@article{almeidaUnifiedCatalog2042021,
  title        = {A Unified Catalog of 204,938 Reference Genomes from
    the Human Gut Microbiome},
  author       = {Almeida, Alexandre and Nayfach, Stephen and Boland,
    Miguel and Strozzi, Francesco and Beracochea, Martin and Shi, Zhou
    Jason and Pollard, Katherine S. and Sakharova, Ekaterina and
    Parks, Donovan H. and Hugenholtz, Philip and Segata, Nicola and
    Kyrpides, Nikos C. and Finn, Robert D.},
  date         = {2020-07-20},
  journaltitle = {Nature Biotechnology},
  shortjournal = {Nat Biotechnol},
  volume       = 39,
  number       = 1,
  pages        = {105--114},
  publisher    = {Nature Publishing Group},
  issn         = {1546-1696},
  doi          = {10.1038/s41587-020-0603-3},
  url          = {https://www.nature.com/articles/s41587-020-0603-3},
  urldate      = {2021-06-15},
  abstract     = {Comprehensive, high-quality reference genomes are
    required for functional characterization and taxonomic assignment
    of the human gut microbiota. We present the Unified Human
    Gastrointestinal Genome (UHGG) collection, comprising 204,938
    nonredundant genomes from 4,644 gut prokaryotes. These genomes
    encode {$>$}170 million protein sequences, which we collated in
    the Unified Human Gastrointestinal Protein (UHGP) catalog. The
    UHGP more than doubles the number of gut proteins in comparison to
    those present in the Integrated Gene Catalog. More than 70\% of
    the UHGG species lack cultured representatives, and 40\% of the
    UHGP lack functional annotations. Intraspecies genomic variation
    analyses revealed a large reservoir of accessory genes and
    single-nucleotide variants, many of which are specific to
    individual human populations. The UHGG and UHGP collections will
    enable studies linking genotypes to phenotypes in the human gut
    microbiome.},
  issue        = 1,
  langid       = {english},
  file         = {/Users/bsmith/Zotero/storage/LJCAD3ZR/Almeida et al.
    - 2021 - A unified catalog of 204,938 reference genomes
    fro.pdf;/Users/bsmith/Zotero/storage/ILFYXWLN/s41587-020-0603-3.html},
}
@article{beghiniIntegratingTaxonomicFunctional2021,
  title        = {Integrating Taxonomic, Functional, and Strain-Level
    Profiling of Diverse Microbial Communities with {{bioBakery}} 3},
  author       = {Beghini, Francesco and McIver, Lauren J and
    Blanco-Míguez, Aitor and Dubois, Leonard and Asnicar, Francesco
    and Maharjan, Sagun and Mailyan, Ana and Manghi, Paolo and Scholz,
    Matthias and Thomas, Andrew Maltez and Valles-Colomer, Mireia and
    Weingart, George and Zhang, Yancong and Zolfo, Moreno and
    Huttenhower, Curtis and Franzosa, Eric A and Segata, Nicola},
  date         = {2021-05-04},
  journaltitle = {eLife},
  volume       = 10,
  pages        = {e65088},
  publisher    = {eLife Sciences Publications, Ltd},
  issn         = {2050-084X},
  doi          = {10.7554/eLife.65088},
  url          = {https://doi.org/10.7554/eLife.65088},
  urldate      = {2022-01-31},
  abstract     = {Culture-independent analyses of microbial
    communities have progressed dramatically in the last decade,
    particularly due to advances in methods for biological profiling
    via shotgun metagenomics. Opportunities for improvement continue
    to accelerate, with greater access to multi-omics, microbial
    reference genomes, and strain-level diversity. To leverage these,
    we present bioBakery 3, a set of integrated, improved methods for
    taxonomic, strain-level, functional, and phylogenetic profiling of
    metagenomes newly developed to build on the largest set of
    reference sequences now available. Compared to current
    alternatives, MetaPhlAn 3 increases the accuracy of taxonomic
    profiling, and HUMAnN 3 improves that of functional potential and
    activity. These methods detected novel disease-microbiome links in
    applications to CRC (1262 metagenomes) and IBD (1635 metagenomes
    and 817 metatranscriptomes). Strain-level profiling of an
    additional 4077 metagenomes with StrainPhlAn 3 and PanPhlAn 3
    unraveled the phylogenetic and functional structure of the common
    gut microbe Ruminococcus bromii, previously described by only 15
    isolate genomes. With open-source implementations and
    cloud-deployable reproducible workflows, the bioBakery 3 platform
    can help researchers deepen the resolution, scale, and accuracy of
    multi-omic profiling for microbial community studies.},
  langid       = {english},
  keywords     = {computational analysis,metagenomics,microbial
    genomics,microbiome},
  file         = {/Users/bsmith/Zotero/storage/HDGEYTPJ/Beghini et
    al_2021_Integrating taxonomic, functional, and strain-level
    profiling of diverse.pdf},
}
@article{blanco-miguezExtendingImprovingMetagenomic2023,
  title        = {Extending and Improving Metagenomic Taxonomic
    Profiling with Uncharacterized Species Using {{MetaPhlAn}} 4},
  author       = {Blanco-Míguez, Aitor and Beghini, Francesco and
    Cumbo, Fabio and McIver, Lauren J. and Thompson, Kelsey N. and
    Zolfo, Moreno and Manghi, Paolo and Dubois, Leonard and Huang, Kun
    D. and Thomas, Andrew Maltez and Nickols, William A. and Piccinno,
    Gianmarco and Piperni, Elisa and Punčochář, Michal and
    Valles-Colomer, Mireia and Tett, Adrian and Giordano, Francesca
    and Davies, Richard and Wolf, Jonathan and Berry, Sarah E. and
    Spector, Tim D. and Franzosa, Eric A. and Pasolli, Edoardo and
    Asnicar, Francesco and Huttenhower, Curtis and Segata, Nicola},
  date         = {2023-02-23},
  journaltitle = {Nature Biotechnology},
  shortjournal = {Nat Biotechnol},
  volume       = 41,
  number       = 11,
  pages        = {1633--1644},
  publisher    = {Nature Publishing Group},
  issn         = {1546-1696},
  doi          = {10.1038/s41587-023-01688-w},
  url          = {https://www.nature.com/articles/s41587-023-01688-w},
  urldate      = {2024-02-16},
  abstract     = {Metagenomic assembly enables new organism discovery
    from microbial communities, but it can only capture few abundant
    organisms from most metagenomes. Here we present MetaPhlAn 4,
    which integrates information from metagenome assemblies and
    microbial isolate genomes for more comprehensive metagenomic
    taxonomic profiling. From a curated collection of 1.01\,M
    prokaryotic reference and metagenome-assembled genomes, we define
    unique marker genes for 26,970 species-level genome bins, 4,992 of
    them taxonomically unidentified at the species level. MetaPhlAn 4
    explains \textasciitilde 20\% more reads in most international
    human gut microbiomes and {$>$}40\% in less-characterized
    environments such as the rumen microbiome and proves more accurate
    than available alternatives on synthetic evaluations while also
    reliably quantifying organisms with no cultured isolates.
    Application of the method to {$>$}24,500 metagenomes highlights
    previously undetected species to be strong biomarkers for host
    conditions and lifestyles in human and mouse microbiomes and shows
    that even previously uncharacterized species can be genetically
    profiled at the resolution of single microbial strains.},
  issue        = 11,
  langid       = {english},
  keywords     = {Data processing,Metagenomics},
  file         = {/Users/bsmith/Zotero/storage/U28X7ZIH/Blanco-Míguez
    et al. - 2023 - Extending and improving metagenomic taxonomic
    prof.pdf},
}
@article{blountUnexhaustedPotentialColi2015,
  title        = {The Unexhausted Potential of {{E}}. Coli},
  author       = {Blount, Zachary D},
  date         = {2015-03-25},
  journaltitle = {eLife},
  volume       = 4,
  pages        = {e05826},
  publisher    = {eLife Sciences Publications, Ltd},
  issn         = {2050-084X},
  doi          = {10.7554/eLife.05826},
  url          = {https://doi.org/10.7554/eLife.05826},
  urldate      = {2024-02-16},
  abstract     = {E. coli's hardiness, versatility, broad palate and
    ease of handling have made it the most intensively studied and
    best understood organism on the planet. However, research on
    E.coli has primarily examined it as a model organism, one that is
    abstracted from any natural history. But E. coli is far more than
    just a microbial lab rat. Rather, it is a highly diverse organism
    with a complex, multi-faceted niche in the wild. Recent studies of
    ‘wild’ E. coli have, for example, revealed a great deal about its
    presence in the environment, its diversity and genomic evolution,
    as well as its role in the human microbiome and disease. These
    findings have shed light on aspects of its biology and ecology
    that pose far-reaching questions and illustrate how an
    appreciation of E. coli's natural history can expand its value as
    a model organism.},
  langid       = {english},
  keywords     = {ecology,microbiome,model organism,natural
    history,pathogen,the natural history of model organism},
  file         = {/Users/bsmith/Zotero/storage/U5JT8IJH/Blount - 2015
    - The unexhausted potential of E. coli.pdf},
}
@article{camargoIdentificationMobileGenetic2023,
  title        = {Identification of Mobile Genetic Elements with
    {{geNomad}}},
  author       = {Camargo, Antonio Pedro and Roux, Simon and Schulz,
    Frederik and Babinski, Michal and Xu, Yan and Hu, Bin and Chain,
    Patrick S. G. and Nayfach, Stephen and Kyrpides, Nikos C.},
  date         = {2023-09-21},
  journaltitle = {Nature Biotechnology},
  shortjournal = {Nat Biotechnol},
  pages        = {1--10},
  publisher    = {Nature Publishing Group},
  issn         = {1546-1696},
  doi          = {10.1038/s41587-023-01953-y},
  url          = {https://www.nature.com/articles/s41587-023-01953-y},
  urldate      = {2024-02-16},
  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.},
  langid       = {english},
  keywords     = {Environmental microbiology,Machine
    learning,Software,Virology},
  file         = {/Users/bsmith/Zotero/storage/UCFRNKA4/Camargo et al.
    - 2023 - Identification of mobile genetic elements with geN.pdf},
}
@article{cantalapiedraEggNOGmapperV2Functional2021,
  title        = {{{eggNOG-mapper}} v2: {{Functional Annotation}},
    {{Orthology Assignments}}, and {{Domain Prediction}} at the
    {{Metagenomic Scale}}},
  shorttitle   = {{{eggNOG-mapper}} V2},
  author       = {Cantalapiedra, Carlos P and Hernández-Plaza, Ana and
    Letunic, Ivica and Bork, Peer and Huerta-Cepas, Jaime},
  date         = {2021-10-01},
  journaltitle = {Molecular Biology and Evolution},
  shortjournal = {Molecular Biology and Evolution},
  volume       = 38,
  number       = 12,
  pages        = {5825--5829},
  issn         = {1537-1719},
  doi          = {10.1093/molbev/msab293},
  url          = {https://doi.org/10.1093/molbev/msab293},
  urldate      = {2023-03-30},
  abstract     = {Even though automated functional annotation of genes
    represents a fundamental step in most genomic and metagenomic
    workflows, it remains challenging at large scales. Here, we
    describe a major upgrade to eggNOG-mapper, a tool for functional
    annotation based on precomputed orthology assignments, now
    optimized for vast (meta)genomic data sets. Improvements in
    version 2 include a full update of both the genomes and functional
    databases to those from eggNOG v5, as well as several efficiency
    enhancements and new features. Most notably, eggNOG-mapper v2 now
    allows for: 1) de novo gene prediction from raw contigs, 2)
    built-in pairwise orthology prediction, 3) fast protein domain
    discovery, and 4) automated GFF decoration. eggNOG-mapper v2 is
    available as a standalone tool or as an online service at
    http://eggnog-mapper.embl.de.},
  langid       = {english},
  file         = {/Users/bsmith/Zotero/storage/ETZDGZG5/Cantalapiedra
    et al. - 2021 - eggNOG-mapper v2 Functional Annotation,
    Orthology.pdf},
}
@article{carrReconstructingGenomicContent2013a,
  title        = {Reconstructing the {{Genomic Content}} of
    {{Microbiome Taxa}} through {{Shotgun Metagenomic Deconvolution}}},
  author       = {Carr, Rogan and Shen-Orr, Shai S. and Borenstein,
    Elhanan},
  date         = {2013-10-17},
  journaltitle = {PLoS Computational Biology},
  shortjournal = {PLoS Comput Biol},
  volume       = 9,
  number       = 10,
  pages        = {e1003292},
  publisher    = {Public Library of Science},
  issn         = {1553-7358},
  doi          = {10.1371/journal.pcbi.1003292},
  url          = {https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1003292},
  urldate      = {2024-02-16},
  abstract     = {Metagenomics has transformed our understanding of
    the microbial world, allowing researchers to bypass the need to
    isolate and culture individual taxa and to directly characterize
    both the taxonomic and gene compositions of environmental samples.
    However, associating the genes found in a metagenomic sample with
    the specific taxa of origin remains a critical challenge. Existing
    binning methods, based on nucleotide composition or alignment to
    reference genomes allow only a coarse-grained classification and
    rely heavily on the availability of sequenced genomes from closely
    related taxa. Here, we introduce a novel computational framework,
    integrating variation in gene abundances across multiple samples
    with taxonomic abundance data to deconvolve metagenomic samples
    into taxa-specific gene profiles and to reconstruct the genomic
    content of community members. This assembly-free method is not
    bounded by various factors limiting previously described methods
    of metagenomic binning or metagenomic assembly and represents a
    fundamentally different approach to metagenomic-based genome
    reconstruction. An implementation of this framework is available
    at http://elbo.gs.washington.edu/software.html. We first describe
    the mathematical foundations of our framework and discuss
    considerations for implementing its various components. We
    demonstrate the ability of this framework to accurately deconvolve
    a set of metagenomic samples and to recover the gene content of
    individual taxa using synthetic metagenomic samples. We
    specifically characterize determinants of prediction accuracy and
    examine the impact of annotation errors on the reconstructed
    genomes. We finally apply metagenomic deconvolution to samples
    from the Human Microbiome Project, successfully reconstructing
    genus-level genomic content of various microbial genera, based
    solely on variation in gene count. These reconstructed genera are
    shown to correctly capture genus-specific properties. With the
    accumulation of metagenomic data, this deconvolution framework
    provides an essential tool for characterizing microbial taxa never
    before seen, laying the foundation for addressing fundamental
    questions concerning the taxa comprising diverse microbial
    communities.},
  langid       = {english},
  keywords     = {Gene prediction,Genome annotation,Genome
    sequencing,Genomics,Metagenomics,Microbiome,Shotgun
    sequencing,Species diversity},
  file         = {/Users/bsmith/Zotero/storage/2S85K8RW/Carr et al. -
    2013 - Reconstructing the Genomic Content of Microbiome T.pdf},
}
@article{carrowStrainDiversityMicrobiome2020a,
  title        = {Strain Diversity in the Microbiome: {{Lessons}} from
    {{Bacteroides}} Fragilis},
  shorttitle   = {Strain Diversity in the Microbiome},
  author       = {Carrow, Hannah C. and Batachari, Lakshmi E. and Chu,
    Hiutung},
  date         = {2020-12-10},
  journaltitle = {PLOS Pathogens},
  shortjournal = {PLoS Pathog},
  volume       = 16,
  number       = 12,
  pages        = {e1009056},
  publisher    = {Public Library of Science},
  issn         = {1553-7374},
  doi          = {10.1371/journal.ppat.1009056},
  url          = {https://journals.plos.org/plospathogens/article?id=10.1371/journal.ppat.1009056},
  urldate      = {2024-02-16},
  langid       = {english},
  keywords     = {Antibiotic resistance,Bacteroides,Gut
    bacteria,Host-pathogen interactions,Immune
    response,Microbiome,Population genetics,Species diversity},
  file         = {/Users/bsmith/Zotero/storage/WV5UBR3M/Carrow et al.
