Long-Read Metagenomics and CAZyme Discovery

  • Protocol
  • First Online:
Carbohydrate-Protein Interactions

Abstract

Microorganisms play a primary role in regulating biogeochemical cycles and are a valuable source of enzymes that have biotechnological applications, such as carbohydrate-active enzymes (CAZymes). However, the inability to culture the majority of microorganisms that exist in natural ecosystems restricts access to potentially novel bacteria and beneficial CAZymes. While commonplace molecular-based culture-independent methods such as metagenomics enable researchers to study microbial communities directly from environmental samples, recent progress in long-read sequencing technologies are advancing the field. We outline key methodological stages that are required as well as describe specific protocols that are currently used for long-read metagenomic projects dedicated to CAZyme discovery.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Protocol
EUR 44.95
Price includes VAT (Germany)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
EUR 149.79
Price includes VAT (Germany)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
EUR 139.09
Price includes VAT (Germany)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free ship** worldwide - see info
Hardcover Book
EUR 192.59
Price includes VAT (Germany)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free ship** worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. La Rosa SL, Ostrowski MP, Vera-Ponce de León A, McKee LS, Larsbrink J, Eijsink VG, Lowe EC, Martens EC, Pope PB (2022) Glycan processing in gut microbiomes. Curr Opin Microbiol 67:102143. https://doi.org/10.1016/j.mib.2022.102143

    Article  CAS  PubMed  Google Scholar 

  2. Warnecke F, Luginbuhl P, Ivanova N, Ghassemian M, Richardson TH, Stege JT, Cayouette M, McHardy AC, Djordjevic G, Aboushadi N, Sorek R, Tringe SG, Podar M, Martin HG, Kunin V, Dalevi D, Madejska J, Kirton E, Platt D, Szeto E, Salamov A, Barry K, Mikhailova N, Kyrpides NC, Matson EG, Ottesen EA, Zhang X, Hernandez M, Murillo C, Acosta LG, Rigoutsos I, Tamayo G, Green BD, Chang C, Rubin EM, Mathur EJ, Robertson DE, Hugenholtz P, Leadbetter JR (2007) Metagenomic and functional analysis of hindgut microbiota of a wood-feeding higher termite. Nature 450(7169):560–565. https://doi.org/10.1038/nature06269

    Article  CAS  PubMed  Google Scholar 

  3. Liu N, Li H, Chevrette MG, Zhang L, Cao L, Zhou H, Zhou X, Zhou Z, Pope PB, Currie CR, Huang Y, Wang Q (2019) Functional metagenomics reveals abundant polysaccharide-degrading gene clusters and cellobiose utilization pathways within gut microbiota of a wood-feeding higher termite. ISME J 13(1):104–117. https://doi.org/10.1038/s41396-018-0255-1

    Article  CAS  PubMed  Google Scholar 

  4. Hagen LH, Brooke CG, Shaw CA, Norbeck AD, Piao H, Arntzen M, Olson HM, Copeland A, Isern N, Shukla A, Roux S, Lombard V, Henrissat B, O’Malley MA, Grigoriev IV, Tringe SG, Mackie RI, Pasa-Tolic L, Pope PB, Hess M (2021) Proteome specialization of anaerobic fungi during ruminal degradation of recalcitrant plant fiber. ISME J 15(2):421–434. https://doi.org/10.1038/s41396-020-00769-x

    Article  CAS  PubMed  Google Scholar 

  5. Naas AE, Solden LM, Norbeck AD, Brewer H, Hagen LH, Heggenes IM, McHardy AC, Mackie RI, Paša-Tolić L, Arntzen M, Eijsink VGH, Koropatkin NM, Hess M, Wrighton KC, Pope PB (2018) “Candidatus Paraporphyromonas polyenzymogenes” encodes multi-modular cellulases linked to the type IX secretion system. Microbiome 6(1):44. https://doi.org/10.1186/s40168-018-0421-8

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Peng X, Wilken SE, Lankiewicz TS, Gilmore SP, Brown JL, Henske JK, Swift CL, Salamov A, Barry K, Grigoriev IV, Theodorou MK, Valentine DL, O’Malley MA (2021) Genomic and functional analyses of fungal and bacterial consortia that enable lignocellulose breakdown in goat gut microbiomes. Nat Microbiol 6(4):499–511. https://doi.org/10.1038/s41564-020-00861-0

