Hostname: page-component-745bb68f8f-g4j75 Total loading time: 0 Render date: 2025-01-22T11:35:52.014Z Has data issue: false hasContentIssue false
  • English
  • Français

Opportunities and challenges in metabarcoding approaches for helminth community identification in wild mammals

Published online by Cambridge University Press:  23 May 2017

TUOMAS AIVELO Open the ORCID record for TUOMAS AIVELO [Opens in a new window]  and
ALAN MEDLAR
TUOMAS AIVELO*
Affiliation:
Department of Evolutionary Biology and Environmental Studies, University of Zürich, Winterthurerstrasse 190, 8057 Zürich, Switzerland
ALAN MEDLAR
Affiliation:
Institute of Biotechnology, University of Helsinki, Viikinkaari 5, PO Box 56, Finland
*
*Corresponding author: Department of Evolutionary Biology and Environmental Studies, University of Zürich, Winterthurerstrasse 190, 8057 Zürich, Switzerland. E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Summary

Despite metabarcoding being widely used to analyse bacterial community composition, its application in parasitological research remains limited. What interest there has been has focused on previously intractable research settings where traditional methods are inappropriate, for example, in longitudinal studies and studies involving endangered species. In settings such as these, non-invasive sampling combined with metabarcoding can provide a fast and accurate assessment of component communities. In this paper we review the use of metabarcoding in the study of helminth communities in wild mammals, outlining the necessary procedures from sample collection to statistical analysis. We highlight the limitations of the metabarcoding approach and speculate on what type of parasitological study would benefit from such methods in the future.

Keywords

mammalsgenetic identificationhigh-throughtput sequencingcestodesnematodescommunity ecology
Type
Special Issue Review
Information
Parasitology , Volume 145 , Special Issue 5: Advances and challenges in barcoding of microbes, parasites, and their vectors and reservoirs , April 2018 , pp. 608 - 621
Check for updates
Copyright
Copyright © Cambridge University Press 2017 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Aivelo, T. (2015). Longitudinal Monitoring of Parasites in Individual Wild Primates. PhD thesis, University of Helsinki, Helsinki, Finland.Google Scholar
Aivelo, T. and Norberg, A. (2016). Parasite-microbiota interactions potentially affect intestinal communities in wild mammals. bioRxiv, New York, NY.Google Scholar
Aivelo, T., Medlar, A., Löytynoja, A., Laakkonen, J. and Jernvall, J. (2015). Tracking year-to-year changes in intestinal nematode communities of rufous mouse lemurs (Microcebus rufus). Parasitology 142, 1095–1107.Google Scholar
Archie, E. A. and Ezenwa, V. O. (2011). Population genetic structure and history of a generalist parasite infecting multiple sympatric host species. International Journal for Parasitology 41, 89–98.CrossRefGoogle ScholarPubMed
Asmundsson, I. M., Mortenson, J. A. and Hoberg, E. P. (2008). Muscleworms, Parelaphostrongylus andersoni (Nematoda: Protostrongylidae), discovered in Columbia white-tailed deer from Oregon and Washington: implications for biogeography and host associations. Journal of Wildlife Diseases 44, 16–17.CrossRefGoogle ScholarPubMed
Astrin, J. J., Zhou, X. and Misof, B. (2013). The importance of biobanking in molecular taxonomy, with proposed definitions for vouchers in a molecular context. ZooKeys 365, 67–70.Google Scholar
Avramenko, R. W., Redman, E. M., Lewis, R., Yazwinski, T. A., Wasmuth, J. D. and Gilleard, J. S. (2015). Exploring the gastrointestinal “nemabiome”: Deep amplicon sequencing to quantify the species composition of parasitic nematode communities. PLoS ONE 10, 1–18.CrossRefGoogle ScholarPubMed
