REFERENCES
1. Bik HM, Porazinska DL, Creer S, Caporaso JG, Knight R, Thomas WK. Sequencing our way towards understanding global eukaryotic biodiversity. Trends Ecol Evol 2012;27:233-43.
2. Rodriguez RJ, White JF Jr, Arnold AE, Redman RS. Fungal endophytes: diversity and functional roles. New Phytol 2009;182:314-30.
4. Kamoun S, Furzer O, Jones JD, et al. The Top 10 oomycete pathogens in molecular plant pathology. Mol Plant Pathol 2015;16:413-34.
6. Laforest-Lapointe I, Arrieta MC. Microbial eukaryotes: a missing link in gut microbiome studies. mSystems 2018;3:e00201-17.
7. Parfrey LW, Walters WA, Lauber CL, et al. Communities of microbial eukaryotes in the mammalian gut within the context of environmental eukaryotic diversity. Front Microbiol 2014;5:298.
8. Sonnenburg ED, Smits SA, Tikhonov M, Higginbottom SK, Wingreen NS, Sonnenburg JL. Diet-induced extinctions in the gut microbiota compound over generations. Nature 2016;529:212-5.
9. Caron DA, Alexander H, Allen AE, et al. Probing the evolution, ecology and physiology of marine protists using transcriptomics. Nat Rev Microbiol 2017;15:6-20.
10. Brussaard L, de Ruiter PC, Brown GG. Soil biodiversity for agricultural sustainability. Agr Ecosyst Environ 2007;121:233-44.
11. James TY, Stajich JE, Hittinger CT, Rokas A. Toward a fully resolved fungal tree of life. Annu Rev Microbiol 2020;74:291-313.
12. Shen XX, Opulente DA, Kominek J, et al. Tempo and mode of genome evolution in the budding yeast subphylum. Cell 2018;175:1533-45.e20.
13. Hernández-Santos N, Klein BS. Through the scope darkly: the gut mycobiome comes into focus. Cell Host Microbe 2017;22:728-9.
14. Alou M, Naud S, Khelaifia S, Bonnet M, Lagier JC, Raoult D. State of the art in the culture of the human microbiota: new interests and strategies. Clin Microbiol Rev 2020;34:e00129-19.
15. Vu D, Groenewald M, Szöke S, et al. DNA barcoding analysis of more than 9000 yeast isolates contributes to quantitative thresholds for yeast species and genera delimitation. Stud Mycol 2016;85:91-105.
16. Makimura K. Species identification system for dermatophytes based on the DNA sequences of nuclear ribosomal internal transcribed spacer 1. Nihon Ishinkin Gakkai Zasshi 2001;42:61-7.
17. Leaw SN, Chang HC, Sun HF, Barton R, Bouchara JP, Chang TC. Identification of medically important yeast species by sequence analysis of the internal transcribed spacer regions. J Clin Microbiol 2006;44:693-9.
18. Del Campo J, Pons MJ, Herranz M, et al. Validation of a universal set of primers to study animal-associated microeukaryotic communities. Environ Microbiol 2019;21:3855-61.
19. del Campo J, Bass D, Keeling PJ, Bennett A. The eukaryome: diversity and role of microeukaryotic organisms associated with animal hosts. Functional Ecology 2020;34:2045-54.
20. Parfrey LW, Walters WA, Knight R. Microbial eukaryotes in the human microbiome: ecology, evolution, and future directions. Front Microbiol 2011;2:153.
21. Andersen LO, Vedel Nielsen H, Stensvold CR. Waiting for the human intestinal Eukaryotome. ISME J 2013;7:1253-5.
22. Franco-Duarte R, Mendes I, Gomes AC, Santos MA, de Sousa B, Schuller D. Genotyping of Saccharomyces cerevisiae strains by interdelta sequence typing using automated microfluidics. Electrophoresis 2011;32:1447-55.
23. Lücking R, Aime MC, Robbertse B, et al. Unambiguous identification of fungi: where do we stand and how accurate and precise is fungal DNA barcoding? IMA Fungus 2020;11:14.