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Solden LM, Naas AE, Roux S, Daly RA, Collins WB, Nicora CD, Purvine SO, Hoyt DW, Schückel J, Jørgensen B, Willats W, Spalinger DE, Firkins JL, Lipton MS, Sullivan MB, Pope PB, Wrighton KC (2018) Interspecies cross-feeding orchestrates carbon degradation in the rumen ecosystem. Nat Microbiol 3(11):1274–1284. https://doi.org/10.1038/s41564-018-0225-4

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Delogu F, Kunath BJ, Evans PN, Arntzen M, Hvidsten TR, Pope PB (2020) Integration of absolute multi-omics reveals dynamic protein-to-RNA ratios and metabolic interplay within mixed-domain microbiomes. Nat Commun 11(1):4708. https://doi.org/10.1038/s41467-020-18543-0

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Singleton CM, Petriglieri F, Kristensen JM, Kirkegaard RH, Michaelsen TY, Andersen MH, Kondrotaite Z, Karst SM, Dueholm MS, Nielsen PH, Albertsen M (2021) Connecting structure to function with the recovery of over 1000 high-quality metagenome-assembled genomes from activated sludge using long-read sequencing. Nat Commun 12(1):2009. https://doi.org/10.1038/s41467-021-22203-2

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Ostrowski MP, La Rosa SL, Kunath BJ, Robertson A, Pereira G, Hagen LH, Varghese NJ, Qiu L, Yao T, Flint G, Li J, McDonald SP, Buttner D, Pudlo NA, Schnizlein MK, Young VB, Brumer H, Schmidt TM, Terrapon N, Lombard V, Henrissat B, Hamaker B, Eloe-Fadrosh EA, Tripathi A, Pope PB, Martens EC (2022) Mechanistic insights into consumption of the food additive xanthan gum by the human gut microbiota. Nat Microbiol 7(4):556–569. https://doi.org/10.1038/s41564-022-01093-0

    Article  CAS  PubMed  Google Scholar 

  11. Bickhart DM, Kolmogorov M, Tseng E, Portik DM, Korobeynikov A, Tolstoganov I, Uritskiy G, Liachko I, Sullivan ST, Shin SB, Zorea A, Andreu VP, Panke-Buisse K, Medema MH, Mizrahi I, Pevzner PA, Smith TPL (2022) Generating lineage-resolved, complete metagenome-assembled genomes from complex microbial communities. Nat Biotechnol 40(5):711–719. https://doi.org/10.1038/s41587-021-01130-z

    Article  CAS  PubMed  Google Scholar 

  12. Sims D, Sudbery I, Ilott NE, Heger A, Ponting CP (2014) Sequencing depth and coverage: key considerations in genomic analyses. Nat Rev Genet 15(2):121–132. https://doi.org/10.1038/nrg3642

    Article  CAS  PubMed  Google Scholar 

  13. Tedersoo L, Albertsen M, Anslan S, Callahan B (2021) Perspectives and benefits of high-throughput long-read sequencing in microbial ecology. Appl Environ Microbiol 87(17):e0062621. https://doi.org/10.1128/aem.00626-21

    Article  CAS  PubMed  Google Scholar 

  14. Kuczynski J, Stombaugh J, Walters WA, González A, Caporaso JG, Knight R (2011) Using QIIME to analyze 16S rRNA gene sequences from microbial communities. Curr Protoc Bioinformatics Chapter 10 36:Unit 10.17

    Google Scholar 

  15. Gilbert JA, Jansson JK, Knight R (2014) The Earth Microbiome project: successes and aspirations. BMC Biol 12:69

    Article  PubMed  PubMed Central  Google Scholar 

  16. Royo-Llonch M, Sánchez P, Ruiz-González C, Salazar G, Pedrós-Alió C, Sebastián M, Labadie K, Paoli L, Ibarbalz FM, Zinger L, Churcheward B, Chaffron S, Eveillard D, Karsenti E, Sunagawa S, Wincker P, Karp-Boss L, Bowler C, Acinas SG (2021) Compendium of 530 metagenome-assembled bacterial and archaeal genomes from the polar Arctic Ocean. Nat Microbiol 6(12):1561–1574. https://doi.org/10.1038/s41564-021-00979-9

    Article  CAS  PubMed  Google Scholar 

  17. Li Z, Wang X, Zhang Y, Yu Z, Zhang T, Dai X, Pan X, **g R, Yan Y, Liu Y, Gao S, Li F, Huang Y, Tian J, Yao J, **ng X, Shi T, Ning J, Yao B, Huang H, Jiang Y (2022) Genomic insights into the phylogeny and biomass-degrading enzymes of rumen ciliates. ISME J 16:2775–2787. https://doi.org/10.1038/s41396-022-01306-8

