Testing the advantages and disadvantages of short- and long- read eukaryotic metagenomics using simulated reads
Open accessPublished version of a preprint
Abstract
Background: The first step in understanding ecological community diversity and dynamics is quantifying community membership. An increasingly common method for doing so is through metagenomics. Because of the rapidly increasing popularity of this approach, a large number of computational tools and pipelines are available for analysing metagenomic data. However, the majority of these tools have been designed and benchmarked using highly accurate short read data (i.e. Illumina), with few studies benchmarking classification accuracy for long error-prone reads (PacBio or Oxford Nanopore). In addition, few tools have been benchmarked for non-microbial communities. Results: Here we compare simulated long reads from Oxford Nanopore and Pacific Biosciences (PacBio) with high accuracy Illumina read sets to systematically investigate the effects of sequence length and taxon type on classification accuracy for metagenomic data from both microbial and non-microbial communities. We show that very generally, classification accuracy is far lower for non-microbial communities, even at low taxonomic resolution (e.g. family rather than genus). We then show that for two popular taxonomic classifiers, long reads can significantly increase classification accuracy, and this is most pronounced for non-microbial communities. Conclusions: This work provides insight on the expected accuracy for metagenomic analyses for different taxonomic groups, and establishes the point at which read length becomes more important than error rate for assigning the correct taxon.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Long-read technology provides more read-length but at the cost of higher error rates.
“By contrast, long‐read technology provides more read‐length but at the cost of higher error rates.82”
From The oral microbiome: Role of key organisms and complex networks in oral health and disease (2021) · cites this paper as [82]
Short-read technology provides abundant sequencing data that is less error prone than long-read technology; however, it can be difficult to assemble complete genomes using short read-lengths due to limitations in distinguishing repetitive elements.
“short‐read technology provides abundant sequencing data that is less error prone than long‐read technology; however, it can be difficult to assemble complete genomes using short read‐lengths due to limitations in the technology to distinguish repetitive elements.”
From The oral microbiome: Role of key organisms and complex networks in oral health and disease (2021) · cites this paper as [82]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
QIIME allows analysis of high-throughput community sequencing data
Caporaso, J. G., Kuczynski, J., Stombaugh, J., Bittinger, K., Bushman, F. D., Costello, E. K., Fierer, N., Peña, A. G., Goodrich, J. K., Gordon, J. I., et al. (2010) · Nature Methods · doi:10.1038/nmeth.f.303 · Cited by 34,887 · see citing works
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., et al. (2009) · Applied and Environmental Microbiology · doi:10.1128/aem.01541-09 · Cited by 20,966 · see citing works
Fast and sensitive protein alignment using DIAMOND
Buchfink, B., Xie, C., & Huson, D. H. (2015) · Nature Methods · doi:10.1038/nmeth.3176 · Cited by 14,201 · see citing works
Stackebrandt, E., & Goebel, B. M. (1994) · INTERNATIONAL JOURNAL OF SYSTEMATIC AND EVOLUTIONARY MICROBIOLOGY · doi:10.1099/00207713-44-4-846 · Cited by 7,156 · see citing works
O'Leary, N. A., Wright, M. W., Brister, J. R., Ciufo, S., Haddad, D., McVeigh, R., Rajput, B., Robbertse, B., Smith-White, B., Ako-Adjei, D., et al. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkv1189 · Cited by 6,357 · see citing works
Kraken: ultrafast metagenomic sequence classification using exact alignments
Wood, D. E., & Salzberg, S. L. (2014) · Genome Biology · doi:10.1186/gb-2014-15-3-r46 · Cited by 4,521 · see citing works
Benson, D. A., Cavanaugh, M., Clark, K., Karsch-Mizrachi, I., Lipman, D. J., Ostell, J., & Sayers, E. W. (2012) · Nucleic Acids Research · doi:10.1093/nar/gks1195 · Cited by 3,261 · see citing works
Performance of neural network basecalling tools for Oxford Nanopore sequencing
Wick, R. R., Judd, L. M., & Holt, K. E. (2019) · Genome Biology · doi:10.1186/s13059-019-1727-y · Cited by 3,221 · see citing works
Bracken: estimating species abundance in metagenomics data
(2016) · doi:10.7717/peerj-cs.104 · Cited by 2,333 · see citing works
Genomic insights that advance the species definition for prokaryotes
Konstantinidis, K. T., & Tiedje, J. M. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0409727102 · Cited by 2,124 · see citing works
Fast and sensitive taxonomic classification for metagenomics with Kaiju
Menzel, P., Ng, K. L., & Krogh, A. (2016) · Nature Communications · doi:10.1038/ncomms11257 · Cited by 2,101 · see citing works
Huson, D. H., Beier, S., Flade, I., Górska, A., El-Hadidi, M., Mitra, S., Ruscheweyh, H.-J., & Tappu, R. (2016) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1004957 · Cited by 1,761 · see citing works
Centrifuge: rapid and sensitive classification of metagenomic sequences
Kim, D., Song, L., Breitwieser, F. P., & Salzberg, S. L. (2016) · Genome Research · doi:10.1101/gr.210641.116 · Cited by 1,438 · see citing works
Federhen, S. (2012) · Nucleic Acids Research · doi:10.1093/nar/gkr1178 · Cited by 1,436 · see citing works
Keeling, P. J., Burki, F., Wilcox, H. M., Allam, B., Allen, E. E., Amaral-Zettler, L. A., Armbrust, E. V., Archibald, J. M., Bharti, A. K., Bell, C. J., et al. (2014) · PLoS Biology · doi:10.1371/journal.pbio.1001889 · Cited by 1,042 · see citing works
