{"paper_id":"4cb30a07-3751-44cc-bff2-0973bb2aeaf7","body_text":"Exploring Quantitative Metagenomics Studies using Oxford Nanopore Sequencing: A Computational and Experimental Protocol | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Methodology Exploring Quantitative Metagenomics Studies using Oxford Nanopore Sequencing: A Computational and Experimental Protocol Rohia ALILI, Eugeni BELDA, Karine CLEMENT, Phuong Le, Edi PRIFTI, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-131495/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: The gut microbiome plays a major role in chronic diseases, several of which are characterized by an altered diversity and composition of bacterial communities. Large-scale sequencing projects allowed the characterization of these microbial community perturbations. However, a gap remains in how these discoveries can be translated into clinical applications. To facilitate routine implementation of microbiome profiling in clinical settings, portable, real-time, and low-cost sequencing technologies are needed. Results: Here, we propose a computational and experimental protocol for whole genome quantitative metagenomics studies of the human gut microbiome with Oxford Nanopore sequencing technology (ONT). We developed a bioinformatic pipeline to process ONT sequences based on the evaluation of different alignment parameters in the estimation of microbial diversity and composition. We also optimized stool collection and DNA extraction methods to maximize read length, a critical parameter for the sequence alignment and classification. Our analytical pipeline was evaluated using simulations of metagenomic communities to reflect naturally occuring compositional variations. We then validated our experimental and analytical pipeline with stool samples from a bariatric surgery cohort sequenced with ONT and Illumina, revealing comparable diversity and microbial composition profiles. These results were compared to those previously obtained with SOLiD sequencing, where differences were observed, possibly explained by variations in library preparation steps. Finally, we found that sequences obtained with ONT allowed assembly of complete genomes for disease-related species. Conclusion: This protocol can be implemented in the clinical or individual setting, bringing rapid personalized whole genome profiling of target microbiome species. Keywords: quantitative metagenomics, microbiome, obesity, gut microbiota, microbial DNA extraction, sequencing, Simulation, Oxford Nanopore Technologies, MinION. General Microbiology quantitative metagenomics microbiome obesity gut microbiota microbial DNA extraction sequencing Simulation Oxford Nanopore Technologies MinION Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Full Text Due to technical limitations, full-text HTML conversion of this manuscript could not be completed. However, the latest manuscript can be downloaded and accessed as a PDF. Supplementary Files SupplFigure1.pdf Additional file S1: Density distributions of mapQ scores in primary alignments of 250 simulated samples stratified by the number of species in reference samples (50 samples per reference species richness). SupplFigure2.pdf Additional file S2: Statistical comparison of differences between reference and simulated samples at different thresholds of mapQ scores. At each level of reference species richness (from 50 to 450 species, 50 samples per level), we compare the distributions of the similarities in species abundances between reference and simulated samples (Spearman’s Rho coefficients of correlations between reference and simulated species abundance vectors) for all possible pairs of mapQ thresholds evaluated with Trukey’s post-hoc pairwise tests. The 95% family-wise confidence level in the difference between pairs of mapQ threshold is represented colored by the significance of the difference according to adjusted P-values in Tukey’s tests. If we focus on the mapQ=5, we observe that higher mapQ values leads to higher similarities between reference and simulated species abundance vectors (positive values in the confidence levels of the differences) in R50 and R150 simulated samples, whereas this is not the case for more compex/rich simulated samples (R250-R450), where we observe that the similarities with the reference decrease as we increase the stringency of the mapQ filtering (negative values in the confidence levels of the differences, being significant for R450 samples). SupplFigure3.pdf Additional file S3: Taxonomic profile of ZymoBIOMICS mock community inferred from Nanopore sequencing. The reference composition of ZymoBIOMICS mock community is compared with the taxonomic profile obtained from Nanopore sequencing data with Centrifuge only and with Centrifuge combined with filtering of read bins by minimap2 mapping against the corresponding reference genomes with parameters derived from simulation experiments (primary alignments only, min. mapQ=5) 2 SupplFigure4.pdf Additional file S4: Comparison of microbial composition of Microbaria samples between Nanopore, Illumina and SOLiD sequencing data. (A) PCoA of samples from Microbaria study based on genus-level MGS abundance data from three