    Article  CAS  PubMed  Google Scholar 

  18. Yilmaz P, Kottmann R, Field D, Knight R, Cole JR, Amaral-Zettler L, Gilbert JA, Karsch-Mizrachi I, Johnston A, Cochrane G, Vaughan R, Hunter C, Park J, Morrison N, Rocca-Serra P, Sterk P, Arumugam M, Bailey M, Baumgartner L, Birren BW, Blaser MJ, Bonazzi V, Booth T, Bork P, Bushman FD, Buttigieg PL, Chain PS, Charlson E, Costello EK, Huot-Creasy H, Dawyndt P, DeSantis T, Fierer N, Fuhrman JA, Gallery RE, Gevers D, Gibbs RA, San Gil I, Gonzalez A, Gordon JI, Guralnick R, Hankeln W, Highlander S, Hugenholtz P, Jansson J, Kau AL, Kelley ST, Kennedy J, Knights D, Koren O, Kuczynski J, Kyrpides N, Larsen R, Lauber CL, Legg T, Ley RE, Lozupone CA, Ludwig W, Lyons D, Maguire E, Methe BA, Meyer F, Muegge B, Nakielny S, Nelson KE, Nemergut D, Neufeld JD, Newbold LK, Oliver AE, Pace NR, Palanisamy G, Peplies J, Petrosino J, Proctor L, Pruesse E, Quast C, Raes J, Ratnasingham S, Ravel J, Relman DA, Assunta-Sansone S, Schloss PD, Schriml L, Sinha R, Smith MI, Sodergren E, Spo A, Stombaugh J, Tiedje JM, Ward DV, Weinstock GM, Wendel D, White O, Whiteley A, Wilke A, Wortman JR, Yatsunenko T, Glockner FO (2011) Minimum information about a marker gene sequence (MIMARKS) and minimum information about any (x) sequence (MIxS) specifications. Nat Biotechnol 29(5):415–420. https://doi.org/10.1038/nbt.1823

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Yilmaz P, Gilbert JA, Knight R, Amaral-Zettler L, Karsch-Mizrachi I, Cochrane G, Nakamura Y, Sansone SA, Glockner FO, Field D (2011) The genomic standards consortium: bringing standards to life for microbial ecology. ISME J 5(10):1565–1567. https://doi.org/10.1038/ismej.2011.39

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Bowers RM, Kyrpides NC, Stepanauskas R, Harmon-Smith M, Doud D, Reddy TBK, Schulz F, Jarett J, Rivers AR, Eloe-Fadrosh EA, Tringe SG, Ivanova NN, Copeland A, Clum A, Becraft ED, Malmstrom RR, Birren B, Podar M, Bork P, Weinstock GM, Garrity GM, Dodsworth JA, Yooseph S, Sutton G, Glöckner FO, Gilbert JA, Nelson WC, Hallam SJ, Jungbluth SP, Ettema TJG, Tighe S, Konstantinidis KT, Liu WT, Baker BJ, Rattei T, Eisen JA, Hedlund B, McMahon KD, Fierer N, Knight R, Finn R, Cochrane G, Karsch-Mizrachi I, Tyson GW, Rinke C, Lapidus A, Meyer F, Yilmaz P, Parks DH, Eren AM, Schriml L, Banfield JF, Hugenholtz P, Woyke T (2017) Minimum information about a single amplified genome (MISAG) and a metagenome-assembled genome (MIMAG) of bacteria and archaea. Nat Biotechnol 35(8):725–731. https://doi.org/10.1038/nbt.3893

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Burke C, Kjelleberg S, Thomas T (2009) Selective extraction of bacterial DNA from the surfaces of macroalgae. Appl Environ Microbiol 75(1):252–256. https://doi.org/10.1128/AEM.01630-08

    Article  CAS  PubMed  Google Scholar 

  22. Solomon R, Wein T, Levy B, Eshed S, Dror R, Reiss V, Zehavi T, Furman O, Mizrahi I, Jami E (2022) Protozoa populations are ecosystem engineers that shape prokaryotic community structure and function of the rumen microbial ecosystem. ISME J 16(4):1187–1197. https://doi.org/10.1038/s41396-021-01170-y

    Article  PubMed  Google Scholar 

  23. Delmont TO, Robe P, Clark I, Simonet P, Vogel TM (2011) Metagenomic comparison of direct and indirect soil DNA extraction approaches. J Microbiol Methods 86(3):397–400. https://doi.org/10.1016/j.mimet.2011.06.013