Metagenomics - a guide from sampling to data analysis
Thomas, T., Gilbert, J. A., & Meyer, F. (2012) · Microbial Informatics and Experimentation · doi:10.1186/2042-5783-2-3 · Cited by 1,037 · see citing works
Song, H., Buhay, J. E., Whiting, M. F., & Crandall, K. A. (2008) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0803076105 · Cited by 1,035 · see citing works
Earth BioGenome Project: Sequencing life for the future of life
Lewin, H. A., Robinson, G. E., Kress, W. J., Baker, W. J., Coddington, J., Crandall, K. A., Durbin, R., Edwards, S. V., Forest, F., Gilbert, M. T. P., et al. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1720115115 · Cited by 962 · see citing works
Ardui, S., Ameur, A., Vermeesch, J. R., & Hestand, M. S. (2018) · Nucleic Acids Research · doi:10.1093/nar/gky066 · Cited by 741 · see citing works
Estimating the total number of phosphoproteins and phosphorylation sites in eukaryotic proteomes
Vlastaridis, P., Kyriakidou, P., Chaliotis, A., de Peer, Y. V., Oliver, S. G., & Amoutzias, G. D. (2017) · GigaScience · doi:10.1093/gigascience/giw015 · Cited by 732 · see citing works
Reliable, verifiable and efficient monitoring of biodiversity via metabarcoding
Ji, Y., Ashton, L., Pedley, S. M., Edwards, D. P., Tang, Y., Nakamura, A., Kitching, R., Dolman, P. M., Woodcock, P., Edwards, F. A., et al. (2013) · Ecology Letters · doi:10.1111/ele.12162 · Cited by 662 · see citing works
Ounit, R., Wanamaker, S. I., Close, T. J., & Lonardi, S. (2015) · BMC Genomics · doi:10.1186/s12864-015-1419-2 · Cited by 624 · see citing works
Accurate phylogenetic classification of variable-length DNA fragments
Mchardy, A., Martín, H., Tsirigos, A., Hugenholtz, P., & Rigoutsos, I. (2007) · Nature Methods · doi:10.1038/nmeth976 · Cited by 552 · see citing works
Genome 10K: A Proposal to Obtain Whole-Genome Sequence for 10 000 Vertebrate Species
Scientists, G. 1. C. O. (2009) · Journal of Heredity · doi:10.1093/jhered/esp086 · Cited by 539 · see citing works
The BLAST Sequence Analysis Tool
Madden, T. (2013) · Cited by 535 · see citing works
Tristan, L., Douady, C. J., Gouy, M., & Gibert, J. (2006) · Molecular Phylogenetics and Evolution · doi:10.1016/j.ympev.2006.03.014 · Cited by 484 · see citing works
Metagenomics for studying unculturable microorganisms: cutting the Gordian knot.
Schloss, P. D., & Handelsman, J. (2005) · Genome biology · doi:10.1186/gb-2005-6-8-229 · Cited by 483 · see citing works
Genome sequencing of the sweetpotato whitefly Bemisia tabaci MED/Q
Xie, W., Chen, C., Yang, Z., Guo, L., Yang, X., Wang, D., Chen, M., Huang, J., Wen, Y., Zeng, Y., et al. (2017) · GigaScience · doi:10.1093/gigascience/gix018 · Cited by 473 · see citing works
The Road to Metagenomics: From Microbiology to DNA Sequencing Technologies and Bioinformatics
Escobar‐Zepeda, A., de León, A. V., & Sánchez‐Flores, A. (2015) · Frontiers in Genetics · doi:10.3389/fgene.2015.00348 · Cited by 363 · see citing works
Comprehensive benchmarking and ensemble approaches for metagenomic classifiers
McIntyre, A. B. R., Ounit, R., Afshinnekoo, E., Prill, R. J., Hénaff, E., Alexander, N., Minot, S. S., Danko, D., Foox, J., Ahsanuddin, S., et al. (2017) · Genome Biology · doi:10.1186/s13059-017-1299-7 · Cited by 342 · see citing works
Madissoon, E., Jouhilahti, E., Vesterlund, L., Töhönen, V., Krjutškov, K., Sophie Petropoulos, A., Einarsdóttir, E., Linnarsson, S., Lanner, F., Månsson, R., et al. (2016) · Scientific Reports · doi:10.1038/srep32053 · Cited by 341 · see citing works
An evaluation of the accuracy and speed of metagenome analysis tools
Lindgreen, S., Adair, K. L., & Gardner, P. P. (2016) · Scientific Reports · doi:10.1038/srep19233 · Cited by 324 · see citing works
Metagenomics: Read Length Matters
Wommack, K. E., Bhavsar, J., & Ravel, J. (2008) · Applied and Environmental Microbiology · doi:10.1128/aem.02181-07 · Cited by 317 · see citing works
Ultra-deep, long-read nanopore sequencing of mock microbial community standards
Nicholls, S. M., Quick, J., Tang, S., & Loman, N. J. (2019) · GigaScience · doi:10.1093/gigascience/giz043 · Cited by 305 · see citing works
NanoSim: nanopore sequence read simulator based on statistical characterization
Yang, C., Chu, J., Warren, R. L., & Birol, İ. (2017) · GigaScience · doi:10.1093/gigascience/gix010 · Cited by 248 · see citing works
Teeling, E. C., Vernes, S. C., Dávalos, L. M., Ray, D. A., Gilbert, M. T. P., & Myers, E. W. (2017) · Annual Review of Animal Biosciences · doi:10.1146/annurev-animal-022516-022811 · Cited by 233 · see citing works
Creating a Buzz About Insect Genomes
Robinson, G. E., Hackett, K. J., Purcell-Miramontes, M., Brown, S. J., Evans, J. D., Goldsmith, M. R., Lawson, D., Okamuro, J. K., Robertson, H. M., & Schneider, D. J. (2011) · Science · doi:10.1126/science.331.6023.1386 · Cited by 208 · see citing works
A Review of bioinformatics tools for bio-prospecting from metagenomic sequence data
Roumpeka, D. D., Wallace, R. J., Escalettes, F., Fotheringham, I., & Watson, M. (2017) · Frontiers in Genetics · doi:10.3389/fgene.2017.00023 · Cited by 199 · see citing works
Genesis, effects and fates of repeats in prokaryotic genomes
Treangen, T. J., Abraham, A., Touchon, M., & Rocha, E. P. C. (2009) · FEMS Microbiology Reviews · doi:10.1111/j.1574-6976.2009.00169.x · Cited by 172 · see citing works
Nasko, D. J., Koren, S., Phillippy, A. M., & Treangen, T. J. (2018) · Genome Biology · doi:10.1186/s13059-018-1554-6 · Cited by 170 · see citing works
Standard Percent DNA Sequence Difference for Insects Does Not Predict Species Boundaries
Cognato, A. I. (2006) · Journal of Economic Entomology · doi:10.1603/0022-0493-99.4.1037 · Cited by 168 · see citing works