    - 2020 - Strain diversity in the microbiome Lessons from B.pdf},
}
@article{chenAccurateCompleteGenomes2020,
  title        = {Accurate and Complete Genomes from Metagenomes},
  author       = {Chen, Lin-Xing and Anantharaman, Karthik and
    Shaiber, Alon and Eren, A. Murat and Banfield, Jillian F.},
  date         = {2020-03},
  journaltitle = {Genome Research},
  shortjournal = {Genome Res.},
  volume       = 30,
  number       = 3,
  eprint       = 32188701,
  eprinttype   = {pmid},
  pages        = {315--333},
  publisher    = {Cold Spring Harbor Lab},
  issn         = {1088-9051, 1549-5469},
  doi          = {10.1101/gr.258640.119},
  url          = {https://genome.cshlp.org/content/30/3/315},
  urldate      = {2022-02-02},
  abstract     = {Genomes are an integral component of the biological
    information about an organism; thus, the more complete the genome,
    the more informative it is. Historically, bacterial and archaeal
    genomes were reconstructed from pure (monoclonal) cultures, and
    the first reported sequences were manually curated to completion.
    However, the bottleneck imposed by the requirement for isolates
    precluded genomic insights for the vast majority of microbial
    life. Shotgun sequencing of microbial communities, referred to
    initially as community genomics and subsequently as
    genome-resolved metagenomics, can circumvent this limitation by
    obtaining metagenome-assembled genomes (MAGs); but gaps, local
    assembly errors, chimeras, and contamination by fragments from
    other genomes limit the value of these genomes. Here, we discuss
    genome curation to improve and, in some cases, achieve complete
    (circularized, no gaps) MAGs (CMAGs). To date, few CMAGs have been
    generated, although notably some are from very complex systems
    such as soil and sediment. Through analysis of about 7000
    published complete bacterial isolate genomes, we verify the value
    of cumulative GC skew in combination with other metrics to
    establish bacterial genome sequence accuracy. The analysis of
    cumulative GC skew identified potential misassemblies in some
    reference genomes of isolated bacteria and the repeat sequences
    that likely gave rise to them. We discuss methods that could be
    implemented in bioinformatic approaches for curation to ensure
    that metabolic and evolutionary analyses can be based on very
    high-quality genomes.},
  langid       = {english},
  file         = {/Users/bsmith/Zotero/storage/EF5FI5JX/Chen et al. -
    2020 - Accurate and complete genomes from
    metagenomes.pdf;/Users/bsmith/Zotero/storage/I5X2KCG3/Chen et al.
    - 2020 - Accurate and complete genomes from
    metagenomes.pdf;/Users/bsmith/Zotero/storage/6F2275BM/315.html},
}
@article{chengDesignConstructionVivo2022,
  title        = {Design, Construction, and in~Vivo Augmentation of a
    Complex Gut Microbiome},
  author       = {Cheng, Alice G. and Ho, Po-Yi and Aranda-Díaz,
    Andrés and Jain, Sunit and Yu, Feiqiao B. and Meng, Xiandong and
    Wang, Min and Iakiviak, Mikhail and Nagashima, Kazuki and Zhao,
    Aishan and Murugkar, Pallavi and Patil, Advait and Atabakhsh,
    Katayoon and Weakley, Allison and Yan, Jia and Brumbaugh, Ariel R.
    and Higginbottom, Steven and Dimas, Alejandra and Shiver, Anthony
    L. and Deutschbauer, Adam and Neff, Norma and Sonnenburg, Justin
    L. and Huang, Kerwyn Casey and Fischbach, Michael A.},
  date         = {2022-09},
  journaltitle = {Cell},
  shortjournal = {Cell},
  volume       = 185,
  number       = 19,
  eprint       = 36070752,
  eprinttype   = {pmid},
  pages        = {3617-3636.e19},
  publisher    = {Elsevier},
  issn         = {0092-8674, 1097-4172},
  doi          = {10.1016/j.cell.2022.08.003},
  url          = {http://www.cell.com/cell/abstract/S0092-8674(22)00990-4},
  urldate      = {2022-09-15},
  langid       = {english},
  file         = {/Users/bsmith/Zotero/storage/DCRRSGPH/Cheng et al. -
    2022 - Design, construction, and in vivo augmentation of
    .pdf;/Users/bsmith/Zotero/storage/Z243MQ3C/Cheng et al. - 2022 -
    Design, construction, and in vivo augmentation of
    .pdf;/Users/bsmith/Zotero/storage/2LIB48GL/S0092-8674(22)00990-4.html;/Users/bsmith/Zotero/storage/CT35JPB7/S0092867422009904.html;/Users/bsmith/Zotero/storage/W5AYPNKL/S0092867422009904.html},
}
@article{davidova-gerzovaHospitalCommunityWastewater2023,
  title        = {Hospital and Community Wastewater as a Source of
    Multidrug-Resistant {{ESBL-producing Escherichia}} Coli},
  author       = {Davidova-Gerzova, Lenka and Lausova, Jarmila and
    Sukkar, Iva and Nesporova, Kristina and Nechutna, Lucie and
    Vlkova, Katerina and Chudejova, Katerina and Krutova, Marcela and
    Palkovicova, Jana and Kaspar, Jakub and Dolejska, Monika},
  date         = {2023-05-15},
  journaltitle = {Frontiers in Cellular and Infection Microbiology},
  shortjournal = {Front. Cell. Infect. Microbiol.},
  volume       = 13,
  publisher    = {Frontiers},
  issn         = {2235-2988},
  doi          = {10.3389/fcimb.2023.1184081},
  url          = {https://www.frontiersin.org/journals/cellular-and-infection-microbiology/articles/10.3389/fcimb.2023.1184081/full},
  urldate      = {2024-10-08},
  abstract     = {{$<$}sec{$><$}title{$>$}Introduction{$<$}/title{$><$}p{$>$}Hospitals
    and wastewater are recognized hot spots for the selection and
    dissemination of antibiotic-resistant bacteria to the environment,
    but the total participation of hospitals in the spread of
    nosocomial pathogens to municipal wastewater treatment plants
    (WWTPs) and adjacent rivers had not previously been
    revealed.{$<$}/p{$><$}/sec{$><$}sec{$><$}title{$>$}Methods{$<$}/title{$><$}p{$>$}We
    used a combination of culturing and whole-genome sequencing to
    explore the transmission routes of {$<$}italic{$>$}Escherichia
    coli{$<$}/italic{$>$} from hospitalized patients suffering from
    urinary tract infections (UTI)
    {$<$}italic{$>$}via{$<$}/italic{$>$} wastewater to the
    environment. Samples were collected in two periods in three
    locations (A, B, and C) and cultured on selective
    antibiotic-enhanced
    plates.{$<$}/p{$><$}/sec{$><$}sec{$><$}title{$>$}Results{$<$}/title{$><$}p{$>$}In
    total, 408 {$<$}italic{$>$}E. coli{$<$}/italic{$>$} isolates were
    obtained from patients with UTI (n=81), raw hospital sewage
    (n=73), WWTPs inflow (n=96)/outflow (n=106), and river upstream
    (n=21)/downstream (n=31) of WWTPs. The majority of the isolates
    produced extended-spectrum beta-lactamase (ESBL), mainly CTX-M-15,
    and showed multidrug resistance (MDR) profiles. Seven
    carbapenemase-producing isolates with GES-5 or OXA-244 were
    obtained in two locations from wastewater and river samples.
    Isolates were assigned to 74 different sequence types (ST), with
    the predominance of ST131 (n=80) found in all sources including
    rivers. Extraintestinal pathogenic lineages frequently found in
    hospital sewage (ST10, ST38, and ST69) were also found in river
    water. Despite generally high genetic diversity, phylogenetic
    analysis of ST10, ST295, and ST744 showed highly related isolates
    (SNP 0-18) from different sources, providing the evidence for the
    transmission of resistant strains through WWTPs to surface
    waters.{$<$}/p{$><$}/sec{$><$}sec{$><$}title{$>$}Discussion{$<$}/title{$><$}p{$>$}Results
    of this study suggest that 1) UTI share a minor participation in
    hospitals wastewaters; 2) a high diversity of STs and phylogenetic
    groups in municipal wastewaters derive from the urban influence
    rather than hospitals; and 3) pathogenic lineages and bacteria
    with emerging resistance genotypes associated with hospitals
    spread into surface waters. Our study highlights the contribution
    of hospital and municipal wastewater to the transmission of ESBL-
    and carbapenemase-producing {$<$}italic{$>$}E.
    coli{$<$}/italic{$>$} with MDR profiles to the
    environment.{$<$}/p{$><$}/sec{$>$}},
  langid       = {english},
  keywords     = {antibiotic resistance,beta-Lactamases,Escherichia
    coli,wastewaters,Whole-genome sequencing},
  file         = {/Users/bsmith/Zotero/storage/QA5N8SMV/Davidova-Gerzova
    et al. - 2023 - Hospital and community wastewater as a source of
    m.pdf},
}
@article{dimonacoNoOneTool2022,
  title        = {No One Tool to Rule Them All: Prokaryotic Gene
    Prediction Tool Annotations Are Highly Dependent on the Organism
    of Study},
  shorttitle   = {No One Tool to Rule Them All},
  author       = {Dimonaco, Nicholas J and Aubrey, Wayne and Kenobi,
    Kim and Clare, Amanda and Creevey, Christopher J},
  date         = {2021-12-07},
  journaltitle = {Bioinformatics},
  shortjournal = {Bioinformatics},
  volume       = 38,
  number       = 5,
  pages        = {1198--1207},
  issn         = {1367-4803},
  doi          = {10.1093/bioinformatics/btab827},
  url          = {https://doi.org/10.1093/bioinformatics/btab827},
  urldate      = {2024-02-16},
  abstract     = {The biases in CoDing Sequence (CDS) prediction
    tools, which have been based on historic genomic annotations from
    model organisms, impact our understanding of novel genomes and
    metagenomes. This hinders the discovery of new genomic information
    as it results in predictions being biased towards existing
    knowledge. To date, users have lacked a systematic and replicable
    approach to identify the strengths and weaknesses of any CDS
    prediction tool and allow them to choose the right tool for their
    analysis.We present an evaluation framework (ORForise) based on a
    comprehensive set of 12 primary and 60 secondary metrics that
    facilitate the assessment of the performance of CDS prediction
    tools. This makes it possible to identify which performs better
    for specific use-cases. We use this to assess 15 ab initio- and
    model-based tools representing those most widely used
    (historically and currently) to generate the knowledge in genomic
    databases. We find that the performance of any tool is dependent
    on the genome being analysed, and no individual tool ranked as the
    most accurate across all genomes or metrics analysed. Even the
    top-ranked tools produced conflicting gene collections, which
    could not be resolved by aggregation. The ORForise evaluation
    framework provides users with a replicable, data-led approach to
    make informed tool choices for novel genome annotations and for
    refining historical annotations.Code and datasets for reproduction
    and customisation are available at
    https://github.com/NickJD/ORForise.Supplementary data are
    available at Bioinformatics online.},
  langid       = {english},
  file         = {/Users/bsmith/Zotero/storage/UK9B78BU/Dimonaco et
    al. - 2022 - No one tool to rule them all prokaryotic gene
    pre.pdf;/Users/bsmith/Zotero/storage/ATRVDA9H/6454948.html},
}
@article{florensaResFinderOpenOnline2022,
  title        = {{{ResFinder}} – an Open Online Resource for
    Identification of Antimicrobial Resistance Genes in
    next-Generation Sequencing Data and Prediction of Phenotypes from
    Genotypes},
  author       = {Florensa, Alfred Ferrer and Kaas, Rolf Sommer and
    Clausen, Philip Thomas Lanken Conradsen and Aytan-Aktug, Derya and
    Aarestrup, Frank M.},
  date         = {2022-01-17},
  journaltitle = {Microbial Genomics},
  shortjournal = {Microb Genom},
  volume       = 8,
  number       = 1,
  eprint       = 35072601,
  eprinttype   = {pmid},
  pages        = 000748,
  issn         = {2057-5858},
  doi          = {10.1099/mgen.0.000748},
  url          = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8914360/},
  urldate      = {2024-02-16},
  abstract     = {Antimicrobial resistance (AMR) is one of the most
    important health threats globally. The ability to accurately
    identify resistant bacterial isolates and the individual
    antimicrobial resistance genes (ARGs) is essential for
    understanding the evolution and emergence of AMR and to provide
    appropriate treatment. The rapid developments in next-generation
    sequencing technologies have made this technology available to
    researchers and microbiologists at routine laboratories around the
    world. However, tools available for those with limited experience
    with bioinformatics are lacking, especially to enable researchers
    and microbiologists in low- and middle-income countries (LMICs) to
    perform their own studies. The CGE-tools (Center for Genomic
    Epidemiology) including ResFinder