24. Schoch CL, Seifert KA, Huhndorf S, et al. Nuclear ribosomal internal transcribed spacer (ITS) region as a universal DNA barcode marker for Fungi. Proc Natl Acad Sci U S A 2012;109:6241-6.
25. Knight R, Vrbanac A, Taylor BC, et al. Best practices for analysing microbiomes. Nat Rev Microbiol 2018;16:410-22.
26. Gillevet PM, Sikaroodi M, Torzilli AP. Analyzing salt-marsh fungal diversity: comparing ARISA fingerprinting with clone sequencing and pyrosequencing. Fungal Ecology 2009;2:160-7.
27. Ghannoum MA, Jurevic RJ, Mukherjee PK, et al. Characterization of the oral fungal microbiome (mycobiome) in healthy individuals. PLoS Pathog 2010;6:e1000713.
28. Kurtzman CP, Sugiyama J. 1 Saccharomycotina and taphrinomycotina: the yeasts and yeastlike fungi of the ascomycota. In: Mclaughlin DJ, Spatafora JW, editors. Systematics and Evolution. Berlin: Springer Berlin Heidelberg; 2015. p. 3-33.
29. Kurtzman CP, Fell JW, Boekhout T. Chapter 1 - Definition, classification and nomenclature of the yeasts. In: The Yeasts. Elsevier; 2011. p. 3-5.
30. Li Y, Steenwyk JL, Chang Y, et al. A genome-scale phylogeny of the kingdom fungi. Curr Biol 2021;31:1653-65.e5.
31. Żymańczyk-duda E, Brzezińska-rodak M, Klimek-ochab M, Duda M, Zerka A. Yeast as a versatile tool in biotechnology. In: Morata A, Loira I, editors. Yeast - Industrial Applications. InTech; 2017.
32. Boekhout T, Aime MC, Begerow D, et al. The evolving species concepts used for yeasts: from phenotypes and genomes to speciation networks. Fungal Divers 2021;109:27-55.
33. Hawksworth DL. The magnitude of fungal diversity: the 1.5 million species estimate revisited. Mycol Res 2001;105:1422-32.
35. Hawksworth DL, Lücking R. Fungal diversity revisited: 2.2 to 3.8 million species. Microbiol Spectr 2017;5.
36. Cheek M, Nic Lughadha E, Kirk P, et al. New scientific discoveries: plants and fungi. Plants People Planet 2020;2:371-88.
37. Lücking R, Aime MC, Robbertse B, et al. Fungal taxonomy and sequence-based nomenclature. Nat Microbiol 2021;6:540-8.
38. Huseyin CE, O'Toole PW, Cotter PD, Scanlan PD. Forgotten fungi-the gut mycobiome in human health and disease. FEMS Microbiol Rev 2017;41:479-511.
39. Naranjo-Ortiz MA, Gabaldón T. Fungal evolution: diversity, taxonomy and phylogeny of the Fungi. Biol Rev Camb Philos Soc 2019;94:2101-37.
40. Suhr MJ, Hallen-Adams HE. The human gut mycobiome: pitfalls and potentials - a mycologist’s perspective. Mycologia 2015;107:1057-73.
41. Hinsu A, Dumadiya A, Joshi A, et al. To culture or not to culture: a snapshot of culture-dependent and culture-independent bacterial diversity from peanut rhizosphere. PeerJ 2021;9:e12035.
42. Strati F, Di Paola M, Stefanini I, et al. Age and gender affect the composition of fungal population of the human gastrointestinal tract. Front Microbiol 2016;7:1227.
43. Browne HP, Forster SC, Anonye BO, et al. Culturing of 'unculturable' human microbiota reveals novel taxa and extensive sporulation. Nature 2016;533:543-6.
44. Gutleben J, Chaib De Mares M, van Elsas JD, Smidt H, Overmann J, Sipkema D. The multi-omics promise in context: from sequence to microbial isolate. Crit Rev Microbiol 2018;44:212-29.