Frank, J. A., Pan, Y., Tooming‐Klunderud, A., Eijsink, V. G. H., McHardy, A. C., Nederbragt, A. J., & Pope, P. B. (2016) · Scientific Reports · doi:10.1038/srep25373 · Cited by 148 · see citing works
Standard Percent DNA Sequence Difference for Insects Does Not Predict Species Boundaries
Cognato, A. I. (2006) · Journal of Economic Entomology · doi:10.1093/jee/99.4.1037 · Cited by 143 · see citing works
MinION™ nanopore sequencing of environmental metagenomes: a synthetic approach
Brown, B. L., Watson, M., Minot, S. S., Rivera, M. C., & Franklin, R. B. (2017) · GigaScience · doi:10.1093/gigascience/gix007 · Cited by 140 · see citing works
Bracken: Estimating species abundance in metagenomics dataPreprint
Lu, J., Breitwieser, F. P., Thielen, P., et al. (2016) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/051813 · Cited by 129 · see citing works
Metagenomics: microbial diversity through a scratched lens
Temperton, B., & Giovannoni, S. J. (2012) · Current Opinion in Microbiology · doi:10.1016/j.mib.2012.07.001 · Cited by 126 · see citing works
Systematics and the Origin of Species from the Viewpoint of a Zoologist
Mayr, & Fisher, M. C. (1999) · Cited by 124 · see citing works
Invertebrate communities in holdfasts of the kelp Macrocystis pyrifera from southern Chile
Ojeda, F. P., & Santelices, B. (1984) · Marine Ecology Progress Series · doi:10.3354/meps016065 · Cited by 112 · see citing works
Goßner, M. M., Lade, P., Rohland, A., Sichardt, N., Kahl, T., Bauhus, J., Weisser, W. W., & Petermann, J. S. (2015) · Journal of Animal Ecology · doi:10.1111/1365-2656.12437 · Cited by 47 · see citing works
Comparison Of Oxford Nanopore Basecalling Tools
Wick, R. R., Judd, L. M., & Holt, K. E. (2017) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.1043611 · Cited by 24 · see citing works
O’Brien, S. J., Haussler, D. H., & Ryder, O. A. (2014) · GigaScience · doi:10.1186/2047-217x-3-32 · Cited by 15 · see citing works
EnSVMB: Metagenomics Fragments Classification using Ensemble SVM and BLAST
Jiang, Y., Wang, J., Xia, D., & Yu, G. (2017) · Scientific Reports · doi:10.1038/s41598-017-09947-y · Cited by 11 · see citing works
Flagged first, then by citations
Research works printed but not linked: 1
- [16]Pearman W, Smith ANH, Breckell G, Dale J, Freed NE, Silander OK. New tools for diet analyses: nanopore sequencing of metagenomic DNA from stomach contents to quantify diet in an invasive population of rats. bioRxiv. 2018:363622 [Cited 2018 Aug 8]. Available from: https://www.biorxiv.org/content/early/2018/07/06/363622.
Cited by
Sorted by citations
The oral microbiome: Role of key organisms and complex networks in oral health and disease
Sedghi, L., DiMassa, V., Harrington, A., Lynch, S. V., & Kapila, Y. L. (2021) · Periodontology 2000 · doi:10.1111/prd.12393 · Cited by 755 · see citing works
The Skin Microbiome: Current Landscape and Future Opportunities
Smythe, P., & Wilkinson, H. N. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms24043950 · Cited by 207 · see citing works
Portik, D. M., Brown, C. T., & Pierce‐Ward, N. T. (2022) · BMC Bioinformatics · doi:10.1186/s12859-022-05103-0 · Cited by 138 · see citing works
Rizal, N. S. M., Neoh, H., Ramli, R., Periyasamy, P. R., Hanafiah, A., Samat, M. N. A., Tan, T. L., Wong, K. K., Nathan, S., Chieng, S., et al. (2020) · Diagnostics · doi:10.3390/diagnostics10100816 · Cited by 134 · see citing works
Metagenomic profiling pipelines improve taxonomic classification for 16S amplicon sequencing data
Odom-Mabey, A. R., Faits, T., Castro‐Nallar, E., Crandall, K. A., & Johnson, W. E. (2023) · Scientific Reports · doi:10.1038/s41598-023-40799-x · Cited by 92 · see citing works
Application and Challenge of 3rd Generation Sequencing for Clinical Bacterial Studies
Khedher, M. B., Ghedira, K., Rolain, J., Ruimy, R., & Croce, O. (2022) · International Journal of Molecular Sciences · doi:10.3390/ijms23031395 · Cited by 76 · see citing works
Marquet, M., Zöllkau, J., Pastuschek, J., Viehweger, A., Schleußner, E., Makarewicz, O., Pletz, M. W., Ehricht, R., & Brandt, C. (2022) · Scientific Reports · doi:10.1038/s41598-022-08003-8 · Cited by 71 · see citing works
Metagenomics and artificial intelligence in the context of human health
Wani, A. K., Roy, P., Gahlawat, V. K., & Mir, T. U. G. (2022) · Infection Genetics and Evolution · doi:10.1016/j.meegid.2022.105267 · Cited by 72 · see citing works
Doherty, A., Wall, A. M., Khaldi, N., & Kussmann, M. (2021) · Frontiers in Genetics · doi:10.3389/fgene.2021.768979 · Cited by 67 · see citing works
Evaluating metagenomics and targeted approaches for diagnosis and surveillance of viruses
Buddle, S., Forrest, L., Akinsuyi, N., Bernal, L. M. M., Brooks, T., Venturini, C., Miller, C. M., Brown, J. R., Storey, N., Atkinson, L., et al. (2024) · Genome Medicine · doi:10.1186/s13073-024-01380-x · Cited by 67 · see citing works
“Several studies have previously compared Illumina and ONT-based metagenomics of bacterial and fungal mock communities [29, 30, 32], simulated bacterial datasets [32, 33, 38] and clinical samples [18, 28, 108, 109].”
BackgroundReddington, K., Eccles, D., O’Grady, J., Drown, D. M., Hansen, L. H., Nielsen, T. K., Ducluzeau, A., Leggett, R. M., Heavens, D., Peel, N., et al. (2020) · GigaScience · doi:10.1093/gigascience/giaa053 · Cited by 62 · see citing works
Eke, M., Tougeron, K., Hamidović, A., Tinkeu, L. S. N., Hance, T., & Renoz, F. (2023) · Animal Microbiome · doi:10.1186/s42523-023-00261-9 · Cited by 65 · see citing works
“Illumina sequencing has the advantage of being relatively affordable but generates short reads (approximately 400 bp) that provide limited resolution for identifying microbial taxa and conducting phylogenetic approaches to infer evolutionary relationships and predict functional profiles [76, 77].”
