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Abstract

Historically, the analysis of airborne biological organisms relied on microscopy and culture-based techniques. However, technological advances such as PCR and next-generation sequencing now provide researchers with the ability to gather vast amounts of data on airborne environmental DNA (eDNA). Studies typically involve capturing airborne biological material, followed by nucleic acid extraction, library preparation, sequencing and taxonomic identification to characterize the eDNA at a given location. These methods have diverse applications, including pathogen detection in agriculture and human health, air quality monitoring, bioterrorism detection and biodiversity monitoring. A variety of methods are used for airborne eDNA analysis, as no single pipeline meets all needs. This review outlines current methods for sampling, extraction, sequencing and bioinformatic analysis, highlighting how different approaches can influence the resulting data and their suitability for specific use cases. It also explores current applications of airborne eDNA sampling and identifies research gaps in the field.

Funding
This study was supported by the:
  • Biotechnology and Biological Sciences Research Council (Award BBX011089/1)
    • Principal Award Recipient: NotApplicable
  • Biotechnology and Biological Sciences Research Council (Award BBX011003/1)
    • Principal Award Recipient: NotApplicable
  • Biotechnology and Biological Sciences Research Council (Award BB/T008717/1)
    • Principal Award Recipient: NotApplicable
  • This is an open-access article distributed under the terms of the Creative Commons Attribution License. This article was made open access via a Publish and Read agreement between the Microbiology Society and the corresponding author’s institution.
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2025-05-28
2026-02-13

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References

  1. Lednicky JA, Shankar SN, Elbadry MA, Gibson JC, Alam MdM et al. Collection of SARS-CoV-2 virus from the air of a clinic within a university student health care center and analyses of the viral genomic sequence. Aerosol Air Qual Res 2020; 20:1167–1171 [View Article]
    [Google Scholar]
  2. Haverkamp THA, Spilsberg B, Johannessen GS, Torp M, Sekse C. Detection and characterization of Campylobacter in air samples from poultry houses using shot-gun metagenomics - a pilot study. BMC Microbiol 2024; 24:399 [View Article] [PubMed]
    [Google Scholar]
  3. Lemos MSC de, Higa Junior MG, Paniago AMM, Melhem M de SC, Takahashi JPF et al. Aspergillus in the indoor air of critical areas of a tertiary hospital in Brazil. J Fungi 2024; 10:538 [View Article] [PubMed]
    [Google Scholar]
  4. Giolai M, Verweij W, Martin S, Pearson N, Nicholson P et al. Measuring air metagenomic diversity in an agricultural ecosystem. Curr Biol 2024; 34:3778–3791 [View Article] [PubMed]
    [Google Scholar]
  5. Huang S, Hu W, Chen J, Wu Z, Zhang D et al. Overview of biological ice nucleating particles in the atmosphere. Environ Int 2021; 146:106197 [View Article]
    [Google Scholar]
  6. Lynggaard C, Bertelsen MF, Jensen CV, Johnson MS, Frøslev TG et al. Airborne environmental DNA for terrestrial vertebrate community monitoring. Curr Biol 2022; 32:701–707 [View Article] [PubMed]
    [Google Scholar]
  7. Thuillet A-C, Morisot D, Renno J-F, Scarcelli N, Serret J et al. Picturing plant biodiversity from airborne environmental DNA. Genetics 2024 [View Article]
    [Google Scholar]
  8. Chappuis C, Tummon F, Clot B, Konzelmann T, Calpini B et al. Automatic pollen monitoring: first insights from hourly data. Aerobiologia 2020; 36:159–170 [View Article]
    [Google Scholar]
  9. Banchi E, Ametrano CG, Tordoni E, Stanković D, Ongaro S et al. Environmental DNA assessment of airborne plant and fungal seasonal diversity. Sci Total Environ 2020; 738:140249 [View Article] [PubMed]
    [Google Scholar]
  10. Ovaskainen O, Abrego N, Furneaux B, Hardwick B, Somervuo P et al. Global spore sampling project: a global, standardized dataset of airborne fungal DNA. Sci Data 2024; 11:561 [View Article] [PubMed]
    [Google Scholar]
  11. Li L, Wang Q, Bi W, Hou J, Xue Y et al. Municipal solid waste treatment system increases ambient airborne bacteria and antibiotic resistance genes. Environ Sci Technol 2020; 54:3900–3908 [View Article] [PubMed]
    [Google Scholar]
  12. Maki T, Hosaka K, Lee KC, Kawabata Y, Kajino M et al. Vertical distribution of airborne microorganisms over forest environments: a potential source of ice-nucleating bioaerosols. Atmos Environ 2023; 302:119726 [View Article]
    [Google Scholar]
  13. Li K, Dong S, Wu Y, Yao M. Comparison of the biological content of air samples collected at ground level and at higher elevation. Aerobiologia 2010; 26:233–244 [View Article]
    [Google Scholar]
  14. Pollegioni P, Cardoni S, Mattioni C, Piredda R, Ristorini M et al. Variability of airborne microbiome at different urban sites across seasons: a case study in Rome. Front Environ Sci 2023; 11: [View Article]
    [Google Scholar]
  15. Shi Y, Lai S, Liu Y, Gromov S, Zhang Y. Fungal aerosol diversity over the northern South China Sea: the influence of land and ocean. JGR Atmospheres 2022; 127:e2021JD035213 [View Article]
    [Google Scholar]