Belden, L. K. and Harris, R. N. (2007). Infectious the diseases in wildlife: ecology context community. Frontiers in Ecology and the Environment 5, 533–539.Google Scholar
Bik, H. M., Porazinska, D. L., Creer, S., Caporaso, J. G., Knight, R. and Thomas, W. K. (2012). Sequencing our way towards understanding global eukaryotic biodiversity. Trends in Ecology & Evolution 27, 233–243.Google Scholar
Blasco-Costa, I., Cutmore, S. C., Miller, T. L. and Nolan, M. J. (2016). Molecular approaches to trematode systematics: “best practice” and implications for future study. Systematic Parasitology 93, 295–306.Google Scholar
Blaxter, M., Mann, J., Chapman, T., Thomas, F., Whitton, C., Floyd, R. and Abebe, E. (2005). Defining operational taxonomic units using DNA barcode data. Philosophical Transactions of the Royal Society B: Biological Sciences 360, 1935–1943.CrossRefGoogle ScholarPubMed
Bordes, F. and Morand, S. (2009). Coevolution between multiple helminth infestations and basal immune investment in mammals: cumulative effects of polyparasitism? Parasitology Research 106, 33–37.Google Scholar
Bordes, F. and Morand, S. (2011). The impact of multiple infections on wild animal hosts: a review. Infection Ecology & Epidemiology 1, 7346.Google Scholar
Bretagne, S., Guillou, J. P., Morand, M. and Houin, R. (1993). Detection of Echinococcus multilocularis DNA in fox faeces using DNA amplification. Parasitology 106, 193–199.Google Scholar
Brooks, J. P., Edwards, D. J., Harwick, M. D. Jr., Rivera, M. C., Fettweis, J. M., Serrano, M. G., Reris, R. A., Sheth, N. U., Huang, B., Girerd, P., Vaginal Microbiome Consortium, Strauss, J. F. III, Jefferson, K. K. and Buck, G. A. (2015). The truth about metagenomics: quantifying and counteracting bias in 16S rRNA studes. BMC Microbiology 15, 66.Google Scholar
Budischak, S. A, Hoberg, E. P., Abrams, A., Jolles, A. E. and Ezenwa, V. O. (2015). A combined parasitological molecular approach for noninvasive characterization of parasitic nematode communities in wild hosts. Molecular Ecology Resources 15, 1112–1119.Google Scholar
Camacho, C., Coulouris, G., Avagyan, V., Ma, N., Papadopoulos, J., Bealer, K. and Madden, T. L. (2009). BLAST+: architecture and applications. BMC Bioinformatics 10, 421.Google Scholar
Caporaso, J. G., Kuczynski, J., Stombaugh, J., Bittinger, K., Bushman, F. D., Costello, E. K., Fierer, N., Gonzalez Peña, A., Goodrich, J. K., Gordon, J. I., Huttley, G. A., Kelley, S. T., Knights, D., Koenig, J. E., Ley, R. E., Lozupone, C. A., McDonald, D., Muegge, B. D., Pirrung, M., Reeder, J., Sevinsky, J. R., Turnbaugh, P. J., Walters, W. A., Widmann, J., Yatsunenko, T., Zaneveld, J. and Knight, R. (2011). QIIME allows analysis of high-throughput community sequencing data. Nature Methods 7, 335–336.Google Scholar
Caron, D. A. (2009). New accomplishments and approaches for assessing protistan diversity and ecology in natural ecosystems. BioScience 59, 287–299.Google Scholar
Casiraghi, M., Labra, M., Ferri, E., Galimberti, A. and De Mattia, F. (2010). DNA barcoding: a six-question tour to improve users’ awareness about the method. Briefings in Bioinformatics 11, 440–453.CrossRefGoogle ScholarPubMed
Clare, E. L., Chain, F. J. J., Littlefair, J. E. and Cristescu, M. E. (2016). The effects of parameter choice on defining molecular operational taxonomic units and resulting ecological analyses of metabarcoding data. Genome 59, 981–990.Google Scholar
Collins, R. A. and Cruickshank, R. H. (2013). The seven deadly sins of DNA barcoding. Molecular Ecology Resources 13, 969–975.Google Scholar
