1
|
Park JW, Braswell WE, Kunta M. Co-Occurrence Analysis of Citrus Root Bacterial Microbiota under Citrus Greening Disease. Plants (Basel) 2023; 13:80. [PMID: 38202388 PMCID: PMC10781011 DOI: 10.3390/plants13010080] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Revised: 11/22/2023] [Accepted: 11/23/2023] [Indexed: 01/12/2024]
Abstract
Candidatus Liberibacter asiaticus (CLas) is associated with Citrus Huanglongbing (HLB), a devastating disease in the US. Previously, we conducted a two-year-long monthly HLB survey by quantitative real-time PCR using root DNA fractions prepared from 112 field grapefruit trees grafted on sour orange rootstock. Approximately 10% of the trees remained CLas-free during the entire survey period. This study conducted 16S metagenomics using the time-series root DNA fractions, monthly prepared during twenty-four consecutive months, followed by microbial co-occurrence network analysis to investigate the microbial factors contributing to the CLas-free phenotype of the aforementioned trees. Based on the HLB status and the time when the trees were first diagnosed as CLas-positive during the survey, the samples were divided into four groups, Stage H (healthy), Stage I (early), II (mid), and III (late) samples. The 16S metagenomics data using Silva 16S database v132 revealed that HLB compromised the diversity of rhizosphere microbiota. At the phylum level, Actinobacteria and Proteobacteria were the predominant bacterial phyla, comprising >93% of total bacterial phyla, irrespective of HLB status. In addition, a temporal change in the rhizosphere microbe population was observed during a two-year-long survey, from which we confirmed that some bacterial families differently responded to HLB disease status. The clustering of the bacterial co-occurrence network data revealed the presence of a subnetwork composed of Streptomycetaceae and bacterial families with plant growth-promoting activity in Stage H and III samples. These data implicated that the Streptomycetaceae subnetwork may act as a functional unit against HLB.
Collapse
Affiliation(s)
- Jong-Won Park
- Citrus Center, Texas A&M University-Kingsville, 312 N. International Blvd., Weslaco, TX 78599, USA
| | - W. Evan Braswell
- Insect Management and Molecular Diagnostic Laboratory, USDA APHIS PPQ S&T, Edinburg, TX 78541, USA
| | - Madhurababu Kunta
- Citrus Center, Texas A&M University-Kingsville, 312 N. International Blvd., Weslaco, TX 78599, USA
| |
Collapse
|
2
|
Liu G, Li T, Zhu X, Zhang X, Wang J. An independent evaluation in a CRC patient cohort of microbiome 16S rRNA sequence analysis methods: OTU clustering, DADA2, and Deblur. Front Microbiol 2023; 14:1178744. [PMID: 37560524 PMCID: PMC10408458 DOI: 10.3389/fmicb.2023.1178744] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Accepted: 06/14/2023] [Indexed: 08/11/2023] Open
Abstract
16S rRNA is the universal gene of microbes, and it is often used as a target gene to obtain profiles of microbial communities via next-generation sequencing (NGS) technology. Traditionally, sequences are clustered into operational taxonomic units (OTUs) at a 97% threshold based on the taxonomic standard using 16S rRNA, and methods for the reduction of sequencing errors are bypassed, which may lead to false classification units. Several denoising algorithms have been published to solve this problem, such as DADA2 and Deblur, which can correct sequencing errors at single-nucleotide resolution by generating amplicon sequence variants (ASVs). As high-resolution ASVs are becoming more popular than OTUs and only one analysis method is usually selected in a particular study, there is a need for a thorough comparison of OTU clustering and denoising pipelines. In this study, three of the most widely used 16S rRNA methods (two denoising algorithms, DADA2 and Deblur, along with de novo OTU clustering) were thoroughly compared using 16S rRNA amplification sequencing data generated from 358 clinical stool samples from the Colorectal Cancer (CRC) Screening Cohort. Our findings indicated that all approaches led to similar taxonomic profiles (with P > 0.05 in PERMNAOVA and P <0.001 in the Mantel test), although the number of ASVs/OTUs and the alpha-diversity indices varied considerably. Despite considerable differences in disease-related markers identified, disease-related analysis showed that all methods could result in similar conclusions. Fusobacterium, Streptococcus, Peptostreptococcus, Parvimonas, Gemella, and Haemophilus were identified by all three methods as enriched in the CRC group, while Roseburia, Faecalibacterium, Butyricicoccus, and Blautia were identified by all three methods as enriched in the healthy group. In addition, disease-diagnostic models generated using machine learning algorithms based on the data from these different methods all achieved good diagnostic efficiency (AUC: 0.87-0.89), with the model based on DADA2 producing the highest AUC (0.8944 and 0.8907 in the training set and test set, respectively). However, there was no significant difference in performance between the models (P >0.05). In conclusion, this study demonstrates that DADA2, Deblur, and de novo OTU clustering display similar power levels in taxa assignment and can produce similar conclusions in the case of the CRC cohort.
