Comparison of Mothur and QIIME for the Analysis of Rumen Microbiota Composition Based on 16S rRNA Amplicon Sequences

López-García, Adrián; Pineda-Quiroga, C.; Atxaerandio, Raquel; Pérez, Adrian; Hernández, Itziar; García-Rodriguez, A.; González-Recio, Óscar

doi:10.3389/fmicb.2018.03010

Cited by 73 publications

(52 citation statements)

References 41 publications

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“…As short reads generated by the Illumina MiSeq do not allow sequencing the entire 16S rRNA gene, we performed a literature survey and selected 11 variable regions for amplification (see Table 1), after which resulting amplicons were sequenced and analyzed with the community-standard Mothur software tool suite [38,[58][59][60], using both the SILVA [44,61] and NCBI 16S [61] reference databases that contain high-quality curated bacterial 16S rRNA gene sequences down to the genus and species level, respectively. Our results generally indicate large variability in classification accuracy between different variable regions.…”

Section: Employing Short-read Second-generation (Illumina) Sequencingmentioning

confidence: 99%

Targeting the 16S rRNA Gene for Bacterial Identification in Complex Mixed Samples: Comparative Evaluation of Second (Illumina) and Third (Oxford Nanopore Technologies) Generation Sequencing Technologies

Winand

Bogaerts

Hoffman

et al. 2019

IJMS

145

131

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Rapid, accurate bacterial identification in biological samples is an important task for microbiology laboratories, for which 16S rRNA gene Sanger sequencing of cultured isolates is frequently used. In contrast, next-generation sequencing does not require intermediate culturing steps and can be directly applied on communities, but its performance has not been extensively evaluated. We present a comparative evaluation of second (Illumina) and third (Oxford Nanopore Technologies (ONT)) generation sequencing technologies for 16S targeted genomics using a well-characterized reference sample. Different 16S gene regions were amplified and sequenced using the Illumina MiSeq, and analyzed with Mothur. Correct classification was variable, depending on the region amplified. Using a majority vote over all regions, most false positives could be eliminated at the genus level but not the species level. Alternatively, the entire 16S gene was amplified and sequenced using the ONT MinION, and analyzed with Mothur, EPI2ME, and GraphMap. Although >99% of reads were correctly classified at the genus level, up to ≈40% were misclassified at the species level. Both technologies, therefore, allow reliable identification of bacterial genera, but can potentially misguide identification of bacterial species, and constitute viable alternatives to Sanger sequencing for rapid analysis of mixed samples without requiring any culturing steps.

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Section: Employing Short-read Second-generation (Illumina) Sequencingmentioning

confidence: 99%

Targeting the 16S rRNA Gene for Bacterial Identification in Complex Mixed Samples: Comparative Evaluation of Second (Illumina) and Third (Oxford Nanopore Technologies) Generation Sequencing Technologies

Winand

Bogaerts

Hoffman

et al. 2019

IJMS

145

131

View full text Add to dashboard Cite

show abstract

“…Differences in environment [46,52], season [48], age [36,50,51], sex [36], diet [18,42] or genetic background [53] can di cult comparisons among studies. In addition, technical differences, such as part of the intestine sampled [52,81], type of sample (adherent, transient or total microbiota) [82,83], DNA extraction techniques or analysis methodology [84,85], can also be a source of variation. However, even though we cannot make statistically sound comparisons among studies, it is safe to say that the typical microbial architecture of gilthead sea bream intestine was found in this study and the different genetic backgrounds of the families do not alter this core composition.…”

Section: Discussionmentioning

confidence: 99%

Genetic selection for growth drives differences in intestinal microbiota composition and parasite disease resistance in gilthead sea bream