Costello, E. K., Stagaman, K., Dethlefsen, L., Bohannan, B. J. M. and Relman, D. A. (2012). The application of ecological theory toward an understanding of the human microbiome. Science 336, 1255–1263.Google Scholar
Cowart, D. A., Pinheiro, M., Mouchel, O., Maguer, M., Grall, J., Miné, J. and Arnaud-Haond, S. (2015). Metabarcoding is powerful yet still blind: a comparative analysis of morphological and molecular surveys of seagrass communities. PLoS ONE 10, 1–26.CrossRefGoogle Scholar
Creer, S., Fonseca, V. G., Porazinska, D. L., Giblin-Davis, R. M., Sung, W., Power, D. M., Packer, M., Carvalho, G. R., Blaxter, M. L., Lambshead, P. J. D. and Thomas, W. K. (2010). Ultrasequencing of the meiofaunal biosphere: practice, pitfalls and promises. Molecular Ecology 19 (Suppl. 1), 4–20.CrossRefGoogle ScholarPubMed
Dawkins, H. J. S. and Spencer, T. L. (1989). The isolation of nucleic acid from nematodes requires an understanding of the parasite and its cuticular structure. Parasitology Today 5, 73–76.Google Scholar
de la Cuesta-Zuluaga, J. & Escobar, J. (2016). Considerations for optimizing microbiome analysis using a marker gene. Frontiers in Nutrition 3, 26.CrossRefGoogle ScholarPubMed
De Schryver, P. and Vadstein, O. (2014). Ecological theory as a foundation to control pathogenic invasion in aquaculture. ISME Journal 8, 2360–2368.CrossRefGoogle ScholarPubMed
Deagle, B. E., Kirkwood, R. and Jarman, S. N. (2009). Analysis of Australian fur seal diet by pyrosequencing prey DNA in faeces. Molecular Ecology 18, 2022–2038.Google Scholar
Deagle, B. E., Thomas, A. C., Shaffer, A. K., Trites, A. W. and Jarman, S. N. (2013). Quantifying sequence proportions in a DNA-based diet study using Ion Torrent amplicon sequencing: which counts count? Molecular Ecology Resources 13, 620–633.Google Scholar
Deagle, B. E., Jarman, S. N., Coissac, E., Pompanon, F. and Taberlet, P. (2014). DNA metabarcoding and the cytochrome c oxidase subunit I marke: not a perfect match. Biology Letters 10, 20140562.Google Scholar
Derycke, S., Vanaverbeke, J., Rigaux, A., Backeljau, T. and Moens, T. (2010). Exploring the use of cytochrome oxidase c subunit 1 (COI) for DNA barcoding of free-living marine nematodes. PLoS ONE 5, e13716.Google Scholar
DeSalle, R., Egan, M. G. and Siddall, M. (2005). The unholy trinity: taxonomy, species delimitation and DNA barcoding. Philosophical Transactions of the Royal Society of London B: Biological sciences 360, 1905–1916.Google Scholar
Dethlefsen, L., McFall-Ngai, M. and Relman, D. A. (2007). An ecological and evolutionary perspective on human-microbe mutualism and disease. Nature 449, 811–818.Google Scholar
Ebach, M. C. and Holdrege, C. (2005). More taxonomy, not DNA barcoding. BioScience 55, 822–823.CrossRefGoogle Scholar
Edgar, R. C. (2010). Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26, 2460–2461.Google Scholar
Ferri, E., Barbuto, M., Bain, O., Galimberti, A., Uni, S., Guerrero, R., Ferté, H., Bandi, C., Martin, C. and Casiraghi, M. (2009). Integrated taxonomy: traditional approach and DNA barcoding for the identification of filarioid worms and related parasites (Nematoda). Frontiers in Zoology 6, 1.Google Scholar
Ficetola, G. F., Pansu, J., Bonin, A., Coissac, E., Giguet-Covex, C., De Barba, M., Gielly, L., Lopes, C. M., Boyer, F., Pompanon, F., Rayé, G. and Taberlet, P. (2015). Replication levels, false presences and the estimation of the presence/absence from eDNA metabarcoding data. Molecular Ecology Resources 15, 543–556.Google Scholar
Floyd, R., Abebe, E., Papert, A. and Blaxter, M. (2002). Molecular barcodes for soil nematode identification. Molecular Ecology 11, 839–850.Google Scholar