45. Borges FM, de Paula TO, Sarmiento MRA, et al. Fungal diversity of human gut microbiota among eutrophic, overweight, and obese individuals based on aerobic culture-dependent approach. Curr Microbiol 2018;75:726-35.
46. Hamad I, Ranque S, Azhar EI, et al. Culturomics and amplicon-based metagenomic approaches for the study of fungal population in human gut microbiota. Sci Rep 2017;7:16788.
47. Huseyin CE, Rubio RC, O’Sullivan O, Cotter PD, Scanlan PD. The fungal frontier: a comparative analysis of methods used in the study of the human gut mycobiome. Front Microbiol 2017;8:1432.
48. Aimanianda V, Clavaud C, Simenel C, Fontaine T, Delepierre M, Latgé JP. Cell wall beta-(1,6)-glucan of Saccharomyces cerevisiae: structural characterization and in situ synthesis. J Biol Chem 2009;284:13401-12.
49. Valiante V, Macheleidt J, Föge M, Brakhage AA. The Aspergillus fumigatus cell wall integrity signaling pathway: drug target, compensatory pathways, and virulence. Front Microbiol 2015;6:325.
50. Gow NAR, Latge JP, Munro CA. The fungal cell wall: structure, biosynthesis, and function. Microbiol Spectr 2017;5.
51. Machová E, Kvapilová K, Kogan G, Sandula J. Effect of ultrasonic treatment on the molecular weight of carboxymethylated chitin-glucan complex from Aspergillus niger. Ultrason Sonochem 1999;5:169-72.
52. Mendonça A, Carvalho-Pereira J, Franco-Duarte R, Sampaio P. Correction to: optimization of a quantitative PCR methodology for detection of Aspergillus spp. and Rhizopus arrhizus. Mol Diagn Ther 2022;26:527.
53. Turnbaugh PJ, Ley RE, Hamady M, Fraser-Liggett CM, Knight R, Gordon JI. The human microbiome project. Nature 2007;449:804-10.
54. Stefanini I, Dapporto L, Legras JL, et al. Role of social wasps in Saccharomyces cerevisiae ecology and evolution. Proc Natl Acad Sci U S A 2012;109:13398-403.
55. Abdelrhman KF, Bacci G, Mancusi C, Mengoni A, Serena F, Ugolini A. A first insight into the gut microbiota of the sea turtle caretta caretta. Front Microbiol 2016;7:1060.
56. Abdelrhman KF, Bacci G, Marras B, et al. Exploring the bacterial gut microbiota of supralittoral talitrid amphipods. Res Microbiol 2017;168:74-84.
57. Ramazzotti M, Bacci G. Chapter 5 - 16S rRNA-based taxonomy profiling in the metagenomics era. In: Nagarajan M, editor. Metagenomics. Academic Press; 2018. p. 103-19.
58. Arranz V, Pearman WS, Aguirre JD, Liggins L. MARES, a replicable pipeline and curated reference database for marine eukaryote metabarcoding. Sci Data 2020;7:209.
59. Frøslev TG, Kjøller R, Bruun HH, et al. Algorithm for post-clustering curation of DNA amplicon data yields reliable biodiversity estimates. Nat Commun 2017;8:1188.
60. Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJ, Holmes SP. DADA2: high-resolution sample inference from Illumina amplicon data. Nat Methods 2016;13:581-3.
61. Nilsson RH, Anslan S, Bahram M, Wurzbacher C, Baldrian P, Tedersoo L. Mycobiome diversity: high-throughput sequencing and identification of fungi. Nat Rev Microbiol 2019;17:95-109.
62. Nilsson RH, Kristiansson E, Ryberg M, Hallenberg N, Larsson KH. Intraspecific ITS variability in the kingdom fungi as expressed in the international sequence databases and its implications for molecular species identification. Evol Bioinform Online 2008;4:193-201.
63. Ali NABM, Mac Aogáin M, Morales RF, Tiew PY, Chotirmall SH. Optimisation and benchmarking of targeted amplicon sequencing for mycobiome analysis of respiratory specimens. Int J Mol Sci 2019;20:4991.