Collapse
Affiliation(s)
- Guang Liu
- School of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, China
- Guangdong Hongyuan Pukong Medical Technology Co., Ltd., Guangzhou, China
| | - Tong Li
- School of Bioscience and Bioengineering, South China University of Technology, Guangzhou, China
| | - Xiaoyan Zhu
- School of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, China
| | - Xuanping Zhang
- School of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, China
| | - Jiayin Wang
- School of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, China
| |
Collapse
|
3
|
Barnes CJ, Rasmussen L, Asplund M, Knudsen SW, Clausen ML, Agner T, Hansen AJ. Comparing DADA2 and OTU clustering approaches in studying the bacterial communities of atopic dermatitis. J Med Microbiol 2020; 69:1293-1302. [PMID: 32965212 PMCID: PMC7717693 DOI: 10.1099/jmm.0.001256] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
Introduction The pathogenesis of atopic dermatitis (AD) is not yet fully understood, but the bacterial composition of AD patients’ skin has been shown to have an increased abundance of Staphylococcus aureus. More recently, coagulase-negative Staphylococcus (CoNS) species were shown to be able to inhibit S. aureus, but further studies are required to determine the effects of Staphylococcus community variation in AD. Aim Here we investigated whether analysing metabarcoding data with the more recently developed DADA2 approach improves metabarcoding analyses compared to the previously used operational taxonomic unit (OTU) clustering, and can be used to study Staphylococcus community dynamics. Methods The bacterial 16S rRNA region from tape strip samples of the stratum corneum of AD patients (non-lesional skin) and non-AD controls was metabarcoded. We processed metabarcoding data with two different bioinformatic pipelines (an OTU clustering method and DADA2), which were analysed with and without technical replication (sampling strategy). Results We found that OTU clustering and DADA2 performed well for community-level studies, as demonstrated by the identification of significant differences in the skin bacterial communities associated with AD. However, the OTU clustering approach inflated bacterial richness, which was worsened by not having technical replication. Data processed with DADA2 likely handled sequencing errors more effectively and thereby did not inflate molecular richness. Conclusion We believe that DADA2 represents an improvement over an OTU clustering approach, and that biological replication rather than technical replication is a more effective use of resources. However, neither OTU clustering nor DADA2 gave insights into Staphylococcus community dynamics, and caution should remain in not overinterpreting the taxonomic assignments at lower taxonomic ranks.