Fouhy, F., Clooney, A. G., Stanton, C., Claesson, M. J. and Cotter, P. D. (2016). 16S rRNA gene sequencing of mock microbial populations- impact of DNA extraction method, primer choice and sequencing platform. BMC Microbiology 16, 123.Google Scholar
Frézal, L. and Leblois, R. (2008). Four years of DNA barcoding: current advances and prospects. Infection, Genetics and Evolution 8, 727–736.Google Scholar
Furlan, E. M., Gleeson, D., Hardy, C. M. and Duncan, R. P. (2016). A framework for estimating the sensitivity of eDNA surveys. Molecular Ecology Resources 16, 641–654.Google Scholar
Galimberti, A., Romano, D. F., Genchi, M., Paoloni, D., Vercillo, F., Bizzarri, L., Sassera, D., Bandi, C., Genchi, C., Ragni, B. and Casiraghi, M. (2012). Integrative taxonomy at work: DNA barcoding of taeniids harboured by wild and domestic cats. Molecular Ecology Resources 12, 403–413.Google Scholar
Gasser, R. B. (2006). Molecular tools – advances, opportunities and prospects. Veterinary Parasitology 136, 69–89.CrossRefGoogle ScholarPubMed
Gasser, R. B., Chilton, N. B., Hoste, H. and Beveridge, I. (1993). Rapid sequencing of rDNA from single worms and eggs of parasitic helminths. Nucleic Acids Research 21, 2525–2526.CrossRefGoogle ScholarPubMed
Gasser, R. B., Bott, N. J., Chilton, N. B., Hunt, P. and Beveridge, I. (2008). Toward practical, DNA-based diagnostic methods for parasitic nematodes of livestock--bionomic and biotechnological implications. Biotechnology Advances 26, 325–334.Google Scholar
Gaugler, R., Bednarek, A. and Campbell, J. F. (1992). Ultraviolet inactivation of heterorhabditid and steinernematid nematodes. Journal of Invertebrate Pathology 59, 155–160.Google Scholar
Gillespie, T. R. (2006). Noninvasive assessment of gastrointestinal parasite infections in free-ranging primates. International Journal of Primatology 27, 1129–1143.Google Scholar
Goldstein, P. Z. and DeSalle, R. (2011). Integrating DNA barcode data and taxonomic practice: determination, discovery, and description. BioEssays 33, 135–147.Google Scholar
Haas, B. J., Gevers, D., Earl, A. M., Feldgarden, M., Ward, D. V., Giannoukos, G., Ciulla, D., Tabbaa, D., Highlander, S. K., Sodergren, E., Methé, B., DeSantis, T. Z., Petrosino, J. F., Knight, R. and Birren, B. W. (2011). Chimeric 16S rRNA sequence formation and detection in Sanger and 454-pyrosequenced PCR amplicons. Genome Research 21, 494–504.Google Scholar
Harmon, A. F., Zarlenga, D. S. and Hildreth, M. B. (2006). Improved methods for isolating DNA from Ostertagia ostertagi eggs in cattle feces. Veterinary Parasitology 135, 297–302.CrossRefGoogle ScholarPubMed
Hebert, P. D. N., Cywinska, A., Ball, S. L. and DeWaard, J. R. (2003 a). Biological identifications through DNA barcodes. Proceedings. Biological sciences/Royal Society 270, 313–321.Google Scholar
Hebert, P. D. N., Ratnasingham, S. and DeWaard, J. R. (2003 b). Barcoding animal life: cytochrome c oxidase subunit 1 divergences among closely related species. Proceedings of the Royal Society B: Biological Sciences 270, S96–S99.Google Scholar
Hu, M., Jex, A. R., Campbell, B. E. and Gasser, R. B. (2007). Long PCR amplification of the entire mitochondrial genome from individual helminths for direct sequencing. Nature Protocols 2, 2339–2344.Google Scholar
Hunter, M. E., Dorazio, R. M., Butterfield, J. S. S., Meigs-Friend, G., Nico, L. G. and Ferrante, J. A. (2016). Detection limits of quantitative and digital PCR assays and their influence in presence-absence surveys of environmental DNA. Molecular Ecology Resources 17, 221–229.Google Scholar
Huson, D. H., Auch, A. F., Qi, J., Huson, D. H., Auch, A. F., Qi, J. and Schuster, S. C. (2007). MEGAN analysis of metagenomic data. Genome Research 17, 377–386.Google Scholar