64. Bokulich NA, Mills DA. Improved selection of internal transcribed spacer-specific primers enables quantitative, ultra-high-throughput profiling of fungal communities. Appl Environ Microbiol 2013;79:2519-26.
65. Tedersoo L, Lindahl B. Fungal identification biases in microbiome projects. Environ Microbiol Rep 2016;8:774-9.
66. Franco-Duarte R, Fernandes I, Gulis V, Cássio F, Pascoal C. ITS rDNA barcodes clarify molecular diversity of aquatic hyphomycetes. Microorganisms 2022;10:1569.
67. Bradshaw MJ, Aime MC, Rokas A, et al. Extensive intragenomic variation in the internal transcribed spacer region of fungi. iScience 2023;26:107317.
68. Bellemain E, Carlsen T, Brochmann C, Coissac E, Taberlet P, Kauserud H. ITS as an environmental DNA barcode for fungi: an in silico approach reveals potential PCR biases. BMC Microbiol 2010;10:189.
69. Mbareche H, Veillette M, Bilodeau G, Duchaine C. Comparison of the performance of ITS1 and ITS2 as barcodes in amplicon-based sequencing of bioaerosols. PeerJ 2020;8:e8523.
70. Hoggard M, Vesty A, Wong G, et al. Characterizing the human mycobiota: a comparison of small subunit rRNA, ITS1, ITS2, and large subunit rRNA genomic targets. Front Microbiol 2018;9:2208.
71. Peterson SW, Kurtzman CP. Ribosomal RNA sequence divergence among sibling species of yeasts. Syst Appl Microbiol 1991;14:124-9.
72. Kurtzman CP, Robnett CJ. Identification and phylogeny of ascomycetous yeasts from analysis of nuclear large subunit (26S) ribosomal DNA partial sequences. Antonie Van Leeuwenhoek 1998;73:331-71.
73. Tang J, Iliev ID, Brown J, Underhill DM, Funari VA. Mycobiome: approaches to analysis of intestinal fungi. J Immunol Methods 2015;421:112-21.
74. Filippis F, Laiola M, Blaiotta G, Ercolini D. Different amplicon targets for sequencing-based studies of fungal diversity. Appl Environ Microbiol 2017;83:e00905-17.
75. Kiss L. Limits of nuclear ribosomal DNA internal transcribed spacer (ITS) sequences as species barcodes for Fungi. Proc Natl Acad Sci U S A 2012;109:E1811; author reply E1812.
76. Stielow JB, Lévesque CA, Seifert KA, et al. One fungus, which genes? Development and assessment of universal primers for potential secondary fungal DNA barcodes. Persoonia 2015;35:242-63.
77. James TY, Kauff F, Schoch CL, et al. Reconstructing the early evolution of fungi using a six-gene phylogeny. Nature 2006;443:818-22.
78. Matheny PB, Liu YJ, Ammirati JF, Hall BD. Using RPB1 sequences to improve phylogenetic inference among mushrooms (Inocybe, Agaricales). Am J Bot 2002;89:688-98.
79. Meyer W, Irinyi L, Hoang MTV, et al. Database establishment for the secondary fungal DNA barcode translational elongation factor 1α (TEF1α)1. Genome 2019;62:160-9.
80. Větrovský T, Kolařík M, Žifčáková L, Zelenka T, Baldrian P. The rpb2 gene represents a viable alternative molecular marker for the analysis of environmental fungal communities. Mol Ecol Resour 2016;16:388-401.
81. Morrison GA, Fu J, Lee GC, et al. Nanopore sequencing of the fungal intergenic spacer sequence as a potential rapid diagnostic assay. J Clin Microbiol 2020;58:e01972-20.
82. Geiser DM, Frisvad JC, Taylor JW. Evolutionary relationships in Aspergillus section Fumigati inferred from partial β-tubulin and hydrophobin DNA sequences. Mycologia 1998;90:831-45.