different sequencing technologies (n=34 for Nanopore (ONT) and SOLiD; n=21 for Illumina). Significant effect of sequencing technology in microbiome composition is observed in PERMANOVA test (P-value=0.001; R2=0.11), with sample points from Illumina and ONT sequencing data (both generated with Invitrogen optimized protocol) closer than sample points from SOLiD sequencing (different DNA extraction method) (B) Hierarchical clustering of Microbaria samples product of different sequencing methods based on same genus-level MGS abundance data as PCoA in panel A. Sample points from Illumina and ONT sequencing over same biological sample tends to cluster together in the dendrogram. SupplFigure5.pdf Additional file S5: Comparison of similarities in the abundance of taxonomic features between Nanopore and SOLiD-Illumina sequencing data. (A) Correlations of taxonomic feature abundances at different levels of taxonomic hierarchy between Nanopore(ONT) abundance data based on Centrifuge approach and Illumina and SOLiD abundance data (based on MGS from IGC catalog). (B) Correlations of taxonomic feature abundances at different levels of taxonomic hierarchy between ONT abundance data and Illumina and SOLiD abundance data based on MGS from IGC catalog. Dashed lines connect the same taxonomic feature across comparisons. ** P-value<0.01, Paired Wilcoxon rank-sum test. SupplFigure6.pdf Additional file S6: Comparison of similarities in KEGG functional modules abundance between Nanopore (ONT) and SOLiD-Illumina sequencing data. (A) Vulcano plots comparing the results of Spearman correlations of individual KEGG functional modules between ONT and Illumina-SOLiD sequencing data. (B) Comparison of similarities in module abundance data (Spearman’s Rho) between ONT abundance data (from Centrifuge and from MGS abundance data) and Illumina and SOLiD abundance data (based on MGS abundance data). P-values from pairwise Wilcoxon rank-sum tests of Spearman’s rho distributions between comparisons in x-6 axis are shown above the violin plots. SupplFigure7.pdf Additional file S7: Scatterplots of KEGG Sporulation module M00485 abundance and microbial diversity across different quantifications of diversity and module abundance based on Nanopore (ONT), SOLiD and Illumina sequencing data. Results of Spearman correlation tests are shown for each comparison. SupplFigure8.pdf Additional file S8: Scatterplots of abundances of KEGG LPS biosynthesis modules (M00060, M00063) and microbial diversity across different quantifications of diversity and module abundance based on Nanopore (ONT), SOLiD and Illumina sequencing data. Results of Spearman correlation tests are shown for each comparison. SupplFigure9.pdf Additional file S9: Summary of Bacteroides vulgatus and Akkermansia muciniphila genomes the assemblies from Microbaria Nanopore sequencing data. (A) Mean ± standard error of the top bacterial species with the highes abundance in 33 Microbaria samples based on Centrifuge workflow. (B) MUMMER alignment dot plot between Bacteroides vulgatus assembly from Nanopore (ONT) reads (y axis) and the reference genome of Bacteroides vulgatus ATCC 8482 (x axis). Each point represents a contig in y-axis matching the reference genome on x-axis, with red representing the same orientation and blue representing inverse orientation of contig in y axis vs. reference genome. Points across the diagonal represents assembled contigs collinear with the reference genome on x-axis. (C) MUMMER alignment dot plot between Akkermansia muciniphila assembly from Nanopore (ONT) reads (y axis) and the reference genome of Akkermansia muciniphila isolate Urmite (x axis). (D) Comparison of log2-gene length distributions between genes from Bacteroides vulgatus Nanopore (ONT) assembly and genes from Bacteroides vulgatus ATCC 8482 reference genome. (E) Comparison of log2-gene length distributions between genes from Akkermansia muciniphila Nanopore (ONT) assembly and genes from Akkermansia muciniphila isolate Urmite reference genome. SupplementalTables.xlsx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {\"props\":{\"pageProps\":{\"initialData\":{\"identity\":\"rs-131495\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":true,\"archivedVersions\":[],\"articleType\":\"Methodology\",\"associatedPublications\":[],\"authors\":[{\"id\":6732232,\"identity\":\"41e64540-ee1e-47c0-953b-367894aa1020\",\"order_by\":0,\"name\":\"Rohia ALILI\",\"email\":\"\",\"orcid\":\"https://orcid.org/0000-0002-0158-4250\",\"institution\":\"Unité de recherche mixte 1269 - NutriOmics\",\"correspondingAuthor\":false,\"submittingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Rohia\",\"middleName\":\"\",\"lastName\":\"ALILI\",\"suffix\":\"\"},{\"id\":6732233,\"identity\":\"70812eed-b93e-4dde-8756-f610498191ed\",\"order_by\":1,\"name\":\"Eugeni BELDA\",\"email\":\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABRklEQVRIie2RMUvDQBSAXzi4LNdmk1dS2l8gRALpIvGv5DhopwpFKELBVgrnonY1+CecxMHhSoYupf4Al3RxTgch0EHP0CI1RRwd7huO9x73Pe7dAzAY/iUMIKXQoNu8CKzR97lXiSj4FEiR4p8VPtoq8NvlLxyoTNOoH3Zu3cl0+f4cXlRRKNJ7Oj51DpLLVQ5h43DXr42qwosWoivrCfHrbwIptiMSz9tntXs+dhkIP1A7iqdYgFySrkRBXVQEKZu31hWZ8IdXXQdQ/HFXOVGslXM57FAU9hrVUCsvGanIj0Kx8rLiAQuAyyTSCrUylSC1r0ErqlD055QUTKoCo8XsSD/Md0HNatKWHoml4HExi1eaxbm6mWZZf9Bs3vHlKlcDpzkmKenJkE/cjq6ch40fymYbm5iVi97+5WzRw5b7GAwGg+ETIBZvdQCWj+EAAAAASUVORK5CYII=\",\"orcid\":\"https://orcid.org/0000-0003-4307-5072\",\"institution\":\"Integrative