scholarly journals Octave plots for visualizing diversity of microbial OTUs

2018 ◽  
Author(s):  
Robert C. Edgar ◽  
Henrik Flyvbjerg
Keyword(s):  
Alpha Diversity ◽  
Read Depth ◽  
Marker Genes ◽  
Sampling Effort ◽  
Logarithmic Scale ◽  
Core Microbiome ◽  
Operational Taxonomic Units ◽  
Generation Sequencing ◽  
Diversity Metrics ◽  
Prostate Cancer Study

AbstractNext-generation sequencing of marker genes such as 16S ribosomal RNA is widely used to survey microbial communities. The abundance distribution (AD) of Operational Taxonomic Units (OTUs) in a sample is typically summarized by alpha diversity metrics, e.g. richness and entropy, discarding information about the AD shape. In this work, we describe octave plots, histograms which visualize the shape of microbial ADs by binning on a logarithmic scale with base 2. Optionally, histogram bars are colored to indicate possible spurious OTUs due to sequence error and cross-talk. Octave plots enable assessment of (a) the shape and completeness of the distribution, (b) the effects of noise on measured diversity, (c) whether low-abundance OTUs should be discarded, (d) whether alpha diversity metrics and estimators are reliable, and (e) the additional sampling effort (i.e., read depth) required to obtain a complete census of the community. The utility of octave plots is illustrated in a re-analysis of a prostate cancer study showing that the reported core microbiome is most likely an artifact of experimental error.

scholarly journals Alpha diversity metrics for noisy OTUs

2018 ◽  
Author(s):  
Robert C. Edgar ◽  
Henrik Flyvbjerg
Keyword(s):  
Alpha Diversity ◽  
Amplicon Sequencing ◽  
Marker Genes ◽  
Next Generation ◽  
Operational Taxonomic Units ◽  
Log Normal ◽  
Next Generation Sequencing Ngs ◽  
Near Future ◽  
Log Normal Distribution ◽  
Generation Sequencing

AbstractNext-generation sequencing (NGS) of marker genes such as 16S ribosomal RNA is widely used to survey microbial communities. The in-sample (alpha) diversity of Operational Taxonomic Units (OTUs) is often summarized by metrics such as richness or entropy which are calculated from observed abundances, or by estimators such as Chao1 which extrapolate to unobserved OTUs. Most such measures are adopted from traditional biodiversity studies, where observational error can often be neglected. However, errors introduced by next-generation amplicon sequencing tend to induce spurious OTUs and spurious counts in OTU tables, both of which are especially prevalent at low abundances. In consequence, traditional metrics may be grossly inaccurate if they are naively applied to NGS OTU tables. In this work, we describe two novel alpha diversity estimators which are calculated from OTU abundances above a specified threshold. The singleton-free estimator (SFE) is a non-parametric estimator which is derived from a similar approach to Chao1 but extrapolates using doublet and triplet abundances rather than singletons and doublets. The octave estimator (OE) fits a log-normal distribution to non-singleton bars of an octave plot. We show that these estimators are effective under suitable conditions, but these conditions rarely apply in practice. We conclude that extrapolating to unobserved OTUs remains an open problem which is unlikely to be solved in the near future.

scholarly journals Denoising the Denoisers: an independent evaluation of microbiome sequence error-correction approaches

PeerJ ◽  
2018 ◽  
Vol 6 ◽  
pp. e5364 ◽  
Author(s):  
Jacob T. Nearing ◽  
Gavin M. Douglas ◽  
André M. Comeau ◽  
Morgan G.I. Langille
Keyword(s):  
Alpha Diversity ◽  
Marker Genes ◽  
Rrna Gene ◽  
Sequencing Errors ◽  
Independent Evaluation ◽  
Common Strategy ◽  
Identity Threshold ◽  
Curtis Dissimilarity ◽  
Nucleotide Resolution ◽  
Diversity Metrics