83. Hu T, Chitnis N, Monos D, Dinh A. Next-generation sequencing technologies: an overview. Hum Immunol 2021;82:801-11.
84. Quince C, Walker AW, Simpson JT, Loman NJ, Segata N. Shotgun metagenomics, from sampling to analysis. Nat Biotechnol 2017;35:833-44.
85. Morgan XC, Huttenhower C. Meta’omic analytic techniques for studying the intestinal microbiome. Gastroenterology 2014;146:1437-48.e1.
86. Microbiome Project Consortium. Structure, function and diversity of the healthy human microbiome. Nature 2012;486:207-14.
87. Nash AK, Auchtung TA, Wong MC, et al. The gut mycobiome of the Human Microbiome Project healthy cohort. Microbiome 2017;5:153.
88. Hoang MTV, Irinyi L, Hu Y, Schwessinger B, Meyer W. Long-reads-based metagenomics in clinical diagnosis with a special focus on fungal infections. Front Microbiol 2021;12:708550.
89. Pollard MO, Gurdasani D, Mentzer AJ, Porter T, Sandhu MS. Long reads: their purpose and place. Hum Mol Genet 2018;27:R234-41.
90. Mantere T, Kersten S, Hoischen A. Long-read sequencing emerging in medical genetics. Front Genet 2019;10:426.
91. Sui Y, Wisniewski M, Droby S, Piombo E, Wu X, Yue J. Genome sequence, assembly, and characterization of the antagonistic yeast candida oleophila used as a biocontrol agent against post-harvest diseases. Front Microbiol 2020;11:295.
92. Cuomo CA, Shea T, Yang B, Rao R, Forche A. Whole genome sequence of the heterozygous clinical isolate candida krusei 81-B-5. G3 2017;7:2883-9.
93. Luo R, Zimin A, Workman R, et al. First draft genome sequence of the pathogenic fungus Lomentospora prolificans (Formerly Scedosporium prolificans). G3 2017;7:3831-6.
94. Vale-Silva L, Beaudoing E, Tran VDT, Sanglard D. Comparative genomics of two sequential candida glabrata clinical isolates. G3 2017;7:2413-26.
95. Panthee S, Hamamoto H, Ishijima SA, Paudel A, Sekimizu K. Utilization of hybrid assembly approach to determine the genome of an opportunistic pathogenic fungus, candida albicans TIMM 1768. Genome Biol Evol 2018;10:2017-22.
96. Rhodes J, Abdolrasouli A, Farrer RA, et al. Genomic epidemiology of the UK outbreak of the emerging human fungal pathogen Candida auris. Emerg Microbes Infect 2018;7:43.
97. Morand SC, Bertignac M, Iltis A, et al. Complete genome sequence of Malassezia restricta CBS 7877, an opportunist pathogen involved in dandruff and seborrheic dermatitis. Microbiol Resour Announc 2019;8:e01543-18.
98. Schultzhaus Z, Cuomo CA, Wang Z. Genome sequence of the black yeast exophiala lecanii-corni. Microbiol Resour Announc 2019;8:e01709-18.
99. Pchelin IM, Azarov DV, Churina MA, et al. Whole genome sequence of first Candida auris strain, isolated in Russia. Med Mycol 2020;58:414-6.
100. Arnaud MB, Chibucos MC, Costanzo MC, et al. The aspergillus genome database, a curated comparative genomics resource for gene, protein and sequence information for the Aspergillus research community. Nucleic Acids Res 2010;38:D420-7.
101. Ratnasingham S, Hebert PD. bold: The barcode of life data system (http://www.barcodinglife.org). Mol Ecol Notes 2007;7:355-64.
102. Inglis DO, Arnaud MB, Binkley J, et al. The Candida genome database incorporates multiple Candida species: multispecies search and analysis tools with curated gene and protein information for Candida albicans and Candida glabrata. Nucleic Acids Res 2012;40:D667-74.
103. Güldener U, Münsterkötter M, Kastenmüller G, et al. CYGD: the comprehensive yeast genome database. Nucleic Acids Res 2005;33:D364-8.