    Article  CAS  PubMed  Google Scholar 

  24. Rosewarne CP, Pope PB, Denman SE, McSweeney CS, O’Cuiv P, Morrison M (2011) High-yield and phylogenetically robust methods of DNA recovery for analysis of microbial biofilms adherent to plant biomass in the herbivore gut. Microb Ecol 61(2):448–454. https://doi.org/10.1007/s00248-010-9745-z

    Article  CAS  PubMed  Google Scholar 

  25. Denman SE, Martinez Fernandez G, Shinkai T, Mitsumori M, McSweeney CS (2015) Metagenomic analysis of the rumen microbial community following inhibition of methane formation by a halogenated methane analog. Front Microbiol 6:1087

    Article  PubMed  PubMed Central  Google Scholar 

  26. Cardenas E, Kranabetter JM, Hope G, Maas KR, Hallam S, Mohn WW (2015) Forest harvesting reduces the soil metagenomic potential for biomass decomposition. ISME J 9:2465–2476

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Marine R, McCarren C, Vorrasane V, Nasko D, Crowgey E, Polson SW, Wommack KE (2014) Caught in the middle with multiple displacement amplification: the myth of pooling for avoiding multiple displacement amplification bias in a metagenome. Microbiome 2:3

    Article  PubMed  PubMed Central  Google Scholar 

  28. Binga EK, Lasken RS, Neufeld JD (2008) Something from (almost) nothing: the impact of multiple displacement amplification on microbial ecology. ISME J 2:233–241

    Article  CAS  PubMed  Google Scholar 

  29. Bragg L, Tyson GW (2014) Metagenomics using next-generation sequencing. Methods Mol Biol 1096:183–201

    Article  CAS  PubMed  Google Scholar 

  30. Laehnemann D, Borkhardt A, McHardy AC (2016) Denoising DNA deep sequencing data-high-throughput sequencing errors and their correction. Brief Bioinform 17:154–179

    Article  CAS  PubMed  Google Scholar 

  31. Karst SM, Ziels RM, Kirkegaard RH, Sørensen EA, McDonald D, Zhu Q, Knight R, Albertsen M (2021) High-accuracy long-read amplicon sequences using unique molecular identifiers with Nanopore or PacBio sequencing. Nat Methods 18(2):165–169. https://doi.org/10.1038/s41592-020-01041-y

    Article  CAS  PubMed  Google Scholar 

  32. Stewart RD, Auffret MD, Warr A, Walker AW, Roehe R, Watson M (2019) Compendium of 4,941 rumen metagenome-assembled genomes for rumen microbiome biology and enzyme discovery. Nat Biotechnol 37(8):953–961. https://doi.org/10.1038/s41587-019-0202-3

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Sereika M, Kirkegaard RH, Karst SM, Michaelsen TY, Sørensen EA, Wollenberg RD, Albertsen M (2022) Oxford Nanopore R10.4 long-read sequencing enables the generation of near-finished bacterial genomes from pure cultures and metagenomes without short-read or reference polishing. Nat Methods 19(7):823–826. https://doi.org/10.1038/s41592-022-01539-7

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Nagarajan N, Pop M (2013) Sequence assembly demystified. Nat Rev Genet 14:157–167

    Article  CAS  PubMed  Google Scholar 

  35. Li D, Liu CM, Luo R, Sadakane K, Lam TW (2015) MEGAHIT: an ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph. Bioinformatics 31:1674–1676

    Article  CAS  PubMed  Google Scholar 

  36. Nurk S, Meleshko D, Korobeynikov A, Pevzner P (2016) metaSPAdes: a new versatile de novo metagenomics assembler. ar**v:160403071

    Google Scholar 

  37. Kolmogorov M, Bickhart DM, Behsaz B, Gurevich A, Rayko M, Shin SB, Kuhn K, Yuan J, Polevikov E, Smith TPL, Pevzner PA (2020) metaFlye: scalable long-read metagenome assembly using repeat graphs. Nat Methods 17(11):1103–1110. https://doi.org/10.1038/s41592-020-00971-x

    Article  CAS  PubMed  Google Scholar 

  38. Kolmogorov M, Yuan J, Lin Y, Pevzner PA (2019) Assembly of long, error-prone reads using repeat graphs. Nat Biotechnol 37(5):540–546. https://doi.org/10.1038/s41587-019-0072-8

    Article  CAS  PubMed  Google Scholar 

  39. Tsai YC, Conlan S, Deming C, Program NCS, Segre JA, Kong HH, Korlach J, Oh J (2016) Resolving the complexity of human skin metagenomes using single-molecule sequencing. MBio 7(1):e01948