BackgroundRapid absolute quantification of pathogens and ARGs by nanopore sequencing
Yu, Y., Che, Y., Liu, L., Wang, C., Yin, X., Deng, Y., Yang, C., & Zhang, T. (2021) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2021.152190 · Cited by 56 · see citing works
Smart, U., Churchill, J. D., & Budowle, B. (2021) · Forensic Science International Genetics · doi:10.1016/j.fsigen.2021.102551 · Cited by 54 · see citing works
Cheng, H., Sun, Y., Yang, Q. Y., Deng, M., Yu, Z., Zhu, G., Qu, J., Liu, L., Yang, L., & Xia, Y. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac453 · Cited by 52 · see citing works
Comparative analysis of metagenomic classifiers for long-read sequencing datasets
Marić, J., Križanović, K., Riondet, S., Nagarajan, N., & Šikić, M. (2024) · BMC Bioinformatics · doi:10.1186/s12859-024-05634-8 · Cited by 49 · see citing works
“Although there are several benchmarking papers on long reads [7–10], our evaluation additionally includes HiFi PacBio reads, and standard long reads mappers and incorporates an assessment of the influence of the database, read lengths and definition of abundance measures on the results in simulated…”
BackgroundGovender, K. N., & Eyre, D. W. (2022) · Microbial Genomics · doi:10.1099/mgen.0.000886 · Cited by 43 · see citing works
“This finding is supported by previous work [38].”
ResultComparative evaluation of soil DNA extraction kits for long read metagenomic sequencing
Child, H. T., Wierzbicki, L., Joslin, G. R., & Tennant, R. (2024) · Access Microbiology · doi:10.1099/acmi.0.000868.v3 · Cited by 39 · see citing works
Wu, H.-Y., & Shaw, P. (2022) · Chinese Medicine · doi:10.1186/s13020-022-00590-y · Cited by 35 · see citing works
“inversions) [45] Semiquantitative estimation possible (for genome skimming without PCR) [21]”
BackgroundInfluentialBrandt, C., Bongcam‐Rudloff, E., & Müller, B. (2020) · Applied Sciences · doi:10.3390/app10217518 · Cited by 34 · see citing works
Fernández-Triana, I., Rubilar, O., Parada, J., Fincheira, P., Benavides‐Mendoza, A., Durán, P., Fernández‐Baldo, M. A., Seabra, A. B., & Tortella, G. (2024) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2024.173494 · Cited by 32 · see citing works
Xu, R., Rajeev, S., & Salvador, L. C. M. (2023) · PLoS ONE · doi:10.1371/journal.pone.0284031 · Cited by 30 · see citing works
“Software with large DBs can report species taxa that are not present in the sample due to failure of searching for the best match in a larger search space, shorter read lengths, or due to sequencing errors in both the query and the sequences contained in the DBs [72, 73].”
BackgroundMiller, M., Rota, C. T., & Welsh, A. B. (2024) · Parasites & Vectors · doi:10.1186/s13071-024-06388-1 · Cited by 29 · see citing works
“A recent comparison of sequencing platforms showed that while the long reads produced by PacBio may have a higher accuracy in assigning taxonomy, Illumina short reads provide higher read depth per sample [69].”
BackgroundUffelen, A. V., Posadas, A., Roosens, N. H. C., Kathleen Marchal, D., Keersmaecker, S. C. J. D., & Vanneste, K. (2024) · Scientific Data · doi:10.1038/s41597-024-03672-8 · Cited by 29 · see citing works
“Other benchmarking studies primarily focused on the nuances of the sequencing technology over the evaluation of classifiers, limiting both the quantity and depth of classifier assessments 48–51 .”
BackgroundApplication of Next-Generation Sequencing (NGS) Techniques for Selected Companion Animals
Domrazek, K., & Jurka, P. (2024) · Animals · doi:10.3390/ani14111578 · Cited by 27 · see citing works
“Lower throughput and higher per-base costs may be prohibitive for large-scale projects requiring deep sequencing coverage [12] Oxford Nanopore Sequencing: Offers real-time sequencing, long read lengths, and portability, making it suitable for field applications, rapid diagnostics, and real-time…”
BackgroundPeng, Y., Li, H., Liu, Z., Zhang, C., Li, K., Gong, Y., Geng, L., Su, J., Guan, X., Liu, L., et al. (2021) · Molecular Ecology Resources · doi:10.1111/1755-0998.13397 · Cited by 27 · see citing works
“The difference in divergence time may be caused by different generation technologies (Alkanaq et al., 2019; Birney, 2011; Paajanen et al., 2019; Pearman et al., 2020).”
BackgroundBee foraging preferences, microbiota and pathogens revealed by direct shotgun metagenomics of honey
Galanis, A., Vardakas, P., Reczko, M., Harokopos, V., Hatzis, P., Skoulakis, E. M. C., Pavlopoulos, G. A., & Patalano, S. (2022) · Molecular Ecology Resources · doi:10.1111/1755-0998.13626 · Cited by 28 · see citing works
Deep Metagenomic Sequencing for Endophthalmitis Pathogen Detection Using a Nanopore Platform
Low, L., Nakamichi, K., Akileswaran, L., Lee, C. S., Lee, A., Moussa, G., Murray, P. I., Wallace, G. R., Gelder, R. N. V., & Rauz, S. (2022) · American Journal of Ophthalmology · doi:10.1016/j.ajo.2022.05.022 · Cited by 26 · see citing works
Novel methods of microbiome analysis in the food industry
Sabater, C., Cobo-Díaz, J. F., Álvarez‐Ordóñez, A., Ruas‐Madiedo, P., Ruíz, L., & Margollés, A. (2021) · International Microbiology · doi:10.1007/s10123-021-00215-8 · Cited by 25 · see citing works
MuDoGeR : Multi‐Domain Genome recovery from metagenomes made easy
da Rocha, U. N., Kasmanas, J. C., Kallies, R., Saraiva, J., Toscan, R. B., Štefanič, P., Bicalho, M. F., Corrêa, F. B., Baştürk, M. N., Fousekis, E., et al. (2023) · Molecular Ecology Resources · doi:10.1111/1755-0998.13904 · Cited by 23 · see citing works
Yan, C., Zhang, N., Wang, Q., Fu, Y., Zhao, H., Wang, J., Wu, G., Wang, F., Li, X., & Liao, H. (2022) · BMC Plant Biology · doi:10.1186/s12870-022-03461-8 · Cited by 22 · see citing works
“Therefore, hybrid sequencing approaches combining SGS and TGS technologies can provide high quality and more complete gene assembly in genome and transcriptome sequencing, which has been well illustrated in the previous studies [30, 31].”
