing data, leading to the discovery of millions of viruses and plasmids that are available through the IMG/VR and IMG/PR databases. geNomad is available at https://portal.nersc.gov/genomad .
Language	English
Publishing date	2023-09-21
Publishing country	United States
Document type	Journal Article
ZDB-ID	1311932-1
ISSN	1546-1696 ; 1087-0156
ISSN (online)	1546-1696
ISSN	1087-0156
DOI	10.1038/s41587-023-01953-y
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Article ; Online: Fast and accurate metagenotyping of the human gut microbiome with GT-Pro.

Shi, Zhou Jason / Dimitrov, Boris / Zhao, Chunyu / Nayfach, Stephen / Pollard, Katherine S

Nature biotechnology

2021 Volume 40, Issue 4, Page(s) 507–516

Abstract: Single nucleotide polymorphisms (SNPs) in metagenomics are used to quantify population structure, track strains and identify genetic determinants of microbial phenotypes. However, existing alignment-based approaches for metagenomic SNP detection require ... ...

Abstract	Single nucleotide polymorphisms (SNPs) in metagenomics are used to quantify population structure, track strains and identify genetic determinants of microbial phenotypes. However, existing alignment-based approaches for metagenomic SNP detection require high-performance computing and enough read coverage to distinguish SNPs from sequencing errors. To address these issues, we developed the GenoTyper for Prokaryotes (GT-Pro), a suite of methods to catalog SNPs from genomes and use unique k-mers to rapidly genotype these SNPs from metagenomes. Compared to methods that use read alignment, GT-Pro is more accurate and two orders of magnitude faster. Using high-quality genomes, we constructed a catalog of 104 million SNPs in 909 human gut species and used unique k-mers targeting this catalog to characterize the global population structure of gut microbes from 7,459 samples. GT-Pro enables fast and memory-efficient metagenotyping of millions of SNPs on a personal computer.
MeSH term(s)	Gastrointestinal Microbiome/genetics ; Genotype ; Humans ; Metagenome/genetics ; Metagenomics/methods ; Microbiota/genetics ; Software
Language	English
Publishing date	2021-12-23
Publishing country	United States
Document type	Journal Article
ZDB-ID	1311932-1
ISSN	1546-1696 ; 1087-0156
ISSN (online)	1546-1696
ISSN	1087-0156
DOI	10.1038/s41587-021-01102-3
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Article ; Online: Toward Accurate and Quantitative Comparative Metagenomics.

Nayfach, Stephen / Pollard, Katherine S

Cell

2016 Volume 166, Issue 5, Page(s) 1103–1116

Abstract: Shotgun metagenomics and computational analysis are used to compare the taxonomic and functional profiles of microbial communities. Leveraging this approach to understand roles of microbes in human biology and other environments requires quantitative ... ...

Abstract	Shotgun metagenomics and computational analysis are used to compare the taxonomic and functional profiles of microbial communities. Leveraging this approach to understand roles of microbes in human biology and other environments requires quantitative data summaries whose values are comparable across samples and studies. Comparability is currently hampered by the use of abundance statistics that do not estimate a meaningful parameter of the microbial community and biases introduced by experimental protocols and data-cleaning approaches. Addressing these challenges, along with improving study design, data access, metadata standardization, and analysis tools, will enable accurate comparative metagenomics. We envision a future in which microbiome studies are replicable and new metagenomes are easily and rapidly integrated with existing data. Only then can the potential of metagenomics for predictive ecological modeling, well-powered association studies, and effective microbiome medicine be fully realized.
MeSH term(s)	Classification ; Computational Biology ; Humans ; Metagenome ; Metagenomics/standards ; Microbiota/genetics ; Models, Statistical
Language	English
Publishing date	2016-08-26
Publishing country	United States
Document type	Journal Article ; Review
ZDB-ID	187009-9
ISSN	1097-4172 ; 0092-8674
ISSN (online)	1097-4172
ISSN	0092-8674
DOI	10.1016/j.cell.2016.08.007
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Article ; Online: iPHoP

Simon Roux / Antonio Pedro Camargo / Felipe H Coutinho / Shareef M Dabdoub / Bas E Dutilh / Stephen Nayfach / Andrew Tritt

PLoS Biology, Vol 21, Iss 4, p e

An integrated machine learning framework to maximize host prediction for metagenome-derived viruses of archaea and bacteria.