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Chandrakumar I, Gauthier NPG, Nelson C, Bonsall MB, Locher K, Charles M, MacDonald C, Krajden M, Manges AR, Chorlton SD (2022) BugSplit enables genome-resolved metagenomics through highly accurate taxonomic binning of metagenomic assemblies. Commun Biol 5(1):151. https://doi.org/10.1038/s42003-022-03114-4

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Koren S, Schatz MC, Walenz BP, Martin J, Howard JT, Ganapathy G, Wang Z, Rasko DA, McCombie WR, Jarvis ED, Adam MP (2012) Hybrid error correction and de novo assembly of single-molecule sequencing reads. Nat Biotechnol 30(7):693–700. https://doi.org/10.1038/nbt.2280

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Frank JA, Pan Y, Tooming-Klunderud A, Eijsink VG, McHardy AC, Nederbragt AJ, Pope PB (2016) Improved metagenome assemblies and taxonomic binning using long-read circular consensus sequence data. Sci Rep 6:25373

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Hess M, Sczyrba A, Egan R, Kim T-W, Chokhawala H, Schroth G, Luo S, Clark DS, Chen F, Zhang T (2011) Metagenomic discovery of biomass-degrading genes and genomes from cow rumen. Science 331:463–467. https://doi.org/10.1126/science.1200387

    Article  CAS  PubMed  Google Scholar 

  44. Eren AM, Esen ÖC, Quince C, Vineis JH, Morrison HG, Sogin ML, Delmont TO (2015) Anvi’o: an advanced analysis and visualization platform for ’omics data. PeerJ 3:e1319

    Article  PubMed  PubMed Central  Google Scholar 

  45. Zhu Z, Niu B, Chen J, Wu S, Sun S, Li W (2013) MGAviewer: a desktop visualization tool for analysis of metagenomics alignment data. Bioinformatics 29:122–123

    Article  CAS  PubMed  Google Scholar 

  46. McHardy AC, Rigoutsos I (2007) What’s in the mix: phylogenetic classification of metagenome sequence samples. Curr Opin Microbiol 10:499–503

    Article  CAS  PubMed  Google Scholar 

  47. Teeling H, Waldmann J, Lombardot T, Bauer M, Glöckner FO (2004) TETRA: a web-service and a stand-alone program for the analysis and comparison of tetranucleotide usage patterns in DNA sequences. BMC Bioinformatics 5:163

    Article  PubMed  PubMed Central  Google Scholar 

  48. Iverson V, Morris RM, Frazar CD, Berthiaume CT, Morales RL, Armbrust EV (2012) Untangling genomes from metagenomes: revealing an uncultured class of marine Euryarchaeota. Science 335:587–590. https://doi.org/10.1126/science.1212665

    Article  CAS  PubMed  Google Scholar 

  49. Wu YW, Tang YH, Tringe SG, Simmons BA, Singer SW (2014) MaxBin: an automated binning method to recover individual genomes from metagenomes using an expectation-maximization algorithm. Microbiome 2:26

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Imelfort M, Parks D, Woodcroft BJ, Dennis P, Hugenholtz P, Tyson GW (2014) GroopM: an automated tool for the recovery of population genomes from related metagenomes. PeerJ 2:e603. https://doi.org/10.7717/peerj.603

    Article  PubMed  PubMed Central  Google Scholar 

  51. Alneberg J, Bjarnason BS, Bruijn I, Schirmer M, Quick J, Ijaz UZ, Lahti L, Loman NJ, Andersson AF, Quince C (2014) Binning metagenomic contigs by coverage and composition. Nat Methods 11:1144–1146. https://doi.org/10.1038/nmeth.3103

    Article  CAS  PubMed  Google Scholar 

  52. Kang DD, Froula J, Egan R, Wang Z (2015) MetaBAT, an efficient tool for accurately reconstructing single genomes from complex microbial communities. PeerJ 3:e1165. https://doi.org/10.7717/peerj.1165

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Albertsen M, Hugenholtz P, Skarshewski A, Nielsen KL, Tyson GW, Nielsen PH (2013) Genome sequences of rare, uncultured bacteria obtained by differential coverage binning of multiple metagenomes. Nat Biotechnol 31:533–538. https://doi.org/10.1038/nbt.2579

    Article  CAS  PubMed  Google Scholar 

  54. Parks DH, Imelfort M, Skennerton CT, Hugenholtz P, Tyson GW (2015) CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Res 25:1043–1055. https://doi.org/10.1101/gr.186072.114