  16. Abrego N, Norros V, Halme P, Somervuo P, Ali-Kovero H et al. Give me a sample of air and I will tell which species are found from your region: molecular identification of fungi from airborne spore samples. Mol Ecol Resour 2018; 18:511–524 [View Article] [PubMed]
    [Google Scholar]
  17. An C, Woo C, Yamamoto N. Introducing DNA-based methods to compare fungal microbiota and concentrations in indoor, outdoor, and personal air. Aerobiologia 2018; 34:1–12 [View Article]
    [Google Scholar]
  18. Mbareche H, Veillette M, Teertstra W, Kegel W, Bilodeau GJ et al. Recovery of fungal cells from air samples: a tale of loss and gain. Appl Environ Microbiol 2019; 85:e02941-18 [View Article] [PubMed]
    [Google Scholar]
  19. Razzini K, Castrica M, Menchetti L, Maggi L, Negroni L et al. SARS-CoV-2 RNA detection in the air and on surfaces in the COVID-19 ward of a hospital in Milan, Italy. Sci Total Environ 2020; 742:140540 [View Article] [PubMed]
    [Google Scholar]
  20. Clare EL, Economou CK, Faulkes CG, Gilbert JD, Bennett F et al. eDNAir: proof of concept that animal DNA can be collected from air sampling. PeerJ 2021; 9:e11030 [View Article]
    [Google Scholar]
  21. Aguayo J, Husson C, Chancerel E, Fabreguettes O, Chandelier A et al. Combining permanent aerobiological networks and molecular analyses for large-scale surveillance of forest fungal pathogens: a proof-of-concept. Plant Pathol 2021; 70:181–194 [View Article]
    [Google Scholar]
  22. Buters J, Clot B, Galán C, Gehrig R, Gilge S et al. Automatic detection of airborne pollen: an overview. Aerobiologia 2024; 40:13–37 [View Article]
    [Google Scholar]
  23. West JS, Kimber RBE. Innovations in air sampling to detect plant pathogens. Ann Appl Biol 2015; 166:4–17 [View Article]
    [Google Scholar]
  24. Ferguson RMW, Garcia-Alcega S, Coulon F, Dumbrell AJ, Whitby C et al. Bioaerosol biomonitoring: sampling optimization for molecular microbial ecology. Mol Ecol Resour 2019; 19:672–690 [View Article] [PubMed]
    [Google Scholar]
  25. Manibusan S, Mainelis G. Passive bioaerosol samplers: a complementary tool for bioaerosol research. A review. J Aerosol Sci 2022; 163:105992 [View Article] [PubMed]
    [Google Scholar]
  26. Check JC, Harkness RJ, Heger L, Sakalidis ML, Chilvers MI et al. It’s a trap! Part I: exploring the applications of rotating-arm impaction samplers in plant pathology. Plant Dis 2024; 108:1910–1922 [View Article]
    [Google Scholar]
  27. Mescioglu E, Paytan A, Mitchell BW, Griffin DW. Efficiency of bioaerosol samplers: a comparison study. Aerobiologia 2021; 37:447–459 [View Article]
    [Google Scholar]
  28. Jeong S ‐Y., Kim TG. Comparison of five membrane filters to collect bioaerosols for airborne microbiome analysis. J Appl Microbiol 2021; 131:780–790 [View Article]
    [Google Scholar]
  29. Hoisington AJ, Maestre JP, King MD, Siegel JA, Kinney KA. Impact of sampler selection on the characterization of the indoor microbiome via high-throughput sequencing. Build Environ 2014; 80:274–282 [View Article]
    [Google Scholar]
  30. Pan M, Bonny TS, Loeb J, Jiang X, Lednicky JA et al. Collection of viable aerosolized influenza virus and other respiratory viruses in a student health care center through water-based condensation growth. mSphere 2017; 2:e00251-17 [View Article] [PubMed]
    [Google Scholar]
  31. Raynor PC, Adesina A, Aboubakr HA, Yang M, Torremorell M et al. Comparison of samplers collecting airborne influenza viruses: 1. Primarily impingers and cyclones. PLoS One 2021; 16:e0244977 [View Article] [PubMed]
    [Google Scholar]
  32. Abeykoon AMH, Poon M, Firestone SM, Stevenson MA, Wiethoelter AK et al. Performance evaluation and validation of air samplers to detect aerosolized Coxiella burnetii. Microbiol Spectr 2022; 10:e0065522 [View Article] [PubMed]
    [Google Scholar]
  33. Polling M, Buij R, Laros I, de Groot GA. Continuous daily sampling of airborne eDNA detects all vertebrate species identified by camera traps. Environ DNA 2024; 6:e591 [View Article]
    [Google Scholar]
  34. Luhung I, Uchida A, Lim SBY, Gaultier NE, Kee C et al. Experimental parameters defining ultra-low biomass bioaerosol analysis. Npj Biofilms Microbiomes 2021; 7:37 [View Article]
    [Google Scholar]
  35. Dommergue A, Amato P, Tignat-Perrier R, Magand O, Thollot A et al. Methods to investigate the global atmospheric microbiome. Front Microbiol 2019; 10: [View Article]
    [Google Scholar]
  36. Littlefair JE, Allerton JJ, Brown AS, Butterfield DM, Robins C et al. Air-quality networks collect environmental DNA with the potential to measure biodiversity at continental scales. Curr Biol 2023; 33:R426–R428 [View Article] [PubMed]
    [Google Scholar]
  37. Agranovski IE, Usachev EV. In-situ rapid bioaerosol detection in the ambient air by miniature multiplex PCR utilizing technique. Atmos Environ 2021; 246:118147 [View Article]
    [Google Scholar]
  38. Mahaffee WF, Margairaz F, Ulmer L, Bailey BN, Stoll R. Catching spores: linking epidemiology, pathogen biology, and physics to ground-based airborne inoculum monitoring. Plant Dis 2023; 107:13–33 [View Article]
    [Google Scholar]
  39. King P, Pham LK, Waltz S, Sphar D, Yamamoto RT et al. Longitudinal metagenomic analysis of hospital air identifies clinically relevant microbes. PLoS One 2016; 11:e0160124 [View Article]
    [Google Scholar]