High-depth sequencing of universal marker genes such as the 16S rRNA gene is a common strategy to profile microbial communities. Traditionally, sequence reads are clustered into operational taxonomic units (OTUs) at a defined identity threshold to avoid sequencing errors generating spurious taxonomic units. However, there have been numerous bioinformatic packages recently released that attempt to correct sequencing errors to determine real biological sequences at single nucleotide resolution by generating amplicon sequence variants (ASVs). As more researchers begin to use high resolution ASVs, there is a need for an in-depth and unbiased comparison of these novel “denoising” pipelines. In this study, we conduct a thorough comparison of three of the most widely-used denoising packages (DADA2, UNOISE3, and Deblur) as well as an open-reference 97% OTU clustering pipeline on mock, soil, and host-associated communities. We found from the mock community analyses that although they produced similar microbial compositions based on relative abundance, the approaches identified vastly different numbers of ASVs that significantly impact alpha diversity metrics. Our analysis on real datasets using recommended settings for each denoising pipeline also showed that the three packages were consistent in their per-sample compositions, resulting in only minor differences based on weighted UniFrac and Bray–Curtis dissimilarity. DADA2 tended to find more ASVs than the other two denoising pipelines when analyzing both the real soil data and two other host-associated datasets, suggesting that it could be better at finding rare organisms, but at the expense of possible false positives. The open-reference OTU clustering approach identified considerably more OTUs in comparison to the number of ASVs from the denoising pipelines in all datasets tested. The three denoising approaches were significantly different in their run times, with UNOISE3 running greater than 1,200 and 15 times faster than DADA2 and Deblur, respectively. Our findings indicate that, although all pipelines result in similar general community structure, the number of ASVs/OTUs and resulting alpha-diversity metrics varies considerably and should be considered when attempting to identify rare organisms from possible background noise.

scholarly journals Comparative analysis of bacterioplankton assemblages from two subtropical karst reservoirs of southwestern China with contrasting trophic status

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Qiang Li ◽  
Yadan Huang ◽  
Shenglin Xin ◽  
Zhongyi Li
Keyword(s):  
Trophic Status ◽  
Alpha Diversity ◽  
Decisive Role ◽  
Sensu Stricto ◽  
Karst Area ◽  
Southwestern China ◽  
Rrna Gene ◽  
Karst Water ◽  
Bacterioplankton Community ◽  
Generation Sequencing

AbstractAlthough bacterioplankton play an important role in aquatic ecosystems, less is known about bacterioplankton assemblages from subtropical karst reservoirs of southwestern China with contrasting trophic status. Here, 16S rRNA gene next-generation sequencing coupled with water chemistry analysis was applied to compare the bacterioplankton communities from a light eutrophic reservoir, DL Reservoir, and a mesotrophic reservoir, WL Reservoir, in subtropical karst area of southwestern China. Our findings indicated that Proteobacteria, Firmicutes, Actinobacteria, Bacteroidetes, Cyanobacteria and Verrucomicrobia dominated bacterioplankton community with contrasting relative frequency in the two subtropical karst reservoirs. Proteobacteria and Bacteroidetes were the core communities, which played important roles in karst biogeochemical cycles. Though WT, TN and DOC play the decisive role in assembling karst aquatic bacterioplankton, trophic status exerted significantly negative direct effects on bacterioplankton community composition and alpha diversity. Due to contrasting trophic status in the two reservoirs, the dominant taxa such as Enterobacter, Clostridium sensu stricto, Candidatus Methylacidiphilum and Flavobacteriia, that harbor potential functions as valuable and natural indicators of karst water health status, differed in DL Reservoir and WL Reservoir.

scholarly journals The Effects of Genetic Relatedness on the Preterm Infant Gut Microbiota

2021 ◽  
Vol 9 (2) ◽  
pp. 278
Author(s):  
Shen Jean Lim ◽  
Miriam Aguilar-Lopez ◽  
Christine Wetzel ◽  
Samia V. O. Dutra ◽  
Vanessa Bray ◽  
...  
Keyword(s):  
Gut Microbiota ◽  
Preterm Infant ◽  
Neonatal Intensive Care ◽  
Genetic Relatedness ◽  
Bovine Milk ◽  
Alpha Diversity ◽  
Neonatal Intensive Care Units ◽  
Rrna Gene ◽  
Microbial Composition ◽  
Operational Taxonomic Units

The preterm infant gut microbiota is influenced by environmental, endogenous, maternal, and genetic factors. Although siblings share similar gut microbial composition, it is not known how genetic relatedness affects alpha diversity and specific taxa abundances in preterm infants. We analyzed the 16S rRNA gene content of stool samples, ≤ and >3 weeks postnatal age, and clinical data from preterm multiplets and singletons at two Neonatal Intensive Care Units (NICUs), Tampa General Hospital (TGH; FL, USA) and Carle Hospital (IL, USA). Weeks on bovine milk-based fortifier (BMF) and weight gain velocity were significant predictors of alpha diversity. Alpha diversity between siblings were significantly correlated, particularly at ≤3 weeks postnatal age and in the TGH NICU, after controlling for clinical factors. Siblings shared higher gut microbial composition similarity compared to unrelated individuals. After residualizing against clinical covariates, 30 common operational taxonomic units were correlated between siblings across time points. These belonged to the bacterial classes Actinobacteria, Bacilli, Bacteroidia, Clostridia, Erysipelotrichia, and Negativicutes. Besides the influence of BMF and weight variables on the gut microbial diversity, our study identified gut microbial similarities between siblings that suggest genetic or shared maternal and environmental effects on the preterm infant gut microbiota.