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Meyer F, Fritz A, Deng ZL, Koslicki D, Lesker TR, Gurevich A, Robertson G, Alser M, Antipov D, Beghini F, Bertrand D, Brito JJ, Brown CT, Buchmann J, Buluç A, Chen B, Chikhi R, Clausen P, Cristian A, Dabrowski PW, Darling AE, Egan R, Eskin E, Georganas E, Goltsman E, Gray MA, Hansen LH, Hofmeyr S, Huang P, Irber L, Jia H, Jørgensen TS, Kieser SD, Klemetsen T, Kola A, Kolmogorov M, Korobeynikov A, Kwan J, LaPierre N, Lemaitre C, Li C, Limasset A, Malcher-Miranda F, Mangul S, Marcelino VR, Marchet C, Marijon P, Meleshko D, Mende DR, Milanese A, Nagarajan N, Nissen J, Nurk S, Oliker L, Paoli L, Peterlongo P, Piro VC, Porter JS, Rasmussen S, Rees ER, Reinert K, Renard B, Robertsen EM, Rosen GL, Ruscheweyh HJ, Sarwal V, Segata N, Seiler E, Shi L, Sun F, Sunagawa S, Sørensen SJ, Thomas A, Tong C, Trajkovski M, Tremblay J, Uritskiy G, Vicedomini R, Wang Z, Wang Z, Wang Z, Warren A, Willassen NP, Yelick K, You R, Zeller G, Zhao Z, Zhu S, Zhu J, Garrido-Oter R, Gastmeier P, Hacquard S, Häußler S, Khaledi A, Maechler F, Mesny F, Radutoiu S, Schulze-Lefert P, Smit N, Strowig T, Bremges A, Sczyrba A, McHardy AC (2022) Critical Assessment of Metagenome Interpretation: the second round of challenges. Nat Methods 19(4):429–440. https://doi.org/10.1038/s41592-022-01431-4

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Kunin V, Copeland A, Lapidus A, Mavromatis K, Hugenholtz P (2008) A bioinformatician’s guide to metagenomics. Microbiol Mol Biol Rev 72(4):557–578, Table of Contents. https://doi.org/10.1128/MMBR.00009-08

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Hyatt D, Chen GL, Locascio PF, Land ML, Larimer FW, Hauser LJ (2010) Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinformatics 11:119. https://doi.org/10.1186/1471-2105-11-119

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Galperin MY, Makarova KS, Wolf YI, Koonin EV (2015) Expanded microbial genome coverage and improved protein family annotation in the COG database. Nucleic Acids Res 43(Database issue):D261–D269. https://doi.org/10.1093/nar/gku1223

    Article  CAS  PubMed  Google Scholar 

  59. Mistry J, Chuguransky S, Williams L, Qureshi M, Salazar GA, Sonnhammer ELL, Tosatto SCE, Paladin L, Raj S, Richardson LJ, Finn RD, Bateman A (2021) Pfam: the protein families database in 2021. Nucleic Acids Res 49(D1):D412–d419. https://doi.org/10.1093/nar/gkaa913

    Article  CAS  PubMed  Google Scholar 

  60. Haft DH, Selengut JD, Richter RA, Harkins D, Basu MK, Beck E (2013) TIGRFAMs and genome properties in 2013. Nucleic Acids Res 41(Database issue):D387–D395. https://doi.org/10.1093/nar/gks1234

    Article  CAS  PubMed  Google Scholar 

  61. Bairoch A (2000) The ENZYME database in 2000. Nucleic Acids Res 28:304–305

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Chang A, Jeske L, Ulbrich S, Hofmann J, Koblitz J, Schomburg I, Neumann-Schaal M, Jahn D, Schomburg D (2021) BRENDA, the ELIXIR core data resource in 2021: new developments and updates. Nucleic Acids Res 49(D1):D498–d508. https://doi.org/10.1093/nar/gkaa1025

    Article  CAS  PubMed  Google Scholar 

  63. Kanehisa M, Sato Y, Kawashima M, Furumichi M, Tanabe M (2015) KEGG as a reference resource for gene and protein annotation. Nucleic Acids Res 43:1–6

    Google Scholar 

  64. Caspi R, Altman T, Billington R, Dreher K, Foerster H, Fulcher CA, Holland TA, Keseler IM, Kothari A, Kubo A, Krummenacker M, Latendresse M, Mueller LA, Ong Q, Paley S, Subhraveti P, Weaver DS, Weerasinghe D, Zhang P, Karp PD (2014) The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of Pathway/Genome Databases. Nucleic Acids Res 42(Database issue):D459–D471. https://doi.org/10.1093/nar/gkt1103