Collapse
Affiliation(s)
- Christopher J Barnes
- Natural History Museum of Denmark, Department of Biology, University of Copenhagen, Denmark.,The Globe Institute, Faculty of Health, University of Copenhagen, Denmark
| | - Linett Rasmussen
- The Globe Institute, Faculty of Health, University of Copenhagen, Denmark
| | - Maria Asplund
- The Globe Institute, Faculty of Health, University of Copenhagen, Denmark
| | | | - Maja-Lisa Clausen
- Department of Dermatology, Bispebjerg Hospital, University of Copenhagen, Denmark
| | - Tove Agner
- Department of Dermatology, Bispebjerg Hospital, University of Copenhagen, Denmark
| | - Anders J Hansen
- The Globe Institute, Faculty of Health, University of Copenhagen, Denmark
| |
Collapse
|
4
|
Lutz S, Procházková L, Benning LG, Nedbalová L, Remias D. Evaluating High-Throughput Sequencing Data of Microalgae Living in Melting Snow: Improvements and Limitations 1. Fottea (Praha) 2019; 19:115-131. [PMID: 33414851 PMCID: PMC7116558 DOI: 10.5507/fot.2019.003] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/20/2023]
Abstract
Melting snow fields are an extremophilic habitat dominated by closely related Chlamydomonadaceae (Chlorophyta). Microscopy-based classification of these cryophilic microalgae is challenging and may not reveal the true diversity. High-throughput sequencing (HTS) allows for a more comprehensive evaluation of the community. However, HTS approaches have been rarely used in such ecosystems and the output of their application has not been evaluated. Furthermore, there is no consensus on the choice for a suitable DNA marker or data processing workflow. We found that the correct placement of taxonomic strings onto OTUs strongly depends on the quality of the reference databases. We improved the assignments of the HST data by generating additional reference sequences of the locally abundant taxa, guided by light microscopy. Furthermore, a manual inspection of all automated OTU assignments, oligotyping of the most abundant 18S OTUs, as well as ITS2 secondary structure analyses were necessary for accurate species assignments. Moreover, the sole use of one marker can cause misleading results, either because of insufficient variability within the locus (18S) or the scarcity of reference sequences (ITS2). Our evaluation reveals that HTS output needs to be thoroughly checked when the studied habitats or organisms are poorly represented in publicly available databases. We recommend an optimized workflow for an improved biodiversity evaluation of not only snow algal communities, but generally 'exotic' ecosystems where similar problems arise. A consistent sampling strategy, two- molecular marker approach, light microscopy-based guidance, generation of appropriate reference sequences and final manual verification of all taxonomic assignments are highly recommended.
Collapse
Affiliation(s)
| | | | - Liane G. Benning
- GFZ German Research Centre for Geosciences, Telegrafenberg, 14473 Potsdam, Germany School of Earth & Environment, University of Leeds, Woodhouse Lane, Leeds LS2 9JT, UK Department of Earth Sciences, Free University of Berlin, 12249 Berlin, Germany
| | - Linda Nedbalová
- Department of Ecology, Faculty of Science, Charles University in Prague, Viničná 7, 128 44 Prague 2, Czech Republic
- The Czech Academy of Sciences, Institute of Botany, Dukelská 135, 379 82 Třeboň, Czech Republic
| | - Daniel Remias
- University of Applied Sciences Upper Austria, Stelzhamerstraße 23, 4600 Wels, Austria
| |
Collapse
|
5
|
Peker N, Garcia-Croes S, Dijkhuizen B, Wiersma HH, van Zanten E, Wisselink G, Friedrich AW, Kooistra-Smid M, Sinha B, Rossen JWA, Couto N. A Comparison of Three Different Bioinformatics Analyses of the 16S-23S rRNA Encoding Region for Bacterial Identification. Front Microbiol 2019; 10:620. [PMID: 31040829 PMCID: PMC6476902 DOI: 10.3389/fmicb.2019.00620] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2018] [Accepted: 03/12/2019] [Indexed: 11/25/2022] Open
Abstract
Rapid and reliable identification of bacterial pathogens directly from patient samples is required for optimizing antimicrobial therapy. Although Sanger sequencing of the 16S ribosomal RNA (rRNA) gene is used as a molecular method, species identification and discrimination is not always achievable for bacteria as their 16S rRNA genes have sometimes high sequence homology. Recently, next generation sequencing (NGS) of the 16S–23S rRNA encoding region has been proposed for reliable identification of pathogens directly from patient samples. However, data analysis is laborious and time-consuming and a database for the complete 16S–23S rRNA encoding region is not available. Therefore, a better, faster, and stronger approach is needed for NGS data analysis of the 16S–23S rRNA encoding region. We compared speed and diagnostic accuracy of different data analysis approaches: de novo assembly followed by Basic Local Alignment Search Tool (BLAST), operational taxonomic unit (OTU) clustering, or mapping using an in-house developed 16S–23S rRNA encoding region database for the identification of bacterial species. De novo assembly followed by BLAST using the in-house database was superior to the other methods, resulting in the shortest turnaround time (2 h and 5 min), approximately 2 h less than OTU clustering and 4.5 h less than mapping, and a sensitivity of 80%. Mapping was the slowest and most laborious data analysis approach with a sensitivity of 60%, whereas OTU clustering was the least laborious approach with 70% sensitivity. Although the in-house database requires more sequence entries to improve the sensitivity, the combination of de novo assembly and BLAST currently appears to be the optimal approach for data analysis.