Update of use of dietary indices to control for diet in human gut microbiota studies

2020 ◽  
Author(s):  
Ruth Bowyer ◽  
Nitin Shivappa ◽  
Philippa M Wells ◽  
Tim D Spector ◽  
Ailsa A Welch ◽  
...  
Keyword(s):  
Inflammatory Disease ◽  
Association Studies ◽  
Alpha Diversity ◽  
Initial Study ◽  
Healthy Eating Index ◽  
Operational Taxonomic Units ◽  
Using Data ◽  
Study Designs ◽  
Dietary Indices

Abstract Background In our recent publication (DOI: https://doi.org/10.1186/s40168-018-0455-y ) we concluded that of the three dietary indices we studied the Healthy Eating Index (HEI) was the index of choice where researchers wish to account for the role of diet in microbiome association studies. Following correspondence from its creators, we replicated our initial study with an additional index, the Dietary inflammation index (DII ® ) using an updated data analysis pipeline for microbiota comparisons that incorporate the use of Amplicon Sequence Variants (ASVs) rather than the previously utilised Operational Taxonomic Units (OTUs). The energy-adjusted DII (E-DII) reflects the inflammatory potential of an individual’s diet, after controlling for total energy intake, which is of interest given the known association between the microbiota and inflammatory disease. Results Using data from 5047 participants of the TwinsUK cohort, we observed the E-DII to be a valid dietary measure within this cohort. The E-DII had a stronger association with frailty, a measure of health deficit, than the HEI (E-DII: β = 0.12, p < 0.001; HEI: β = −0.09, p < 0.001) But not with BMI (E-DII: β = 0.04, p <0.01, HEI: β = −0.07, p <0.001). In a subset of individuals (n = 1853) with existing microbiota data, the E-DII had a stronger association with measures of microbiota species (alpha) diversity; the HEI had a higher number of associations with differences of taxa abundance. Of interest are the differences where the two indexes do not overlap). For example, the genus with the strongest association with the E-DII but not significantly associated with the HEI was assigned as Escherichia/Shigella (logFC=0.692, q<0.001). Both indexes were associated with PCoAs of weighted UniFrac distances. Conclusion Both the E-DII and HEI are strong candidates for use as covariates in microbiota association studies where measures of the microbiota are captured using ASVs. The E-DII is an interesting alternative to the HEI particularly where study designs are looking to assess causality of the microbiota in driving inflammation and inflammatory disease.

scholarly journals Comparative analysis and characterization of the gut microbiota of four farmed snakes from southern China

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e6658 ◽  
Author(s):  
Bing Zhang ◽  
Jing Ren ◽  
Daode Yang ◽  
Shuoran Liu ◽  
Xinguo Gong
Keyword(s):  
Gut Microbiota ◽  
Host Species ◽  
High Throughput Sequencing ◽  
Southern China ◽  
Alpha Diversity ◽  
Operational Taxonomic Units ◽  
Close Relationship ◽  
Elaphe Carinata ◽  
Deinagkistrodon Acutus

Background The gut microbiota plays an important role in host immunity and metabolic homeostasis. Although analyses of gut microbiotas have been used to assess host health and foster disease prevention and treatment, no comparative comprehensive study, assessing gut microbiotas among several species of farmed snake, is yet available. In this study, we characterized and compared the gut microbiotas of four species of farmed snakes (Naja atra, Ptyas mucosa, Elaphe carinata, and Deinagkistrodon acutus) using high-throughput sequencing of the 16S rDNA gene in southern China and tested whether there was a relationship between gut microbiotal composition and host species. Results A total of 629 operational taxonomic units across 22 samples were detected. The five most abundant phyla were Bacteroidetes, Proteobacteria, Firmicutes, Fusobacteria, and Actinobacteria, while the five most abundant genera were Bacteroides, Cetobacterium, Clostridium, Plesiomonas, and Paeniclostridium. This was the first report of the dominance of Fusobacteria and Cetobacterium in the snake gut. Our phylogenetic analysis recovered a relatively close relationship between Fusobacteria and Bacteroidetes. Alpha diversity analysis indicated that species richness and diversity were highest in the gut microbiota of D. acutus and lowest in that of E. carinata. Significant differences in alpha diversity were detected among the four farmed snake species. The gut microbiotas of conspecifics were more similar to each other than to those of heterospecifics. Conclusion This study provides the first comparative study of gut microbiotas among several species of farmed snakes, and provides valuable data for the management of farmed snakes. In farmed snakes, host species affected the species composition and diversity of the gut microbiota.