    Article  CAS  PubMed  Google Scholar 

  65. Lu S, Wang J, Chitsaz F, Derbyshire MK, Geer RC, Gonzales NR, Gwadz M, Hurwitz DI, Marchler GH, Song JS, Thanki N, Yamashita RA, Yang M, Zhang D, Zheng C, Lanczycki CJ, Marchler-Bauer A (2020) CDD/SPARCLE: the conserved domain database in 2020. Nucleic Acids Res 48(D1):D265–D268. https://doi.org/10.1093/nar/gkz991

    Article  CAS  PubMed  Google Scholar 

  66. Potter SC, Luciani A, Eddy SR, Park Y, Lopez R, Finn RD (2018) HMMER web server: 2018 update. Nucleic Acids Res 46(W1):W200–W204. https://doi.org/10.1093/nar/gky448

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Edgar RC (2010) Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26(19):2460–2461. https://doi.org/10.1093/bioinformatics/btq461

    Article  CAS  PubMed  Google Scholar 

  68. Chen IA, Chu K, Palaniappan K, Ratner A, Huang J, Huntemann M, Hajek P, Ritter S, Varghese N, Seshadri R, Roux S, Woyke T, Eloe-Fadrosh EA, Ivanova NN, Kyrpides NC (2021) The IMG/M data management and analysis system v.6.0: new tools and advanced capabilities. Nucleic Acids Res 49(D1):D751–D763. https://doi.org/10.1093/nar/gkaa939

    Article  CAS  PubMed  Google Scholar 

  69. Drula E, Garron ML, Dogan S, Lombard V, Henrissat B, Terrapon N (2022) The carbohydrate-active enzyme database: functions and literature. Nucleic Acids Res 50(D1):D571–D577. https://doi.org/10.1093/nar/gkab1045

    Article  CAS  PubMed  Google Scholar 

  70. Cantarel BL, Coutinho PM, Rancurel C, Bernard T, Lombard V, Henrissat B (2009) The Carbohydrate-Active EnZymes database (CAZy): an expert resource for glycogenomics. Nucleic Acids Res 37(suppl_1):233–238

    Article  Google Scholar 

  71. Zhang H, Yohe T, Huang L, Entwistle S, Wu P, Yang Z, Busk PK, Xu Y, Yin Y (2018) dbCAN2: a meta server for automated carbohydrate-active enzyme annotation. Nucleic Acids Res 46(W1):W95–W101. https://doi.org/10.1093/nar/gky418

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Park BH, Karpinets TV, Syed MH, Leuze MR, Uberbacher EC (2010) CAZymes Analysis Toolkit (CAT): web service for searching and analyzing carbohydrate-active enzymes in a newly sequenced organism using CAZy database. Glycobiology 20:1574–1584

    Article  CAS  PubMed  Google Scholar 

  73. Marz M, Beerenwinkel N, Drosten C, Fricke M, Frishman D, Hofacker IL, Hoffmann D, Middendorf M, Rattei T, Stadler PF, Töpfer A (2014) Challenges in RNA virus bioinformatics. Bioinformatics 30(13):1793–1799. https://doi.org/10.1093/bioinformatics/btu105

    Article  CAS  PubMed  Google Scholar 

  74. Aramaki T, Blanc-Mathieu R, Endo H, Ohkubo K, Kanehisa M, Goto S, Ogata H (2020) KofamKOALA: KEGG Ortholog assignment based on profile HMM and adaptive score threshold. Bioinformatics 36(7):2251–2252. https://doi.org/10.1093/bioinformatics/btz859

    Article  CAS  PubMed  Google Scholar 

  75. Suzek BE, Wang Y, Huang H, McGarvey PB, Wu CH (2015) UniRef clusters: a comprehensive and scalable alternative for improving sequence similarity searches. Bioinformatics 31(6):926–932. https://doi.org/10.1093/bioinformatics/btu739

    Article  CAS  PubMed  Google Scholar 

  76. Rawlings ND, Barrett AJ, Bateman A (2010) MEROPS: the peptidase database. Nucleic Acids Res 38(Database issue):D227–D233. https://doi.org/10.1093/nar/gkp971

    Article  CAS  PubMed  Google Scholar 

  77. Shaffer M, Borton MA, McGivern BB, Zayed AA, La Rosa SL, Solden LM, Liu P, Narrowe AB, Rodríguez-Ramos J, Bolduc B, Gazitúa MC, Daly RA, Smith GJ, Vik DR, Pope PB, Sullivan MB, Roux S, Wrighton KC (2020) DRAM for distilling microbial metabolism to automate the curation of microbiome function. Nucleic Acids Res 48(16):8883–8900. https://doi.org/10.1093/nar/gkaa621