104. Stajich JE, Harris T, Brunk BP, et al. FungiDB: an integrated functional genomics database for fungi. Nucleic Acids Res 2012;40:D675-81.
105. Grossetête S, Labedan B, Lespinet O. FUNGIpath: a tool to assess fungal metabolic pathways predicted by orthology. BMC Genomics 2010;11:81.
106. Geiser DM, del Mar Jiménez-gasco M, Kang S, et al. FUSARIUM-ID v. 1.0: a DNA sequence database for identifying fusarium. Eur J Plant Pathol 2004;110:473-9.
107. O’donnell K, Sutton DA, Rinaldi MG, et al. Internet-accessible DNA sequence database for identifying fusaria from human and animal infections. J Clin Microbiol 2010;48:3708-18.
108. Irinyi L, Serena C, Garcia-Hermoso D, et al. International Society of Human and Animal Mycology (ISHAM)-ITS reference DNA barcoding database - the quality controlled standard tool for routine identification of human and animal pathogenic fungi. Med Mycol 2015;53:313-37.
109. Ahrendt SR, Mondo SJ, Haridas S, Grigoriev IV. MycoCosm, the JGI’s fungal genome portal for comparative genomic and multiomics data analyses. In: Martin F, Uroz S, editors. Microbial Environmental Genomics (MEG). New York: Springer US; 2023. p. 271-91.
110. Wood V, Harris MA, McDowall MD, et al. PomBase: a comprehensive online resource for fission yeast. Nucleic Acids Res 2012;40:D695-9.
111. Cherry JM, Hong EL, Amundsen C, et al. Saccharomyces genome database: the genomics resource of budding yeast. Nucleic Acids Res 2012;40:D700-5.
112. Abarenkov K, Henrik Nilsson R, Larsson KH, et al. The UNITE database for molecular identification of fungi - recent updates and future perspectives. New Phytol 2010;186:281-5.
113. Prakash PY, Irinyi L, Halliday C, Chen S, Robert V, Meyer W. Online databases for taxonomy and identification of pathogenic fungi and proposal for a cloud-based dynamic data network platform. J Clin Microbiol 2017;55:1011-24.
114. Kuczynski J, Lauber CL, Walters WA, et al. Experimental and analytical tools for studying the human microbiome. Nat Rev Genet 2011;13:47-58.
115. Schloss PD, Westcott SL, Ryabin T, et al. Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities. Appl Environ Microbiol 2009;75:7537-41.
116. Gweon HS, Oliver A, Taylor J, et al. PIPITS: an automated pipeline for analyses of fungal internal transcribed spacer sequences from the Illumina sequencing platform. Methods Ecol Evol 2015;6:973-80.
117. Rognes T, Flouri T, Nichols B, Quince C, Mahé F. VSEARCH: a versatile open source tool for metagenomics. PeerJ 2016;4:e2584.
118. Mysara M, Njima M, Leys N, Raes J, Monsieurs P. From reads to operational taxonomic units: an ensemble processing pipeline for MiSeq amplicon sequencing data. Gigascience 2017;6:1-10.
119. Bolyen E, Rideout JR, Dillon MR, et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat Biotechnol 2019;37:852-7.
120. He Y, Caporaso JG, Jiang XT, et al. Stability of operational taxonomic units: an important but neglected property for analyzing microbial diversity. Microbiome 2015;3:20.
121. Callahan BJ, McMurdie PJ, Holmes SP. Exact sequence variants should replace operational taxonomic units in marker-gene data analysis. ISME J 2017;11:2639-43.
122. Chiarello M, McCauley M, Villéger S, Jackson CR. Ranking the biases: the choice of OTUs vs. ASVs in 16S rRNA amplicon data analysis has stronger effects on diversity measures than rarefaction and OTU identity threshold. PLoS One 2022;17:e0264443.
123. Wood DE, Salzberg SL. Kraken: ultrafast metagenomic sequence classification using exact alignments. Genome Biol 2014;15:R46.