Phenomics\",\"correspondingAuthor\":true,\"submittingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Eugeni\",\"middleName\":\"\",\"lastName\":\"BELDA\",\"suffix\":\"\"},{\"id\":6732234,\"identity\":\"599b1a08-d0c3-4571-935a-4ff7cb3c5540\",\"order_by\":2,\"name\":\"Karine CLEMENT\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"UMRS 1269 - NutriOmics\",\"correspondingAuthor\":false,\"submittingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Karine\",\"middleName\":\"\",\"lastName\":\"CLEMENT\",\"suffix\":\"\"},{\"id\":6732235,\"identity\":\"1d9dff70-ef62-4ea2-add2-1a3d09451ac3\",\"order_by\":3,\"name\":\"Phuong Le\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Sorbonne Université, \",\"correspondingAuthor\":false,\"submittingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Phuong\",\"middleName\":\"\",\"lastName\":\"Le\",\"suffix\":\"\"},{\"id\":6732236,\"identity\":\"b053e988-45d4-4d86-957a-697dcd13030d\",\"order_by\":4,\"name\":\"Edi PRIFTI\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"IRD : UMMISCO\",\"correspondingAuthor\":false,\"submittingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Edi\",\"middleName\":\"\",\"lastName\":\"PRIFTI\",\"suffix\":\"\"},{\"id\":6732237,\"identity\":\"4c722c24-d9f7-4e07-ac10-9eb7f5744a87\",\"order_by\":5,\"name\":\"Jean-Daniel ZUCKER\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"IRD : UMMISCO\",\"correspondingAuthor\":false,\"submittingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Jean-Daniel\",\"middleName\":\"\",\"lastName\":\"ZUCKER\",\"suffix\":\"\"},{\"id\":6732238,\"identity\":\"22af82b3-4761-4f76-b0ea-e9947044f661\",\"order_by\":6,\"name\":\"Thierry WIRTH\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Institut de Systématique Evolution Biodiversité: Institut de Systematique Evolution Biodiversite\",\"correspondingAuthor\":false,\"submittingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Thierry\",\"middleName\":\"\",\"lastName\":\"WIRTH\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2020-12-18 13:04:07\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-131495/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-131495/v1\",\"draftVersion\":[],\"editorialEvents\":[],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":4447317,\"identity\":\"3d1ceaba-d787-482c-acaa-a816f1628dad\",\"added_by\":\"auto\",\"created_at\":\"2020-12-22 17:02:17\",\"extension\":\"png\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":143327,\"visible\":true,\"origin\":\"\",\"legend\":\"Summary of the workflow : (A) Simulated data processing. (B) Wet-lab optimization. (C) Summary of ONT sequencing comparison with Illumina and SOLiD technologies. \",\"description\":\"\",\"filename\":\"Figure1.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-131495/v1/c45e3222d02b2db4bcb1c2a9.png\"},{\"id\":4447312,\"identity\":\"27c4309b-fdec-47f0-8a0e-8ab25a220c94\",\"added_by\":\"auto\",\"created_at\":\"2020-12-22 17:02:15\",\"extension\":\"png\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":193830,\"visible\":true,\"origin\":\"\",\"legend\":\"Metagenomic profiles from simulated samples between minimap2 results and minimap2 results filtered from secondary alignments. (A) Boxplots of mean lengths of Nanopore reads of 250 simulated samples (y axis) between those aligned and unaligned over the 506 reference genomes from minimap2 results (x-axis). (B) Boxplots of recall values of species richness estimates in 250 simulated samples (y-axis) between metagenomic profiles inferred from all minimap2 alignments (mmap2 raw) and from minimap2 primary alignments only (mmap2APfilt, x-axis). (C) Boxplots of precision values of species richness estimates in 250 simulated samples (y-axis) between metagenomic profiles inferred from all minimap2 alignments (mmap2 raw) and from minimap2 primary alignments only (mmap2APfilt, x-axis). (D) Principal Coordinates Analysis (PCoA) of metagenomic profiles from the reference and 250 simulated samples inferred from all minimap2 alignments (mmap2raw) and from minimap2 primary alignments only (mmap2APfilt, x-axis). Dashed lines connect points coming from the same sample (reference, simulated ones; 3 points per sample). \",\"description\":\"\",\"filename\":\"Figure2.