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  78. Rosewarne CP, Pope PB, Cheung JL, Morrison M (2014) Analysis of the bovine rumen microbiome reveals a diversity of Sus-like polysaccharide utilization loci from the bacterial phylum Bacteroidetes. J Ind Microbiol Biotechnol 41(3):601–606

    Article  CAS  PubMed  Google Scholar 

  79. Zhou Y, Pope PB, Li S, Wen B, Tan F, Cheng S, Chen J, Yang J, Liu F, Lei X, Su Q, Zhou C, Zhao J, Dong X, ** T, Zhou X, Yang S, Zhang G, Yang H, Wang J, Yang R, Eijsink VG, Wang J (2014) Omics-based interpretation of synergism in a soil-derived cellulose-degrading microbial community. Sci Rep 4:5288

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  80. Martens EC, Koropatkin NM, Smith TJ, Gordon JI (2009) Complex glycan catabolism by the human gut microbiota: the bacteroidetes Sus-like paradigm. J Biol Chem 284:24673–24677. https://doi.org/10.1074/jbc.R109.022848

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  81. Hemsworth GR, Henrissat B, Davies GJ, Walton PH (2014) Discovery and characterization of a new family of lytic polysaccharide monooxygenases. Nat Chem Biol 10:122–126

    Article  CAS  PubMed  Google Scholar 

  82. Asnicar F, Thomas AM, Beghini F, Mengoni C, Manara S, Manghi P, Zhu Q, Bolzan M, Cumbo F, May U, Sanders JG, Zolfo M, Kopylova E, Pasolli E, Knight R, Mirarab S, Huttenhower C, Segata N (2020) Precise phylogenetic analysis of microbial isolates and genomes from metagenomes using PhyloPhlAn 3.0. Nat Commun 11(1):2500. https://doi.org/10.1038/s41467-020-16366-7

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  83. Eren AM, Kiefl E, Shaiber A, Veseli I, Miller SE, Schechter MS, Fink I, Pan JN, Yousef M, Fogarty EC, Trigodet F, Watson AR, Esen ÖC, Moore RM, Clayssen Q, Lee MD, Kivenson V, Graham ED, Merrill BD, Karkman A, Blankenberg D, Eppley JM, Sjödin A, Scott JJ, Vázquez-Campos X, McKay LJ, McDaniel EA, Stevens SLR, Anderson RE, Fuessel J, Fernandez-Guerra A, Maignien L, Delmont TO, Willis AD (2021) Community-led, integrated, reproducible multi-omics with anvi’o. Nat Microbiol 6(1):3–6. https://doi.org/10.1038/s41564-020-00834-3

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  84. Nguyen LT, Schmidt HA, von Haeseler A, Minh BQ (2015) IQ-TREE: a fast and effective stochastic algorithm for estimating maximum-likelihood phylogenies. Mol Biol Evol 32(1):268–274. https://doi.org/10.1093/molbev/msu300

    Article  CAS  PubMed  Google Scholar 

  85. Letunic I, Bork P (2021) Interactive Tree Of Life (iTOL) v5: an online tool for phylogenetic tree display and annotation. Nucleic Acids Res 49(W1):W293–W296. https://doi.org/10.1093/nar/gkab301

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  86. Yu G (2020) Using ggtree to visualize data on tree-like structures. Curr Protoc Bioinformatics 69(1):e96. https://doi.org/10.1002/cpbi.96

    Article  PubMed  Google Scholar 

  87. Jonassen KR, Hagen LH, Vick SHW, Arntzen M, Eijsink VGH, Frostegård Å, Lycus P, Molstad L, Pope PB, Bakken LR (2022) Nitrous oxide respiring bacteria in biogas digestates for reduced agricultural emissions. ISME J 16(2):580–590. https://doi.org/10.1038/s41396-021-01101-x

    Article  CAS  PubMed  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Live Heldal Hagen .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature

About this protocol

Check for updates. Verify currency and authenticity via CrossMark

Cite this protocol

Ferrillo, A. et al. (2023). Long-Read Metagenomics and CAZyme Discovery. In: Abbott, D.W., Zandberg, W.F. (eds) Carbohydrate-Protein Interactions. Methods in Molecular Biology, vol 2657. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-3151-5_19

Download citation

  • DOI: https://doi.org/10.1007/978-1-0716-3151-5_19

  • Published:

  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-3150-8

  • Online ISBN: 978-1-0716-3151-5

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics

Navigation