MethodMeasuring air metagenomic diversity in an agricultural ecosystem
Giolai, M., Verweij, W., MARTIN, S. J., Pearson, N., Nicholson, P., Leggett, R. M., & Clark, M. D. (2024) · Current Biology · doi:10.1016/j.cub.2024.07.030 · Cited by 30 · see citing works
Saraiva, J., Bartholomäus, A., Toscan, R. B., Baldrián, P., & da Rocha, U. N. (2023) · Molecular Ecology Resources · doi:10.1111/1755-0998.13776 · Cited by 21 · see citing works
Inferring Species Compositions of Complex Fungal Communities from Long- and Short-Read Sequence Data
Hu, Y., Irinyi, L., Hoang, M. T. V., Eenjes, T., Graetz, A., Stone, E. A., Meyer, W., Schwessinger, B., & Rathjen, J. P. (2022) · mBio · doi:10.1128/mbio.02444-21 · Cited by 20 · see citing works
Luo, W., He, Y., Xu, J., Zhang, S., Li, C., Lv, J., Shen, Y., Ou, Z., & Dong, H. (2023) · Discovery Medicine · doi:10.24976/discov.med.202335176.34 · Cited by 20 · see citing works
Garrido‐Sanz, L., Senar, M. À., & Piñol, J. (2022) · PLoS ONE · doi:10.1371/journal.pone.0275790 · Cited by 19 · see citing works
“The metagenomic classifiers are not able to deal with all the genomic peculiarities of every species on Earth [33, 37].”
BackgroundMethodInfluentialChanges in Mitochondrial Epigenome in Type 2 Diabetes Mellitus
Low, H. C., Chilian, W. M., Ratnam, W., Karupaiah, T., Noh, M. F. M., Mansor, F., Ng, Z. X., & Pung, Y. F. (2023) · British Journal of Biomedical Science · doi:10.3389/bjbs.2023.10884 · Cited by 19 · see citing works
“Also, it has a lower throughput and accuracy than shortread Next-Generation sequencing (87).”
BackgroundTaylor, W. S., Pearson, J. F., Miller, A. L., Schmeier, S., Frizelle, F., & Purcell, R. V. (2020) · PLoS ONE · doi:10.1371/journal.pone.0233170 · Cited by 19 · see citing works
Garrido‐Sanz, L., Senar, M. À., & Piñol, J. (2021) · Molecular Ecology Resources · doi:10.1111/1755-0998.13464 · Cited by 18 · see citing works
Malicka, M., Magurno, F., & Piotrowska‐Seget, Z. (2022) · International Journal of Molecular Sciences · doi:10.3390/ijms232012585 · Cited by 18 · see citing works
“Given the difficulties of finding suitable LSU primers specific for Glomeromycota and amplifying a portion with a size compatible with the Illumina requirements, we tested the possibility of using a newly designed set of LSU primers which were not Glomeromycota-specific.”
MethodInfluentialRing, N., Low, A. S., Wee, B. A., Paterson, G. K., Nuttall, T. J., Gally, D. L., Mellanby, R. J., & Fitzgerald, J. R. (2023) · Microbial Genomics · doi:10.1099/mgen.0.001066 · Cited by 18 · see citing works
Silveira, G. O., Coelho, H. S., Amaral, M. S., & Verjovski‐Almeida, S. (2021) · Parasitology Research · doi:10.1007/s00436-021-07384-5 · Cited by 15 · see citing works
Metagenomics approaches to understanding soil health in environmental research - a review
Duque-Zapata, J. D., Flórez, J. E. M., & López, D. (2023) · Soil Science Annual · doi:10.37501/soilsa/163080 · Cited by 15 · see citing works
Li, S., Rao, C., Zang, X., Yang, Y., Yang, W., Huang, X., Li, J., Sun, J., Liu, Y., & Ye, D. (2025) · Food Research International · doi:10.1016/j.foodres.2025.116676 · Cited by 15 · see citing works
The importance of precision medicine in modern molecular oncology
Wang, Y., & Zheng, D. (2021) · Clinical Genetics · doi:10.1111/cge.13998 · Cited by 15 · see citing works
“However, both PacBio and Nanopore technologies have far higher error rates (88%– 94% accuracy for Nanopore and 85%–87% for PacBio).(91,92)”
BackgroundZaytsev, P., Rodin, V. A., Zaytseva, A., Zvereva, M. I., & Solovchenko, A. E. (2024) · Journal of Applied Phycology · doi:10.1007/s10811-024-03267-1 · Cited by 14 · see citing works
“Comparison of simulation results for different platforms showed that 50% exceed of sequencing launch capacity for Illumina over Oxford Nanopore can provide maximum accuracy of read classification and taxa identification (Pearman et al. 2020).”
Chen, B., Ding, Z., Zhou, X., Wang, Y., Huang, F., Sun, J., Chen, J., & Han, W. (2022) · Frontiers in Genetics · doi:10.3389/fgene.2022.932832 · Cited by 13 · see citing works
Estimation of the impact of three different bioinformatic pipelines on sheep nemabiome analysis
Baltrušis, P., Halvarsson, P., & Höglund, J. (2022) · Parasites & Vectors · doi:10.1186/s13071-022-05399-0 · Cited by 13 · see citing works
“communities [32, 33] and has also more recently been demonstrated to perform on par with the Illumina platform in terms of correctly classifying sequences belonging to different taxa from multiple organism kingdoms [34].”