Janzen, D. H., Hallwachs, W., Blandin, P., Burns, J. M., Cadiou, J. M., Chacon, I., Dapkey, T., Deans, A. R., Epstein, M. E., Espinoza, B., Franclemont, J. G., Haber, W. A., Hajibabaei, M., Hall, J. P. W., Hebert, P. D. N., Gauld, I. D., Harvey, D. J., Hausmann, A., Kitching, I. J., Lafontaine, D., Landry, J. F., Lemaire, C., Miller, J. Y., Miller, J. S., Miller, L., Miller, S. E., Montero, J., Munroe, E., Green, S. R., Ratnasingham, S. et al. (2009). Integration of DNA barcoding into an ongoing inventory of complex tropical biodiversity. Molecular Ecology Resources 9, 1–26.Google Scholar
Jarman, S. N., Deagle, B. E. and Gales, N. J. (2004). Group-specific polymerase chain reaction for DNA-based analysis of species diversity and identity in dietary samples. Molecular Ecology 13, 1313–1322.Google Scholar
Jenkins, E. J., Appleyard, G. D., Hoberg, E. P., Rosenthal, B. M., Kutz, S. J., Veitch, A. M., Schwantje, H. M., Elkin, B. T. and Polley, L. (2005). Geographic distribution of the muscle-dwelling nematode Parelaphostrongylus odocoilei in North America, using molecular identification of first-stage larvae. Journal of Parasitology 91, 574–584.Google Scholar
Jorge, F., Carretero, M. A., Roca, V., Poulin, R. and Perera, A. (2013). What you get is what they have? Detectability of intestinal parasites in reptiles using faeces. Parasitology Research 112, 4001–4007.CrossRefGoogle ScholarPubMed
King, R. A., Read, D. S., Traugott, M. and Symondson, W. O. C. (2008). Molecular analysis of predation: a review of best practice for DNA-based approaches. Molecular Ecology 17, 947–963.Google Scholar
Krauth, S. J., Coulibaly, J. T., Knopp, S., Traoré, M., N'Goran, E. K. and Utzinger, J. (2012). An in-depth analysis of a piece of shit: distribution of Schistosoma mansoni and hookworm eggs in human stool. PLoS Neglected Tropical Diseases 6, e1969.Google Scholar
Kutz, S. J., Checkley, S., Verocai, G. G., Dumond, M., Hoberg, E. P., Peacock, R., Wu, J. P., Orsel, K., Seegers, K., Warren, A. L. and Abrams, A. (2013). Invasion, establishment, and range expansion of two parasitic nematodes in the Canadian Arctic. Global Change Biology 19, 3254–3262.Google Scholar
Lee, M. S. Y. (2004). The molecularisation of taxonomy. Invertebrate Systematics 18, 1–6.Google Scholar
Leung, T. L. F., Donald, K. M., Keeney, D. B., Koehler, A. V., Peoples, R. C. and Poulin, R. (2009). Trematode parasites of Otago Harbour (New Zealand) soft-sediment intertidal ecosystems: life cycles, ecological roles and DNA barcodes. New Zealand Journal of Marine and Freshwater Research 43, 857–865.CrossRefGoogle Scholar
Locke, S. A., McLaughlin, J. D., Lapierre, A. R., Johnson, P. T. J. and Marcogliese, D. J. (2011). Linking larvae and adults of Apharyngostrigea cornu, Hysteromorpha triloba, and Alaria mustelae (Diplostomoidea: Digenea) using molecular data. Journal of Parasitology 97, 846–851.Google Scholar
Lott, M. J., Eldridge, M. D. B, Hose, G. C. and Power, M. L. (2012). Nematode community structure in the brush-tailed rock-wallaby, Petrogale penicillata: Implications of captive breeding and the translocation of wildlife. Experimental Parasitology 132, 185–192.Google Scholar
Lott, M. J., Hose, G. C. and Power, M. L. (2015). Parasitic nematode communities of the red kangaroo, Macropus rufus: richness and structuring in captive systems. Parasitology Research 114, 2925–2932.Google Scholar
Martin, L. K. and Beaver, P. C. (1968). Evaluation of Kato thick-smear technique for quantitative diagnosis of helminth infections. American Journal of Tropical Medicine and Hygiene 17, 382–391.Google Scholar
Mathis, A. and Deplazes, P. (2006). Copro-DNA tests for diagnosis of animal taeniid cestodes. Parasitology International 55, S87–S90.Google Scholar