  40. Richardson M, Gottel N, Gilbert JA, Gordon J, Gandhi P et al. Concurrent measurement of microbiome and allergens in the air of bedrooms of allergy disease patients in the Chicago area. Microbiome 2019; 7:82 [View Article]
    [Google Scholar]
  41. Birgand G, Peiffer-Smadja N, Fournier S, Kerneis S, Lescure F-X et al. Assessment of air contamination by SARS-CoV-2 in hospital settings. JAMA Netw Open 2020; 3:e2033232 [View Article] [PubMed]
    [Google Scholar]
  42. Nicolaisen M, West JS, Sapkota R, Canning GGM, Schoen C et al. Fungal communities including plant pathogens in near surface air are similar across Northwestern Europe. Front Microbiol 2017; 8:1729 [View Article] [PubMed]
    [Google Scholar]
  43. Gusareva ES, Gaultier NPE, Premkrishnan BNV, Kee C, Lim SBY et al. Taxonomic composition and seasonal dynamics of the air microbiome in West Siberia. Sci Rep 2020; 10:21515 [View Article] [PubMed]
    [Google Scholar]
  44. Sullivan AR, Karlsson E, Svensson D, Brindefalk B, Villegas JA et al. Airborne eDNA captures three decades of ecosystem biodiversity. Ecology 2023 [View Article]
    [Google Scholar]
  45. Glawe DA. The powdery mildews: a review of the world’s most familiar (yet poorly known) plant pathogens. Annu Rev Phytopathol 2008; 46:27–51 [View Article] [PubMed]
    [Google Scholar]
  46. Lagomarsino Oneto D, Golan J, Mazzino A, Pringle A, Seminara A. Timing of fungal spore release dictates survival during atmospheric transport. Proc Natl Acad Sci USA 2020; 117:5134–5143 [View Article] [PubMed]
    [Google Scholar]
  47. Jang GI, Hwang CY, Cho BC. Effects of heavy rainfall on the composition of airborne bacterial communities. Front Environ Sci Eng 2018; 12:12 [View Article]
    [Google Scholar]
  48. Niu M, Hu W, Cheng B, Wu L, Ren L et al. Influence of rainfall on fungal aerobiota in the urban atmosphere over Tianjin, China: a case study. Atmospheric Environ X 2021; 12:100137 [View Article]
    [Google Scholar]
  49. Uetake J, Tobo Y, Uji Y, Hill TCJ, DeMott PJ et al. Seasonal changes of airborne bacterial communities over Tokyo and influence of local meteorology. Front Microbiol 2019; 10:1572 [View Article] [PubMed]
    [Google Scholar]
  50. Franco Ortega S, Ferrocino I, Adams I, Silvestri S, Spadaro D et al. Monitoring and surveillance of aerial mycobiota of rice paddy through DNA metabarcoding and qPCR. J Fungi 2020; 6:372 [View Article]
    [Google Scholar]
  51. Arigela R, Jose C, Gopalakrishnan S, Gunthe SS, Raghunathan R. Effect of relative humidity on passive spore release from substrate surfaces. J Aerosol Sci 2025; 183:106477 [View Article]
    [Google Scholar]
  52. Hu Z, Liu H, Zhang H, Zhang X, Zhou M et al. Temporal discrepancy of airborne total bacteria and pathogenic bacteria between day and night. Environ Res 2020; 186:109540 [View Article]
    [Google Scholar]
  53. Clauss M. Particle size distribution of airborne micro-organisms in the environment-a review. Landbauforsch Volkenrode 2015; 65:77–100 [View Article]
    [Google Scholar]
  54. Tsuda A, Henry FS, Butler JP. Particle transport and deposition: basic physics of particle kinetics. Compr Physiol 2013; 3:1437–1471 [View Article] [PubMed]
    [Google Scholar]
  55. Alsved M, Fraenkel C-J, Bohgard M, Widell A, Söderlund-Strand A et al. Sources of airborne norovirus in hospital outbreaks. Clin Infect Dis 2020; 70:2023–2028 [View Article] [PubMed]
    [Google Scholar]
  56. Yamamoto N, Bibby K, Qian J, Hospodsky D, Rismani-Yazdi H et al. Particle-size distributions and seasonal diversity of allergenic and pathogenic fungi in outdoor air. ISME J 2012; 6:1801–1811 [View Article] [PubMed]
    [Google Scholar]
  57. Ferguson RMW, Neath CEE, Nasir ZA, Garcia-Alcega S, Tyrrel S et al. Size fractionation of bioaerosol emissions from green-waste composting. Environ Int 2021; 147:106327 [View Article] [PubMed]
    [Google Scholar]
  58. Jaing C, Thissen J, Morrison M, Dillon MB, Waters SM et al. Sierra Nevada sweep: metagenomic measurements of bioaerosols vertically distributed across the troposphere. Sci Rep 2020; 10:12399 [View Article] [PubMed]
    [Google Scholar]
  59. Salter SJ, Cox MJ, Turek EM, Calus ST, Cookson WO et al. Reagent and laboratory contamination can critically impact sequence-based microbiome analyses. BMC Biol 2014; 12:87 [View Article] [PubMed]
    [Google Scholar]
  60. Brooks JP, Edwards DJ, Harwich MD Jr, Rivera MC, Fettweis JM et al. The truth about metagenomics: quantifying and counteracting bias in 16S rRNA studies. BMC Microbiol 2015; 15:66 [View Article] [PubMed]
    [Google Scholar]
  61. Banchi E, Pallavicini A, Muggia L. Relevance of plant and fungal DNA metabarcoding in aerobiology. Aerobiologia 2020; 36:9–23 [View Article]
    [Google Scholar]
  62. Bøifot KO, Gohli J, Moen LV, Dybwad M. Performance evaluation of a new custom, multi-component DNA isolation method optimized for use in shotgun metagenomic sequencing-based aerosol microbiome research. Environ Microbiome 2020; 15:1 [View Article] [PubMed]
    [Google Scholar]
  63. Quick J. The “Three Peaks” faecal DNA extraction method for long-read sequencing v2; 2019 10.17504/protocols.io.7rshm6e
    [Google Scholar]
  64. Brodie EL, DeSantis TZ, Parker JPM, Zubietta IX, Piceno YM et al. Urban aerosols harbor diverse and dynamic bacterial populations. Proc Natl Acad Sci USA 2007; 104:299–304 [View Article]
    [Google Scholar]