Collapse
Affiliation(s)
- Nilay Peker
- Department of Medical Microbiology and Infection Prevention, University Medical Center Groningen, University of Groningen, Groningen, Netherlands
| | - Sharron Garcia-Croes
- Department of Medical Microbiology and Infection Prevention, University Medical Center Groningen, University of Groningen, Groningen, Netherlands
| | - Brigitte Dijkhuizen
- Department of Medical Microbiology and Infection Prevention, University Medical Center Groningen, University of Groningen, Groningen, Netherlands
| | - Henry H Wiersma
- Department of Medical Microbiology and Infection Prevention, University Medical Center Groningen, University of Groningen, Groningen, Netherlands
| | - Evert van Zanten
- Department of Medical Microbiology, Certe, Groningen, Netherlands
| | - Guido Wisselink
- Department of Medical Microbiology, Certe, Groningen, Netherlands
| | - Alex W Friedrich
- Department of Medical Microbiology and Infection Prevention, University Medical Center Groningen, University of Groningen, Groningen, Netherlands
| | - Mirjam Kooistra-Smid
- Department of Medical Microbiology and Infection Prevention, University Medical Center Groningen, University of Groningen, Groningen, Netherlands.,Department of Medical Microbiology, Certe, Groningen, Netherlands
| | - Bhanu Sinha
- Department of Medical Microbiology and Infection Prevention, University Medical Center Groningen, University of Groningen, Groningen, Netherlands
| | - John W A Rossen
- Department of Medical Microbiology and Infection Prevention, University Medical Center Groningen, University of Groningen, Groningen, Netherlands
| | - Natacha Couto
- Department of Medical Microbiology and Infection Prevention, University Medical Center Groningen, University of Groningen, Groningen, Netherlands
| |
Collapse
|
6
|
Caruso V, Song X, Asquith M, Karstens L. Performance of Microbiome Sequence Inference Methods in Environments with Varying Biomass. mSystems 2019; 4:e00163-18. [PMID: 30801029 DOI: 10.1128/mSystems.00163-18] [Citation(s) in RCA: 77] [Impact Index Per Article: 15.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2018] [Accepted: 01/22/2019] [Indexed: 02/07/2023] Open
Abstract
Microbial communities have important ramifications for human health, but determining their impact requires accurate characterization. Current technology makes microbiome sequence data more accessible than ever. However, popular software methods for analyzing these data are based on algorithms developed alongside older sequencing technology and smaller data sets and thus may not be adequate for modern, high-throughput data sets. Additionally, samples from environments where microbes are scarce present additional challenges to community characterization relative to high-biomass environments, an issue that is often ignored. We found that a new class of microbiome sequence processing tools, called amplicon sequence variant (ASV) methods, outperformed conventional methods. In samples representing low-biomass communities, where sample contamination becomes a significant confounding factor, the improved accuracy of ASV methods may allow more-robust computational identification of contaminants. Microbiome community composition plays an important role in human health, and while most research to date has focused on high-microbial-biomass communities, low-biomass communities are also important. However, contamination and technical noise make determining the true community signal difficult when biomass levels are low, and the influence of varying biomass on sequence processing methods has received little attention. Here, we benchmarked six methods that infer community composition from 16S rRNA sequence reads, using samples of varying biomass. We included two operational taxonomic unit (OTU) clustering algorithms, one entropy-based method, and three more-recent amplicon sequence