BackgroundLima, C. O. D. C., de Castro, G. M., Solar, R., Vaz, A. B. M., Lobo, F. P., Pereira, G. V. D. M., Rodrigues, C., Vandenberghe, L. P. D. S., Pinto, L. R. M., da Costa, A. M., et al. (2022) · Frontiers in Microbiology · doi:10.3389/fmicb.2022.994524 · Cited by 11 · see citing works
“Nonetheless, the aforementioned advantages are still impaired by a high error rate if compared to short-read sequencing and; therefore, the lower accuracy of long-read sequencing affects the success rate of current classification methods, as well as there are few algorithms specifically designed to exploit long-read d…”
BackgroundDecadt, H., Díaz-Muñoz, C., Vermote, L., Pradal, I., Vuyst, L. D., & Weckx, S. (2025) · Frontiers in Microbiology · doi:10.3389/fmicb.2025.1543079 · Cited by 11 · see citing works
Metabarcoding assessment of fungal diversity in brown algae and sponges of Mauritius
Chin, J. M. W., Puchooa, D., Bahorun, T., Neergheen, V. S., Aullybux, A. A., Beedessee, G., Nazurally, N., Alrefaei, A. F., & Jeewon, R. (2022) · Frontiers in Microbiology · doi:10.3389/fmicb.2022.1003790 · Cited by 11 · see citing works
Gong, M., Liu, H., Liu, Z., Wang, Y., Qi, S., Guo, H. W., & Jin, S. L. (2025) · Frontiers in Endocrinology · doi:10.3389/fendo.2025.1439341 · Cited by 10 · see citing works
The National Ecological Observatory Network’s soil metagenomes: assembly and basic analysis
Werbin, Z. R., Hackos, B., Lopez-Nava, J., Dietze, M. C., & Bhatnagar, J. M. (2021) · F1000Research · doi:10.12688/f1000research.51494.1 · Cited by 10 · see citing works
Gut microbiome profiling in systemic sclerosis: a metagenomic approach
Tan, T. C., Chandrasekaran, L. K., Leung, Y. Y., Purbojati, R. W., Pettersson, S., & Low, A. H. L. (2022) · Clinical and Experimental Rheumatology · doi:10.55563/clinexprheumatol/jof7nx · Cited by 10 · see citing works
Metaviromic analyses of DNA virus community from sediments of the N-Choe stream, North India
Mukhopadhyay, A., Choudhury, S., & Kumar, M. (2023) · Virus Research · doi:10.1016/j.virusres.2023.199110 · Cited by 9 · see citing works
“Presently, metagenomic/metaviromic studies are utilizing longread-based, even error-prone nanopore sequencing owing to its capability of increasing classification accuracy (Ji et al., 2020; Pearman et al., 2020; Reddington et al., 2020; Warwick-Dugdale et al., 2019).”
BackgroundMarine Mycobiomes Colonize Mediterranean Sponge Hosts in a Random Fashion
Mazzella, V., Zahn, G. L., Dell’Anno, A., & Núñez‐Pons, L. (2025) · Microbial Ecology · doi:10.1007/s00248-025-02523-2 · Cited by 8 · see citing works
“The unculturable fungal biosphere has been more recently investigated since the emergence of omics techniques, allowing the scientific community to examine the diversity and composition of marine mycobiomes [31].”
Rabapane, K. J., Ijoma, G. N., & Matambo, T. S. (2022) · Frontiers in Genetics · doi:10.3389/fgene.2022.946449 · Cited by 8 · see citing works
“, Furthermore, as a direct result of most bioinformatics techniques’ inability to completely piece together without redundancy the various contigs produced by the platform, low-quality full-length reference sequences are submitted to most databases, affecting even taxonomic representations (Schloss et al., 2016; Pearm…”
BackgroundConventional tobacco products harbor unique and heterogenous microbiomes
Chattopadhyay, S., Ramachandran, P., Malayil, L., Mongodin, E. F., & Sapkota, A. R. (2022) · Environmental Research · doi:10.1016/j.envres.2022.115205 · Cited by 8 · see citing works
Serghiou, I. R., Baker, D., Evans, R., Dalby, M. J., Kiu, R., Trampari, E., Phillips, S., Watt, R., Atkinson, T., Murphy, B., et al. (2023) · Microbial Genomics · doi:10.1099/mgen.0.001058 · Cited by 8 · see citing works
“This method is typically performed using the Illumina sequencing technology, which results in short reads for taxonomic classification to genus level (Pearman, et al., 2020).”
BackgroundMethodBludau, D., Sieber, G., Shah, M., Deep, A., Boenigk, J., & Beißer, D. (2025) · Environmental DNA · doi:10.1002/edn3.70084 · Cited by 9 · see citing works
Green, S. J., Torok, T., Allen, J., Eloe‐Fadrosh, E. A., Jackson, S. A., Jiang, S. C., Levine, S. S., Levy, S., Schriml, L. M., Thomas, W. K., et al. (2023) · Astrobiology · doi:10.1089/ast.2022.0044 · Cited by 7 · see citing works
Jokhakar, P. H., Godhaniya, M., Vaghamshi, N., Patel, R. K., Ghelani, A., & Dudhagara, P. R. (2022) · Ecological Genetics and Genomics · doi:10.1016/j.egg.2022.100144 · Cited by 7 · see citing works
Comprehensive benchmarking of metagenomic classification tools for long-read sequencing dataPreprint
Marić, J., Križanović, K., Riondet, S., Nagarajan, N., & Šikić, M. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.11.25.397729 · Cited by 7 · see citing works
“ONT1 (18) 1 2162(15) 135 (14) 2365 (15) 905 (15) 51 (15) 596 (15) 547 (15) 428 (15) 592 (15) 127 (15) 5 375 (14) 135 (14) 502 (14) 223 (15) 20 (15) 78 (15) 160 (15) 132 (14) 154 (15) 54 (15) 50 33 (14) 42 (14) 42 (14) 26 (15) 3 (14) 10 (15) 18 (14) 21 (14) 21 (15) 13 (15) ONT2 (8) 1 2033 (6) 87 (6) 2318 (6) 596 (6) 62…”
Marquet, M., Zöllkau, J., Pastuschek, J., Viehweger, A., Schleußner, E., Makarewicz, O., Pletz, M. W., Ehricht, R., & Brandt, C. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.09.15.460450 · Cited by 7 · see citing works
Berg, M., Reiter, T. E., Emerson, J., Brown, C. T., & Roux, S. (2025) · NAR Genomics and Bioinformatics · doi:10.1093/nargab/lqaf163 · Cited by 6 · see citing works
“In eukaryotes, a comparison of simulated SR and LR showed that LR improved taxon classification accuracy [ 5 ].”