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-131495/v1/401b8368dea0a70af6fa010e.png\"},{\"id\":4447216,\"identity\":\"7e3cccf2-01c3-46b2-b7a7-00af748a1c10\",\"added_by\":\"auto\",\"created_at\":\"2020-12-22 16:59:17\",\"extension\":\"png\",\"order_by\":3,\"title\":\"Figure 3\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":422776,\"visible\":true,\"origin\":\"\",\"legend\":\"Impact of filtering minimap2 primary alignments of Nanopore reads at different thresholds of sequence identity. Boxplots of recall (A) and precision (B) values of species richness estimates in 250 simulated samples (y-axis) between metagenomic profiles inferred from primary alignments of Nanopore reads filtered by different thresholds of sequence identity (from 0 to 90%; x-axis) stratified by the number of species in reference metagenomic profiles. (C) PCoA of reference metagenomic profiles and metagenomic profiles of the 250 simulated samples inferred from minimap2 primary alignments filtered by different thresholds of sequence identity (from 0 to 90%; x-axis). Dashed lines connect points coming from the same sample (reference, simulated ones; 11 points per sample) with different shapes assigned to samples from different reference species richness. Points corresponding to reference samples and simulated samples with no filtering by sequence identity (id_0) are highlighted with larger point sizes. (D) Boxplots of Spearman’s Rho coefficients in correlation analyses between taxonomic profiles of reference and simulated samples (y-axis) at different thresholds of sequence identity (from 0 to 90%; x-axis) stratified by the number of species in reference metagenomic profiles. Points are colored according with the sequencing depth of simulated samples.\",\"description\":\"\",\"filename\":\"Figure3.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-131495/v1/a4a801f7baf06b384df6fef2.png\"},{\"id\":4447314,\"identity\":\"4bd11cb4-be59-489f-b55b-574e20fffa5b\",\"added_by\":\"auto\",\"created_at\":\"2020-12-22 17:02:16\",\"extension\":\"png\",\"order_by\":4,\"title\":\"Figure 4\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":427093,\"visible\":true,\"origin\":\"\",\"legend\":\"Impact of filtering minimap2 primary alignments of Nanopore reads at different thresholds of mapQ score. Boxplots of recall (A) and precision (B) values of species richness estimates in 250 simulated samples (y-axis) between metagenomic profiles inferred from primary alignments of Nanopore reads filtered by different thresholds of mapQ score (from 0 to 50; x-axis) stratified by the number of species in reference metagenomic profiles. (C) PCoA of reference metagenomic profiles and metagenomic profiles of the 250 simulated samples inferred from minimap2 primary alignments filtered by different thresholds of mapQ score (from 0 to 50; x-axis). Dashed lines connect points coming from the same sample (reference, simulated ones) with different shapes assigned to samples from different reference species richness. Points corresponding to reference samples and simulated samples with no filtering by mapQ (0) and filtered by mapQ\\u003e5 (5) are highlighted with larger point sizes. (D) Boxplots of Spearman’s Rho coefficients in correlation analyses between taxonomic profiles of reference and simulated samples (y-axis) at different thresholds of sequence identity (from 0 to 90%; x-axis) stratified by the number of species in reference metagenomic profiles. Points are colored according with the sequencing depth of simulated samples. \",\"description\":\"\",\"filename\":\"Figure4.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-131495/v1/2eeaf4fe372e0009554ff87d.png\"},{\"id\":4447376,\"identity\":\"0df897d7-7207-4cea-96e7-245eae6d83ed\",\"added_by\":\"auto\",\"created_at\":\"2020-12-22 17:05:16\",\"extension\":\"png\",\"order_by\":5,\"title\":\"Figure 5\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":137575,\"visible\":true,\"origin\":\"\",\"legend\":\"DNA extraction kits, fragmentation and end repair impact over human stool metagenomic composition from Nanopore sequencing data. Read length distributions of nanopore reads across different DNA extraction kits (A, n=29) and between DNA fragmentation (B, n=6 paired samples Fragmented/non-fragmented) and DNA end repair (C, n=6 paired samples end vs. no end repair) steps for Invitrogen samples. Blue dashed lines correspond to the median value of log2-transformed read lengths used to stratify reads as long or short. (D) Differences between the fraction of classified reads by Centrifuge approach between long and short reads for 29 samples in panel A. (E) Differences in microbial diversity (observed species) between extraction kits (n=29). (F) Differences in microbial diversity by DNA fragmentation (n=4 paired samples). (G) Differences in microbial diversity by DNA end-repair step (n=4 paired samples). (H) PCoA ordination of 29 samples in panel A colored by extraction kit. (I) PCoA ordination of 8 samples in panel E. (J) PCoA of 8 samples of panel G colored by DNA end-repair step. ns (panel F, G) =Non-significant differences in paired Wilcoxon rank-sum tests. ***=Pvalue\\u003c0.0001 in paired Wilcoxon rank-sum test \",\"description\":\"\",\"filename\":\"Figure5.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-131495/v1/0340c095f3e2837f43969fa8.png\"},{\"id\":4447326,\"identity\":\"082c19b1-310d-439e-9875-67b8e77ed1bb\",\"added_by\":\"auto\",\"created_at\":\"2020-12-22 17:02:18\",\"extension\":\"png\",\"order_by\":6,\"title\":\"Figure 6\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":106171,\"visible\":true,\"origin\":\"\",\"legend\":\"Optimization of DNA extraction and library preparation protocols. (A) Steps of the bacterial DNA extraction protocol. In black, the steps include in the protocol of the Invitrogen kit, in red the improvement steps recommended by the IHMS consortium. B) Improvement of library preparation by application of NEB recommendation and decrease the SPRI/DNA ratio.