2023 Volume 3002083

Abstract	The extraordinary diversity of viruses infecting bacteria and archaea is now primarily studied through metagenomics. While metagenomes enable high-throughput exploration of the viral sequence space, metagenome-derived sequences lack key information compared to isolated viruses, in particular host association. Different computational approaches are available to predict the host(s) of uncultivated viruses based on their genome sequences, but thus far individual approaches are limited either in precision or in recall, i.e., for a number of viruses they yield erroneous predictions or no prediction at all. Here, we describe iPHoP, a two-step framework that integrates multiple methods to reliably predict host taxonomy at the genus rank for a broad range of viruses infecting bacteria and archaea, while retaining a low false discovery rate. Based on a large dataset of metagenome-derived virus genomes from the IMG/VR database, we illustrate how iPHoP can provide extensive host prediction and guide further characterization of uncultivated viruses.
Keywords	Biology (General) ; QH301-705.5
Language	English
Publishing date	2023-04-01T00:00:00Z
Publisher	Public Library of Science (PLoS)
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Phylogeny-corrected identification of microbial gene families relevant to human gut colonization.

Bradley, Patrick H / Nayfach, Stephen / Pollard, Katherine S

PLoS computational biology

2018 Volume 14, Issue 8, Page(s) e1006242

Abstract: The mechanisms by which different microbes colonize the healthy human gut versus other body sites, the gut in disease states, or other environments remain largely unknown. Identifying microbial genes influencing fitness in the gut could lead to new ways ... ...

Abstract	The mechanisms by which different microbes colonize the healthy human gut versus other body sites, the gut in disease states, or other environments remain largely unknown. Identifying microbial genes influencing fitness in the gut could lead to new ways to engineer probiotics or disrupt pathogenesis. We approach this problem by measuring the statistical association between a species having a gene and the probability that the species is present in the gut microbiome. The challenge is that closely related species tend to be jointly present or absent in the microbiome and also share many genes, only a subset of which are involved in gut adaptation. We show that this phylogenetic correlation indeed leads to many false discoveries and propose phylogenetic linear regression as a powerful solution. To apply this method across the bacterial tree of life, where most species have not been experimentally phenotyped, we use metagenomes from hundreds of people to quantify each species' prevalence in and specificity for the gut microbiome. This analysis reveals thousands of genes potentially involved in adaptation to the gut across species, including many novel candidates as well as processes known to contribute to fitness of gut bacteria, such as acid tolerance in Bacteroidetes and sporulation in Firmicutes. We also find microbial genes associated with a preference for the gut over other body sites, which are significantly enriched for genes linked to fitness in an in vivo competition experiment. Finally, we identify gene families associated with higher prevalence in patients with Crohn's disease, including Proteobacterial genes involved in conjugation and fimbria regulation, processes previously linked to inflammation. These gene targets may represent new avenues for modulating host colonization and disease. Our strategy of combining metagenomics with phylogenetic modeling is general and can be used to identify genes associated with adaptation to any environment.
MeSH term(s)	Bacteria/genetics ; Gastrointestinal Microbiome/genetics ; Gastrointestinal Microbiome/physiology ; Gene Expression Regulation, Bacterial/genetics ; Genes, Microbial/genetics ; Humans ; Metagenome ; Metagenomics/methods ; Microbiota/genetics ; Phylogeny
Language	English
Publishing date	2018-08-09
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't ; Research Support, U.S. Gov't, Non-P.H.S.
ZDB-ID	2193340-6
ISSN	1553-7358 ; 1553-734X
ISSN (online)	1553-7358
ISSN	1553-734X
DOI	10.1371/journal.pcbi.1006242
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