scholarly journals Metabarcoding Analyses of Gut Microbiota of Nile Tilapia (Oreochromis niloticus) from Lake Awassa and Lake Chamo, Ethiopia

2020 ◽  
Vol 8 (7) ◽  
pp. 1040
Author(s):  
Negash Kabtimer Bereded ◽  
Manuel Curto ◽  
Konrad J. Domig ◽  
Getachew Beneberu Abebe ◽  
Solomon Workneh Fanta ◽  
...  
Keyword(s):  
Gut Microbiota ◽  
Oreochromis Niloticus ◽  
Nile Tilapia ◽  
Large Scale ◽  
Alpha Diversity ◽  
Illumina Miseq ◽  
Operational Taxonomic Units ◽  
Nile Tilapia Oreochromis Niloticus ◽  
Miseq Platform ◽  
Ethiopian Lakes

The Nile tilapia (Oreochromis niloticus) gut harbors a diverse microbial community; however, their variation across gut regions, lumen and mucosa is not fully elucidated. In this study, gut microbiota of all samples across gut regions and sample types (luminal content and mucosa) were analyzed and compared from two Ethiopian lakes. Microbiota were characterized using 16S rRNA Illumina MiSeq platform sequencing. A total of 2061 operational taxonomic units (OTUs) were obtained and the results indicated that Nile tilapia from Lake Chamo harbored a much more diversified gut microbiota than Lake Awassa. In addition, the gut microbiota diversity varied significantly across the gut region based on the Chao1, Shannon and Simpson index. The microbiome analyses of all samples in the midgut region showed significantly higher values for alpha diversity (Chao 1, Shannon and Simpson). Beta diversity analysis revealed a clear separation of samples according to sampling areas and gut regions. The most abundant genera were Clostridium_sensu_stricto and Clostridium_XI genera across all samples. Between the two sampling lakes, two phyla, Phylum Fusobacteria and Cyanobacteria, were found to be significantly different. On the other hand, six phyla (Actinobacteria, Bacteroidetes, Chloroflexi, Firmicutes, Proteobacteria and Cyanobacteria) were significantly different across gut regions. In this study, we found that all samples shared a large core microbiota, comprising a relatively large number of OTUs, which was dominated by Proteobacteria, Firmicutes, Cyanobacteria, Fusobacteria and Actinobacteria. This study has established the bases for future large-scale investigations of gut microbiota of fishes in Ethiopian lakes.

scholarly journals 21 Profiling sow vaginal and fecal and piglet gastrointestinal microbiome and the passage of sow microbiome to their progeny from birth to weaning

2019 ◽  
Vol 97 (Supplement_3) ◽  
pp. 21-21
Author(s):  
Huyen Tran ◽  
Brenda de Rodas ◽  
Manohar M Lahoti ◽  
Timothy J Johnson
Keyword(s):  
Alpha Diversity ◽  
Illumina Miseq ◽  
Amplicon Sequencing ◽  
Gastrointestinal Microbiome ◽  
Core Microbiome ◽  
Linear Discriminant ◽  
Fecal Microbiome ◽  
The Core ◽  
Vaginal Swabs ◽  
Sampling Points