Matsen, F. A., Kodner, R. B. and Armbrust, E. V. (2010). pplacer: linear time maximum-likelihood and Bayesian phylogenetic placement of sequences onto a fixed reference tree pplacer: linear time maximum-likelihood and Bayesian phy- logenetic placement of sequences onto a fixed reference tree. BMC Bioinformatics 11, 538.Google Scholar
Medlar, A., Aivelo, T. and Löytynoja, A. (2014). Séance: reference-based phylogenetic analysis for 18S rRNA studies. BMC Evolutionary Biology 14, 235.Google Scholar
Mes, T. H. M. (2003). Technical variability and required sample size of helminth egg isolation procedures. Veterinary Parasitology 115, 311–320.Google Scholar
Meyer, C. P. and Paulay, G. (2005). DNA barcoding: error rates based on comprehensive sampling. PLoS Biology 3, e422.Google Scholar
Mills, D. K., Entry, J. A., Voss, J. D., Gillevet, P. M. and Mathee, K. (2006). An assessment of the hypervariable domains of the 16S rRNA genes for their value in determining microbial community diversity: the paradox of traditional ecological indices. FEMS Microbiology Ecology 57, 496–503.Google Scholar
Mitchell, A. (2011). DNA barcoding is useful for taxonomy: a reply to Ebach. Zootaxa 2772, 67–68.Google Scholar
Moszczynska, A., Locke, S. A., McLaughlin, J. D., Marcogliese, D. J. and Crease, T. J. (2009). Development of primers for the mitochondrial cytochrome c oxidase I gene in digenetic trematodes (Platyhelminthes) illustrates the challenge of barcoding parasitic helminths. Molecular Ecology Resources 9, 75–82.Google Scholar
Ogedengbe, J. D., Hanner, R. H. and Barta, J. R. (2011). DNA barcoding identifies Eimeria species and contributes to the phylogenetics of coccidian parasites (Eimeriorina, Apicomplexa, Alveolata). International Journal for Parasitology 41, 843–850.Google Scholar
Porazinska, D. L., Giblin-Davis, R. M., Faller, L., Farmerie, W., Kanzaki, N., Morris, K., Powers, T. O., Tucker, A. E., Sung, W. and Thomas, W. K. (2009). Evaluating high-throughput sequencing as a method for metagenomic analysis of nematode diversity. Molecular Ecology Resources 9, 1439–1450.Google Scholar
Poulin, R. (2010). Decay of similarity with host phylogenetic distance in parasite faunas. Parasitology 137, 733–741.Google Scholar
Poulin, R. and Morand, S. (2000). The diversity of parasites. Quarterly Review of Biology 75, 277–293.Google Scholar
Powers, T. (2004). Nematode molecular diagnostics: from bands to barcodes. Annual Review of Phytopathology 42, 367–383.CrossRefGoogle ScholarPubMed
Powers, T., Harris, T., Higgins, R., Mullin, P., Sutton, L. and Powers, K. (2011). MOTUs, morphology, and biodiversity estimation: a case study using nematodes of the suborder Criconematina and a conserved 18S DNA barcode. Journal of Nematology 43, 35–48.Google Scholar
Prichard, R. and Tait, A. (2001). The role of molecular biology in veterinary parasitology. Veterinary Parasitology 98, 169–194.CrossRefGoogle ScholarPubMed
Prosser, S. W. J., Velarde-Aguilar, M. G., León-Règagnon, V. and Hebert, P. D. N. (2013). Advancing nematode barcoding: a primer cocktail for the cytochrome c oxidase subunit I gene from vertebrate parasitic nematodes. Molecular Ecology Resources 13, 1108–1115.Google Scholar
Quail, M., Smith, M. E., Coupland, P., Otto, T. D., Harris, S. R., Connor, T. R., Bertoni, A., Swerdlow, H. P., Gu, Y., Rothberg, J., Hinz, W., Rearick, T., Schultz, J., Mileski, W., Davey, M., Leamon, J., Johnson, K., Milgrew, M., Edwards, M., Eid, J., Fehr, A., Gray, J., Luong, K., Lyle, J., Otto, G., Peluso, P., Rank, D., Baybayan, P., Bettman, B., Bentley, D. et al. (2012). A tale of three next generation sequencing platforms: comparison of Ion torrent, pacific biosciences and illumina MiSeq sequencers. BMC Genomics 13, 341.Google Scholar