\\n\",\"description\":\"\",\"filename\":\"Figure6.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-131495/v1/aeefbd8de0ed3542cf661aee.png\"},{\"id\":4447380,\"identity\":\"4fa73e71-730f-41be-9ff3-305c4df29bc6\",\"added_by\":\"auto\",\"created_at\":\"2020-12-22 17:05:18\",\"extension\":\"png\",\"order_by\":7,\"title\":\"Figure 7\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":40163,\"visible\":true,\"origin\":\"\",\"legend\":\" Impact of Invitrogen optimized protocol over human stool metagenomic composition from Nanopore sequencing data. (A) Read length distributions of ONT reads across different DNA extraction kits including the optimized Invitrogen protocol. Blue dashed lines correspond to the median value of log2- transformed read lengths used to stratify reads as long or short. (B) Differences in the fraction of classified reads between Invitrogen optimized kit and original Invitrogen kit (n=6 paired samples; *=P-value\\u003c0.05, Paired Wilcoxon rank-sum test). (C) Differences in microbial diversity between Invitrogen optimized kit and original Invitrogen kit (n=6 paired samples; ns=P-value\\u003e0.05, Paired Wilcoxon rank-sum test). (D) PCoA ordination of 12 samples extracted with Invitrogen optimized kit and original Invitrogen kit. Dashed lines connect samples coming from the same fecal stool sample collected at different dates. \",\"description\":\"\",\"filename\":\"Figure7.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-131495/v1/b1e3f4fb561dd158c677d86e.png\"},{\"id\":4447218,\"identity\":\"8025a325-20d1-4f6a-abce-70ccff54aef4\",\"added_by\":\"auto\",\"created_at\":\"2020-12-22 16:59:17\",\"extension\":\"png\",\"order_by\":8,\"title\":\"Figure 8\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":184694,\"visible\":true,\"origin\":\"\",\"legend\":\"Collection kits and storage conditions Impact on metagenomic human stool composition from Nanopore sequencing data. (A) log2-read length distribution of ONT reads across collection kits and temperature storage conditions. For comparison, the log2-read length distribution of initial Invitrogen optimized reads is included. Dashed blue line represents the median log2-read length from the entire dataset. (B) Difference in the fraction of classified reads by Centrifuge strategy between collection kits stratified by storage condition. (C) Differences in microbial diversity between collection kits stratified by storage condition. (D) Differences in microbial diversity between donors of fecal samples in this experiment. (E) Impact of difference experimental variables (donor, temperature, collection kit) over microbiome composition of studied samples. The barplot represents the effect sizes (R2) from PERMANOVA tests of variables in Y-axis over a beta-diversity distance matrix computed from Centrifuge-based genus abundance data (*=P-value\\u003c0.05, PERMANOVA test). (F) PCoA ordination of samples from collection kits experiments coloured by donor. Dashed lines connect samples collected with same collection kits (Omnigen, Ozyme, Norgen). \",\"description\":\"\",\"filename\":\"Figure8.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-131495/v1/9f65f153a29be1ccb2bef3a1.png\"},{\"id\":4447322,\"identity\":\"c6020102-dca6-4ad2-89f2-6340bfce2e36\",\"added_by\":\"auto\",\"created_at\":\"2020-12-22 17:02:17\",\"extension\":\"png\",\"order_by\":9,\"title\":\"Figure 9\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":216982,\"visible\":true,\"origin\":\"\",\"legend\":\"Comparison of quantitative metagenomic profiles of Microbaria samples between sequencing technologies. Correlation between gene richness from SOLiD sequencing (x-axis) and Observed Species inferred from Nanopore(ONT) sequencing data using Centrifuge approach (A, n=33), gene richness inferred from ONT sequencing data (B, n=33) and gene richness inferred from Illumina sequencing data (C, n=21). The strength of the similarities was evaluated with Spearman correlation test (Spearman’s Rho and P-value included in the scatter plots). (D) Lineplots representing the scaled diversity (from zero to 1) of Microbaria samples from different diversity metrics based on SOLiD, ONT and Illumina sequencing data. Samples in x-axis are ordered based on the scaled diversity of the gene richness from the original Microbaria study (GeneRichness3.9SOLiD). (E) Heatmap of Spearman’s Rho representing similarities in abundance vectors of taxonomic features in x-axis between ONT quantifications based on Centrifuge data and Illumina and SOLiD quantifications based on metagenomic species of the IGC gene catalog (y-axis; #=P- valueadj\\u003c0.05, BH method; *=P-value\\u003c0.05). On the bottom of the heatmap is represented the prevalence of taxonomic features in x-axis based on ONT sequencing data. \",\"description\":\"\",\"filename\":\"Figure9.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-131495/v1/622298b69dc289ede1d64874.png\"},{\"id\":13567323,\"identity\":\"67ef832c-17c4-442d-8aaa-d40b717434b2\",\"added_by\":\"auto\",\"created_at\":\"2021-09-17 03:32:29\",\"extension\":\"pdf\",\"order_by\":1,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":1385479,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"Exploringquantitativemetagenomicsstudies.