  65. Keeling PJ, Burki F, Wilcox HM, Allam B, Allen EE et al. The Marine Microbial Eukaryote Transcriptome Sequencing Project (MMETSP): illuminating the functional diversity of eukaryotic life in the oceans through transcriptome sequencing. PLoS Biol 2014; 12:e1001889 [View Article] [PubMed]
    [Google Scholar]
  66. Chen W, Hambleton S, Seifert KA, Carisse O, Diarra MS et al. Assessing performance of spore samplers in monitoring aeromycobiota and fungal plant pathogen diversity in Canada. Appl Environ Microbiol 2018; 84:e02601-17 [View Article] [PubMed]
    [Google Scholar]
  67. Scibetta S, Schena L, Abdelfattah A, Pangallo S, Cacciola SO. Selection and experimental evaluation of universal primers to study the fungal microbiome of higher plants. Phytobiomes J 2018; 2:225–236 [View Article]
    [Google Scholar]
  68. Bakker MG. A fungal mock community control for amplicon sequencing experiments. Mol Ecol Resour 2018; 18:541–556 [View Article] [PubMed]
    [Google Scholar]
  69. Low L, Fuentes-Utrilla P, Hodson J, O’Neil JD, Rossiter AE et al. Evaluation of full-length nanopore 16S sequencing for detection of pathogens in microbial keratitis. PeerJ 2021; 9:e10778 [View Article] [PubMed]
    [Google Scholar]
  70. Schloss PD, Jenior ML, Koumpouras CC, Westcott SL, Highlander SK. Sequencing 16S rRNA gene fragments using the PacBio SMRT DNA sequencing system. PeerJ 2016; 4:e1869 [View Article] [PubMed]
    [Google Scholar]
  71. Cuber P, Chooneea D, Geeves C, Salatino S, Creedy TJ et al. Comparing the accuracy and efficiency of third generation sequencing technologies, Oxford Nanopore Technologies, and Pacific Biosciences, for DNA barcode sequencing applications. Ecol Genet Genomics 2023; 28:100181 [View Article]
    [Google Scholar]
  72. Santos A, van Aerle R, Barrientos L, Martinez-Urtaza J. Computational methods for 16S metabarcoding studies using Nanopore sequencing data. Comput Struct Biotechnol J 2020; 18:296–305 [View Article] [PubMed]
    [Google Scholar]
  73. Silverman JD, Bloom RJ, Jiang S, Durand HK, Dallow E et al. Measuring and mitigating PCR bias in microbiota datasets. PLoS Comput Biol 2021; 17:e1009113 [View Article] [PubMed]
    [Google Scholar]
  74. Parada AE, Needham DM, Fuhrman JA. Every base matters: assessing small subunit rRNA primers for marine microbiomes with mock communities, time series and global field samples. Environ Microbiol 2016; 18:1403–1414 [View Article] [PubMed]
    [Google Scholar]
  75. Kelly RP, Shelton AO, Gallego R. Understanding PCR processes to draw meaningful conclusions from environmental dna studies. Sci Rep 2019; 9:12133 [View Article] [PubMed]
    [Google Scholar]
  76. Forry SP, Servetas SL, Kralj JG, Soh K, Hadjithomas M et al. Variability and bias in microbiome metagenomic sequencing: an interlaboratory study comparing experimental protocols. Sci Rep 2024; 14:9785 [View Article] [PubMed]
    [Google Scholar]
  77. Peng W, Li X, Wang C, Cao H, Cui Z. Metagenome complexity and template length are the main causes of bias in PCR-based bacteria community analysis. J Basic Microbiol 2018; 58:987–997 [View Article] [PubMed]
    [Google Scholar]
  78. Větrovský T, Baldrian P. The variability of the 16S rRNA gene in bacterial genomes and its consequences for bacterial community analyses. PLoS One 2013; 8:e57923 [View Article] [PubMed]
    [Google Scholar]
  79. Schloss PD. Identifying and overcoming threats to reproducibility, replicability, robustness, and generalizability in microbiome research. mBio 2018; 9:00525–18 [View Article] [PubMed]
    [Google Scholar]
  80. Regueira-Iglesias A, Balsa-Castro C, Blanco-Pintos T, Tomás I. Critical review of 16S rRNA gene sequencing workflow in microbiome studies: from primer selection to advanced data analysis. Mol Oral Microbiol 2023; 38:347–399 [View Article] [PubMed]
    [Google Scholar]
  81. Leung MHY, Tong X, Bøifot KO, Bezdan D, Butler DJ et al. Characterization of the public transit air microbiome and resistome reveals geographical specificity. Microbiome 2021; 9:112 [View Article]
    [Google Scholar]
  82. Jiang C, Zhang X, Gao P, Chen Q, Snyder M. Decoding personal biotic and abiotic airborne exposome. Nat Protoc 2021; 16:1129–1151 [View Article] [PubMed]
    [Google Scholar]
  83. Qin N, Liang P, Wu C, Wang G, Xu Q et al. Longitudinal survey of microbiome associated with particulate matter in a megacity. Genome Biol 2020; 21:55 [View Article] [PubMed]
    [Google Scholar]
  84. Heavens D, Chooneea D, Giolai M, Cuber P, Aanstad P et al. How low can you go? Driving down the DNA input requirements for nanopore sequencing. Genomics 2021 [View Article]
    [Google Scholar]
  85. Ruppert KM, Kline RJ, Rahman MS. Past, present, and future perspectives of environmental DNA (eDNA) metabarcoding: a systematic review in methods, monitoring, and applications of global eDNA. Glob Ecol Conserv 2019; 17:e00547 [View Article]
    [Google Scholar]
  86. Yang L, Chen J. Benchmarking differential abundance analysis methods for correlated microbiome sequencing data. Brief Bioinform 2023; 24:bbac607 [View Article]
    [Google Scholar]
  87. Boshuizen HC, Te Beest DE. Pitfalls in the statistical analysis of microbiome amplicon sequencing data. Mol Ecol Resour 2023; 23:539–548 [View Article] [PubMed]
    [Google Scholar]