variant (ASV) methods. We first compared inference results from high-biomass mock communities to assess baseline performance. We then benchmarked the methods on a dilution series made from a single mock community—samples that varied only in biomass. ASVs/OTUs inferred by each method were classified as representing expected community, technical noise, or contamination. With the high-biomass data, we found that the ASV methods had good sensitivity and precision, whereas the other methods suffered in one area or in both. Inferred contamination was present only in small proportions. With the dilution series, contamination represented an increasing proportion of the data from the inferred communities, regardless of the inference method used. However, correlation between inferred contaminants and sample biomass was strongest for the ASV methods and weakest for the OTU methods. Thus, no inference method on its own can distinguish true community sequences from contaminant sequences, but ASV methods provide the most accurate characterization of community and contaminants. IMPORTANCE Microbial communities have important ramifications for human health, but determining their impact requires accurate characterization. Current technology makes microbiome sequence data more accessible than ever. However, popular software methods for analyzing these data are based on algorithms developed alongside older sequencing technology and smaller data sets and thus may not be adequate for modern, high-throughput data sets. Additionally, samples from environments where microbes are scarce present additional challenges to community characterization relative to high-biomass environments, an issue that is often ignored. We found that a new class of microbiome sequence processing tools, called amplicon sequence variant (ASV) methods, outperformed conventional methods. In samples representing low-biomass communities, where sample contamination becomes a significant confounding factor, the improved accuracy of ASV methods may allow more-robust computational identification of contaminants.
Collapse
|
7
|
Liu B, Zhang X, Bakken LR, Snipen L, Frostegård Å. Rapid Succession of Actively Transcribing Denitrifier Populations in Agricultural Soil During an Anoxic Spell. Front Microbiol 2019; 9:3208. [PMID: 30671037 PMCID: PMC6331397 DOI: 10.3389/fmicb.2018.03208] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2018] [Accepted: 12/11/2018] [Indexed: 12/25/2022] Open
Abstract
Denitrification allows sustained respiratory metabolism during periods of anoxia, an advantage in soils with frequent anoxic spells. However, the gains may be more than evened out by the energy cost of producing the denitrification machinery, particularly if the anoxic spell is short. This dilemma could explain the evolution of different regulatory phenotypes observed in model strains, such as sequential expression of the four denitrification genes needed for a complete reduction of nitrate to N2, or a “bet hedging” strategy where all four genes are expressed only in a fraction of the cells. In complex environments such strategies would translate into progressive onset of transcription by the members of the denitrifying community. We exposed soil microcosms to anoxia, sampled for amplicon sequencing of napA/narG, nirK/nirS, and nosZ genes and transcripts after 1, 2 and 4 h, and monitored the kinetics of NO, N2O, and N2. The cDNA libraries revealed a succession of transcribed genes from active denitrifier populations, which probably reflects various regulatory phenotypes in combination with cross-talks via intermediates (NO2−, NO) produced by the “early onset” denitrifying populations. This suggests that the regulatory strategies observed in individual isolates are also displayed in complex communities, and pinpoint the importance for successive sampling when identifying active key player organisms.