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-131495/v1_covered.pdf\"},{\"id\":10658990,\"identity\":\"bcbedc5f-2926-4d0a-bf42-e54700d3ef59\",\"added_by\":\"auto\",\"created_at\":\"2021-06-22 19:38:00\",\"extension\":\"pdf\",\"order_by\":1,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":1381962,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"Exploringquantitativemetagenomicsstudies.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-131495/v1_covered.pdf\"},{\"id\":4447493,\"identity\":\"3203582f-ad23-4b00-a6e7-894a41a7ecbb\",\"added_by\":\"auto\",\"created_at\":\"2020-12-22 17:11:18\",\"extension\":\"pdf\",\"order_by\":1,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":431958,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"Exploringquantitativemetagenomicsstudies.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-131495/v1_stamped.pdf\"},{\"id\":4447207,\"identity\":\"039badde-6591-4816-bb08-4db8f5011397\",\"added_by\":\"auto\",\"created_at\":\"2020-12-22 16:59:16\",\"extension\":\"pdf\",\"order_by\":1,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":1581175,\"visible\":true,\"origin\":\"\",\"legend\":\"Additional file S1: Density distributions of mapQ scores in primary alignments of 250 simulated samples stratified by the number of species in reference samples (50 samples per reference species richness). \",\"description\":\"\",\"filename\":\"SupplFigure1.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-131495/v1/b71fdc487f8b23dd8af7da88.pdf\"},{\"id\":4447316,\"identity\":\"5c782e52-2034-429f-b21d-d07f6f7dd3b1\",\"added_by\":\"auto\",\"created_at\":\"2020-12-22 17:02:17\",\"extension\":\"pdf\",\"order_by\":2,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":399620,\"visible\":true,\"origin\":\"\",\"legend\":\"Additional file S2: Statistical comparison of differences between reference and simulated samples at different thresholds of mapQ scores. At each level of reference species richness (from 50 to 450 species, 50 samples per level), we compare the distributions of the similarities in species abundances between reference\\n and simulated samples (Spearman’s Rho coefficients of correlations between reference and simulated species abundance vectors) for all possible pairs of mapQ thresholds evaluated with Trukey’s post-hoc pairwise tests. The 95% family-wise confidence level in the difference between pairs of mapQ threshold is represented colored by the significance of the difference according to adjusted P-values in Tukey’s tests. If we focus on the mapQ=5, we observe that higher mapQ values leads to higher similarities between reference and simulated species abundance vectors (positive values in the confidence levels of the differences) in R50 and R150 simulated samples, whereas this is not the case for more compex/rich simulated samples (R250-R450), where we observe that the similarities with the reference decrease as we increase the stringency of the mapQ filtering (negative values in the confidence levels of the\\n differences, being significant for R450 samples). \",\"description\":\"\",\"filename\":\"SupplFigure2.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-131495/v1/02a843f03cd9e86452a6a429.pdf\"},{\"id\":4447214,\"identity\":\"2aef4170-e455-4a6f-b100-34e773c377c2\",\"added_by\":\"auto\",\"created_at\":\"2020-12-22 16:59:17\",\"extension\":\"pdf\",\"order_by\":3,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":148702,\"visible\":true,\"origin\":\"\",\"legend\":\"Additional file S3: Taxonomic profile of ZymoBIOMICS mock community inferred\\n from Nanopore sequencing. The reference composition of ZymoBIOMICS mock community is compared with the taxonomic profile obtained from Nanopore \\n sequencing data with Centrifuge only and with Centrifuge combined with filtering of \\n read bins by minimap2 mapping against the corresponding reference genomes with parameters derived from simulation experiments (primary alignments only, min. \\n mapQ=5) 2\",\"description\":\"\",\"filename\":\"SupplFigure3.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-131495/v1/bd5d83b5bce147395702ee6f.pdf\"},{\"id\":4447313,\"identity\":\"2a8ef62e-200f-4fea-9376-06cfa129eb1c\",\"added_by\":\"auto\",\"created_at\":\"2020-12-22 17:02:16\",\"extension\":\"pdf\",\"order_by\":4,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":270624,\"visible\":true,\"origin\":\"\",\"legend\":\"Additional file S4: Comparison of microbial composition of Microbaria samples\\n between Nanopore, Illumina and SOLiD sequencing data. (A) PCoA of samples\\n from Microbaria study based on genus-level MGS abundance data from three different\\n sequencing technologies (n=34 for Nanopore (ONT) and SOLiD; n=21 for Illumina).\\n Significant effect of sequencing technology in microbiome composition is observed in\\n PERMANOVA test (P-value=0.001; R2=0.11), with sample points from Illumina and\\n ONT sequencing data (both generated with Invitrogen optimized protocol) closer than\\n sample points from SOLiD sequencing (different DNA extraction method) (B)\\n Hierarchical clustering of Microbaria samples product of different sequencing methods\\n based on same genus-level MGS abundance data as PCoA in panel A. Sample points\\n from Illumina and ONT sequencing over same biological sample tends to cluster\\n together in the dendrogram.