Quince, C., Lanzen, A., Davenport, R. J. and Turnbaugh, P. J. (2011). Removing noise from pyrosequenced amplicons. BMC Bioinformatics 12, 38.Google Scholar
Robinson, C. J., Bohannan, B. J. M. and Young, V. B. (2010). From structure to function: the ecology of host-associated microbial communities. Microbiology and Molecular Biology Reviews 74, 453–476.Google Scholar
Routtu, J., Grunberg, D., Izhar, R., Dagan, Y., Guttel, Y., Ucko, M. and Ben-Ami, F. (2014). Selective and universal primers for trematode barcoding in freshwater snails. Parasitology Research 113, 2535–2540.Google Scholar
Santos, A. M. C., Besnard, G. and Quicke, D. L. J. (2011). Applying DNA barcoding for the study of geographical variation in host-parasitoid interactions. Molecular Ecology Resources 11, 46–59.Google Scholar
Schloss, P. D., Westcott, S. L., Ryabin, T., Hall, J. R., Hartmann, M., Hollister, E. B., Lesniewski, R. A., Oakley, B. B., Parks, D. H., Robinson, C. J., Sahl, J. W., Stres, B., Thallinger, G. G., Van Horn, D. J. and Weber, C. F. (2009). Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities. Applied and Environmental Microbiology 75, 7537–7541.Google Scholar
Schloss, P. D., Gevers, D. and Westcott, S. L. (2011). Reducing the effects of PCR amplification and sequencing Artifacts on 16s rRNA-based studies. PLoS ONE 6, e27310.Google Scholar
Shokralla, S., Spall, J. L., Gibson, J. F. and Hajibabaei, M. (2012). Next-generation sequencing technologies for environmental DNA research. Molecular Ecology 21, 1794–1805.Google Scholar
Siddall, M. E., Kvist, S., Phillips, A. and Oceguera-Figueroa, A. (2012). DNA barcoding of parasitic nematodes: is it kosher? Journal of Parasitology 98, 692–694.Google Scholar
Silva, N. R. R., Da Silva, M. C., Genevois, V. F., Esteves, A. M., Ley, P., De Decraemer, W., Rieger, T. T. and Correia, M. T. D. S. (2010). Marine nematode taxonomy in the age of DNA: the present and future of molecular tools to assess their biodiversity. Nematology 12, 661–672.Google Scholar
Smith, D. P. and Peay, K. G. (2014). Sequence depth, not PCR replication, improves ecological inference from next generation DNA sequencing. PLoS ONE 9, e90234.Google Scholar
Srivathsan, A., Sha, J. C. M., Vogler, A. P. and Meier, R. (2015). Comparing the effectiveness of metagenomics and metabarcoding for diet analysis of a leaf-feeding monkey (Pygathrix nemaeus). Molecular Ecology Resources 15, 250–261.Google Scholar
Srivathsan, A., Ang, A., Vogler, A. P. and Meier, R. (2016). Fecal metagenomics for the simultaneous assessment of diet, parasites, and population genetics of an understudied primate. Frontiers in Zoology 13, 17.CrossRefGoogle ScholarPubMed
Stear, M. J., Bishop, S. C., Duncan, J. L., Mckellar, Q. A. and Murray, M. (1995). The repeatability of faecal egg counts, peripheral eosinophil counts, and plasma pepsinogen concentrations during deliberate infection with Ostertagia circmumcincta. International Journal for Parasitology 25, 375–380.Google Scholar
Stear, M. J., Abuagob, O., Benothman, M., Bishop, S. C., Innocent, G., Kerr, A. and Mitchell, S. (2006). Variation among faecal egg counts following natural nematode infection in Scottish Blackface lambs. Parasitology 132, 275–280.Google Scholar
Symondson, W. O. C. (2002). Molecular identification of prey in predator diets. Molecular Ecology 11, 627–641.Google Scholar
Tanaka, R., Hino, A., Tsai, I. J., Palomares-Rius, J. E., Yoshida, A., Ogura, Y., Hayashi, T., Maruyama, H. and Kikuchi, T. (2014). Assessment of helminth biodiversity in wild rats using 18S rDNA based metagenomics. PLoS ONE 9, e110769.Google Scholar