    (https://cge.cbs.dtu.dk/services/ResFinder/) was developed to
    provide freely available easy to use online bioinformatic tools
    allowing inexperienced researchers and microbiologists to perform
    simple bioinformatic analyses. The main purpose was and is to
    provide these solutions for people involved in frontline diagnosis
    especially in LMICs. Since its original publication in 2012,
    ResFinder has undergone a number of improvements including
    improvement of the code and databases, inclusion of point
    mutations for selected bacterial species and predictions of
    phenotypes also for selected species. As of 28 September 2021, 820
    803 analyses have been performed using ResFinder from 61 776
    IP-addresses in 171 countries. ResFinder clearly fulfills a need
    for several people around the globe and we hope to be able to
    continue to provide this service free of charge in the future. We
    also hope and expect to provide further improvements including
    phenotypic predictions for additional bacterial species.},
  langid       = {english},
  pmcid        = {PMC8914360},
  file         = {/Users/bsmith/Zotero/storage/D9JU2TUS/Florensa et
    al. - 2022 - ResFinder – an open online resource for
    identifica.pdf},
}
@article{fuCDHITAcceleratedClustering2012,
  title        = {{{CD-HIT}}: Accelerated for Clustering the
    next-Generation Sequencing Data},
  shorttitle   = {{{CD-HIT}}},
  author       = {Fu, Limin and Niu, Beifang and Zhu, Zhengwei and Wu,
    Sitao and Li, Weizhong},
  date         = {2012-10-11},
  journaltitle = {Bioinformatics},
  shortjournal = {Bioinformatics},
  volume       = 28,
  number       = 23,
  eprint       = 23060610,
  eprinttype   = {pmid},
  pages        = {3150--3152},
  issn         = {1367-4811},
  doi          = {10.1093/bioinformatics/bts565},
  abstract     = {SUMMARY: CD-HIT is a widely used program for
    clustering biological sequences to reduce sequence redundancy and
    improve the performance of other sequence analyses. In response to
    the rapid increase in the amount of sequencing data produced by
    the next-generation sequencing technologies, we have developed a
    new CD-HIT program accelerated with a novel parallelization
    strategy and some other techniques to allow efficient clustering
    of such datasets. Our tests demonstrated very good speedup derived
    from the parallelization for up to ∼24 cores and a quasi-linear
    speedup for up to ∼8 cores. The enhanced CD-HIT is capable of
    handling very large datasets in much shorter time than previous
    versions. AVAILABILITY: http://cd-hit.org. CONTACT: liwz@sdsc.edu
    SUPPLEMENTARY INFORMATION: Supplementary data are available at
    Bioinformatics online.},
  langid       = {english},
  pmcid        = {PMC3516142},
  keywords     = {Algorithms,Cluster Analysis,Computational
    Biology,Sequence Analysis Protein,Software},
  file         = {/Users/bsmith/Zotero/storage/QU8R59IN/Fu et al. -
    2012 - CD-HIT accelerated for clustering the next-genera.pdf},
}
@article{hackerExclusiveEnteralNutrition2024,
  title        = {Exclusive Enteral Nutrition Initiates Individual
    Protective Microbiome Changes to Induce Remission in Pediatric
    {{Crohn}}’s Disease},
  author       = {Häcker, Deborah and Siebert, Kolja and Smith, Byron
    J. and Köhler, Nikolai and Riva, Alessandra and Mahapatra, Aritra
    and Heimes, Helena and Nie, Jiatong and Metwaly, Amira and Hölz,
    Hannes and Manz, Quirin and De Zen, Federica and Heetmeyer,
    Jeannine and Socas, Katharina and Le Thi, Giang and Meng, Chen and
    Kleigrewe, Karin and Pauling, Josch K. and Neuhaus, Klaus and
    List, Markus and Pollard, Katherine S. and Schwerd, Tobias and
    Haller, Dirk},
  date         = {2024-10-25},
  journaltitle = {Cell Host \& Microbe},
  shortjournal = {Cell Host \& Microbe},
  issn         = {1931-3128},
  doi          = {10.1016/j.chom.2024.10.001},
  url          = {https://www.sciencedirect.com/science/article/pii/S1931312824003597},
  urldate      = {2024-10-27},
  abstract     = {Exclusive enteral nutrition (EEN) is a first-line
    therapy for pediatric Crohn’s disease (CD), but protective
    mechanisms remain unknown. We established a prospective pediatric
    cohort to characterize the function of fecal microbiota and
    metabolite changes of treatment-naive CD patients in response to
    EEN (German Clinical Trials DRKS00013306). Integrated multi-omics
    analysis identified network clusters from individually variable
    microbiome profiles, with Lachnospiraceae and medium-chain fatty
    acids as protective features. Bioorthogonal non-canonical amino
    acid tagging selectively identified bacterial species in response
    to medium-chain fatty acids. Metagenomic analysis identified high
    strain-level dynamics in response to EEN. Functional changes in
    diet-exposed fecal microbiota were further validated using gut
    chemostat cultures and microbiota transfer into germ-free
    Il10-deficient mice. Dietary model conditions induced individual
    patient-specific strain signatures to prevent or cause
    inflammatory bowel disease (IBD)-like inflammation in gnotobiotic
    mice. Hence, we provide evidence that EEN therapy operates through
    explicit functional changes of temporally and individually
    variable microbiome profiles.},
  keywords     = {bacterial strain dynamics,EEN,exclusive enteral
    nutrition,fiber,FMT,gut chemostat model,medium-chain fatty
    acids,metagenomics,microbiome,multi-omics data
    integration,pediatric Crohn’s disease},
  file         = {/Users/bsmith/Zotero/storage/3CFCELAS/S1931312824003597.html},
}
@article{hovhannisyanCROSSMAPPEREstimatingCrossmapping2020,
  title        = {{{CROSSMAPPER}}: Estimating Cross-Mapping Rates and
    Optimizing Experimental Design in Multi-Species Sequencing Studies},
  shorttitle   = {{{CROSSMAPPER}}},
  author       = {Hovhannisyan, Hrant and Hafez, Ahmed and Llorens,
    Carlos and Gabaldón, Toni},
  date         = {2020-02-01},
  journaltitle = {Bioinformatics},
  shortjournal = {Bioinformatics},
  volume       = 36,
  number       = 3,
  pages        = {925--927},
  issn         = {1367-4803},
  doi          = {10.1093/bioinformatics/btz626},
  url          = {https://doi.org/10.1093/bioinformatics/btz626},
  urldate      = {2024-11-08},
  abstract     = {Numerous sequencing studies, including
    transcriptomics of host-pathogen systems, sequencing of hybrid
    genomes, xenografts, mixed species systems, metagenomics and
    meta-transcriptomics, involve samples containing genetic material
    from divergent organisms. A crucial step in these studies is
    identifying from which organism each sequencing read originated,
    and the experimental design should be directed to minimize biases
    caused by cross-mapping of reads to incorrect source genomes.
    Additionally, pooling of sufficiently different genetic material
    into a single sequencing library could significantly reduce
    experimental costs but requires careful planning and assessment of
    the impact of cross-mapping. Having these applications in mind we
    designed Crossmapper, the first to our knowledge tool able to
    assess cross-mapping prior to sequencing, therefore allowing
    optimization of experimental design.Using any combination of
    reference genomes, Crossmapper performs read simulation and
    back-mapping of those reads to the pool of references, quantifies
    and reports the cross-mapping rates for each organism. Crossmapper
    performs these analyses with numerous user-specified parameters,
    including, among others, read length, read layout, coverage,
    mapping parameters, genomic or transcriptomic data. Additionally,
    it outputs the results in highly interactive and publication-ready
    reports. This allows the user to perform multiple comparisons at
    once and choose the experimental setup minimizing cross-mapping
    rates. Moreover, Crossmapper can be used for resource optimization
    in sequencing facilities by pooling different samples into one
    sequencing library.Crossmapper is a command line tool implemented
    in Python 3.6 and available as a conda package, allowing
    effortless installation. The source code, detailed information and
    a step-by-step tutorial is available at our GitHub page
    https://github.com/Gabaldonlab/crossmapper.Supplementary data are
    available at Bioinformatics online.},
  file         = {/Users/bsmith/Zotero/storage/HZBR2LUD/Hovhannisyan
    et al. - 2020 - CROSSMAPPER estimating cross-mapping rates and
    optimizing experimental design in multi-species
    sequ.pdf;/Users/bsmith/Zotero/storage/867PGCSQ/5544929.html},
}
@article{huStrainPanDALinkedReconstruction2022,
  title        = {{{StrainPanDA}}: {{Linked}} Reconstruction of Strain
    Composition and Gene Content Profiles via Pangenome‐based
    Decomposition of Metagenomic Data},
  shorttitle   = {{{StrainPanDA}}},
  author       = {Hu, Han and Tan, Yuxiang and Li, Chenhao and Chen,
    Junyu and Kou, Yan and Xu, Zhenjiang Zech and Liu, Yang‐Yu and
    Tan, Yan and Dai, Lei},
  date         = {2022-08},
  journaltitle = {iMeta},
  shortjournal = {iMeta},
  volume       = 1,
  number       = 3,
  pages        = {e41},
  issn         = {2770-596X},
  doi          = {10.1002/imt2.41},
  url          = {https://onlinelibrary.wiley.com/doi/abs/10.1002/imt2.41},
  urldate      = {2022-10-18},
  abstract     = {Microbial strains of variable functional capacities
    coexist in microbiomes. Current bioinformatics methods of strain
    analysis cannot provide the direct linkage between strain
    composition and their gene contents from metagenomic data. Here we
    present Strain-level Pangenome Decomposition Analysis
    (StrainPanDA), a novel method that uses the pangenome coverage
    profile of multiple metagenomic samples to simultaneously
    reconstruct the composition and gene content variation of
    coexisting strains in microbial communities. We systematically
    validate the accuracy and robustness of StrainPanDA using
    synthetic data sets. To demonstrate the power of gene-centric
    strain profiling, we then apply StrainPanDA to analyze the gut
    microbiome samples of infants, as well as patients treated with
    fecal microbiota transplantation. We show that the linked
    reconstruction of strain composition and gene content profiles is
    critical for understanding the relationship between microbial
    adaptation and strain-specific functions (e.g., nutrient
    utilization and pathogenicity). Finally, StrainPanDA has minimal
    requirements for computing resources and can be scaled to process
    multiple species in a community in parallel. In short, StrainPanDA
    can be applied to metagenomic data sets to detect the association
    between molecular functions and microbial/host phenotypes to
    formulate testable hypotheses and gain novel biological insights
    at the strain or subspecies level.},
  langid       = {english},
  keywords     = {gene content
    profile,metagenomics,microbiome,pangenome,strain analysis},
  file         = {/Users/bsmith/Zotero/storage/AFKEWDKZ/Hu et
    al_2022_StrainPanDA.pdf;/Users/bsmith/Zotero/storage/WUEE3YN7/imt2.html},
}
@article{hyattGeneTranslationInitiation2012,
  title        = {Gene and Translation Initiation Site Prediction in
    Metagenomic Sequences},
  author       = {Hyatt, Doug and LoCascio, Philip F. and Hauser,
    Loren J. and Uberbacher, Edward C.},
  date         = {2012-07-12},
  journaltitle = {Bioinformatics},
  shortjournal = {Bioinformatics},
  volume       = 28,
  number       = 17,
  pages        = {2223--2230},
  issn         = {1367-4803},
  doi          = {10.1093/bioinformatics/bts429},
  url          = {https://doi.org/10.1093/bioinformatics/bts429},
  urldate      = {2024-02-16},
  abstract     = {Motivation: Gene prediction in metagenomic sequences
    remains a difficult problem. Current sequencing technologies do
    not achieve sufficient coverage to assemble the individual genomes
    in a typical sample; consequently, sequencing runs produce a large
    number of short sequences whose exact origin is unknown. Since
    these sequences are usually smaller than the average length of a
    gene, algorithms must make predictions based on very little
    data.Results: We present MetaProdigal, a metagenomic version of
    the gene prediction program Prodigal, that can identify genes in
    short, anonymous coding sequences with a high degree of accuracy.