124. Caporaso JG, Kuczynski J, Stombaugh J, et al. QIIME allows analysis of high-throughput community sequencing data. Nat Methods 2010;7:335-6.
125. Abarenkov K, Tedersoo L, Nilsson RH, et al. PlutoF - a web based workbench for ecological and taxonomic research, with an online implementation for fungal ITS sequences. Evol Bioinform Online 2010;6:EBO.S6271.
126. Kumar S, Carlsen T, Mevik BH, et al. CLOTU: an online pipeline for processing and clustering of 454 amplicon reads into OTUs followed by taxonomic annotation. BMC Bioinform 2011;12:182.
127. White JR, Maddox C, White O, Angiuoli SV, Fricke WF. CloVR-ITS: automated internal transcribed spacer amplicon sequence analysis pipeline for the characterization of fungal microbiota. Microbiome 2013;1:6.
128. Albanese D, Fontana P, De Filippo C, Cavalieri D, Donati C. MICCA: a complete and accurate software for taxonomic profiling of metagenomic data. Sci Rep 2015;5:9743.
129. Fosso B, Santamaria M, Marzano M, et al. BioMaS: a modular pipeline for Bioinformatic analysis of Metagenomic AmpliconS. BMC Bioinform 2015;16:203.
130. Odom AR, Faits T, Castro-Nallar E, Crandall KA, Johnson WE. Metagenomic profiling pipelines improve taxonomic classification for 16S amplicon sequencing data. Sci Rep 2023;13:13957.
131. Edgar RC. UPARSE: highly accurate OTU sequences from microbial amplicon reads. Nat Methods 2013;10:996-8.
132. Edgar RC, Flyvbjerg H. Error filtering, pair assembly and error correction for next-generation sequencing reads. Bioinformatics 2015;31:3476-82.
133. Truong DT, Franzosa EA, Tickle TL, et al. MetaPhlAn2 for enhanced metagenomic taxonomic profiling. Nat Methods 2015;12:902-3.
134. Wood DE, Lu J, Langmead B. Improved metagenomic analysis with Kraken 2. Genome Biol 2019;20:257.
135. Buchfink B, Xie C, Huson DH. Fast and sensitive protein alignment using DIAMOND. Nat Methods 2015;12:59-60.
136. Olson ND, Treangen TJ, Hill CM, et al. Metagenomic assembly through the lens of validation: recent advances in assessing and improving the quality of genomes assembled from metagenomes. Brief Bioinform 2019;20:1140-50.
137. Jünemann S, Kleinbölting N, Jaenicke S, et al. Bioinformatics for NGS-based metagenomics and the application to biogas research. J Biotechnol 2017;261:10-23.
138. Nurk S, Meleshko D, Korobeynikov A, Pevzner PA. metaSPAdes: a new versatile metagenomic assembler. Genome Res 2017;27:824-34.
139. Li D, Liu CM, Luo R, Sadakane K, Lam TW. MEGAHIT: an ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph. Bioinformatics 2015;31:1674-6.
140. Peng Y, Leung HC, Yiu SM, Chin FY. IDBA-UD: a de novo assembler for single-cell and metagenomic sequencing data with highly uneven depth. Bioinformatics 2012;28:1420-8.
141. Kang DD, Li F, Kirton E, et al. MetaBAT 2: an adaptive binning algorithm for robust and efficient genome reconstruction from metagenome assemblies. PeerJ 2019;7:e7359.
142. Alneberg J, Bjarnason BS, de Bruijn I, et al. Binning metagenomic contigs by coverage and composition. Nat Methods 2014;11:1144-6.
143. Wu YW, Simmons BA, Singer SW. MaxBin 2.0: an automated binning algorithm to recover genomes from multiple metagenomic datasets. Bioinformatics 2016;32:605-7.
144. Uritskiy GV, DiRuggiero J, Taylor J. MetaWRAP-a flexible pipeline for genome-resolved metagenomic data analysis. Microbiome 2018;6:158.