High-resolution microbiome analysis of host-rich samples using 2bRAD-M without host depletion
Jiang, Y., Liu, J., Zhang, Y., Zhou, L., Kao, E., Hou, S., Niu, Q., Liu, Y., Xu, Z. Z., Ding, T., et al. (2025) · npj Biofilms and Microbiomes · doi:10.1038/s41522-025-00851-2 · Cited by 6 · see citing works
Woo, C., Kumari, P., Eo, K.-Y., Lee, W.-S., Kimura, J., & Yamamoto, N. (2023) · PLoS ONE · doi:10.1371/journal.pone.0281245 · Cited by 6 · see citing works
Using nanopore sequencing to identify bacterial infection in joint replacements: a preliminary study
Wilkinson, H., McDonald, J., McCarthy, H. S., Perry, J., Wright, K. T., Hulme, C. H., & Cool, P. (2024) · Briefings in Functional Genomics · doi:10.1093/bfgp/elae008 · Cited by 6 · see citing works
“High variation from this value suggests that the sample may be contaminated and is not suitable for genomic sequencing [22].”
BackgroundMethodInfluentialFoster, A. J., Mills, A. A. S., & McCallum, J. (2024) · Canadian Journal of Plant Pathology · doi:10.1080/07060661.2024.2386299 · Cited by 6 · see citing works
“While the Oxford Nanopore sequencer exhibits a high error rate for individual bases, it has been demonstrated to provide reliable results in fungal metagenomic analyses (Pearman et al. 2020).”
MuDoGeR: Multi-Domain Genome Recovery from metagenomes made easyPreprint
da Rocha, U. N., Kasmanas, J. C., Kallies, R., Saraiva, J., Toscan, R. B., Štefanič, P., Bicalho, M. F., Corrêa, F. B., Baştürk, M. N., Fousekis, E., et al. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.06.21.496983 · Cited by 6 · see citing works
“genomes using short reads (Pearman et al., 2020), a limited number of reference genomes (Pawlowski et al.”
BackgroundImproved Equine Fecal Microbiome Characterization Using Target Enrichment by Hybridization Capture
Álvarez‐Narváez, S., Beaudry, M. S., Norris, C. G., Bartlett, P., Glenn, T. C., & Sánchez, S. (2024) · Animals · doi:10.3390/ani14030445 · Cited by 5 · see citing works
Automated environmental metagenomics using Oxford nanopore sequencing
Child, H. T., Wierzbicki, L., Joslin, G. R., Roper, K., Haxhiraj, Q., & Tennant, R. (2025) · BMC Genomics · doi:10.1186/s12864-025-11989-w · Cited by 4 · see citing works
Examining correlation between ocular microbiome and ocular diseases including keratitis
Shivaji, S. (2023) · Expert Review of Ophthalmology · doi:10.1080/17469899.2023.2284313 · Cited by 4 · see citing works
“Nanopore technology generates long reads which are useful for metagenomic classification due to their increased length and the classification accuracy exceeds that of Illumina which generates short reads [112].”
Kumar, A., Robertsen, E. M., Willassen, N. P., Fu, J., & Hjerde, E. (2023) · Genomics & Informatics · doi:10.5808/gi.23072 · Cited by 4 · see citing works
Hu, J., Blazier, J. C., Gitter, A., Gregory, L. F., & Gentry, T. J. (2025) · Applied and Environmental Microbiology · doi:10.1128/aem.01723-25 · Cited by 4 · see citing works
“Although Kraken2 was originally developed for short-read data, recent studies have demonstrated its effectiveness in classifying long-read sequences (57, 58).”
Roy, N. S., Basnet, P., Ramekar, R. V., Um, T., Yu, J., Park, K.-C., & Choi, I. (2022) · Agronomy · doi:10.3390/agronomy12071685 · Cited by 4 · see citing works
Freed, N. E., Pearman, W. S., Smith, A. N. H., Breckell, G., Dale, J., & Silander, O. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/363622 · Cited by 4 · see citing works
Self-citationUnlocking River Biofilm Microbial Diversity: A Comparative Analysis of Sequencing Technologies
Anderson, M. A. J., Thorpe, A. C., Busi, S. B., Gweon, H. S., Warren, J., Walsh, K. A., & Read, D. S. (2025) · Molecular Ecology Resources · doi:10.1111/1755-0998.70075 · Cited by 3 · see citing works
Leo, D. D., Nilsson, E., Krinos, A. I., Pinhassi, J., & Lundin, D. (2025) · PeerJ · doi:10.7717/peerj.20328 · Cited by 3 · see citing works
Ruiz, A. R., & Dam, A. R. V. (2023) · PeerJ · doi:10.7717/peerj.16276 · Cited by 3 · see citing works
Splicing the Difference: Harnessing the Complexity of the Transcriptome in Hematopoiesis
Maul-Newby, H. M., & Halene, S. (2024) · Experimental Hematology · doi:10.1016/j.exphem.2024.104655 · Cited by 3 · see citing works
Xiao, S., Ma, Y., Hao, J., Ye, G., Zhang, D., Dong, J., Zhao, M., Yang, N., & Wang, X. (2025) · BMC Plant Biology · doi:10.1186/s12870-025-06662-z · Cited by 3 · see citing works
“Hybrid sequencing approaches combining SGS and TGS are more popular as it can provide more accurate and complete gene assembly in genome and transcriptome studies [25, 26].”
Bourreau, M., Singh, P., Armbrecht, L., Utge, J., Bowler, C., Sabourdy, M., Escutia, C., Müller, J., Bohoyo, F., Crosta, X., et al. (2025) · Marine Micropaleontology · doi:10.1016/j.marmicro.2025.102498 · Cited by 3 · see citing works
Oberc, C. A., & Li, P. C. H. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e29104 · Cited by 3 · see citing works
Microbiome Analysis Using Next-Generation Sequencing in Urinary Tract Infections
Han, H. H., & Lee, J. Y. (2022) · Urogenital Tract Infection · doi:10.14777/uti.2022.17.1.1 · Cited by 3 · see citing works
Eenjes, T., Hu, Y., Irinyi, L., Hoang, M. T. V., Smith, L. M. A., Linde, C. C., Milgate, A., Meyer, W., Stone, E. A., Rathjen, J. P., et al. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.05.01.442223 · Cited by 3 · see citing works
“This is consistent with previous work with long-read MinION 278 nanopore data, where Kraken2 classification success never exceeded that for BLAST, another 279 alignment-based classification program, when using the default 35 bp k-mers [38].”