    The novel value of the method consists of enhanced translation
    initiation site identification, ability to identify sequences that
    use alternate genetic codes and confidence values for each gene
    call. We compare the results of MetaProdigal with other methods
    and conclude with a discussion of future
    improvements.Availability: The Prodigal software is freely
    available under the General Public License from
    http://code.google.com/p/prodigal/.Contact:
    ~hyattpd@ornl.govSupplementary Information: Supplementary data are
    available at Bioinformatics online.},
  langid       = {english},
  file         = {/Users/bsmith/Zotero/storage/4QC9P398/Hyatt et al. -
    2012 - Gene and translation initiation site prediction
    in.pdf;/Users/bsmith/Zotero/storage/SQ4TSJAV/246063.html},
}
@article{hyattProdigalProkaryoticGene2010,
  title        = {Prodigal: Prokaryotic Gene Recognition and
    Translation Initiation Site Identification},
  shorttitle   = {Prodigal},
  author       = {Hyatt, Doug and Chen, Gwo-Liang and LoCascio, Philip
    F and Land, Miriam L and Larimer, Frank W and Hauser, Loren J},
  date         = {2010-03-08},
  journaltitle = {BMC Bioinformatics},
  shortjournal = {BMC Bioinformatics},
  volume       = 11,
  number       = 1,
  pages        = 119,
  issn         = {1471-2105},
  doi          = {10.1186/1471-2105-11-119},
  url          = {https://doi.org/10.1186/1471-2105-11-119},
  urldate      = {2024-03-06},
  abstract     = {The quality of automated gene prediction in
    microbial organisms has improved steadily over the past decade,
    but there is still room for improvement. Increasing the number of
    correct identifications, both of genes and of the translation
    initiation sites for each gene, and reducing the overall number of
    false positives, are all desirable goals.},
  langid       = {english},
  keywords     = {Bias Score,Codon Position,Halobacterium
    Salinarum,Ribosomal Binding Site,Translation Initiation Site},
  file         = {/Users/bsmith/Zotero/storage/GTUEDGP9/Hyatt et al. -
    2010 - Prodigal prokaryotic gene recognition and
    transla.pdf;/Users/bsmith/Zotero/storage/MFIEMPYB/1471-2105-11-119.html},
}
@article{jinCulturingComplexGut2023,
  title        = {Culturing of a Complex Gut Microbial Community in
    Mucin-Hydrogel Carriers Reveals Strain- and Gene-Associated
    Spatial Organization},
  author       = {Jin, Xiaofan and Yu, Feiqiao B. and Yan, Jia and
    Weakley, Allison M. and Dubinkina, Veronika and Meng, Xiandong and
    Pollard, Katherine S.},
  date         = {2023-06-14},
  journaltitle = {Nature Communications},
  shortjournal = {Nat Commun},
  volume       = 14,
  number       = 1,
  eprint       = 37316519,
  eprinttype   = {pmid},
  pages        = 3510,
  issn         = {2041-1723},
  doi          = {10.1038/s41467-023-39121-0},
  url          = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10267222/},
  urldate      = {2023-09-04},
  abstract     = {Microbial community function depends on both
    taxonomic composition and spatial organization. While composition
    of the human gut microbiome has been deeply characterized, less is
    known about the organization of microbes between regions such as
    lumen and mucosa and the microbial genes regulating this
    organization. Using a defined 117 strain community for which we
    generate high-quality genome assemblies, we model mucosa/lumen
    organization with in vitro cultures incorporating mucin hydrogel
    carriers as surfaces for bacterial attachment. Metagenomic
    tracking of carrier cultures reveals increased diversity and
    strain-specific spatial organization, with distinct strains
    enriched on carriers versus liquid supernatant, mirroring
    mucosa/lumen enrichment in vivo. A comprehensive search for
    microbial genes associated with this spatial organization
    identifies candidates with known adhesion-related functions, as
    well as novel links. These findings demonstrate that carrier
    cultures of defined communities effectively recapitulate
    fundamental aspects of gut spatial organization, enabling
    identification of key microbial strains and genes., The
    organization of gut microbes in lumen and mucosa and the microbial
    genes regulating this organization remain poorly understood. Here,
    using in vitro cultures incorporating a complex gut microbial
    community in mucin-hydrogel carriers, the authors show greater
    richness and strain-specific spatial organization, enabling
    discovery of associated genes.},
  langid       = {english},
  pmcid        = {PMC10267222},
  file         = {/Users/bsmith/Zotero/storage/68Y8FVIX/Jin et al. -
    2023 - Culturing of a complex gut microbial community in .pdf},
}
@article{joglekarGeneticVariationSusC2018a,
  title        = {Genetic {{Variation}} of the {{SusC}}/{{SusD
    Homologs}} from a {{Polysaccharide Utilization Locus Underlies
    Divergent Fructan Specificities}} and {{Functional Adaptation}} in
    {{{\emph{Bacteroides}}}}{\emph{ Thetaiotaomicron}} {{Strains}}},
  author       = {Joglekar, Payal and Sonnenburg, Erica D. and
    Higginbottom, Steven K. and Earle, Kristen A. and Morland, Carl
    and Shapiro-Ward, Sarah and Bolam, David N. and Sonnenburg, Justin
    L.},
  date         = {2018-06-27},
  journaltitle = {mSphere},
  shortjournal = {mSphere},
  volume       = 3,
  number       = 3,
  publisher    = {American Society for Microbiology},
  doi          = {10.1128/mspheredirect.00185-18},
  url          = {https://journals.asm.org/doi/full/10.1128/mspheredirect.00185-18},
  urldate      = {2024-02-16},
  abstract     = {Genomic differences between gut-resident bacterial
    strains likely underlie significant interindividual variation in
    microbiome function. Traditional methods of determining community
    composition, such as 16S rRNA gene amplicon sequencing, fail to
    capture this functional diversity. Metagenomic approaches are a
    significant step forward in identifying strain-level sequence
    variants; however, given the current paucity of biochemical
    information, they too are limited to mainly low-resolution and
    incomplete functional predictions. Using genomic, biochemical, and
    molecular approaches, we identified differences in the fructan
    utilization profiles of two closely related Bacteroides
    thetaiotaomicron strains. B.~thetaiotaomicron 8736 (Bt-8736)
    contains a fructan polysaccharide utilization locus (PUL) with a
    divergent susC/susD homolog gene pair that enables it to utilize
    inulin, differentiating this strain from other characterized Bt
    strains. Transfer of the distinct pair of susC/susD genes from
    Bt-8736 into the noninulin using type strain B.~thetaiotaomicron
    VPI-5482 resulted in inulin use by the recipient strain,
    Bt(8736-2). The presence of the divergent susC/susD gene pair
    alone enabled the hybrid Bt(8736-2) strain to outcompete the
    wild-type strain in vivo in mice fed an inulin diet. Further, we
    discovered that the susC/susD homolog gene pair facilitated import
    of inulin into the periplasm without surface predigestion by an
    endo-acting enzyme, possibly due to the short average chain length
    of inulin compared to many other polysaccharides. Our data builds
    upon recent reports of dietary polysaccharide utilization
    mechanisms found in members of the Bacteroides genus and
    demonstrates how the acquisition of two genes can alter the
    functionality and success of a strain within the gut. IMPORTANCE
    Dietary polysaccharides play a dominant role in shaping the
    composition and functionality of our gut microbiota. Dietary
    interventions using these microbiota-accessible carbohydrates
    (MACs) serve as a promising tool for manipulating the gut
    microbial community. However, our current gap in knowledge
    regarding microbial metabolic pathways that are involved in the
    degradation of these MACs has made the design of rational
    interventions difficult. The issue is further complicated by the
    diversity of pathways observed for the utilization of similar
    MACs, even in closely related microbial strains. Our current work
    focuses on divergent fructan utilization pathways in two closely
    related B.~thetaiotaomicron strains and provides an integrated
    approach to characterize the molecular basis for strain-level
    functional differences.},
  langid       = {english},
  file         = {/Users/bsmith/Zotero/storage/I9VH6FSW/Joglekar et
    al. - 2018 - Genetic Variation of the SusCSusD Homologs from a.pdf},
}
@article{johanssonDetectionMobileGenetic2021,
  title        = {Detection of Mobile Genetic Elements Associated with
    Antibiotic Resistance in {{{\emph{Salmonella}}}}{\emph{ Enterica}}
    Using a Newly Developed Web Tool: {{MobileElementFinder}}},
  shorttitle   = {Detection of Mobile Genetic Elements Associated with
    Antibiotic Resistance in {{Salmonella}} Enterica Using a Newly
    Developed Web Tool},
  author       = {Johansson, Markus H K and Bortolaia, Valeria and
    Tansirichaiya, Supathep and Aarestrup, Frank M and Roberts, Adam P
    and Petersen, Thomas N},
  date         = {2020-10-03},
  journaltitle = {Journal of Antimicrobial Chemotherapy},
  shortjournal = {J Antimicrob Chemother},
  volume       = 76,
  number       = 1,
  eprint       = 33009809,
  eprinttype   = {pmid},
  pages        = {101--109},
  issn         = {1460-2091},
  doi          = {10.1093/jac/dkaa390},
  abstract     = {OBJECTIVES: Antimicrobial resistance (AMR) in
    clinically relevant bacteria is a growing threat to public health
    globally. In these bacteria, antimicrobial resistance genes are
    often associated with mobile genetic elements (MGEs), which
    promote their mobility, enabling them to rapidly spread throughout
    a bacterial community. METHODS: The tool MobileElementFinder was
    developed to enable rapid detection of MGEs and their genetic
    context in assembled sequence data. MGEs are detected based on
    sequence similarity to a database of 4452 known elements augmented
    with annotation of resistance genes, virulence factors and
    detection of plasmids. RESULTS: MobileElementFinder was applied to
    analyse the mobilome of 1725 sequenced Salmonella enterica
    isolates of animal origin from Denmark, Germany and the USA. We
    found that the MGEs were seemingly conserved according to
    multilocus ST and not restricted to either the host or the country
    of origin. Moreover, we identified putative translocatable units
    for specific aminoglycoside, sulphonamide and tetracycline genes.
    Several putative composite transposons were predicted that could
    mobilize, among others, AMR, metal resistance and
    phosphodiesterase genes associated with macrophage survivability.
    This is, to our knowledge, the first time the
    phosphodiesterase-like pdeL has been found to be potentially
    mobilized into S. enterica. CONCLUSIONS: MobileElementFinder is a
    powerful tool to study the epidemiology of MGEs in a large number
    of genome sequences and to determine the potential for genomic
    plasticity of bacteria. This web service provides a convenient
    method of detecting MGEs in assembled sequence data.
    MobileElementFinder can be accessed at
    https://cge.cbs.dtu.dk/services/MobileElementFinder/.},
  langid       = {english},
  pmcid        = {PMC7729385},
  keywords     = {Animals,Anti-Bacterial Agents,Drug Resistance
    Microbial,Germany,Interspersed Repetitive
    Sequences,Plasmids,Salmonella enterica},
  file         = {/Users/bsmith/Zotero/storage/GBF6PI8S/Johansson et
    al. - 2021 - Detection of mobile genetic elements associated
    wi.pdf},
}
@article{langmeadFastGappedreadAlignment2012,
  title        = {Fast Gapped-Read Alignment with {{Bowtie}} 2},
  author       = {Langmead, Ben and Salzberg, Steven L},
  date         = {2012-03-04},
  journaltitle = {Nature Methods},
  shortjournal = {Nat Methods},
  volume       = 9,
  number       = 4,
  eprint       = 22388286,
  eprinttype   = {pmid},
  pages        = {357--359},
  issn         = 15487091,
  doi          = {10.1038/nmeth.1923},
  abstract     = {As the rate of sequencing increases, greater
    throughput is demanded from read aligners. The full-text minute
    index is often used to make alignment very fast and
    memory-efficient, but the approach is ill-suited to finding
    longer, gapped alignments. Bowtie 2 combines the strengths of the
    full-text minute index with the flexibility and speed of
    hardware-accelerated dynamic programming algorithms to achieve a
    combination of high speed, sensitivity and accuracy.},
  isbn         = {1548-7105 (Electronic) 1548-7091 (Linking)},
  langid       = {english},
  file         = {/Users/bsmith/Zotero/storage/622U7QX3/Langmead,
    Salzberg - 2012 - Fast gapped-read alignment with Bowtie 2.pdf},
}
@article{liIntegratedCatalogReference2014,
  title        = {An Integrated Catalog of Reference Genes in the
    Human Gut Microbiome},
  author       = {Li, Junhua and Jia, Huijue and Cai, Xianghang and
    Zhong, Huanzi and Feng, Qiang and Sunagawa, Shinichi and Arumugam,
    Manimozhiyan and Kultima, Jens Roat and Prifti, Edi and Nielsen,
    Trine and Juncker, Agnieszka Sierakowska and Manichanh, Chaysavanh
    and Chen, Bing and Zhang, Wenwei and Levenez, Florence and Wang,
    Juan and Xu, Xun and Xiao, Liang and Liang, Suisha and Zhang,
    Dongya and Zhang, Zhaoxi and Chen, Weineng and Zhao, Hailong and
    Al-Aama, Jumana Yousuf and Edris, Sherif and Yang, Huanming and
    Wang, Jian and Hansen, Torben and Nielsen, Henrik Bjørn and
    Brunak, Søren and Kristiansen, Karsten and Guarner, Francisco and
    Pedersen, Oluf and Doré, Joel and Ehrlich, S. Dusko and Bork, Peer
    and Wang, Jun},
  date         = {2014-08},
  journaltitle = {Nature Biotechnology},
  shortjournal = {Nat Biotechnol},
  volume       = 32,
  number       = 8,
  pages        = {834--841},
  publisher    = {Nature Publishing Group},
  issn         = {1546-1696},
  doi          = {10.1038/nbt.2942},
  url          = {https://www.nature.com/articles/nbt.2942},
  urldate      = {2024-02-16},
  abstract     = {Li et al. provide the largest catalog of genes in
    the human gut microbiome based on samples from {$>$}1,200
    individuals across three continents.},
  issue        = 8,
  langid       = {english},
  keywords     = {Metagenomics,Microbiome},
  file         = {/Users/bsmith/Zotero/storage/WVALRG8X/Li et al. -
    2014 - An integrated catalog of reference genes in the hu.pdf},
}
@article{linGenomicLandscapeReference2023,
  title        = {The Genomic Landscape of Reference Genomes of
    Cultivated Human Gut Bacteria},
  author       = {Lin, Xiaoqian and Hu, Tongyuan and Chen, Jianwei and
    Liang, Hewei and Zhou, Jianwei and Wu, Zhinan and Ye, Chen and
    Jin, Xin and Xu, Xun and Zhang, Wenwei and Jing, Xiaohuan and
    Yang, Tao and Wang, Jian and Yang, Huanming and Kristiansen,
    Karsten and Xiao, Liang and Zou, Yuanqiang},
  date         = {2023-03-25},
  journaltitle = {Nature Communications},
  shortjournal = {Nat Commun},
  volume       = 14,
  number       = 1,
  eprint       = 36966151,
  eprinttype   = {pmid},
  pages        = 1663,
  issn         = {2041-1723},
  doi          = {10.1038/s41467-023-37396-x},
  abstract     = {Culture-independent metagenomic studies have
    revolutionized our understanding of the gut microbiota. However,
    the lack of full genomes from cultured species is still a
    limitation for in-depth studies of the gut microbiota. Here we
    present a substantially expanded version of our Cultivated Genome
    Reference (CGR), termed CGR2, providing 3324 high-quality draft
    genomes from isolates selected from a large-scale cultivation of
    bacterial isolates from fecal samples of healthy Chinese
    individuals. The CGR2 classifies 527 species (179 previously
    unidentified species) from 8 phyla, and uncovers a genomic and
    functional diversity of Collinsella aerofaciens. The CGR2 genomes
    match 126 metagenome-assembled genomes without cultured
    representatives in the Unified Human Gastrointestinal Genome
    (UHGG) collection and harbor 3767 unidentified secondary
    metabolite biosynthetic gene clusters, providing a source of
    natural compounds with pharmaceutical potentials. We uncover
    accurate phage-bacterium linkages providing information on the
    evolutionary characteristics of interaction between bacteriophages
    and bacteria at the strain level.},
  langid       = {english},
  pmcid        = {PMC10039858},
  keywords     = {Bacteria,Bacteriophages,Gastrointestinal
    Microbiome,Genome
    Bacterial,Genomics,Humans,Metagenome,Metagenomics},
  file         = {/Users/bsmith/Zotero/storage/LE4LESZH/Lin et al. -
    2023 - The genomic landscape of reference genomes of cultivated
    human gut bacteria.pdf},
}
@article{lloyd-priceStrainsFunctionsDynamics2017,
  title        = {Strains, Functions and Dynamics in the Expanded
    {{Human Microbiome Project}}},
  author       = {Lloyd-Price, Jason and Mahurkar, Anup and Rahnavard,
    Gholamali and Crabtree, Jonathan and Orvis, Joshua and Hall, A.
    Brantley and Brady, Arthur and Creasy, Heather H. and McCracken,
    Carrie and Giglio, Michelle G. and McDonald, Daniel and Franzosa,
    Eric A. and Knight, Rob and White, Owen and Huttenhower, Curtis},
  date         = {2017-09-20},
  journaltitle = {Nature},
  shortjournal = {Nature},
  volume       = 550,
  number       = 7674,
  pages        = {61--66},
  publisher    = {Nature Publishing Group},
  issn         = {1476-4687},
  doi          = {10.1038/nature23889},
  url          = {https://www.nature.com/articles/nature23889},
  urldate      = {2024-02-16},
  abstract     = {The characterization of baseline microbial and
    functional diversity in the human microbiome has enabled studies
    of microbiome-related disease, diversity, biogeography, and
    molecular function. The National Institutes of Health Human
    Microbiome Project has provided one of the broadest such
    characterizations so far. Here we introduce a second wave of data
    from the study, comprising 1,631 new metagenomes (2,355 total)
    targeting diverse body sites with multiple time points in 265
    individuals. We applied updated profiling and assembly methods to
    provide new characterizations of microbiome personalization.