  88. Kim C, Pongpanich M, Porntaveetus T. Unraveling metagenomics through long-read sequencing: a comprehensive review. J Transl Med 2024; 22:111 [View Article]
    [Google Scholar]
  89. Liu Y-X, Qin Y, Chen T, Lu M, Qian X et al. A practical guide to amplicon and metagenomic analysis of microbiome data. Protein Cell 2021; 12:315–330 [View Article]
    [Google Scholar]
  90. Hemstrom W, Grummer JA, Luikart G, Christie MR. Next-generation data filtering in the genomics era. Nat Rev Genet 2024; 25:750–767
    [Google Scholar]
  91. Ayling M, Clark MD, Leggett RM. New approaches for metagenome assembly with short reads. Brief Bioinform 2020; 21:584–594
    [Google Scholar]
  92. Pilo P, Lawless C, Tiley AMM, Karki SJ, Burke JI et al. Comparison of microscopic and metagenomic approaches to identify cereal pathogens and track fungal spore release in the field. Front Plant Sci 2022
    [Google Scholar]
  93. Ghurye JS, Cepeda-Espinoza V, Pop M. Metagenomic assembly: overview, challenges and applications. Yale J Biol Med 2016; 89:353–362 [PubMed]
    [Google Scholar]
  94. Mallawaarachchi V, Wickramarachchi A, Xue H, Papudeshi B, Grigson SR et al. Solving genomic puzzles: computational methods for metagenomic binning. Brief Bioinform 2024; 25:bbae372 [View Article]
    [Google Scholar]
  95. Meyer F, Fritz A, Deng Z-L, Koslicki D, Lesker TR et al. Critical assessment of metagenome interpretation: the second round of challenges. Nat Methods 2022; 19:429–440 [View Article] [PubMed]
    [Google Scholar]
  96. Hakimzadeh A, Abdala Asbun A, Albanese D, Bernard M, Buchner D et al. A pile of pipelines: an overview of the bioinformatics software for metabarcoding data analyses. Mol Ecol Resour 2024; 24:e13847 [View Article] [PubMed]
    [Google Scholar]
  97. Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ. Basic local alignment search tool. J Mol Biol 1990; 215:403–410 [View Article] [PubMed]
    [Google Scholar]
  98. Wood DE, Lu J, Langmead B. Improved metagenomic analysis with Kraken 2. Genome Biol 2019; 20:257 [View Article] [PubMed]
    [Google Scholar]
  99. Blanco-Miguez A, Beghini F, Cumbo F, McIver LJ, Thompson KN et al. Extending and improving metagenomic taxonomic profiling with uncharacterized species with MetaPhlAn 4. Bioinformatics 2022 [View Article]
    [Google Scholar]
  100. Smith RH, Glendinning L, Walker AW, Watson M. Investigating the impact of database choice on the accuracy of metagenomic read classification for the rumen microbiome. Anim Microbiome 2022; 4:57 [View Article] [PubMed]
    [Google Scholar]
  101. Nasko DJ, Koren S, Phillippy AM, Treangen TJ. RefSeq database growth influences the accuracy of k-mer-based lowest common ancestor species identification. Genome Biol 2018; 19:165 [View Article] [PubMed]
    [Google Scholar]
  102. Chorlton SD. Ten common issues with reference sequence databases and how to mitigate them. Front Bioinform 2024; 4: [View Article]
    [Google Scholar]
  103. Reska T, Pozdniakova S, Borràs S, Perlas A, Sauerborn E et al. Air monitoring by nanopore sequencing. ISME Commun 2024; 4:ycae099 [View Article] [PubMed]
    [Google Scholar]
  104. Nilsson RH, Larsson K-H, Taylor AFS, Bengtsson-Palme J, Jeppesen TS et al. The UNITE database for molecular identification of fungi: handling dark taxa and parallel taxonomic classifications. Nucleic Acids Res 2019; 47:D259–D264 [View Article]
    [Google Scholar]
  105. Apangu GP, Frisk CA, Petch GM, Muggia L, Pallavicini A et al. Environmental DNA reveals diversity and abundance of Alternaria species in neighbouring heterogeneous landscapes in Worcester, UK. Aerobiologia 2022; 38:457–481 [View Article]
    [Google Scholar]
  106. Pruesse E, Quast C, Knittel K, Fuchs BM, Ludwig W et al. SILVA: a comprehensive online resource for quality checked and aligned ribosomal RNA sequence data compatible with ARB. Nucleic Acids Res 2007; 35:7188–7196 [View Article] [PubMed]
    [Google Scholar]
  107. Gomez-Silvan C, Leung MHY, Grue KA, Kaur R, Tong X et al. A comparison of methods used to unveil the genetic and metabolic pool in the built environment. Microbiome 2018; 6:71 [View Article]
    [Google Scholar]
  108. Mayol E, Arrieta JM, Jiménez MA, Martínez-Asensio A, Garcias-Bonet N et al. Long-range transport of airborne microbes over the global tropical and subtropical ocean. Nat Commun 2017; 8:201 [View Article]
    [Google Scholar]
  109. Prussin AJ II, Torres PJ, Shimashita J, Head SR, Bibby KJ et al. Seasonal dynamics of DNA and RNA viral bioaerosol communities in a daycare center. Microbiome 2019; 7:53 [View Article]
    [Google Scholar]
  110. Urban M, Cuzick A, Seager J, Wood V, Rutherford K et al. PHI-base: the pathogen–host interactions database. Nucleic Acids Res 2020; 48:D613–20 [View Article]
    [Google Scholar]
  111. Jia B, Raphenya AR, Alcock B, Waglechner N, Guo P et al. CARD 2017: expansion and model-centric curation of the comprehensive antibiotic resistance database. Nucleic Acids Res 2017; 45:D566–D573 [View Article] [PubMed]
    [Google Scholar]
  112. Yin X, Jiang X-T, Chai B, Li L, Yang Y et al. ARGs-OAP v2.0 with an expanded SARG database and hidden Markov models for enhancement characterization and quantification of antibiotic resistance genes in environmental metagenomes. Bioinformatics 2018; 34:2263–2270 [View Article] [PubMed]
    [Google Scholar]
