Basic report for Microbiome of Moscow subway: pilot project

Report summary

Perform preprocessing of user data and analyze essential taxonomic and functional composition (without analysis of factors = meta-data about the samples).

Created04/01/2020
Updated05/01/2020
TypeBasic report
ProjectMicrobiome of Moscow subway: pilot project
Uploaded samples40

Data quality

Assessment of raw data quality.

Number of reads

Read quantity distribution

Number of the reads per sample before and after the quality filtering. Quality filtering (using split_libraries_fastq.py QIIME script) included: trimming of low-quality read ends (quality threshold = 20) and discarding of trimmed reads shorter than 75% of the initial length. Vertical line denotes minimal number of reads (3000 reads).

Download read_quant_distr.svg

Samples with low coverage

List of samples that had insufficient number of high-quality reads after the quality filtering (< 3000 reads) and were excluded from further analysis.

All samples passed the filter.

Read classification statistics

The reads were denoised using Deblur (target read length parameter value was determined as the most frequent read length across all samples). Taxonomy was assigned using a scikit-learn naive Bayes machine-learning classifier from QIIME2. The classifier was trained on Greengenes database v. 13.5, 97% OTU similarity. No subsequent filtering by minimum fraction of mapped reads is performed.

Proportion of classified reads

Distribution of the successfully classified reads for each sample.

Download classif_reads_frac.svg

Taxonomic composition

Heatmap of taxonomic composition

The interactive heatmap represents relative abundance of major microbial taxa (columns) in the samples (rows). Using the drop-down list “Heatmap settings” on the right of the heatmap, users can select taxonomic rank of interest. For convenience of comparison between close values, clicking on a cell “freezes” the displayed value of cell on the Legend and additionally the displayed abundance of top 10 taxa of corresponding sample (click again or on the cross near sample name to “unfreeze”). Use the Top control to change the way of major composition display between the top features in the selected sample and the top features across all samples on the average.

Major taxa

The boxplots represent distribution of relative abundance for 25 most abundant taxa across all samples (for each taxonomic rank). For proper display on log scale, zero values were replaced with a pseudocount not higher than minimum value of relative abundance of major taxa.

Complete taxonomic composition

The table contains relative abundance of all microbial taxa for each taxonomic rank.

Taxonomic core

The plot represents the proportion of OTUs shared across the varying proportion of samples.

Download taxa_core.svg

Analysis of outliers

Automatic filtering of the user samples with extreme taxonomic composition (based on the combined analysis of user and external data). Analysis of outliers: samples in upper 1% tail of distribution of median distance between each sample and closest 50% of neighbours approximated by normal distribution. List of outliers:

No outliers detected.

PCoA visualization based on taxonomic composition

Distribution of the samples by their taxonomic composition in reduced dimensionality. The closer the samples (points) on the plot, the more similar their composition. Vectors show the directions in which the levels of the respective major taxa increase. Method of dimension reduction: PCoA (Principal Coordinate Analysis); dissimilarity metric: Bray-Curtis. Clicking on a dot “freezes” the detailed information about the sample on the right of the plot (click again or on the cross near sample name to “unfreeze”). Switch between the display modes with or without outliers and with or without vectors showing major microbial “drivers” using the respective controls.

Enterotypes

Enterotyping (cluster analysis of samples by their composition) was performed according to the original protocol (Arumugam et al, 2011). The optimal number of clusters was determined according to the highest Calinski-Harabasz index. Silhouette width is a measure of the clustering quality. For each of the enterotypes, there is a list of its drivers – microbial taxa distinguishing the samples belonging to the cluster from the other samples.

Number of enterotypes

2

Calinski-Harabasz index

4.407

Average silhouette width of the clusters

0.063

Microbial drivers

Enterotype name: Enterotype 1

Table

taxon score
k__Bacteria;p__Proteobacteria;c__Alphaproteobacteria;o__Caulobacterales;f__Caulobacteraceae;g__Brevundimonas 0.43
k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Clostridiaceae;g__Proteiniclasticum 0.27
k__Bacteria;p__Bacteroidetes;c__Sphingobacteriia;o__Sphingobacteriales;f__Sphingobacteriaceae;g__Sphingobacterium 0.22
k__Bacteria;p__Proteobacteria;c__Betaproteobacteria;o__Burkholderiales;f__Alcaligenaceae;g__Achromobacter 0.21
k__Bacteria;p__Actinobacteria;c__Actinobacteria;o__Actinomycetales;f__Dietziaceae;g__Dietzia 0.20
k__Bacteria;p__Proteobacteria;c__Alphaproteobacteria;o__Rhodobacterales;f__Rhodobacteraceae;g__(Paracoccus/unclassified/Octadecabacter) 0.19
k__Bacteria;p__Proteobacteria;c__Alphaproteobacteria;o__Caulobacterales;f__Caulobacteraceae;g__ 0.18
k__Bacteria;p__Proteobacteria;c__Alphaproteobacteria;o__Rhodobacterales;f__Rhodobacteraceae;g__ 0.18
k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Clostridiaceae;g__ 0.18
k__Bacteria;p__Proteobacteria;c__Betaproteobacteria;o__Burkholderiales;f__Comamonadaceae;g__ 0.17