    Strain identification revealed subspecies clades specific to body
    sites; it also quantified species with phylogenetic diversity
    under-represented in isolate genomes. Body-wide functional
    profiling classified pathways into universal, human-enriched, and
    body site-enriched subsets. Finally, temporal analysis decomposed
    microbial variation into rapidly variable, moderately variable,
    and stable subsets. This study furthers our knowledge of baseline
    human microbial diversity and enables an understanding of
    personalized microbiome function and dynamics.},
  issue        = 7674,
  langid       = {english},
  keywords     = {Metagenomics,Microbial ecology,Microbiome},
  file         = {/Users/bsmith/Zotero/storage/57C2LC3H/Lloyd-Price et
    al. - 2017 - Strains, functions and dynamics in the expanded
    Hu.pdf},
}
@article{milaneseMicrobialAbundanceActivity2019b,
  title        = {Microbial Abundance, Activity and Population Genomic
    Profiling with {{mOTUs2}}},
  author       = {Milanese, Alessio and Mende, Daniel R and Paoli,
    Lucas and Salazar, Guillem and Ruscheweyh, Hans-Joachim and
    Cuenca, Miguelangel and Hingamp, Pascal and Alves, Renato and
    Costea, Paul I and Coelho, Luis Pedro and Schmidt, Thomas S. B.
    and Almeida, Alexandre and Mitchell, Alex L and Finn, Robert D.
    and Huerta-Cepas, Jaime and Bork, Peer and Zeller, Georg and
    Sunagawa, Shinichi},
  date         = {2019-03-04},
  journaltitle = {Nature Communications},
  shortjournal = {Nat Commun},
  volume       = 10,
  number       = 1,
  pages        = 1014,
  publisher    = {Nature Publishing Group},
  issn         = {2041-1723},
  doi          = {10.1038/s41467-019-08844-4},
  url          = {https://www.nature.com/articles/s41467-019-08844-4},
  urldate      = {2024-02-16},
  abstract     = {Metagenomic sequencing has greatly improved our
    ability to profile the composition of environmental and
    host-associated microbial communities. However, the dependency of
    most methods on reference genomes, which are currently unavailable
    for a substantial fraction of microbial species, introduces
    estimation biases. We present an updated and functionally extended
    tool based on universal (i.e., reference-independent),
    phylogenetic marker gene (MG)-based operational taxonomic units
    (mOTUs) enabling the profiling of {$>$}7700 microbial species. As
    more than 30\% of them could not previously be quantified at this
    taxonomic resolution, relative abundance estimates based on mOTUs
    are more accurate compared to other methods. As a new feature, we
    show that mOTUs, which are based on essential housekeeping genes,
    are demonstrably well-suited for quantification of basal
    transcriptional activity of community members. Furthermore, single
    nucleotide variation profiles estimated using mOTUs reflect those
    from whole genomes, which allows for comparing microbial strain
    populations (e.g., across different human body sites).},
  issue        = 1,
  langid       = {english},
  keywords     = {Microbiome,Software},
  file         = {/Users/bsmith/Zotero/storage/WBMNZRDZ/Milanese et
    al. - 2019 - Microbial abundance, activity and population
    genom.pdf},
}
@article{minotGeneshotGenelevelMetagenomics2021,
  title        = {Geneshot: Gene-Level Metagenomics Identifies Genome
    Islands Associated with Immunotherapy Response},
  shorttitle   = {Geneshot},
  author       = {Minot, Samuel S. and Barry, Kevin C. and Kasman,
    Caroline and Golob, Jonathan L. and Willis, Amy D.},
  date         = {2021-05-05},
  journaltitle = {Genome Biology},
  shortjournal = {Genome Biol},
  volume       = 22,
  number       = 1,
  pages        = 135,
  issn         = {1474-760X},
  doi          = {10.1186/s13059-021-02355-6},
  url          = {https://doi.org/10.1186/s13059-021-02355-6},
  urldate      = {2024-02-16},
  abstract     = {Researchers must be able to generate experimentally
    testable hypotheses from sequencing-based observational microbiome
    experiments to discover the mechanisms underlying the influence of
    gut microbes on human health. We describe geneshot, a novel
    bioinformatics tool for identifying testable hypotheses based on
    gene-level metagenomic analysis of WGS microbiome data. By
    applying geneshot to two independent previously published cohorts,
    we identify microbial genomic islands consistently associated with
    response to immune checkpoint inhibitor (ICI)-based cancer
    treatment in culturable type strains. The identified genomic
    islands are within operons involved in type II secretion,
    TonB-dependent transport, and bacteriophage growth.},
  langid       = {english},
  file         = {/Users/bsmith/Zotero/storage/EIJ52XGZ/Minot et al. -
    2021 - geneshot gene-level metagenomics identifies
    genom.pdf;/Users/bsmith/Zotero/storage/BDDP2LFB/s13059-021-02355-6.html},
}
@article{molderSustainableDataAnalysis2021a,
  title        = {Sustainable Data Analysis with {{Snakemake}}},
  author       = {Mölder, Felix and Jablonski, Kim Philipp and
    Letcher, Brice and Hall, Michael B. and Tomkins-Tinch, Christopher
    H. and Sochat, Vanessa and Forster, Jan and Lee, Soohyun and
    Twardziok, Sven O. and Kanitz, Alexander and Wilm, Andreas and
    Holtgrewe, Manuel and Rahmann, Sven and Nahnsen, Sven and Köster,
    Johannes},
  date         = {2021-04-19},
  number       = {10:33},
  publisher    = {F1000Research},
  doi          = {10.12688/f1000research.29032.2},
  url          = {https://f1000research.com/articles/10-33},
  urldate      = {2024-02-16},
  abstract     = {Data analysis often entails a multitude of
    heterogeneous steps, from the application of various command line
    tools to the usage of scripting languages like R or Python for the
    generation of plots and tables. It is widely recognized that data
    analyses should ideally be conducted in a reproducible
    way.\&nbsp;Reproducibility enables technical validation and
    regeneration of results on the original or even new data. However,
    reproducibility alone is by no means sufficient to deliver an
    analysis that is of lasting impact (i.e., sustainable) for the
    field, or even just one research group. We postulate that it is
    equally important to ensure adaptability and transparency. The
    former describes the ability to modify the analysis to answer
    extended or slightly different research questions. The latter
    describes the ability to understand the analysis in order to judge
    whether it is not only technically, but methodologically valid.
    Here, we analyze the properties needed for a data analysis to
    become reproducible, adaptable, and transparent. We show how the
    popular workflow management system Snakemake can be used to
    guarantee this, and how it enables an ergonomic, combined, unified
    representation of all steps involved in data analysis, ranging
    from raw data processing, to quality control and fine-grained,
    interactive exploration and plotting of final results.},
  langid       = {english},
  keywords     = {adaptability,data
    analysis,reproducibility,scalability,sustainability,transparency,workflow
    management},
  file         = {/Users/bsmith/Zotero/storage/9F52UEGJ/Mölder et al.
    - 2021 - Sustainable data analysis with Snakemake.pdf},
}
@article{navarro-garciaTypeVISecretion2019,
  title        = {Type {{VI Secretion System}} in {{Pathogenic
    Escherichia}} Coli: {{Structure}}, {{Role}} in {{Virulence}}, and
    {{Acquisition}}},
  shorttitle   = {Type {{VI Secretion System}} in {{Pathogenic
    Escherichia}} Coli},
  author       = {Navarro-Garcia, Fernando and Ruiz-Perez, Fernando
    and Cataldi, Ángel and Larzábal, Mariano},
  date         = {2019-08-30},
  journaltitle = {Frontiers in Microbiology},
  shortjournal = {Front. Microbiol.},
  volume       = 10,
  eprint       = 31543869,
  eprinttype   = {pmid},
  pages        = 1965,
  issn         = {1664-302X},
  doi          = {10.3389/fmicb.2019.01965},
  url          = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6730261/},
  urldate      = {2024-04-02},
  abstract     = {Bacterial pathogens utilize a myriad of mechanisms
    to invade mammalian hosts, damage tissue sites, and evade the
    immune system. One essential strategy of Gram-negative bacteria is
    the secretion of virulence factors through both inner and outer
    membranes to reach a potential target. Most secretion systems are
    harbored in mobile elements including transposons, plasmids,
    pathogenicity islands, and phages, and Escherichia coli is one of
    the more versatile bacteria adopting this genetic information by
    horizontal gene transfer. Additionally, E. coli is a bacterial
    species with members of the commensal intestinal microbiota and
    pathogens associated with numerous types of infections such as
    intestinal, urinary, and systemic in humans and other animals.
    T6SS cluster plasticity suggests evolutionarily divergent systems
    were acquired horizontally. T6SS is a secretion nanomachine that
    is extended through the bacterial double membrane; from this
    apparatus, substrates are conveyed straight from the cytoplasm of
    the bacterium into a target cell or to the extracellular space.
    This nanomachine consists of three main complexes: proteins in the
    inner membrane that are T4SS component-like, the baseplate
    complex, and the tail complex, which are formed by components
    evolutionarily related to contractile bacteriophage tails.
    Advances in the T6SS understanding include the functional and
    structural characterization of at least 13 subunits (so-called
    core components), which are thought to comprise the minimal
    apparatus. So far, the main role of T6SS is on bacterial
    competition by using it to kill neighboring non-immune bacteria
    for which antibacterial proteins are secreted directly into the
    periplasm of the bacterial target after cell–cell contact.
    Interestingly, a few T6SSs have been associated directly to
    pathogenesis, e.g., roles in biofilm formation and macrophage
    survival. Here, we focus on the advances on T6SS from the
    perspective of E. coli pathotypes with emphasis in the secretion
    apparatus architecture, the mechanisms of pathogenicity of
    effector proteins, and the events of lateral gene transfer that
    led to its spread.},
  pmcid        = {PMC6730261},
  file         = {/Users/bsmith/Zotero/storage/48QGT7TR/Navarro-Garcia
    et al. - 2019 - Type VI Secretion System in Pathogenic
    Escherichia.pdf},
}
@article{nayfachIntegratedMetagenomicsPipeline2016a,
  title        = {An Integrated Metagenomics Pipeline for Strain
    Profiling Reveals Novel Patterns of Bacterial Transmission and
    Biogeography},
  author       = {Nayfach, Stephen and Rodriguez-Mueller, Beltran and
    Garud, Nandita and Pollard, Katherine S.},
  date         = {2016-10-18},
  journaltitle = {Genome Research},
  shortjournal = {Genome Res.},
  volume       = 26,
  number       = 11,
  eprint       = 27803195,
  eprinttype   = {pmid},
  pages        = {1612--1625},
  publisher    = {Cold Spring Harbor Lab},
  issn         = {1088-9051, 1549-5469},
  doi          = {10.1101/gr.201863.115},
  url          = {https://genome.cshlp.org/content/26/11/1612},
  urldate      = {2024-02-16},
  abstract     = {We present the Metagenomic Intra-species Diversity
    Analysis System (MIDAS), which is an integrated computational
    pipeline for quantifying bacterial species abundance and
    strain-level genomic variation, including gene content and
    single-nucleotide polymorphisms (SNPs), from shotgun metagenomes.
    Our method leverages a database of more than 30,000 bacterial
    reference genomes that we clustered into species groups. These
    cover the majority of abundant species in the human microbiome but
    only a small proportion of microbes in other environments,
    including soil and seawater. We applied MIDAS to stool metagenomes
    from 98 Swedish mothers and their infants over one year and used
    rare SNPs to track strains between hosts. Using this approach, we
    found that although species compositions of mothers and infants
    converged over time, strain-level similarity diverged.
    Specifically, early colonizing bacteria were often transmitted
    from an infant’s mother, while late colonizing bacteria were often
    transmitted from other sources in the environment and were
    enriched for spore-formation genes. We also applied MIDAS to 198
    globally distributed marine metagenomes and used gene content to
    show that many prevalent bacterial species have population
    structure that correlates with geographic location. Strain-level
    genetic variants present in metagenomes clearly reveal extensive
    structure and dynamics that are obscured when data are analyzed at
    a coarser taxonomic resolution.},
  langid       = {english},
  file         = {/Users/bsmith/Zotero/storage/TKQTP5MN/Nayfach et al.
    - 2016 - An integrated metagenomics pipeline for strain pro.pdf},
}
@article{pakbinVirulenceFactorsEnteric2021b,
  title        = {Virulence {{Factors}} of {{Enteric Pathogenic
    Escherichia}} Coli: {{A Review}}},
  shorttitle   = {Virulence {{Factors}} of {{Enteric Pathogenic
    Escherichia}} Coli},
  author       = {Pakbin, Babak and Brück, Wolfram M. and Rossen, John
    W. A.},
  date         = {2021-09-14},
  journaltitle = {International Journal of Molecular Sciences},
  shortjournal = {IJMS},
  volume       = 22,
  number       = 18,
  pages        = 9922,
  publisher    = {Multidisciplinary Digital Publishing Institute},
  issn         = {1422-0067},
  doi          = {10.3390/ijms22189922},
  url          = {https://www.mdpi.com/1422-0067/22/18/9922},
  urldate      = {2024-02-16},
  abstract     = {Escherichia coli are remarkably versatile
    microorganisms and important members of the normal intestinal
    microbiota of humans and animals. This harmless commensal organism
    can acquire a mixture of comprehensive mobile genetic elements
    that contain genes encoding virulence factors, becoming an
    emerging human pathogen capable of causing a broad spectrum of
    intestinal and extraintestinal diseases. Nine definite enteric E.
    coli pathotypes have been well characterized, causing diseases
    ranging from various gastrointestinal disorders to urinary tract
    infections. These pathotypes employ many virulence factors and
    effectors subverting the functions of host cells to mediate their
    virulence and pathogenesis. This review summarizes new
    developments in our understanding of diverse virulence factors
    associated with encoding genes used by different pathotypes of
    enteric pathogenic E. coli to cause intestinal and extraintestinal
    diseases in humans.},
  issue        = 18,
  langid       = {english},
  keywords     = {<i>E. coli</i> pathotypes,enteric pathogenic
    <i>Escherichia coli</i>,virulence factor genes},
  file         = {/Users/bsmith/Zotero/storage/JTTZ34H2/Pakbin et al.