\",\"description\":\"\",\"filename\":\"SupplFigure4.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-131495/v1/a120acc495aaff5efdcd5578.pdf\"},{\"id\":4447221,\"identity\":\"1bd06c7d-26f1-4803-b97c-0d7bef43a40c\",\"added_by\":\"auto\",\"created_at\":\"2020-12-22 16:59:18\",\"extension\":\"pdf\",\"order_by\":5,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":646075,\"visible\":true,\"origin\":\"\",\"legend\":\"Additional file S5: Comparison of similarities in the abundance of taxonomic\\n features between Nanopore and SOLiD-Illumina sequencing data. (A)\\n Correlations of taxonomic feature abundances at different levels of taxonomic\\n hierarchy between Nanopore(ONT) abundance data based on Centrifuge approach\\n and Illumina and SOLiD abundance data (based on MGS from IGC catalog). (B)\\n Correlations of taxonomic feature abundances at different levels of taxonomic\\n hierarchy between ONT abundance data and Illumina and SOLiD abundance data\\n based on MGS from IGC catalog. Dashed lines connect the same taxonomic feature\\n across comparisons. ** P-value\\u003c0.01, Paired Wilcoxon rank-sum test.\",\"description\":\"\",\"filename\":\"SupplFigure5.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-131495/v1/57aac7300c848ba6fb969590.pdf\"},{\"id\":4447217,\"identity\":\"bf80c0d1-2d1c-4b95-8425-da59ded54f43\",\"added_by\":\"auto\",\"created_at\":\"2020-12-22 16:59:17\",\"extension\":\"pdf\",\"order_by\":6,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":1740432,\"visible\":true,\"origin\":\"\",\"legend\":\"Additional file S6: Comparison of similarities in KEGG functional modules\\nabundance between Nanopore (ONT) and SOLiD-Illumina sequencing data. (A)\\nVulcano plots comparing the results of Spearman correlations of individual KEGG\\nfunctional modules between ONT and Illumina-SOLiD sequencing data. (B)\\nComparison of similarities in module abundance data (Spearman’s Rho) between ONT\\nabundance data (from Centrifuge and from MGS abundance data) and Illumina and\\n SOLiD abundance data (based on MGS abundance data). P-values from pairwise\\n Wilcoxon rank-sum tests of Spearman’s rho distributions between comparisons in x-6\\n axis are shown above the violin plots.\",\"description\":\"\",\"filename\":\"SupplFigure6.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-131495/v1/7057a081569853cd0593ce74.pdf\"},{\"id\":4447204,\"identity\":\"9236e5ba-64f4-4875-ba36-a6e22754a7b9\",\"added_by\":\"auto\",\"created_at\":\"2020-12-22 16:59:15\",\"extension\":\"pdf\",\"order_by\":7,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":301453,\"visible\":true,\"origin\":\"\",\"legend\":\"Additional file S7: Scatterplots of KEGG Sporulation module M00485 abundance and\\n microbial diversity across different quantifications of diversity and module abundance\\n based on Nanopore (ONT), SOLiD and Illumina sequencing data. Results of Spearman\\n correlation tests are shown for each comparison.\",\"description\":\"\",\"filename\":\"SupplFigure7.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-131495/v1/421bc8f2c968b0c180fad959.pdf\"},{\"id\":4447401,\"identity\":\"a45787df-e501-48cf-9b69-dd0f32d40f4d\",\"added_by\":\"auto\",\"created_at\":\"2020-12-22 17:08:18\",\"extension\":\"pdf\",\"order_by\":8,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":432213,\"visible\":true,\"origin\":\"\",\"legend\":\"Additional file S8: Scatterplots of abundances of KEGG LPS biosynthesis modules\\n (M00060, M00063) and microbial diversity across different quantifications of diversity\\n and module abundance based on Nanopore (ONT), SOLiD and Illumina sequencing\\n data. Results of Spearman correlation tests are shown for each comparison.\",\"description\":\"\",\"filename\":\"SupplFigure8.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-131495/v1/dd575fab738eead706011b89.pdf\"},{\"id\":4447227,\"identity\":\"7817fd5d-cd94-48a8-a317-d95d7919ced9\",\"added_by\":\"auto\",\"created_at\":\"2020-12-22 16:59:18\",\"extension\":\"pdf\",\"order_by\":9,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":563698,\"visible\":true,\"origin\":\"\",\"legend\":\"Additional file S9: Summary of Bacteroides vulgatus and Akkermansia muciniphila genomes the assemblies from Microbaria Nanopore sequencing data. (A) Mean ± standard error of the top bacterial species with the highes abundance in 33 Microbaria samples based on Centrifuge workflow. (B) MUMMER alignment dot plot between Bacteroides vulgatus assembly from Nanopore (ONT) reads (y axis) and the reference genome of Bacteroides vulgatus ATCC 8482 (x axis). Each point represents a contig in y-axis matching the reference genome on x-axis, with red representing the same orientation and blue representing inverse orientation of contig in y axis vs. reference genome. Points across the diagonal represents assembled contigs collinear with the reference genome on x-axis. (C) MUMMER alignment dot plot between Akkermansia muciniphila assembly from Nanopore (ONT) reads (y axis) and the reference genome of Akkermansia muciniphila isolate Urmite (x axis). (D) Comparison of log2-gene length distributions between genes from\\n Bacteroides vulgatus Nanopore (ONT) assembly and genes from Bacteroides vulgatus\\n ATCC 8482 reference genome. (E) Comparison of log2-gene length distributions\\n between genes from Akkermansia muciniphila Nanopore (ONT) assembly and genes\\n from Akkermansia muciniphila isolate Urmite reference genome. \",\"description\":\"\",\"filename\":\"SupplFigure9.