Enterotype name: Enterotype 2

Table

taxon score
k__Bacteria;p__Proteobacteria;c__Gammaproteobacteria;o__Xanthomonadales;f__Xanthomonadaceae;g__Stenotrophomonas 0.75
k__Bacteria;p__Proteobacteria;c__Alphaproteobacteria;o__Rhizobiales;f__Aurantimonadaceae;g__ 0.40
k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__[Tissierellaceae];g__Anaerococcus 0.36
k__Bacteria;p__Chloroflexi;c__Thermomicrobia;o__JG30-KF-CM45;f__;g__ 0.34
k__Bacteria;p__Actinobacteria;c__Actinobacteria;o__Actinomycetales;f__Micrococcaceae;g__Micrococcus 0.33
k__Bacteria;p__Actinobacteria;c__Actinobacteria;o__Actinomycetales;f__Nocardiaceae;g__Rhodococcus 0.31
k__Bacteria;p__Firmicutes;c__Bacilli;o__Lactobacillales;f__Aerococcaceae;g__Facklamia 0.31
k__Bacteria;p__Actinobacteria;c__Actinobacteria;o__Actinomycetales;f__Microbacteriaceae;g__Agromyces 0.30
k__Bacteria;p__Firmicutes;c__Bacilli;o__Lactobacillales;f__Aerococcaceae;g__Aerococcus 0.28
k__Bacteria;p__Firmicutes;c__Bacilli;o__Lactobacillales;f__Carnobacteriaceae;g__Trichococcus 0.27

Hierarchical clustering

The tree shows clustering of the samples by similarity of their taxonomic composition at varying levels of detail. Dissimilarity metric: Bray-Curtis; linkage: Ward’s method.

Alpha-diversity

Interactive plot

The measure describes the conditional number of taxa in each sample. Metric: Shannon index. Clicking on a dot “freezes” the displayed value on Y axis and additionally the abundance of top 10 taxa (click on it or on the cross near the sample name to “unfreeze”). In addition, the mean and confidence interval value appear when the mouse is over the boxplot. Controls at the top and bottom-right allow to change the displayed data.

Static plots

Shannon index

Chao1 index

Taxa co-occurence analysis

Co-occurence graph

Co-occurrence of microbial genera was analyzed basing on correlation analysis of their relative abundance using SPIEC-EASI software. In the graph, vertices show genera; pairs of highly co-occurring genera are connected with blue lines. The graph shows the members of the cooperatives - groups of highly co-occurring genera corresponding to isolated components (singleton vertices are omitted). Parameters of SPIEC-EASI algorithm: Meinshausen and Bühlmann neighbourhood selection method (MB), minimum lambda ratio= 0.1, number of lambda iterations = 20, model selection using StARS algorithm (number of StARS subsamples = 50).

Members of the cooperatives

Relative abundance of each cooperative in the samples.

Download sample_cooperative.csv

Reconstruction of metabolic potential

Predicted functional composition of microbiota.

Heatmap of functional composition

The interactive heatmap represents relative abundance of major pathways (columns) in the samples (rows). To switch between KEGG or MetaCyc nomenclatures, use the drop-down list in “Heatmap settings”. For convenience of comparison between close values, clicking on a cell “freezes” the displayed value of the cell in the displayed abundance of top features of the sample (click again or on the cross near the sample name to “unfreeze”). Use the Top control to change the way of major composition display between the top features in the selected sample and the top features across all samples on the average.

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Vitamins synthesis

Gut microbes are known to produce a number of vitamins. The boxplots represent median, standard deviation and quartiles of the vitamin biosynthesis pathways in the samples.

Gene groups

Relative abundance of KEGG Ortology gene groups involved in vitamins synthesis.

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Pathways

Relative abundance of pathways involved in vitamins synthesis.

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Plots

Total relative abundance of the genes involved in vitamins biosynthesis summed across the respective pathways.

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Description of pathways

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Complete functional composition

The table contains relative abundance of all functional features.

Percents

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Synthesis of short-chain fatty acids (SCFAs)

Gut microbes are known to produce SCFAs. The boxplots represent median, standard deviation and quartiles of the SCFAs biosynthesis pathways in the samples.

Synthesis of butyrate

Gene groups

Relative abundance of KEGG Ortology gene groups involved in butyrate synthesis.

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Pathways

Relative abundance of pathways involved in butyrate synthesis.

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Plots

Total relative abundance of the genes involved in butyrate synthesis summed across the respective pathways.

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Description of pathways

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Synthesis of propionate

Gene groups

Relative abundance of KEGG Ortology gene groups involved in propionate synthesis.

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Pathways

Relative abundance of pathways involved in propionate synthesis

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Plots

Total relative abundance of the genes involved in propionate synthesis summed across the respective pathways.

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Description of pathways

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All features tables

All calculated features can be downloaded here.

Alpha-diversity data

The table contains alpha-diversity values of all samples.

Download alpha_diversity.xlsx

Complete taxonomic composition

The table contains relative abundance of all microbial taxa for each taxonomic rank.

Complete functional composition

The table contains relative abundance of all functional features.

Percents

Nothing to show

Beta-diversity data

Table of Bray-Curtis dissimilarities between samples

Download beta_diversity.csv

knb_interactive: 2.0.2
knb_lib: 4.8.45
datalab: 3.10.0