    - 2021 - Virulence Factors of Enteric Pathogenic Escherichi.pdf},
}
@article{parksGTDBOngoingCensus2022,
  title        = {{{GTDB}}: An Ongoing Census of Bacterial and
    Archaeal Diversity through a Phylogenetically Consistent, Rank
    Normalized~and Complete Genome-Based Taxonomy},
  shorttitle   = {{{GTDB}}},
  author       = {Parks, Donovan H and Chuvochina, Maria and Rinke,
    Christian and Mussig, Aaron J and Chaumeil, Pierre-Alain and
    Hugenholtz, Philip},
  date         = {2021-09-14},
  journaltitle = {Nucleic Acids Research},
  shortjournal = {Nucleic Acids Research},
  volume       = 50,
  number       = {D1},
  pages        = {D785-D794},
  issn         = {0305-1048},
  doi          = {10.1093/nar/gkab776},
  url          = {https://doi.org/10.1093/nar/gkab776},
  urldate      = {2024-02-16},
  abstract     = {The Genome Taxonomy Database (GTDB;
    https://gtdb.ecogenomic.org) provides a phylogenetically
    consistent and rank normalized genome-based taxonomy for
    prokaryotic genomes sourced from the NCBI Assembly database. GTDB
    R06-RS202 spans 254 090 bacterial and 4316 archaeal genomes, a
    270\% increase since the introduction of the GTDB in November,
    2017. These genomes are organized into 45 555 bacterial and 2339
    archaeal species clusters which is a 200\% increase since the
    integration of species clusters into the GTDB in June, 2019. Here,
    we explore prokaryotic diversity from the perspective of the GTDB
    and highlight the importance of metagenome-assembled genomes in
    expanding available genomic representation. We also discuss
    improvements to the GTDB website which allow tracking of taxonomic
    changes, easy assessment of genome assembly quality, and
    identification of genomes assembled from type material or used as
    species representatives. Methodological updates and policy changes
    made since the inception of the GTDB are then described along with
    the procedure used to update species clusters in the GTDB. We
    conclude with a discussion on the use of average nucleotide
    identities as a pragmatic approach for delineating prokaryotic
    species.},
  langid       = {english},
  file         = {/Users/bsmith/Zotero/storage/AKKBHEZJ/Parks et al. -
    2022 - GTDB an ongoing census of bacterial and archaeal
    .pdf;/Users/bsmith/Zotero/storage/NUDBMF4N/6370255.html},
}
@article{plazaonateMSPminerAbundancebasedReconstitution2019a,
  title        = {{{MSPminer}}: Abundance-Based Reconstitution of
    Microbial Pan-Genomes from Shotgun Metagenomic Data},
  shorttitle   = {{{MSPminer}}},
  author       = {Plaza Oñate, Florian and Le Chatelier, Emmanuelle
    and Almeida, Mathieu and Cervino, Alessandra C L and Gauthier,
    Franck and Magoulès, Frédéric and Ehrlich, S Dusko and Pichaud,
    Matthieu},
  date         = {2018-09-25},
  journaltitle = {Bioinformatics},
  shortjournal = {Bioinformatics},
  volume       = 35,
  number       = 9,
  pages        = {1544--1552},
  issn         = {1367-4803},
  doi          = {10.1093/bioinformatics/bty830},
  url          = {https://doi.org/10.1093/bioinformatics/bty830},
  urldate      = {2024-02-16},
  abstract     = {Analysis toolkits for shotgun metagenomic data
    achieve strain-level characterization of complex microbial
    communities by capturing intra-species gene content variation.
    Yet, these tools are hampered by the extent of reference genomes
    that are far from covering all microbial variability, as many
    species are still not sequenced or have only few strains
    available. Binning co-abundant genes obtained from de novo
    assembly is a powerful reference-free technique to discover and
    reconstitute gene repertoire of microbial species. While current
    methods accurately identify species core parts, they miss many
    accessory genes or split them into small gene groups that remain
    unassociated to core clusters.We introduce MSPminer, a
    computationally efficient software tool that reconstitutes
    Metagenomic Species Pan-genomes (MSPs) by binning co-abundant
    genes across metagenomic samples. MSPminer relies on a new robust
    measure of proportionality coupled with an empirical classifier to
    group and distinguish not only species core genes but accessory
    genes also. Applied to a large scale metagenomic dataset, MSPminer
    successfully delineates in a few hours the gene repertoires of
    1661 microbial species with similar specificity and higher
    sensitivity than existing tools. The taxonomic annotation of MSPs
    reveals microorganisms hitherto unknown and brings coherence in
    the nomenclature of the species of the human gut microbiota. The
    provided MSPs can be readily used for taxonomic profiling and
    biomarkers discovery in human gut metagenomic samples. In
    addition, MSPminer can be applied on gene count tables from other
    ecosystems to perform similar analyses.The binary is freely
    available for non-commercial users at
    www.enterome.com/downloads.Supplementary data are available at
    Bioinformatics online.},
  langid       = {english},
  file         = {/Users/bsmith/Zotero/storage/FBMA4BZL/Plaza Oñate et
    al. - 2019 - MSPminer abundance-based reconstitution of
    microb.pdf;/Users/bsmith/Zotero/storage/TZ6QD6G3/5106712.html},
}
@article{prenticeBacterialMicrocompartmentsTheir2021,
  title        = {Bacterial Microcompartments and Their Role in
    Pathogenicity},
  author       = {Prentice, Michael B},
  date         = {2021-10},
  journaltitle = {Current Opinion in Microbiology},
  shortjournal = {Current Opinion in Microbiology},
  volume       = 63,
  pages        = {19--28},
  issn         = {1369-5274},
  doi          = {10.1016/j.mib.2021.05.009},
  url          = {https://www.sciencedirect.com/science/article/pii/S1369527421000709},
  urldate      = {2023-07-02},
  abstract     = {Catabolic bacterial microcompartments (BMC), or
    metabolosomes, are self-assembling structures formed by enzymes
    enclosed by porous protein shells. They provide a specialised
    environment inside bacterial cells separating a short catabolic
    pathway with reactive or toxic intermediates from the cytoplasm.
    Substrates for microcompartment metabolism like ethanolamine and
    1,2-propanediol are constantly produced in the human intestine by
    bacterial metabolism of food or host cell components. Enteric
    pathogens gain a competitive advantage in the intestine by
    metabolising these substrates, an advantage enhanced by the host
    inflammatory response. They exploit the intestinal specificity of
    signature metabolosome substrates by adopting substrate sensors
    and regulators encoded by BMC operons for governance of
    non-metabolic processes in pathogenesis. In turn, products of
    microcompartment metabolism regulate the host immune system.},
  langid       = {english},
  file         = {/Users/bsmith/Zotero/storage/JNK3HEIL/Prentice -
    2021 - Bacterial microcompartments and their role in
    path.pdf;/Users/bsmith/Zotero/storage/WCJVTTV5/S1369527421000709.html},
}
@article{proctorIntegrativeHumanMicrobiome2019,
  title        = {The {{Integrative Human Microbiome Project}}},
  author       = {Proctor, Lita M. and Creasy, Heather H. and
    Fettweis, Jennifer M. and Lloyd-Price, Jason and Mahurkar, Anup
    and Zhou, Wenyu and Buck, Gregory A. and Snyder, Michael P. and
    Strauss, Jerome F. and Weinstock, George M. and White, Owen and
    Huttenhower, Curtis and {The Integrative HMP (iHMP) Research
    Network Consortium}},
  date         = {2019-05},
  journaltitle = {Nature},
  volume       = 569,
  number       = 7758,
  pages        = {641--648},
  publisher    = {Nature Publishing Group},
  issn         = {1476-4687},
  doi          = {10.1038/s41586-019-1238-8},
  url          = {https://www.nature.com/articles/s41586-019-1238-8},
  urldate      = {2024-10-26},
  abstract     = {The NIH Human Microbiome Project (HMP) has been
    carried out over ten years and two phases to provide resources,
    methods, and discoveries that link interactions between humans and
    their microbiomes to health-related outcomes. The recently
    completed second phase, the Integrative Human Microbiome Project,
    comprised studies of dynamic changes in the microbiome and host
    under three conditions: pregnancy and preterm birth; inflammatory
    bowel diseases; and stressors that affect individuals with
    prediabetes. The associated research begins to elucidate
    mechanisms of host–microbiome interactions under these conditions,
    provides unique data resources (at the HMP Data Coordination
    Center), and represents a paradigm for future multi-omic studies
    of the human microbiome.},
  langid       = {english},
  keywords     = {Inflammatory bowel disease,Microbiome,Reproductive
    disorders,Type 2 diabetes},
  file         = {/Users/bsmith/Zotero/storage/N665HQMY/Proctor et al.
    - 2019 - The Integrative Human Microbiome Project.pdf},
}
@article{quinceDESMANNewTool2017,
  title        = {{{DESMAN}}: A New Tool for de Novo Extraction of
    Strains from Metagenomes},
  author       = {Quince, Christopher and Delmont, Tom O. and
    Raguideau, Sébastien and Alneberg, Johannes and Darling, Aaron E.
    and Collins, Gavin and Eren, A. Murat},
  date         = {2017-09-21},
  journaltitle = {Genome Biology},
  shortjournal = {Genome Biol},
  volume       = 18,
  number       = 1,
  eprint       = 28934976,
  eprinttype   = {pmid},
  pages        = {1--22},
  publisher    = {Genome Biology},
  issn         = {1474760X},
  doi          = {10.1186/s13059-017-1309-9},
  abstract     = {We introduce DESMAN for De novo Extraction of
    Strains from Metagenomes. Large multi-sample metagenomes are being
    generated but strain variation results in fragmentary
    co-assemblies. Current algorithms can bin contigs into
    metagenome-assembled genomes but are unable to resolve
    strain-level variation. DESMAN identifies variants in core genes
    and uses co-occurrence across samples to link variants into
    haplotypes and abundance profiles. These are then searched for
    against non-core genes to determine the accessory genome of each
    strain. We validated DESMAN on a complex 50-species 210-genome
    96-sample synthetic mock data set and then applied it to the Tara
    Oceans microbiome.},
  langid       = {english},
  keywords     = {Metagenomes,Niche,Strain},
  file         = {/Users/bsmith/Zotero/storage/DFMZ2GT3/Quince et al.
    - 2017 - DESMAN a new tool for de novo extraction of
    strai.pdf;/Users/bsmith/Zotero/storage/UPTVAJZW/Quince et al. -
    2017 - DESMAN A new tool for de novo extraction of strains from
    metagenomes.pdf;/Users/bsmith/Zotero/storage/5P78WXE6/s13059-017-1309-9.html},
}
@article{quinceSTRONGMetagenomicsStrain2021,
  title        = {{{STRONG}}: Metagenomics Strain Resolution on
    Assembly Graphs},
  shorttitle   = {{{STRONG}}},
  author       = {Quince, Christopher and Nurk, Sergey and Raguideau,
    Sebastien and James, Robert and Soyer, Orkun S. and Summers, J.
    Kimberly and Limasset, Antoine and Eren, A. Murat and Chikhi,
    Rayan and Darling, Aaron E.},
  date         = {2021-07-26},
  journaltitle = {Genome Biology},
  shortjournal = {Genome Biology},
  volume       = 22,
  number       = 1,
  pages        = 214,
  issn         = {1474-760X},
  doi          = {10.1186/s13059-021-02419-7},
  url          = {https://doi.org/10.1186/s13059-021-02419-7},
  urldate      = {2021-08-02},
  abstract     = {We introduce STrain Resolution ON assembly Graphs
    (STRONG), which identifies strains de novo, from multiple
    metagenome samples. STRONG performs coassembly, and binning into
    metagenome assembled genomes (MAGs), and stores the coassembly
    graph prior to variant simplification. This enables the subgraphs
    and their unitig per-sample coverages, for individual single-copy
    core genes (SCGs) in each MAG, to be extracted. A Bayesian
    algorithm, BayesPaths, determines the number of strains present,
    their haplotypes or sequences on the SCGs, and abundances. STRONG
    is validated using synthetic communities and for a real anaerobic
    digestor time series generates haplotypes that match those
    observed from long Nanopore reads.},
  keywords     = {Assembly graph,Bayesian,Metagenome,Microbial
    community,Microbiome,Strains},
  file         = {/Users/bsmith/Zotero/storage/KPVQD5XK/Quince et
    al_2021_STRONG.pdf;/Users/bsmith/Zotero/storage/DCNITXVV/s13059-021-02419-7.html},
}
@incollection{rayMolecularMechanismDrug2017,
  title        = {Molecular {{Mechanism}} of {{Drug Resistance}}},
  booktitle    = {Drug {{Resistance}} in {{Bacteria}}, {{Fungi}},
    {{Malaria}}, and {{Cancer}}},
  author       = {Ray, Shilpa and Das, Susmita and Suar, Mrutyunjay},
  editor       = {Arora, Gunjan and Sajid, Andaleeb and Kalia, Vipin
    Chandra},
  date         = 2017,
  pages        = {47--110},
  publisher    = {Springer International Publishing},
  location     = {Cham},
  doi          = {10.1007/978-3-319-48683-3_3},
  url          = {https://doi.org/10.1007/978-3-319-48683-3_3},
  urldate      = {2024-02-16},
  abstract     = {The treatment of microbial infections has suffered
    greatly in this present century of pathogen dominance. Inspite of
    extensive research efforts and scientific advancements, the
    worldwide emergence of microbial tolerance continues to plague
    survivability. The innate property of microbe to resist any
    antibiotic due to evolution is the virtue of intrinsic resistance.
    However, the classical genetic mutations and extrachromosomal
    segments causing gene exchange attribute to acquired tolerance
    development. Rampant use of antimicrobials causes certain
    selection pressure which increases the resistance frequency.
    Genomic duplication, enzymatic site modification, target
    alteration, modulation in membrane permeability, and the efflux
    pump mechanism are the major contributors of multidrug resistance
    (MDR), specifically antibiotic tolerance development. MDRs will
    lead to clinical failures for treatment and pose health crisis.
    The molecular mechanisms of antimicrobial resistance are diverse
    as well as complex and still are exploited for new discoveries in
    order to prevent the surfacing of “superbugs.” Antimicrobial
    chemotherapy has diminished the threat of infectious diseases to
    some extent. To avoid the indiscriminate use of antibiotics, the
    new ones licensed for use have decreased with time. Additionally,
    in vitro assays and genomics for anti-infectives are novel
    approaches used in resolving the issues of microbial resistance.