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-131495/v1/9f643c896880addab5245891.pdf\"},{\"id\":4447224,\"identity\":\"84c5b4ab-c0ac-4e65-8f3a-545a4407b56a\",\"added_by\":\"auto\",\"created_at\":\"2020-12-22 16:59:18\",\"extension\":\"xlsx\",\"order_by\":10,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":716901,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"SupplementalTables.xlsx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-131495/v1/b8db868fd22d78982cacdf2b.xlsx\"}],\"financialInterests\":\"\",\"formattedTitle\":\"\\u003cp\\u003eExploring Quantitative Metagenomics Studies using Oxford Nanopore Sequencing: A Computational and Experimental Protocol\\u003c/p\\u003e\",\"fulltext\":[{\"header\":\"Full Text\",\"content\":\"Due to technical limitations, full-text HTML conversion of this manuscript could not be completed. However, the latest manuscript can be downloaded and \\u003ca href='/article/rs-131495/latest.pdf' target='_blank'\\u003e accessed as a PDF.\\u003c/a\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":false,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":true,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":false,\"isAuthorSuppliedPdf\":true,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true},\"keywords\":\" quantitative metagenomics, microbiome, obesity, gut microbiota, microbial DNA extraction, sequencing, Simulation, Oxford Nanopore Technologies, MinION\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-131495/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-131495/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003e\\u003cstrong\\u003eBackground:\\u003c/strong\\u003e The gut microbiome plays a major role in chronic diseases, several of which are characterized by an altered diversity and composition of bacterial communities. Large-scale sequencing projects allowed the characterization of these microbial community perturbations. However, a gap remains in how these discoveries can be translated into clinical applications. To facilitate routine implementation of microbiome profiling in clinical settings, portable, real-time, and low-cost sequencing technologies are needed.\\u003c/p\\u003e\\u003cp\\u003e\\u003cstrong\\u003eResults:\\u003c/strong\\u003e Here, we propose a computational and experimental protocol for whole genome quantitative metagenomics studies of the human gut microbiome with Oxford Nanopore sequencing technology (ONT). We developed a bioinformatic pipeline to process ONT sequences based on the evaluation of different alignment parameters in the estimation of microbial diversity and composition. We also optimized stool collection and DNA extraction methods to maximize read length, a critical parameter for the sequence alignment and classification. Our analytical pipeline was evaluated using simulations of metagenomic communities to reflect naturally occuring compositional variations. We then validated our experimental and analytical pipeline with stool samples from a bariatric surgery cohort sequenced with ONT and Illumina, revealing comparable diversity and microbial composition profiles. These results were compared to those previously obtained with SOLiD sequencing, where differences were observed, possibly explained by variations in library preparation steps. Finally, we found that sequences obtained with ONT allowed assembly of complete genomes for disease-related species.\\u003c/p\\u003e\\u003cp\\u003e\\u003cstrong\\u003eConclusion:\\u003c/strong\\u003e This protocol can be implemented in the clinical or individual setting, bringing rapid personalized whole genome profiling of target microbiome species. Keywords: quantitative metagenomics, microbiome, obesity, gut microbiota, microbial DNA extraction, sequencing, Simulation, Oxford Nanopore Technologies, MinION.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Exploring Quantitative Metagenomics Studies using Oxford Nanopore Sequencing: A Computational and Experimental Protocol\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2020-12-22 16:59:12\",\"doi\":\"10.21203/rs.3.rs-131495/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true}}],\"origin\":\"\",\"ownerIdentity\":\"c12b9f51-13e6-4b39-9193-f36f04844178\",\"owner\":[],\"postedDate\":\"December 22nd, 2020\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"posted\",\"subjectAreas\":[{\"id\":1589564,\"name\":\"General Microbiology\"}],\"tags\":[],\"updatedAt\":\"2021-06-22T19:34:50+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2020-12-22 16:59:12\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-131495\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-131495\",\"identity\":\"rs-131495\",\"version\":[\"v1\"]},\"buildId\":\"rHA-KDH7Qsr4HCuvH75dn\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}