Collapse
Affiliation(s)
- Binbin Liu
- Faculty of Chemistry, Biotechnology and Food Science, Norwegian University of Life Sciences, Ås, Norway
| | - Xiaojun Zhang
- State Key Laboratory of Microbial Metabolism and School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
| | - Lars R Bakken
- Faculty of Chemistry, Biotechnology and Food Science, Norwegian University of Life Sciences, Ås, Norway
| | - Lars Snipen
- Faculty of Chemistry, Biotechnology and Food Science, Norwegian University of Life Sciences, Ås, Norway
| | - Åsa Frostegård
- Faculty of Chemistry, Biotechnology and Food Science, Norwegian University of Life Sciences, Ås, Norway
| |
Collapse
|
8
|
Mysara M, Njima M, Leys N, Raes J, Monsieurs P. From reads to operational taxonomic units: an ensemble processing pipeline for MiSeq amplicon sequencing data. Gigascience 2017; 6:1-10. [PMID: 28369460 PMCID: PMC5466709 DOI: 10.1093/gigascience/giw017] [Citation(s) in RCA: 38] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2016] [Accepted: 12/27/2016] [Indexed: 01/09/2023] Open
Abstract
The development of high-throughput sequencing technologies has provided microbial ecologists with an efficient approach to assess bacterial diversity at an unseen depth, particularly with the recent advances in the Illumina MiSeq sequencing platform. However, analyzing such high-throughput data is posing important computational challenges, requiring specialized bioinformatics solutions at different stages during the processing pipeline, such as assembly of paired-end reads, chimera removal, correction of sequencing errors, and clustering of those sequences into Operational Taxonomic Units (OTUs). Individual algorithms grappling with each of those challenges have been combined into various bioinformatics pipelines, such as mothur, QIIME, LotuS, and USEARCH. Using a set of well-described bacterial mock communities, state-of-the-art pipelines for Illumina MiSeq amplicon sequencing data are benchmarked at the level of the amount of sequences retained, computational cost, error rate, and quality of the OTUs. In addition, a new pipeline called OCToPUS is introduced, which is making an optimal combination of different algorithms. Huge variability is observed between the different pipelines in respect to the monitored performance parameters, where in general the amount of retained reads is found to be inversely proportional to the quality of the reads. By contrast, OCToPUS achieves the lowest error rate, minimum number of spurious OTUs, and the closest correspondence to the existing community, while retaining the uppermost amount of reads when compared to other pipelines. The newly introduced pipeline translates Illumina MiSeq amplicon sequencing data into high-quality and reliable OTUs, with improved performance and accuracy compared to the currently existing pipelines.
Collapse
Affiliation(s)
- Mohamed Mysara
- Unit of Microbiology, Belgian Nuclear Research Centre (SCK-CEN), Boeretang 200, 2400 Mol, Belgium.,Department of Bio-Engineering Sciences, Vrije Universiteit Brussel (VUB), Pleinlaan 2, 1050 Brussel, Belgium.,VIB Center for the Biology of Disease, VIB, Herestraat 49 - box 1028, 3000 Leuven, Belgium.,Department of Microbiology and Immunology, REGA institute, Herestraat 49 - box 1028, 3000 Leuven, Belgium
| | - Mercy Njima
- Unit of Microbiology, Belgian Nuclear Research Centre (SCK-CEN), Boeretang 200, 2400 Mol, Belgium
| | - Natalie Leys
- Unit of Microbiology, Belgian Nuclear Research Centre (SCK-CEN), Boeretang 200, 2400 Mol, Belgium
| | - Jeroen Raes
- Department of Bio-Engineering Sciences, Vrije Universiteit Brussel (VUB), Pleinlaan 2, 1050 Brussel, Belgium.,VIB Center for the Biology of Disease, VIB, Herestraat 49 - box 1028, 3000 Leuven, Belgium.,Department of Microbiology and Immunology, REGA institute, Herestraat 49 - box 1028, 3000 Leuven, Belgium
| | - Pieter Monsieurs
- Unit of Microbiology, Belgian Nuclear Research Centre (SCK-CEN), Boeretang 200, 2400 Mol, Belgium
| |
Collapse
|
9
|
Mysara M, Vandamme P, Props R, Kerckhof FM, Leys N, Boon N, Raes J, Monsieurs P. Reconciliation between operational taxonomic units and species boundaries. FEMS Microbiol Ecol 2017; 93:3065615. [PMID: 28334218 PMCID: PMC5812548 DOI: 10.1093/femsec/fix029] [Citation(s) in RCA: 49] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2016] [Accepted: 03/09/2017] [Indexed: 01/20/2023] Open
Abstract
The development of high-throughput sequencing technologies has revolutionised the field of microbial ecology via 16S rRNA gene amplicon sequencing approaches. Clustering those amplicon sequencing reads into operational taxonomic units (OTUs) using a fixed cut-off is a commonly used approach to estimate microbial diversity. A 97% threshold was chosen with the intended purpose that resulting OTUs could be interpreted as a proxy for bacterial species. Our results show that the robustness of such a generalised cut-off is questionable when applied to short amplicons only covering one or two variable regions of the 16S rRNA gene. It will lead to biases in diversity metrics and makes it hard to compare results obtained with amplicons derived with different primer sets. The method introduced within this work takes into account the differential evolutional rates of taxonomic lineages in order to define a dynamic and taxonomic-dependent OTU clustering cut-off score. For a taxonomic family consisting of species showing high evolutionary conservation in the amplified variable regions, the cut-off will be more stringent than 97%. By taking into consideration the amplified variable regions and the taxonomic family when defining this cut-off, such a threshold will lead to more robust results and closer correspondence between OTUs and species. This approach has been implemented in a publicly available software package called DynamiC.