ResultSaraiva, J., Bartholomäus, A., Toscan, R. B., Baldrián, P., & da Rocha, U. N. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.04.07.487146 · Cited by 3 · see citing works
Fauzia, K. A., Rathnayake, J., Doohan, D., Lamawansa, M. D., Alfaray, R. I., Saruuljavkhlan, B., Phuc, B. H., Waskito, L. A., Tuan, V. P., Tshibangu‐Kabamba, E., et al. (2025) · Microorganisms · doi:10.3390/microorganisms13020420 · Cited by 2 · see citing works
Govindharaj, G., Annamalai, M., Choudhary, J. S., Khan, R. M., Gowda, G. B., Patil, N. B., Panda, R. M., Srivastava, K., & Mohapatra, S. D. (2025) · Scientific Reports · doi:10.1038/s41598-025-93048-8 · Cited by 2 · see citing works
“However, the increased length of reads through PacBio and Nanopore are more useful for metagenomic classification despite being more error-prone than Illumina reads 36 .”
Nanopore sequencing of DNA barcodes to unveil the diversity of fungal mock communities
Pedroso-Roussado, C., Guppy, F. M., Bowler, L. D., & Inácio, J. (2023) · Open Research Europe · doi:10.12688/openreseurope.15485.1 · Cited by 2 · see citing works
Nanopore sequencing of DNA barcodes succeeds in unveilling the diversity of fungal mock communities
Pedroso-Roussado, C., Guppy, F. M., Bowler, L. D., & Inácio, J. (2023) · Open Research Europe · doi:10.12688/openreseurope.15485.2 · Cited by 1 · see citing works
Methodological aspects of investigating the resistome in pig farm environments
Ladyhina, V., Rajala, E. L., Lewerin, S. S., Nasirzadeh, L., Bongcam‐Rudloff, E., & Dicksved, J. (2025) · Journal of Microbiological Methods · doi:10.1016/j.mimet.2025.107103 · Cited by 1 · see citing works
“…for characterizing complex microbial communities (Stevens et al., 2023), however significant improvement in the quality of long read sequencing with ONT has lead to increase in the accuracy of classification and relative estimates and might be preferred for taxonomy studies (Pearman et al., 2020).”
Hunter, E. S., Fo, S., Literman, R., Uva, R. H., Wolny, J. L., & Handy, S. M. (2025) · Applications in Plant Sciences · doi:10.1002/aps3.70013 · Cited by 1 · see citing works
Nanopore sequencing of metagenomic DNA from rat stomach contents to infer diet
Freed, N. E., Smith, A. N. H., Breckell, G. L., Dale, J., & Silander, O. (2023) · New Zealand Journal of Ecology · doi:10.20417/nzjecol.47.3554 · Cited by 1 · see citing works
“However, there was wide variation in read length, with almost 10% of all reads being longer than 1200 bp. Longer reads, although more error prone, are often more accurate in determining taxonomy than shorter reads (Pearman et al. 2020).”
BackgroundSelf-citationCirculating Nucleic Acids in Colorectal Cancer: Diagnostic and Prognostic Value
Igder, S., Zamani, M., Fakher, S., Siri, M., Ashktorab, H., Azarpira, N., & Mokarram, P. (2024) · Disease Markers · doi:10.1155/2024/9943412 · Cited by 1 · see citing works
“…a a cc u r a c y o f S B S [ 76 ] N o l o n g l e n g t h s o f D N A s e q u e n ce s c a n b e ob t a i n e d u s i n g t h e s e m e t h o d s [ 77 ] B D A cc u r i C 6 ® T h e i n t e rr o g a t i o n o f h a r v e s t e d b e a d s b y fl u o r e s ce n t p r ob e s t h a t s p ec i fi c a ll…”
Kogoj, R., Bosilj, M., Šturm, A. C., Korva, M., Smrdel, K. S., Kvas, E., Pirš, M., Lepen, L., & Triglav, T. (2025) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2025.104841 · Cited by 1 · see citing works
Proaño-Cuenca, F., Carrera-Lopez, D., Zeller, K. A., Espíndola, A. S., & Cardwell, K. F. (2025) · PhytoFrontiers™ · doi:10.1094/phytofr-11-24-0128-fi · Cited by 1 · see citing works
Rapid detection of difficult-to-culture bacterial pathogens using real-time nanopore sequencing
Gündoğdu, A., Ulu-Kılıç, A., Kılıç, H., & Nalbantoğlu, Ö. U. (2019) · Infectious Diseases and Clinical Microbiology · doi:10.36519/idcm.2019.19015 · Cited by 1 · see citing works
Foodborne Bacterial Pathogen Big Data – Genomic Analysis
Marquis-Hrabe, J., Jung, D., Park, S.-Y., Boray, A., & Ronholm, J. (2022) · Food microbiology and food safety · doi:10.1007/978-3-031-07179-9_2 · Cited by 1 · see citing works
- Authors
- Pearman, W. S., Freed, N. E., & Silander, O. K.
- Affiliations
- Massey University (NZ)
- Corresponding author
- Pearman, William S.
- Silander, Olin K.
- Venue
- BMC Bioinformatics
- Published
- 2020-05-29
- Volume
- 21
- Issue
- 1
- Pages
- 220-220
- ISSN
- 1471-2105 (ISSN-L)
- PMC
- PMC7257156
- OpenAlex
- W3031621849
- Cited by
- 114
- References
- 50
- Citations by year
- 2026: 7 · 2025: 20 · 2024: 18 · 2023: 20 · 2022: 29 · 2021: 13 · 2020: 5 · 2019: 1 · 2018: 1
- FWCI
- 5.43
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- BMC Bioinformatics (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
- Research Square (acceptedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- ResearchSpace (University of Auckland) (submittedVersion, cc-by)
- Funders
- Massey University (RM21565)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Physical Sciences › Environmental Science › Ecology › Environmental DNA in Biodiversity Studies
- Physical Sciences › Environmental Science › Ecology › Microbial Community Ecology and Physiology
- Keywords
- metagenomics, long-read sequencing, short-read sequencing, taxonomic classification, classification accuracy, simulated reads, Oxford Nanopore, PacBio, eukaryotic communities, read length, error rate, taxonomic resolution, benchmarking
- MeSH terms
- Nanopore Sequencing, Computer Simulation, Sequence Analysis, DNA, Metagenomics, Eukaryota, High-Throughput Nucleotide Sequencing
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