  113. He P, Wu Y, Huang W, Wu X, Lv J et al. Characteristics of and variation in airborne ARGs among urban hospitals and adjacent urban and suburban communities: a metagenomic approach. Environ Int 2020; 139:105625 [View Article] [PubMed]
    [Google Scholar]
  114. Song L, Ma J, Jiang G, Wang C, Zhang Y et al. Comparison of airborne antibiotic resistance genes in the chicken farm during winter and summer. Indoor Air 2024; 2024:1707863 [View Article]
    [Google Scholar]
  115. Nguyen NH, Song Z, Bates ST, Branco S, Tedersoo L et al. FUNGuild: an open annotation tool for parsing fungal community datasets by ecological guild. Fungal Ecol 2016; 20:241–248 [View Article]
    [Google Scholar]
  116. Cao Y, Yu X, Ju F, Zhan H, Jiang B et al. Airborne bacterial community diversity, source and function along the Antarctic Coast. Sci Total Environ 2021; 765:142700 [View Article]
    [Google Scholar]
  117. Jiang X, Wang C, Guo J, Hou J, Guo X et al. Global Meta-analysis of Airborne Bacterial Communities and Associations with Anthropogenic Activities. Environ Sci Technol 2022; 56:9891–9902 [View Article]
    [Google Scholar]
  118. Yan X, Ma J, Chen X, Lei M, Li T et al. Characteristics of airborne bacterial communities and antibiotic resistance genes under different air quality levels. Environ Int 2022; 161:107127 [View Article]
    [Google Scholar]
  119. Feng G, Xie T, Wang X, Bai J, Tang L et al. Metagenomic analysis of microbial community and function involved in cd-contaminated soil. BMC Microbiol 2018; 18:11 [View Article]
    [Google Scholar]
  120. Bengtsson-Palme J, Alm Rosenblad M, Molin M, Blomberg A. Metagenomics reveals that detoxification systems are underrepresented in marine bacterial communities. BMC Genom 2014; 15:749 [View Article] [PubMed]
    [Google Scholar]
  121. Wen T, Niu G, Chen T, Shen Q, Yuan J et al. The best practice for microbiome analysis using R. Protein Cell 2023; 14:713–725 [View Article] [PubMed]
    [Google Scholar]
  122. Pan AY. Statistical analysis of microbiome data: the challenge of sparsity. Curr Opin Endocr Metab Res 2021; 19:35–40 [View Article]
    [Google Scholar]
  123. Lutz KC, Jiang S, Neugent ML, De Nisco NJ, Zhan X et al. A survey of statistical methods for microbiome data analysis. Front Appl Math Stat 2022; 8:884810 [View Article] [PubMed]
    [Google Scholar]
  124. Wieczorek TM, Jørgensen LN, Hansen AL, Munk L, Justesen AF. Early detection of sugar beet pathogen Ramularia beticola in leaf and air samples using qPCR. Eur J Plant Pathol 2014; 138:775–785 [View Article]
    [Google Scholar]
  125. Ginn O, Rocha-Melogno L, Bivins A, Lowry S, Cardelino M et al. Detection and quantification of enteric pathogens in aerosols near open wastewater canals in cities with poor sanitation. Environ Sci Technol 2021; 55:14758–14771 [View Article]
    [Google Scholar]
  126. Van der Heyden H, Dutilleul P, Charron J-B, Bilodeau GJ, Carisse O. Monitoring airborne inoculum for improved plant disease management. A review. Agron Sustain Dev 2021; 41:40 [View Article]
    [Google Scholar]
  127. Bowers RM, Lauber CL, Wiedinmyer C, Hamady M, Hallar AG et al. Characterization of airborne microbial communities at a high-elevation site and their potential to act as atmospheric ice nuclei. Appl Environ Microbiol 2009; 75:5121–5130 [View Article]
    [Google Scholar]
  128. Folloni S, Kagkli D-M, Rajcevic B, Guimarães NCC, Van Droogenbroeck B et al. Detection of airborne genetically modified maize pollen by real-time PCR. Mol Ecol Resour 2012; 12:810–821 [View Article] [PubMed]
    [Google Scholar]
  129. Drautz-Moses DI, Luhung I, Gusareva ES, Kee C, Gaultier NE et al. Vertical stratification of the air microbiome in the lower troposphere. Proc Natl Acad Sci USA 2022; 119:e2117293119 [View Article]
    [Google Scholar]
  130. Song H, Crawford I, Lloyd J, Robinson C, Boothman C et al. Airborne bacterial and eukaryotic community structure across the United Kingdom revealed by high-throughput sequencing. Atmosphere 2020; 11:802 [View Article]
    [Google Scholar]
  131. Métris KL, Métris J. Aircraft surveys for air eDNA: probing biodiversity in the sky. PeerJ 2023; 11:e15171 [View Article]
    [Google Scholar]
  132. Kobziar LN, Vuono D, Moore R, Christner BC, Dean T et al. Wildland fire smoke alters the composition, diversity, and potential atmospheric function of microbial life in the aerobiome. ISME Commun 2022; 2:8 [View Article] [PubMed]
    [Google Scholar]
  133. Gould S, Atkinson B, Onianwa O, Spencer A, Furneaux J et al. Air and surface sampling for monkeypox virus in a UK hospital: an observational study. Lancet Microbe 2022; 3:e904–e911 [View Article]
    [Google Scholar]
  134. Ingram RJ, Ludwig HD, Scherm H. Epidemiology of Exobasidium leaf and fruit spot of rabbiteye blueberry: pathogen overwintering, primary infection, and disease progression on leaves and fruit. Plant Dis 2019; 103:1293–1301 [View Article] [PubMed]
    [Google Scholar]
  135. Urel H, Benassou S, Reska T, Marti H, Rayo E et al. Nanopore- and AI-empowered metagenomic viability inference. Bioinformatics [View Article]
    [Google Scholar]
  136. Prussin AJ, Garcia EB, Marr LC. Total virus and bacteria concentrations in indoor and outdoor air. Environ Sci Technol Lett 2015; 2:84–88 [View Article] [PubMed]
    [Google Scholar]
  137. Johnson MD, Cox RD, Grisham BA, Lucia D, Barnes MA. Airborne eDNA reflects human activity and seasonal changes on a landscape scale. Front Environ Sci 2021; 8:276 [View Article]