    Proper use of drugs can keep it under check and minimize the risk
    of MDR spread.},
  isbn         = {978-3-319-48683-3},
  langid       = {english},
  keywords     = {Drug Resistance,Efflux Pump,Efflux
    Transporter,Resistance Development,Resistance Mechanism},
  file         = {/Users/bsmith/Zotero/storage/K9E353YP/Ray et al. -
    2017 - Molecular Mechanism of Drug Resistance.pdf},
}
@article{rognesVSEARCHVersatileOpen2016,
  title        = {{{VSEARCH}}: A Versatile Open Source Tool for
    Metagenomics},
  shorttitle   = {{{VSEARCH}}},
  author       = {Rognes, Torbjørn and Flouri, Tomáš and Nichols, Ben
    and Quince, Christopher and Mahé, Frédéric},
  date         = {2016-10-18},
  journaltitle = {PeerJ},
  shortjournal = {PeerJ},
  volume       = 4,
  eprint       = 27781170,
  eprinttype   = {pmid},
  pages        = {e2584},
  issn         = {2167-8359},
  doi          = {10.7717/peerj.2584},
  url          = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5075697/},
  urldate      = {2024-02-16},
  abstract     = {Background VSEARCH is an open source and free of
    charge multithreaded 64-bit tool for processing and preparing
    metagenomics, genomics and population genomics nucleotide sequence
    data. It is designed as an alternative to the widely used USEARCH
    tool () for which the source code is not publicly available,
    algorithm details are only rudimentarily described, and only a
    memory-confined 32-bit version is freely available for academic
    use. Methods When searching nucleotide sequences, VSEARCH uses a
    fast heuristic based on words shared by the query and target
    sequences in order to quickly identify similar sequences, a
    similar strategy is probably used in USEARCH. VSEARCH then
    performs optimal global sequence alignment of the query against
    potential target sequences, using full dynamic programming instead
    of the seed-and-extend heuristic used by USEARCH. Pairwise
    alignments are computed in parallel using vectorisation and
    multiple threads. Results VSEARCH includes most commands for
    analysing nucleotide sequences available in USEARCH version 7 and
    several of those available in USEARCH version 8, including
    searching (exact or based on global alignment), clustering by
    similarity (using length pre-sorting, abundance pre-sorting or a
    user-defined order), chimera detection (reference-based or de
    novo), dereplication (full length or prefix), pairwise alignment,
    reverse complementation, sorting, and subsampling. VSEARCH also
    includes commands for FASTQ file processing, i.e., format
    detection, filtering, read quality statistics, and merging of
    paired reads. Furthermore, VSEARCH extends functionality with
    several new commands and improvements, including shuffling,
    rereplication, masking of low-complexity sequences with the
    well-known DUST algorithm, a choice among different similarity
    definitions, and FASTQ file format conversion. VSEARCH is here
    shown to be more accurate than USEARCH when performing searching,
    clustering, chimera detection and subsampling, while on a par with
    USEARCH for paired-ends read merging. VSEARCH is slower than
    USEARCH when performing clustering and chimera detection, but
    significantly faster when performing paired-end reads merging and
    dereplication. VSEARCH is available at
    https://github.com/torognes/vsearch under either the BSD 2-clause
    license or the GNU General Public License version 3.0. Discussion
    VSEARCH has been shown to be a fast, accurate and full-fledged
    alternative to USEARCH. A free and open-source versatile tool for
    sequence analysis is now available to the metagenomics community.},
  langid       = {english},
  pmcid        = {PMC5075697},
  file         = {/Users/bsmith/Zotero/storage/BILYRPUH/Rognes et al.
    - 2016 - VSEARCH a versatile open source tool for metageno.pdf},
}
@article{seemannProkkaRapidProkaryotic2014,
  title        = {Prokka: Rapid Prokaryotic Genome Annotation},
  shorttitle   = {Prokka},
  author       = {Seemann, Torsten},
  date         = {2014-03-18},
  journaltitle = {Bioinformatics},
  shortjournal = {Bioinformatics},
  volume       = 30,
  number       = 14,
  pages        = {2068--2069},
  issn         = {1367-4803},
  doi          = {10.1093/bioinformatics/btu153},
  url          = {https://doi.org/10.1093/bioinformatics/btu153},
  urldate      = {2024-03-06},
  abstract     = {Summary: The multiplex capability and high yield of
    current day DNA-sequencing instruments has made bacterial whole
    genome sequencing a routine affair. The subsequent de novo
    assembly of reads into contigs has been well addressed. The final
    step of annotating all relevant genomic features on those contigs
    can be achieved slowly using existing web- and email-based
    systems, but these are not applicable for sensitive data or
    integrating into computational pipelines. Here we introduce
    Prokka, a command line software tool to fully annotate a draft
    bacterial genome in about 10 min on a typical desktop computer. It
    produces standards-compliant output files for further analysis or
    viewing in genome browsers. Availability and implementation:
    Prokka is implemented in Perl and is freely available under an
    open source GPLv2 license from http://vicbioinformatics.com/ .
    Contact: ~torsten.seemann@monash.edu},
  langid       = {english},
  file         = {/Users/bsmith/Zotero/storage/GBVFI9MH/Seemann - 2014
    - Prokka rapid prokaryotic genome
    annotation.pdf;/Users/bsmith/Zotero/storage/67PRAR5S/2390517.html},
}
@article{shiFastAccurateMetagenotyping2022,
  title        = {Fast and Accurate Metagenotyping of the Human Gut
    Microbiome with {{GT-Pro}}},
  author       = {Shi, Zhou Jason and Dimitrov, Boris and Zhao, Chunyu
    and Nayfach, Stephen and Pollard, Katherine S.},
  date         = {2022-04},
  journaltitle = {Nature Biotechnology},
  shortjournal = {Nat Biotechnol},
  volume       = 40,
  number       = 4,
  pages        = {507--516},
  publisher    = {Nature Publishing Group},
  issn         = {1546-1696},
  doi          = {10.1038/s41587-021-01102-3},
  url          = {https://www.nature.com/articles/s41587-021-01102-3},
  urldate      = {2024-11-08},
  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.},
  langid       = {english},
  keywords     = {Gastrointestinal Microbiome,Genome
    informatics,Genotype,Humans,Metagenome,Metagenomics,Microbiota,Software},
  file         = {/Users/bsmith/Zotero/storage/4S7M3KXZ/Shi et al. -
    2022 - Fast and accurate metagenotyping of the human gut
    .pdf;/Users/bsmith/Zotero/storage/E66VGRXT/Shi et al_2021_Fast and
    accurate metagenotyping of the human gut microbiome with
    GT-Pro.pdf;/Users/bsmith/Zotero/storage/U4IAC5LW/Shi et al. - 2022
    - Fast and accurate metagenotyping of the human gut microbiome
    with
    GT-Pro.pdf;/Users/bsmith/Zotero/storage/ADHXHNRR/s41587-021-01102-3.html},
}
@article{smithScalableMicrobialStrain2022a,
  title        = {Scalable {{Microbial Strain Inference}} in
    {{Metagenomic Data Using StrainFacts}}},
  author       = {Smith, Byron J. and Li, Xiangpeng and Shi, Zhou
    Jason and Abate, Adam and Pollard, Katherine S.},
  date         = {2022-05-16},
  journaltitle = {Frontiers in Bioinformatics},
  shortjournal = {Front. Bioinform.},
  volume       = 2,
  publisher    = {Frontiers},
  issn         = {2673-7647},
  doi          = {10.3389/fbinf.2022.867386},
  url          = {https://www.frontiersin.org/articles/10.3389/fbinf.2022.867386},
  urldate      = {2024-04-30},
  abstract     = {While genome databases are nearing a complete
    catalog of species commonly inhabiting the human gut, their
    representation of intraspecific diversity is lacking for all but
    the most abundant and frequently studied taxa. Statistical
    deconvolution of allele frequencies from shotgun metagenomic data
    into strain genotypes and relative abundances is a promising
    approach, but existing methods are limited by computational
    scalability. Here we introduce StrainFacts, a method for strain
    deconvolution that enables inference across tens of thousands of
    metagenomes. We harness a “fuzzy” genotype approximation that
    makes the underlying graphical model fully differentiable, unlike
    existing methods. This allows parameter estimates to be optimized
    with gradient-based methods, speeding up model fitting by two
    orders of magnitude. A GPU implementation provides additional
    scalability. Extensive simulations show that StrainFacts can
    perform strain inference on thousands of metagenomes and has
    comparable accuracy to more computationally intensive tools. We
    further validate our strain inferences using single-cell genomic
    sequencing from a human stool sample. Applying StrainFacts to a
    collection of more than 10,000 publicly available human stool
    metagenomes, we quantify patterns of strain diversity,
    biogeography, and linkage-disequilibrium that agree with and
    expand on what is known based on existing reference genomes.
    StrainFacts paves the way for large-scale biogeography and
    population genetic studies of microbiomes using metagenomic data.},
  langid       = {english},
  keywords     = {biogeography,Metagenomics,microbiome,Model-based
    inference,Population Genetics,strains},
  file         = {/Users/bsmith/Zotero/storage/6HX8VF3U/Smith et al. -
    2022 - Scalable Microbial Strain Inference in Metagenomic.pdf},
}
@article{smithStrainresolvedAnalysisRandomized2022,
  ids          = {Smith2022,zotero-6579},
  title        = {Strain-Resolved Analysis in a Randomized Trial of
    Antibiotic Pretreatment and Maintenance Dose Delivery Mode with
    Fecal Microbiota Transplant for Ulcerative Colitis},
  author       = {Smith, Byron J. and Piceno, Yvette and Zydek, Martin
    and Zhang, Bing and Syriani, Lara Aboud and Terdiman, Jonathan P.
    and Kassam, Zain and Ma, Averil and Lynch, Susan V. and Pollard,
    Katherine S. and El-Nachef, Najwa},
  date         = {2022-04-01},
  journaltitle = {Scientific Reports},
  shortjournal = {Sci Rep},
  volume       = 12,
  number       = 1,
  pages        = 5517,
  publisher    = {Nature Publishing Group},
  issn         = {2045-2322},
  doi          = {10.1038/s41598-022-09307-5},
  url          = {https://www.nature.com/articles/s41598-022-09307-5},
  urldate      = {2022-04-14},
  abstract     = {Fecal microbiota transplant is a promising therapy
    for ulcerative colitis. Parameters maximizing effectiveness and
    tolerability are not yet clear, and it is not known how import the
    transmission of donor microbes to patients is. Here
    (clinicaltrails.gov: NCT03006809) we have tested the effects of
    antibiotic pretreatment and compared two modes of maintenance dose
    delivery, capsules versus enema, in a randomized, pilot,
    open-label, 2\,×\,2 factorial design with 22 patients analyzed
    with mild to moderate UC. Clinically, the treatment was
    well-tolerated with favorable safety profile. Of patients who
    received antibiotic pretreatment, 6 of 11 experienced remission
    after 6 weeks of treatment, versus 2 of 11 non-pretreated patients
    (log~odds ratio: 1.69, 95\% confidence interval: −0.25 to 3.62).
    No significant differences were found between maintenance dosing
    via capsules versus enema. In exploratory analyses, microbiome
    turnover at both the species and strain levels was extensive and
    significantly more pronounced in the pretreated patients.
    Associations were also revealed between taxonomic turnover and
    changes in the composition of primary and secondary bile acids.
    Together these findings suggest that antibiotic pretreatment
    contributes to microbiome engraftment and possibly clinical
    effectiveness, and validate longitudinal strain tracking as a
    powerful way to monitor the dynamics and impact of microbiota
    transfer.},
  issue        = 1,
  langid       = {english},
  keywords     = {Microbiome,Ulcerative colitis},
  file         = {/Users/bsmith/Zotero/storage/KHZIQDRN/41598_2022_9307_Author.pdf;/Users/bsmith/Zotero/storage/PFAF2PSI/Smith
    et al. - 2021 - Strain-resolved analysis in a randomized trial of
    .pdf;/Users/bsmith/Zotero/storage/QN7ZARUJ/Smith et
    al_2022_Strain-resolved analysis in a randomized trial of
    antibiotic pretreatment
    and.pdf;/Users/bsmith/Zotero/storage/XHZFWEJ6/s41598-022-09307-5.html},
}
@article{trimbleShortreadReadingframePredictors2012,
  title        = {Short-Read Reading-Frame Predictors Are Not Created
    Equal: Sequence Error Causes Loss of Signal},
  shorttitle   = {Short-Read Reading-Frame Predictors Are Not Created
    Equal},
  author       = {Trimble, William L and Keegan, Kevin P and D’Souza,
    Mark and Wilke, Andreas and Wilkening, Jared and Gilbert, Jack and
    Meyer, Folker},
  date         = {2012-07-28},
  journaltitle = {BMC Bioinformatics},
  shortjournal = {BMC Bioinformatics},
  volume       = 13,
  number       = 1,
  eprint       = 22839106,
  eprinttype   = {pmid},
  pages        = 183,
  issn         = {1471-2105},
  doi          = {10.1186/1471-2105-13-183},
  url          = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3526449/},
  urldate      = {2024-02-16},
  abstract     = {Background Gene prediction algorithms (or gene
    callers) are an essential tool for analyzing shotgun nucleic acid
    sequence data. Gene prediction is a ubiquitous step in sequence
    analysis pipelines; it reduces the volume of data by identifying
    the most likely reading frame for a fragment, permitting the
    out-of-frame translations to be ignored. In this study we evaluate
    five widely used ab initio gene-calling algorithms—FragGeneScan,
    MetaGeneAnnotator, MetaGeneMark, Orphelia, and Prodigal—for
    accuracy on short (75–1000\,bp) fragments containing sequence
    error from previously published artificial data and “real”
    metagenomic datasets. Results While gene prediction tools have
    similar accuracies predicting genes on error-free fragments, in
    the presence of sequencing errors considerable differences between
    tools become evident. For error-containing short reads,
    FragGeneScan finds more prokaryotic coding regions than does
    MetaGeneAnnotator, MetaGeneMark, Orphelia, or Prodigal. This
    improved detection of genes in error-containing fragments,
    however, comes at the cost of much lower (50\%) specificity and
    overprediction of genes in noncoding regions. Conclusions Ab
    initio gene callers offer a significant reduction in the
    computational burden of annotating individual nucleic acid reads
    and are used in many metagenomic annotation systems. For
    predicting reading frames on raw reads, we find the hidden Markov
    model approach in FragGeneScan is more sensitive than other gene
    prediction tools, while Prodigal, MGA, and MGM are better suited
    for higher-quality sequences such as assembled contigs.},
  langid       = {english},
  pmcid        = {PMC3526449},
  file         = {/Users/bsmith/Zotero/storage/97WZ863P/Trimble et al.