Abstract The objectives of this study were 1) to profile the sow vaginal and fecal microbiome and the corresponding piglet gastrointestinal microbiome from birth to weaning, and 2) to identify the core microbiome shared between sows and piglets. A total of 226 samples collected from sows (vaginal swabs pre-farrow; fecal samples at farrow, d 3, 7, 10, 17 post-farrow) and their progenies (stomach, ileum, and colon digesta at birth, d 2, and 14 after birth) were used for the analyses of microbial community structure using 16S rRNA V4 amplicon sequencing with Illumina MiSeq. Our data indicated that the piglet and sow microbiome were quite distinct. Piglets had lower bacterial alpha diversity (chao1, richness, Shannon, Simpson indices; P &lt; 0.01) than sows across all timepoints. Beta diversity of piglets by sample types was significantly different (P &lt; 0.001) than sows by sample types when averaged across all timepoints or separation by timepoints. Feature selection by the Linear discriminant analysis effect size (LEfSe) indicated that the genera associated with piglets included those classified as Lactobacillus, unclassified Micrococcaceae, and Rothia when averaged across sampling points and sample types. Genera associated with sows included those classified as Treponema, YRC22, Unclassified RF39, Unclassified Christensenellaceae, Turicibacter, Unclassified RFP12, Unclassified F16, Collinsella, Coprococcus, Unclassified Coriobacteriaceae, and Unclassified Mogibacteriaceae. The genera shared between sow vaginal samples and piglets included those classified as Bacteroides, Fusobacterium, Haemophilus, Prevotella, Veillonella, and unclassified Clostridiadiaceae. The genera shared between sow fecal and piglet samples included those classified as Bacteroides, Lactobacillus, unclassified Clostridiadiaceae, unclassified Ruminococceae, and Prevotella. Overall, there are evidences that bacterial genera were passed from sows to piglets and influenced the microbial communities of piglets later in life.

The assessment of populations of the aphid Sitobion avenae in field trials

1984 ◽  
Vol 102 (2) ◽  
pp. 487-497 ◽  
Author(s):  
H. J. B. Lowe
Keyword(s):  
Field Trials ◽  
Sitobion Avenae ◽  
Sampling Effort ◽  
Logarithmic Scale ◽  
Small Populations ◽  
Aphid Infestation ◽  
Large Numbers ◽  
The Mean ◽  
Adult Plants

SummaryVariation in infestation of S. avenae on plots of wheat was assessed by combining techniques of enhancing field infestations with scoring individual shoots according to the number of aphids on them, using a logarithmic scale with base 2. The mean score appeared the most useful way of describing the aphid infestation on each plot.In 1982, the sampling effort required for assessment of S. avenae on many plots was considerably reduced by this approach. The artificial increase in numbers of aphids avoided problems associated with sampling small populations, and the logarithmic classification of shoots reduced the time needed to assess large numbers. Scoring plots as a whole, although much quicker, gave results that differed from the assessments based on shoot sampling, and should be used with caution. The differences observed among cultivars in infestation of adult plants in the field in 1982 were not always the same as those observed on younger plants in the glasshouse.

scholarly journals Assessment of the incorporation of CNV surveillance into gene panel next-generation sequencing testing for inherited retinal diseases

2017 ◽  
Vol 55 (2) ◽  
pp. 114-121 ◽  
Author(s):  
Jamie M Ellingford ◽  
Bradley Horn ◽  
Christopher Campbell ◽  
Gavin Arno ◽  
Stephanie Barton ◽  
...  
Keyword(s):  
Next Generation Sequencing ◽  
Diagnostic Yield ◽  
Diagnostic Testing ◽  
Read Depth ◽  
Gene Panel ◽  
Genomic Variation ◽  
Next Generation ◽  
Diagnostic Laboratory ◽  
Inherited Retinal Dystrophies ◽  
Generation Sequencing

BackgroundDiagnostic use of gene panel next-generation sequencing (NGS) techniques is commonplace for individuals with inherited retinal dystrophies (IRDs), a highly genetically heterogeneous group of disorders. However, these techniques have often failed to capture the complete spectrum of genomic variation causing IRD, including CNVs. This study assessed the applicability of introducing CNV surveillance into first-tier diagnostic gene panel NGS services for IRD.MethodsThree read-depth algorithms were applied to gene panel NGS data sets for 550 referred individuals, and informatics strategies used for quality assurance and CNV filtering. CNV events were confirmed and reported to referring clinicians through an accredited diagnostic laboratory.ResultsWe confirmed the presence of 33 deletions and 11 duplications, determining these findings to contribute to the confirmed or provisional molecular diagnosis of IRD for 25 individuals. We show that at least 7% of individuals referred for diagnostic testing for IRD have a CNV within genes relevant to their clinical diagnosis, and determined a positive predictive value of 79% for the employed CNV filtering techniques.ConclusionIncorporation of CNV analysis increases diagnostic yield of gene panel NGS diagnostic tests for IRD, increases clarity in diagnostic reporting and expands the spectrum of known disease-causing mutations.

Sign in / Sign up

Export Citation Format

Share Document