Collapse
Affiliation(s)
- Mohamed Mysara
- Unit of Microbiology, Belgian Nuclear Research Centre (SCK-CEN), 2400 Mol, Belgium.,Department of Bio-Engineering sciences, Vrije Universiteit Brussel (VUB), 1050 Brussels, Belgium.,VIB lab for Bioinformatics and (eco-)systems biology, VIB, 3000 Leuven, Belgium.,Department of Microbiology and Immunology, REGA institute, KU Leuven, 3000 Leuven, Belgium
| | - Peter Vandamme
- Department of Biochemistry and microbiology, Ghent University, 9000 Ghent, Belgium
| | - Ruben Props
- Department of Biochemical and microbial technology, Ghent University, 9000 Ghent, Belgium
| | | | - Natalie Leys
- Unit of Microbiology, Belgian Nuclear Research Centre (SCK-CEN), 2400 Mol, Belgium
| | - Nico Boon
- Department of Biochemical and microbial technology, Ghent University, 9000 Ghent, Belgium
| | - Jeroen Raes
- VIB lab for Bioinformatics and (eco-)systems biology, VIB, 3000 Leuven, Belgium.,Department of Microbiology and Immunology, REGA institute, KU Leuven, 3000 Leuven, Belgium
| | - Pieter Monsieurs
- Unit of Microbiology, Belgian Nuclear Research Centre (SCK-CEN), 2400 Mol, Belgium
| |
Collapse
|
10
|
Bacci G, Bani A, Bazzicalupo M, Ceccherini MT, Galardini M, Nannipieri P, Pietramellara G, Mengoni A. Evaluation of the Performances of Ribosomal Database Project (RDP) Classifier for Taxonomic Assignment of 16S rRNA Metabarcoding Sequences Generated from Illumina-Solexa NGS. J Genomics 2015; 3:36-9. [PMID: 25653722 PMCID: PMC4316179 DOI: 10.7150/jgen.9204] [Citation(s) in RCA: 38] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023] Open
Abstract
Here we report a benchmark of the effect of bootstrap cut-off values of the RDP Classifier tool in terms of data retention along the different taxonomic ranks by using Illumina reads. Results provide guidelines for planning sequencing depths and selection of bootstrap cut-off in taxonomic assignments.