    - 2012 - Short-read reading-frame predictors are not create.pdf},
}
@article{yangFecalIgALevels2020,
  title        = {Fecal {{IgA Levels Are Determined}} by
    {{Strain-Level Differences}} in {{Bacteroides}} Ovatus and {{Are
    Modifiable}} by {{Gut Microbiota Manipulation}}},
  author       = {Yang, Chao and Mogno, Ilaria and Contijoch, Eduardo
    J. and Borgerding, Joshua N. and Aggarwala, Varun and Li, Zhihua
    and Siu, Sophia and Grasset, Emilie K. and Helmus, Drew S. and
    Dubinsky, Marla C. and Mehandru, Saurabh and Cerutti, Andrea and
    Faith, Jeremiah J.},
  date         = {2020-03},
  journaltitle = {Cell Host \&amp; Microbe},
  shortjournal = {Cell Host \&amp; Microbe},
  volume       = 27,
  number       = 3,
  eprint       = 32075742,
  eprinttype   = {pmid},
  pages        = {467-475.e6},
  publisher    = {Elsevier},
  issn         = {1931-3128},
  doi          = {10.1016/j.chom.2020.01.016},
  url          = {https://www.cell.com/cell-host-microbe/abstract/S1931-3128(20)30055-X},
  urldate      = {2024-02-16},
  langid       = {english},
  keywords     = {Bacteroides ovatus,gut microbiota,immune
    modulation,immunoglobulin A,strains},
  file         = {/Users/bsmith/Zotero/storage/REHLMQR4/Yang et al. -
    2020 - Fecal IgA Levels Are Determined by Strain-Level Di.pdf},
}
@article{zhaoGenotypingMicrobialCommunities2022,
  title        = {Genotyping {{Microbial Communities}} with
    {{MIDAS2}}: {{From Metagenomic Reads}} to {{Allele Tables}}},
  shorttitle   = {Genotyping {{Microbial Communities}} with {{MIDAS2}}},
  author       = {Zhao, Chunyu and Goldman, Miriam and Smith, Byron J.
    and Pollard, Katherine S.},
  date         = 2022,
  journaltitle = {Current Protocols},
  volume       = 2,
  number       = 12,
  pages        = {e604},
  issn         = {2691-1299},
  doi          = {10.1002/cpz1.604},
  url          = {https://onlinelibrary.wiley.com/doi/abs/10.1002/cpz1.604},
  urldate      = {2024-11-08},
  abstract     = {The Metagenomic Intra-Species Diversity Analysis
    System 2 (MIDAS2) is a scalable pipeline that identifies single
    nucleotide variants and gene copy number variants in metagenomes
    using comprehensive reference databases built from public
    microbial genome collections (metagenotyping). MIDAS2 is the first
    metagenotyping tool with functionality to control metagenomic read
    mapping filters and to customize the reference database to the
    microbial community, features that improve the precision and
    recall of detected variants. In this article we present four basic
    protocols for the most common use cases of MIDAS2, along with
    supporting protocols for installation and use. In addition, we
    provide in-depth guidance on adjusting command line parameters,
    editing the reference database, optimizing hardware utilization,
    and understanding the metagenotyping results. All the steps of
    metagenotyping, from raw sequencing reads to population genetic
    analysis, are demonstrated with example data in two downloadable
    sequencing libraries of single-end metagenomic reads representing
    a mixture of multiple bacterial species. This set of protocols
    empowers users to accurately genotype hundreds of species in
    thousands of samples, providing rich genetic data for studying the
    evolution and strain-level ecology of microbial communities. ©
    2022 The Authors. Current Protocols published by Wiley Periodicals
    LLC. Basic Protocol 1: Species prescreening Basic Protocol 2:
    Download MIDAS reference database Basic Protocol 3: Population
    single nucleotide variant calling Basic Protocol 4: Pan-genome
    copy number variant calling Support Protocol 1: Installing MIDAS2
    Support Protocol 2: Command line inputs Support Protocol 3:
    Metagenotyping with a custom collection of genomes Support
    Protocol 4: Metagenotyping with advanced parameters},
  langid       = {english},
  keywords     = {copy number
    variant,genotyping,microbiome,MIDAS2,single nucleotide
    variant,strain},
  file         = {/Users/bsmith/Zotero/storage/CBEEQ5QA/Zhao et al. -
    2022 - Genotyping Microbial Communities with MIDAS2 From
    Metagenomic Reads to Allele
    Tables.pdf;/Users/bsmith/Zotero/storage/RPQVZMRC/cpz1.html},
}
@article{zhaoMIDAS2MetagenomicIntraspecies2023,
  title        = {{{MIDAS2}}: {{Metagenomic Intra-species Diversity
    Analysis System}}},
  shorttitle   = {{{MIDAS2}}},
  author       = {Zhao, Chunyu and Dimitrov, Boris and Goldman, Miriam
    and Nayfach, Stephen and Pollard, Katherine S},
  date         = {2022-11-02},
  journaltitle = {Bioinformatics},
  shortjournal = {Bioinformatics},
  volume       = 39,
  number       = 1,
  pages        = {btac713},
  issn         = {1367-4811},
  doi          = {10.1093/bioinformatics/btac713},
  url          = {https://doi.org/10.1093/bioinformatics/btac713},
  urldate      = {2024-04-30},
  abstract     = {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.The source code is available at
    https://github.com/czbiohub/MIDAS2.Supplementary data are
    available at Bioinformatics online.},
  langid       = {english},
  file         = {/Users/bsmith/Zotero/storage/Y6UPLSI3/Zhao et al. -
    2023 - MIDAS2 Metagenomic Intra-species Diversity
    Analys.pdf;/Users/bsmith/Zotero/storage/IX7C8HK6/6793850.html},
}
@article{zhaoPitfallsGenotypingMicrobial2023,
  title        = {Pitfalls of Genotyping Microbial Communities with
    Rapidly Growing Genome Collections},
  author       = {Zhao, Chunyu and Shi, Zhou Jason and Pollard,
    Katherine S.},
  date         = {2023-02},
  journaltitle = {Cell Systems},
  shortjournal = {Cell Systems},
  volume       = 14,
  number       = 2,
  pages        = {160-176.e3},
  issn         = 24054712,
  doi          = {10.1016/j.cels.2022.12.007},
  url          = {https://linkinghub.elsevier.com/retrieve/pii/S2405471222004951},
  urldate      = {2024-04-30},
  abstract     = {Detecting genetic variants in metagenomic data is a
    priority for understanding the evolution, ecology, and functional
    characteristics of microbial communities. Many tools that perform
    this metagenotyping rely on aligning reads of unknown origin to a
    database of sequences from many species before calling variants.
    In this synthesis, we investigate how databases of increasingly
    diverse and closely related species have pushed the limits of
    current alignment algorithms, thereby degrading the performance of
    metagenotyping tools. We identify multi-mapping reads as a
    prevalent source of errors and illustrate a trade-off between
    retaining correct alignments versus limiting incorrect alignments,
    many of which map reads to the wrong species. Then we evaluate
    several actionable mitigation strategies and review emerging
    methods showing promise to further improve metagenotyping in
    response to the rapid growth in genome collections. Our results
    have implications beyond metagenotyping to the many tools in
    microbial genomics that depend upon accurate read mapping.},
  langid       = {english},
  file         = {/Users/bsmith/Zotero/storage/MXI6UNER/Zhao et al. -
    2023 - Pitfalls of genotyping microbial communities with .pdf},
}
@article{zhongIntegratingPangenomeMetagenome2021a,
  title        = {Integrating Pan-Genome with Metagenome for Microbial
    Community Profiling},
  author       = {Zhong, Chaofang and Chen, Chaoyun and Wang, Lusheng
    and Ning, Kang},
  date         = 2021,
  journaltitle = {Computational and Structural Biotechnology Journal},
  shortjournal = {Computational and Structural Biotechnology Journal},
  volume       = 19,
  pages        = {1458--1466},
  issn         = {2001-0370},
  doi          = {10.1016/j.csbj.2021.02.021},
  url          = {https://www.sciencedirect.com/science/article/pii/S2001037021000726},
  urldate      = {2024-02-16},
  abstract     = {Advances in sequencing technology have led to the
    increased availability of genomes and metagenomes, which has
    greatly facilitated microbial pan-genome and metagenome analysis
    in the community. In line with this trend, studies on microbial
    genomes and phenotypes have gradually shifted from individuals to
    environmental communities. Pan-genomics and metagenomics are
    powerful strategies for in-depth profiling study of microbial
    communities. Pan-genomics focuses on genetic diversity, dynamics,
    and phylogeny at the multi-genome level, while metagenomics
    profiles the distribution and function of culture-free microbial
    communities in special environments. Combining pan-genome and
    metagenome analysis can reveal the microbial complicated
    connections from an individual complete genome to a mixture of
    genomes, thereby extending the catalog of traditional individual
    genomic profile to community microbial profile. Therefore, the
    combination of pan-genome and metagenome approaches has become a
    promising method to track the sources of various microbes and
    decipher the population-level evolution and ecosystem functions.
    This review summarized the pan-genome and metagenome approaches,
    the combined strategies of pan-genome and metagenome, and
    applications of these combined strategies in studies of microbial
    dynamics, evolution, and function in communities. We discussed
    emerging strategies for the study of microbial communities that
    integrate information in both pan-genome and metagenome. We
    emphasized studies in which the integrating pan-genome with
    metagenome approach improved the understanding of models of
    microbial community profiles, both structural and functional.
    Finally, we illustrated future perspectives of microbial community
    profile: more advanced analytical techniques, including big-data
    based artificial intelligence, will lead to an even better
    understanding of the patterns of microbial communities.},
  langid       = {english},
  keywords     = {Integrated pan-genomics with
    metagenomics,Metagenome,Microbial community,Pan-genome},
  file         = {/Users/bsmith/Zotero/storage/S5DML2K5/Zhong et al. -
    2021 - Integrating pan-genome with metagenome for
    microbi.pdf;/Users/bsmith/Zotero/storage/HMC6GUI7/S2001037021000726.html},
}
@article{zhouCAFAChallengeReports2019,
  title        = {The {{CAFA}} Challenge Reports Improved Protein
    Function Prediction and New Functional Annotations for Hundreds of
    Genes through Experimental Screens},
  author       = {Zhou, Naihui and Jiang, Yuxiang and Bergquist,
    Timothy R. and Lee, Alexandra J. and Kacsoh, Balint Z. and
    Crocker, Alex W. and Lewis, Kimberley A. and Georghiou, George and
    Nguyen, Huy N. and Hamid, Md Nafiz and Davis, Larry and Dogan,
    Tunca and Atalay, Volkan and Rifaioglu, Ahmet S. and Dalkıran,
    Alperen and Cetin Atalay, Rengul and Zhang, Chengxin and Hurto,
    Rebecca L. and Freddolino, Peter L. and Zhang, Yang and Bhat,
    Prajwal and Supek, Fran and Fernández, José M. and Gemovic,
    Branislava and Perovic, Vladimir R. and Davidović, Radoslav S. and
    Sumonja, Neven and Veljkovic, Nevena and Asgari, Ehsaneddin and
    Mofrad, Mohammad R.K. and Profiti, Giuseppe and Savojardo,
    Castrense and Martelli, Pier Luigi and Casadio, Rita and Boecker,
    Florian and Schoof, Heiko and Kahanda, Indika and Thurlby, Natalie
    and McHardy, Alice C. and Renaux, Alexandre and Saidi, Rabie and
    Gough, Julian and Freitas, Alex A. and Antczak, Magdalena and
    Fabris, Fabio and Wass, Mark N. and Hou, Jie and Cheng, Jianlin
    and Wang, Zheng and Romero, Alfonso E. and Paccanaro, Alberto and
    Yang, Haixuan and Goldberg, Tatyana and Zhao, Chenguang and Holm,
    Liisa and Törönen, Petri and Medlar, Alan J. and Zosa, Elaine and
    Borukhov, Itamar and Novikov, Ilya and Wilkins, Angela and
    Lichtarge, Olivier and Chi, Po-Han and Tseng, Wei-Cheng and
    Linial, Michal and Rose, Peter W. and Dessimoz, Christophe and
    Vidulin, Vedrana and Dzeroski, Saso and Sillitoe, Ian and Das,
    Sayoni and Lees, Jonathan Gill and Jones, David T. and Wan, Cen
    and Cozzetto, Domenico and Fa, Rui and Torres, Mateo and Warwick
    Vesztrocy, Alex and Rodriguez, Jose Manuel and Tress, Michael L.
    and Frasca, Marco and Notaro, Marco and Grossi, Giuliano and
    Petrini, Alessandro and Re, Matteo and Valentini, Giorgio and
    Mesiti, Marco and Roche, Daniel B. and Reeb, Jonas and Ritchie,
    David W. and Aridhi, Sabeur and Alborzi, Seyed Ziaeddin and
    Devignes, Marie-Dominique and Koo, Da Chen Emily and Bonneau,
    Richard and Gligorijević, Vladimir and Barot, Meet and Fang, Hai
    and Toppo, Stefano and Lavezzo, Enrico and Falda, Marco and
    Berselli, Michele and Tosatto, Silvio C.E. and Carraro, Marco and
    Piovesan, Damiano and Ur Rehman, Hafeez and Mao, Qizhong and
    Zhang, Shanshan and Vucetic, Slobodan and Black, Gage S. and Jo,
    Dane and Suh, Erica and Dayton, Jonathan B. and Larsen, Dallas J.
    and Omdahl, Ashton R. and McGuffin, Liam J. and Brackenridge,
    Danielle A. and Babbitt, Patricia C. and Yunes, Jeffrey M. and
    Fontana, Paolo and Zhang, Feng and Zhu, Shanfeng and You, Ronghui
    and Zhang, Zihan and Dai, Suyang and Yao, Shuwei and Tian, Weidong
    and Cao, Renzhi and Chandler, Caleb and Amezola, Miguel and
    Johnson, Devon and Chang, Jia-Ming and Liao, Wen-Hung and Liu,
    Yi-Wei and Pascarelli, Stefano and Frank, Yotam and Hoehndorf,
    Robert and Kulmanov, Maxat and Boudellioua, Imane and Politano,
    Gianfranco and Di Carlo, Stefano and Benso, Alfredo and Hakala,
    Kai and Ginter, Filip and Mehryary, Farrokh and Kaewphan, Suwisa
    and Björne, Jari and Moen, Hans and Tolvanen, Martti E.E. and
    Salakoski, Tapio and Kihara, Daisuke and Jain, Aashish and Šmuc,
    Tomislav and Altenhoff, Adrian and Ben-Hur, Asa and Rost, Burkhard
    and Brenner, Steven E. and Orengo, Christine A. and Jeffery,
    Constance J. and Bosco, Giovanni and Hogan, Deborah A. and Martin,
    Maria J. and O’Donovan, Claire and Mooney, Sean D. and Greene,
    Casey S. and Radivojac, Predrag and Friedberg, Iddo},
  date         = {2019-11-19},
  journaltitle = {Genome Biology},
  shortjournal = {Genome Biol},
  volume       = 20,
  number       = 1,
  pages        = 244,
  issn         = {1474-760X},
  doi          = {10.1186/s13059-019-1835-8},
  url          = {https://doi.org/10.1186/s13059-019-1835-8},
  urldate      = {2024-02-16},
  abstract     = {The Critical Assessment of Functional Annotation
    (CAFA) is an ongoing, global, community-driven effort to evaluate
    and improve the computational annotation of protein function.},
  langid       = {english},
  keywords     = {Biofilm,Community challenge,Critical
    assessment,Long-term memory,Protein function prediction},
  file         = {/Users/bsmith/Zotero/storage/2F9IQIZM/Zhou et al. -
    2019 - The CAFA challenge reports improved protein
    functi.pdf;/Users/bsmith/Zotero/storage/HY2TBQ78/s13059-019-1835-8.html},
}