Tang, C. Q., Leasi, F., Obertegger, U., Kieneke, A., Barraclough, T. G. and Fontaneto, D. (2012). The widely used small subunit 18S rDNA molecule greatly underestimates true diversity in biodiversity surveys of the meiofauna. Proceedings of the National Academy of Sciences of the United States of America 109, 16208–16212.CrossRefGoogle ScholarPubMed
Taylor, H. R. and Harris, W. E. (2012). An emergent science on the brink of irrelevance: a review of the past 8 years of DNA barcoding. Molecular Ecology Resources 12, 377–388.Google Scholar
Thomas, A. C., Deagle, B. E., Eveson, J. P., Harsch, C. H. and Trites, A. W. (2016). Quantitative DNA metabarcoding: improved estimates of species proportional biomass using correction factors derived from control material. Molecular Ecology Resources 16, 714–726.Google Scholar
Trosvik, P. and de Muinck, E. J. (2015). Ecology of bacteria in the human gastrointestinal tract--identification of keystone and foundation taxa. Microbiome 3, 44.CrossRefGoogle ScholarPubMed
Valentini, A., Pompanon, F. and Taberlet, P. (2009). DNA barcoding for ecologists. Trends in Ecology & Evolution 24, 110–117.Google Scholar
Vanhove, M. P. M., Tessens, B., Schoelinck, C., Jondelius, U., Littlewood, D. T. J., Artois, T. and Huyse, T. (2013). Problematic barcoding in flatworms: a case-study on monogeneans and rhabdocoels (Platyhelminthes). ZooKeys 365, 355–379.Google Scholar
Van Steenkiste, N., Locke, S. A., Castelin, M., Marcogliese, D. J. and Abbott, C. L. (2015). New primers for DNA barcoding of digeneans and cestodes (Platyhelminthes). Molecular Ecology Resources 15, 945–952.Google Scholar
Viney, M. E. and Graham, A. L. (2013). Patterns and processes in parasite co-infection. Advances in Parasitology 82, 321–369.Google Scholar
Wang, Q., Garrity, G. M., Tiedje, J. M. and Cole, J. R. (2007). Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Applied and Environmental Microbiology 73, 5261–5267.Google Scholar
Warton, D. I., Blanchet, F. G., O'Hara, R. B., Ovaskainen, O., Taskinen, S., Walker, S. C. and Hui, F. K. C. (2015). So many variables: joint modeling in community ecology. Trends in Ecology & Evolution 30, 766–779.Google Scholar
Wilson, J. J., Rougerie, R., Schonfeld, J., Janzen, D. H., Hallwachs, W., Hajibabaei, M., Kitching, I. J., Haxaire, J. and Hebert, P. D. N. (2011). When species matches are unavailable are DNA barcodes correctly assigned to higher taxa? An assessment using sphingid moths. BMC Ecology 11, 18.Google Scholar
Woodstock, L., Cook, J. A., Peters, P. A. and Warren, K. S. (1971). Random distribution of schistosome eggs in the feces of patients with Schistosomiasis mansoni. Journal of Infectious Diseases 124, 613–614.Google Scholar
Xu, B., Xu, W., Yang, F., Li, J., Yang, Y., Tang, X., Mu, Y., Zhou, J. and Huang, Z. (2013). Metagenomic analysis of the pygmy loris fecal microbiome reveals unique functional capacity related to metabolism of aromatic compounds. PLoS ONE 8, e56565.Google Scholar
Ye, X. P., Donnelly, C. A., Anderson, R. M., Fu, Y. L. and Agnew, A. (1998). The distribution of Schistosoma japonicum eggs in faeces and the effect of stirring faecal specimens. Annals of Tropical Medicine and Parasitology 92, 181–185.Google Scholar
Yu, J. M., de Vlas, S. J., Yuan, H. C., Gryseels, B. (1998). Variations in fecal Schistosoma japonicum egg counts. American Journal of Tropical Medicine and Hygiene 59, 370–375.Google Scholar
Zhou, X., Li, Y., Liu, S., Yang, Q., Su, X., Zhou, L., Tang, M., Fu, R., Li, J. and Huang, Q. (2013). Ultra-deep sequencing enables high-fidelity recovery of biodiversity for bulk arthropod samples without PCR amplification. Giga Science 2, 4.Google Scholar