Collapse
Affiliation(s)
- Giovanni Bacci
- 1. Department of Biology, University of Florence, via Madonna del Piano 6, I-50019 Sesto Fiorentino, Firenze, Italy. ; 2. Consiglio per la Ricerca e la Sperimentazione in Agricoltura, Centro di Ricerca per lo Studio delle Relazioni tra Pianta e Suolo (CRA-RPS), Via della Navicella 2/4, I-00184 Roma, Italy
| | - Alessia Bani
- 1. Department of Biology, University of Florence, via Madonna del Piano 6, I-50019 Sesto Fiorentino, Firenze, Italy
| | - Marco Bazzicalupo
- 1. Department of Biology, University of Florence, via Madonna del Piano 6, I-50019 Sesto Fiorentino, Firenze, Italy
| | - Maria Teresa Ceccherini
- 3. Department of Agrifood Production and Environmental Science, University of Florence, P.le delle Cascine 28, I-50144, Firenze, Italy
| | - Marco Galardini
- 1. Department of Biology, University of Florence, via Madonna del Piano 6, I-50019 Sesto Fiorentino, Firenze, Italy
| | - Paolo Nannipieri
- 3. Department of Agrifood Production and Environmental Science, University of Florence, P.le delle Cascine 28, I-50144, Firenze, Italy
| | - Giacomo Pietramellara
- 3. Department of Agrifood Production and Environmental Science, University of Florence, P.le delle Cascine 28, I-50144, Firenze, Italy
| | - Alessio Mengoni
- 1. Department of Biology, University of Florence, via Madonna del Piano 6, I-50019 Sesto Fiorentino, Firenze, Italy
| |
Collapse
|
11
|
Eren AM, Maignien L, Sul WJ, Murphy LG, Grim SL, Morrison HG, Sogin ML. Oligotyping: Differentiating between closely related microbial taxa using 16S rRNA gene data. Methods Ecol Evol 2013; 4. [PMID: 24358444 PMCID: PMC3864673 DOI: 10.1111/2041-210x.12114] [Citation(s) in RCA: 420] [Impact Index Per Article: 38.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Abstract
Bacteria comprise the most diverse domain of life on Earth, where they occupy nearly every possible ecological niche and play key roles in biological and chemical processes. Studying the composition and ecology of bacterial ecosystems and understanding their function are of prime importance. High-throughput sequencing technologies enable nearly comprehensive descriptions of bacterial diversity through 16S ribosomal RNA gene amplicons. Analyses of these communities generally rely upon taxonomic assignments through reference data bases or clustering approaches using de facto sequence similarity thresholds to identify operational taxonomic units. However, these methods often fail to resolve ecologically meaningful differences between closely related organisms in complex microbial data sets. In this paper, we describe oligotyping, a novel supervised computational method that allows researchers to investigate the diversity of closely related but distinct bacterial organisms in final operational taxonomic units identified in environmental data sets through 16S ribosomal RNA gene data by the canonical approaches. Our analysis of two data sets from two different environments demonstrates the capacity of oligotyping at discriminating distinct microbial populations of ecological importance. Oligotyping can resolve the distribution of closely related organisms across environments and unveil previously overlooked ecological patterns for microbial communities. The URL http://oligotyping.org offers an open-source software pipeline for oligotyping.
Collapse
Affiliation(s)
- A Murat Eren
- Josephine Bay Paul Center for Comparative Molecular Biology and Evolution, Marine Biological Laboratory, Woods Hole, MA 02543 USA
| | - Loïs Maignien
- Josephine Bay Paul Center for Comparative Molecular Biology and Evolution, Marine Biological Laboratory, Woods Hole, MA 02543 USA
| | - Woo Jun Sul
- Josephine Bay Paul Center for Comparative Molecular Biology and Evolution, Marine Biological Laboratory, Woods Hole, MA 02543 USA
| | - Leslie G Murphy
- Josephine Bay Paul Center for Comparative Molecular Biology and Evolution, Marine Biological Laboratory, Woods Hole, MA 02543 USA
| | - Sharon L Grim
- Josephine Bay Paul Center for Comparative Molecular Biology and Evolution, Marine Biological Laboratory, Woods Hole, MA 02543 USA
| | - Hilary G Morrison
- Josephine Bay Paul Center for Comparative Molecular Biology and Evolution, Marine Biological Laboratory, Woods Hole, MA 02543 USA
| | - Mitchell L Sogin
- Josephine Bay Paul Center for Comparative Molecular Biology and Evolution, Marine Biological Laboratory, Woods Hole, MA 02543 USA
| |
Collapse
|