    [Google Scholar]
  138. Cando‐Dumancela C, Liddicoat C, McLeod D, Young JM, Breed MF. A guide to minimize contamination issues in microbiome restoration studies. Restor Ecol 2021; 29:e13358 [View Article]
    [Google Scholar]
  139. Sepulveda AJ, Hutchins PR, Forstchen M, Mckeefry MN, Swigris AM. The elephant in the lab (and field): contamination in aquatic environmental DNA studies. Front Ecol Evol 2020; 8: [View Article]
    [Google Scholar]
  140. Jiang W, Liang P, Wang B, Fang J, Lang J et al. Optimized DNA extraction and metagenomic sequencing of airborne microbial communities. Nat Protoc 2015; 10:768–779 [View Article] [PubMed]
    [Google Scholar]
  141. Weyrich LS, Farrer AG, Eisenhofer R, Arriola LA, Young J et al. Laboratory contamination over time during low-biomass sample analysis. Mol Ecol Resour 2019; 19:982–996 [View Article] [PubMed]
    [Google Scholar]
  142. Whitmore L, McCauley M, Farrell JA, Stammnitz MR, Koda SA et al. Inadvertent human genomic bycatch and intentional capture raise beneficial applications and ethical concerns with environmental DNA. Nat Ecol Evol 2023; 7:873–888 [View Article] [PubMed]
    [Google Scholar]
  143. Gloor GB, Macklaim JM, Pawlowsky-Glahn V, Egozcue JJ. Microbiome datasets are compositional: and this is not optional. Front Microbiol 2017; 8:2224 [View Article] [PubMed]
    [Google Scholar]
  144. Sigsgaard EE, Jensen MR, Winkelmann IE, Møller PR, Hansen MM et al. Population‐level inferences from environmental DNA—current status and future perspectives. Evol Appl 2020; 13:245–262 [View Article] [PubMed]
    [Google Scholar]
  145. Grewling Ł, Magyar D, Chłopek K, Grinn-Gofroń A, Gwiazdowska J et al. Bioaerosols on the atmospheric super highway: an example of long distance transport of Alternaria spores from the Pannonian Plain to Poland. Sci Total Environ 2022; 819:153148 [View Article] [PubMed]
    [Google Scholar]
  146. Stein AF, Draxler RR, Rolph GD, Stunder BJB, Cohen MD et al. NOAA’s HYSPLIT atmospheric transport and dispersion modeling system. Bull Am Meteorol Soc 2015; 96:2059–2077 [View Article]
    [Google Scholar]
  147. Hanson MC, Petch GM, Ottosen T-B, Skjøth CA. Climate change impact on fungi in the atmospheric microbiome. Sci Total Environ 2022; 830:154491 [View Article] [PubMed]
    [Google Scholar]
  148. Chen W, Newlands N, Hambleton S, Laroche A, Davoodi SM et al. Optimizing an integrated biovigilance toolbox to study the spatial distribution and dynamic changes of airborne mycobiota, with a focus on cereal rust fungi in western Canada. Mol Ecol Resour 2024; 24:e13983 [View Article] [PubMed]
    [Google Scholar]
  149. Memon R, Niazi JH, Qureshi A. Biosensors for detection of airborne pathogenic fungal spores: a review. Nanoscale 2024; 16:15419–15445 [View Article] [PubMed]
    [Google Scholar]
  150. Laurent B, Douillet A, Beslay A, Bordes J, Delmotte F et al. The VISA network: a collaborative project between research institutes and vineyard owners to create the first epidemiological monitoring network of downy mildew epidemic based on aerial spore capture. BIO Web Conf 2022; 50:04007 [View Article]
    [Google Scholar]
  151. Singh BK, Delgado-Baquerizo M, Egidi E, Guirado E, Leach JE et al. Climate change impacts on plant pathogens, food security and paths forward. Nat Rev Microbiol 2023; 21:640–656 [View Article] [PubMed]
    [Google Scholar]
  152. Huffman JA, Perring AE, Savage NJ, Clot B, Crouzy B et al. Real-time sensing of bioaerosols: review and current perspectives. Aerosol Sci Technol 2020; 54:465–495 [View Article]
    [Google Scholar]
  153. Taş N, de Jong AE, Li Y, Trubl G, Xue Y et al. Metagenomic tools in microbial ecology research. Curr Opin Biotechnol 2021; 67:184–191 [View Article]
    [Google Scholar]
  154. Martin M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet J 2011; 17:10 [View Article]
    [Google Scholar]
  155. Chen S, Zhou Y, Chen Y, Gu J. fastp: an ultra-fast all-in-one FASTQ preprocessor. Bioinformatics 2018; 34:i884–i890 [View Article]
    [Google Scholar]
  156. Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 2014; 30:2114–2120 [View Article] [PubMed]
    [Google Scholar]
  157. Schubert M, Lindgreen S, Orlando L. AdapterRemoval v2: rapid adapter trimming, identification, and read merging. BMC Res Notes 2016; 9:88 [View Article] [PubMed]
    [Google Scholar]
  158. Leggett RM, Clavijo BJ, Clissold L, Clark MD, Caccamo M. NextClip: an analysis and read preparation tool for Nextera long mate pair libraries. Bioinformatics 2014; 30:566–568 [View Article] [PubMed]
    [Google Scholar]
  159. Fu L, Niu B, Zhu Z, Wu S, Li W. CD-HIT: accelerated for clustering the next-generation sequencing data. Bioinformatics 2012; 28:3150–3152 [View Article] [PubMed]
    [Google Scholar]
  160. Li H. New strategies to improve minimap2 alignment accuracy. Bioinformatics 2021; 37:4572–4574 [View Article]
    [Google Scholar]
  161. Langmead B, Trapnell C, Pop M, Salzberg SL. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol 2009; 10:R25 [View Article] [PubMed]
    [Google Scholar]
  162. Zhang J, Kobert K, Flouri T, Stamatakis A. PEAR: a fast and accurate illumina paired-end read merger | bioinformatics | oxford academic. Bioinformatics 2014; 30:614–620 [View Article]
    [Google Scholar]