Piazzon

Naya-Català

Perera

et al. 2020

Preprint

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Background The key effects of intestinal microbiota in animal health has led to an increasing interest in manipulating these bacterial populations to improve animal welfare. The aquaculture sector is no exception and in the last years many studies have described these populations in different fish species. However, this is not an easy task, as intestinal microbiota is composed of very dynamic populations that are influenced by different factors, such as diet, environment, host age and genetics. In the current study, we aimed to determine whether the genetic background of gilthead sea bream ( Sparus aurata ) influences the intestinal microbial composition, how these bacterial populations are modulated by dietary changes, and the effect of selection by growth on intestinal disease resistance. To that aim, three different groups of five families of gilthead sea bream that were selected during two generations for fast, intermediate or slow growth (F3 generation) were kept together in the same open-flow tanks and fed a control or a well-balanced plant-based diet during nine months. Six animals per family and dietary treatment were sacrificed and the adherent bacteria from the anterior intestinal portion were sequenced. In parallel, fish of the fast- and slow-growth groups were infected with the intestinal parasite Enteromyxum leei and the disease signs, prevalence, intensity and parasite abundance were evaluated. Results No differences were detected in alpha diversity indexes among families, and the core bacterial architecture was the prototypical composition of gilthead sea bream intestinal microbiota, indicating no dysbiosis in any of the groups. The plant-based diet significantly changed the microbiota in the intermediate- and slow-growth families, with a much lower effect on the fast-growth group. Interestingly, the smaller changes detected in the fast-growth families potentially accounted for more changes at the metabolic level when compared to the other families. Upon parasitic infection, the fast-growth group showed significantly lower disease signs and parasite intensity and abundance than the slow-growth animals. Conclusions These results show a clear genome-metagenome interaction indicating that the fast-growth families harbor a microbiota that is more flexible upon dietary changes. These animals also showed a better ability to cope with intestinal infections.

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“…Differences in environment [49,55], season [51], age [36,53,54], sex [36], diet [18,42] or genetic background [46] can di cult comparisons among studies. In addition, technical differences, such as part of the intestine sampled [55,81], type of sample (adherent, transient or total microbiota) [82,83], DNA extraction techniques or analysis methodology [84,85], can also be a source of variation. However, even though we cannot make statistically sound comparisons among studies, it is safe to say that the typical microbial architecture of gilthead sea bream intestine was found in this study and the different genetic backgrounds of the families do not alter this core composition.…”

Section: Discussionmentioning

confidence: 99%

Genetic selection for growth drives differences in intestinal microbiota composition and parasite disease resistance in gilthead sea bream

Piazzon

Naya-Català

Perera

et al. 2020

Preprint

View full text Add to dashboard Cite

Background: The key effects of intestinal microbiota in animal health has led to an increasing interest in manipulating these bacterial populations to improve animal welfare. The aquaculture sector is no exception and in the last years many studies have described these populations in different fish species. However, this is not an easy task, as intestinal microbiota is composed of very dynamic populations that are influenced by different factors, such as diet, environment, host age and genetics. In the current study, we aimed to determine whether the genetic background of gilthead sea bream (Sparus aurata) influences the intestinal microbial composition, how these bacterial populations are modulated by dietary changes, and the effect of selection by growth on intestinal disease resistance. To that aim, three different groups of five families of gilthead sea bream that were selected during two generations for fast, intermediate or slow growth (F3 generation) were kept together in the same open-flow tanks and fed a control or a well-balanced plant-based diet during nine months. Six animals per family and dietary treatment were sacrificed and the adherent bacteria from the anterior intestinal portion were sequenced. In parallel, fish of the fast- and slow-growth groups were infected with the intestinal parasite Enteromyxum leei and the disease signs, prevalence, intensity and parasite abundance were evaluated. Results: No differences were detected in alpha diversity indexes among families, and the core bacterial architecture was the prototypical composition of gilthead sea bream intestinal microbiota, indicating no dysbiosis in any of the groups. The plant-based diet significantly changed the microbiota in the intermediate- and slow-growth families, with a much lower effect on the fast-growth group. Interestingly, the smaller changes detected in the fast-growth families potentially accounted for more changes at the metabolic level when compared to the other families. Upon parasitic infection, the fast-growth group showed significantly lower disease signs and parasite intensity and abundance than the slow-growth animals. Conclusions: These results show a clear genome-metagenome interaction indicating that the fast-growth families harbor a microbiota that is more flexible upon dietary changes. These animals also showed a better ability to cope with intestinal infections.

show abstract

Comparison of Mothur and QIIME for the Analysis of Rumen Microbiota Composition Based on 16S rRNA Amplicon Sequences

Cited by 73 publications

References 41 publications

Targeting the 16S rRNA Gene for Bacterial Identification in Complex Mixed Samples: Comparative Evaluation of Second (Illumina) and Third (Oxford Nanopore Technologies) Generation Sequencing Technologies

Targeting the 16S rRNA Gene for Bacterial Identification in Complex Mixed Samples: Comparative Evaluation of Second (Illumina) and Third (Oxford Nanopore Technologies) Generation Sequencing Technologies

Genetic selection for growth drives differences in intestinal microbiota composition and parasite disease resistance in gilthead sea bream

Genetic selection for growth drives differences in intestinal microbiota composition and parasite disease resistance in gilthead sea bream

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