  163. Yang C, Chowdhury D, Zhang Z, Cheung WK, Lu A et al. A review of computational tools for generating metagenome-assembled genomes from metagenomic sequencing data. Comput Struct Biotechnol J 2021; 19:6301–6314 [View Article]
    [Google Scholar]
  164. Li D, Liu C-M, Luo R, Sadakane K, Lam T-W. MEGAHIT: an ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph. Bioinformatics 2015; 31:1674–1676 [View Article] [PubMed]
    [Google Scholar]
  165. Nurk S, Meleshko D, Korobeynikov A, Pevzner PA. metaSPAdes: a new versatile metagenomic assembler. Genome Res 2017; 27:824–834 [View Article]
    [Google Scholar]
  166. Kolmogorov M, Bickhart DM, Behsaz B, Gurevich A, Rayko M et al. metaFlye: scalable long-read metagenome assembly using repeat graphs. Nat Methods 2020; 17:1103–1110 [View Article]
    [Google Scholar]
  167. Benoit G, Raguideau S, James R, Phillippy AM, Chikhi R et al. High-quality metagenome assembly from long accurate reads with metaMDBG. Nat Biotechnol 2024; 42:1378–1383 [View Article] [PubMed]
    [Google Scholar]
  168. Rho M, Tang H, Ye Y. FragGeneScan: predicting genes in short and error-prone reads. Nucleic Acids Res 2010; 38:e191 [View Article]
    [Google Scholar]
  169. Kang DD, Froula J, Egan R, Wang Z. MetaBAT, an efficient tool for accurately reconstructing single genomes from complex microbial communities. PeerJ 2015; 3:e1165 [View Article]
    [Google Scholar]
  170. Wu Y-W, Tang Y-H, Tringe SG, Simmons BA, Singer SW. MaxBin: an automated binning method to recover individual genomes from metagenomes using an expectation-maximization algorithm. Microbiome 2014; 2:26 [View Article] [PubMed]
    [Google Scholar]
  171. Alneberg J, Bjarnason BS, de Bruijn I, Schirmer M, Quick J et al. Binning metagenomic contigs by coverage and composition. Nat Methods 2014; 11:1144–1146 [View Article] [PubMed]
    [Google Scholar]
  172. de Hoon MJL, Imoto S, Nolan J, Miyano S. Open source clustering software. Bioinformatics 2004; 20:1453–1454 [View Article] [PubMed]
    [Google Scholar]
  173. Uritskiy GV, DiRuggiero J, Taylor J. MetaWRAP-a flexible pipeline for genome-resolved metagenomic data analysis. Microbiome 2018; 6:158 [View Article] [PubMed]
    [Google Scholar]
  174. Terrón-Camero LC, Gordillo-González F, Salas-Espejo E, Andrés-León E. Comparison of metagenomics and metatranscriptomics tools: a guide to making the right choice. Genes 2022; 13:2280 [View Article] [PubMed]
    [Google Scholar]
  175. Li H, Durbin R. Fast and accurate long-read alignment with burrows-wheeler transform. Bioinformatics 2010; 26:589–595 [View Article] [PubMed]
    [Google Scholar]
  176. Kim D, Song L, Breitwieser FP, Salzberg SL. Centrifuge: rapid and sensitive classification of metagenomic sequences. Genome Res 2016; 26:1721–1729 [View Article] [PubMed]
    [Google Scholar]
  177. Ondov BD, Treangen TJ, Melsted P, Mallonee AB, Bergman NH et al. Mash: fast genome and metagenome distance estimation using MinHash. Genome Biol 2016; 17:132 [View Article] [PubMed]
    [Google Scholar]
  178. Franzosa EA, McIver LJ, Rahnavard G, Thompson LR, Schirmer M et al. Species-level functional profiling of metagenomes and metatranscriptomes. Nat Methods 2018; 15:962–968 [View Article] [PubMed]
    [Google Scholar]
  179. Kaminski J, Gibson MK, Franzosa EA, Segata N, Dantas G et al. High-specificity targeted functional profiling in microbial communities with ShortBRED. PLoS Comput Biol 2015; 11:e1004557 [View Article] [PubMed]
    [Google Scholar]
  180. Wemheuer F, Taylor JA, Daniel R, Johnston E, Meinicke P et al. Tax4Fun2: prediction of habitat-specific functional profiles and functional redundancy based on 16S rRNA gene sequences. Environ Microbiome 2020; 15:11 [View Article] [PubMed]
    [Google Scholar]
  181. Douglas GM, Maffei VJ, Zaneveld JR, Yurgel SN, Brown JR et al. PICRUSt2 for prediction of metagenome functions. Nat Biotechnol 2020; 38:685–688 [View Article] [PubMed]
    [Google Scholar]
  182. Oksanen J, Simpson GL, Blanchet FG, Kindt R, Legendre P et al. Vegan: community ecology package; 2024 10.32614/CRAN.package.vegan
    [Google Scholar]
  183. McMurdie PJ, Holmes S. phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS One 2013; 8:e61217 [View Article] [PubMed]
    [Google Scholar]
  184. Valero-Mora PM. Ggplot2: elegant graphics for data analysis. J Stat Softw 2010; 35:1–3 [View Article]
    [Google Scholar]
  185. Herbig A, Maixner F, Bos KI, Zink A, Krause J et al. MALT: fast alignment and analysis of metagenomic DNA sequence data applied to the Tyrolean Iceman. Bioinformatics 2016 [View Article]
    [Google Scholar]
  186. Huson DH, Auch AF, Qi J, Schuster SC. MEGAN analysis of metagenomic data. Genome Res 2007; 17:377–386 [View Article] [PubMed]
    [Google Scholar]
  187. Peel N, Martin S, Heavens D, Yu DW, Clark MD et al. MARTi: a real-time analysis and visualisation tool for nanopore metagenomics. bioRxiv [View Article]
    [Google Scholar]
  188. Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJA et al. DADA2: High-resolution sample inference from Illumina amplicon data. Nat Methods 2016; 13:581–583 [View Article] [PubMed]
    [Google Scholar]
  189. Sharma VK, Kumar N, Prakash T, Taylor TD. Fast and accurate taxonomic assignments of metagenomic sequences using MetaBin. PLoS One 2012; 7:e34030 [View Article] [PubMed]
    [Google Scholar]
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