SERVICES

Mouse Run Report

Report generated on November 10, 2021

Submitted Samples

Sample names and analysis groups were taken directly from the submission form. The table was created with the kableExtra R package^[1]

Number of Samples Submitted: 10

Run Report

Sequencer Used: Illumina MiniSeq
NGS Sequencing Method: Paired-End Amplicon Sequencing
Bacterial Region Sequenced: 16S (V3, V4, and V6)
Amplicon Size: 160-300bp
Mock Communities Used: BEI Mock Even and Mock Staggered Communities for 16S

Total Reads: 7,871,350
Total Paired Reads: 3,935,675
Minimum Paired Read Count: 237,641
Maximum Paired Read Count: 410,966
Average Read Size: 128 bases

Read Counts Per Sample

Raw paired-end reads from each sample are reported in the following bar graph and corresponding table. All samples must pass this threshold for successful reporting of low abundant bacterial organisms. The submitted samples are labeled with the user-provided IDs from the submission form. Submitted samples are listed alphabetically, and duplicates are denoted with an underscore and numbered sequentially. The bar graph was generated with the ggplot2 and plotly R packages^{[2, 3]}, and the table was created with the kableExtra R package^[1]

Read Counts After Filtering

Paired-end reads were filtered with Fastp^[4] and new FastQ files were generated with the passing reads. To ensure proper alignments and classification, reads with ambigous bases in the forward or reverse reads were removed from the read count. Additionally, reads with less than 50 bases in either direction were filtered out. The resulting read counts after Fastp filtering are shown in the bar graph and table below. Raw read counts are shown as a blue bar and read counts following Fastp filtering are shown as a yellow bar on the graph. The percentages of passing paired-reads after Fastp filtering are reported in the table. The bar graph was generated with the ggplot2 and plotly R packages^{[2, 3]}, and the table was created with the kableExtra R package^[1]

Read Counts After Mothur Filtering

Read alignment and taxonomic classification were done using the Mothur pipeline for 16S data^[5]. Paired-end reads were used to generate a Fasta file that was used for 16S analysis. The Fasta sequences were aligned to the reference 16S V4 region obtained from the SILVA database^[6–8]. Aligned sequences were then classified using the RDP database^[9]. Classified reads were filtered to remove sequences outside of the 16S region, sequences with non-bacterial classification, and poor quality sequences. The remaining read counts are reported in the following graph and table. The percentages of passing reads after Mothur filtering are listed in the table. The bar graph was generated with the ggplot2 and plotly R packages^{[2, 3]}, and the table was created with the kableExtra R package^[1]

Group Diversity

The sequences were divide into organizational taxonomic units (OTUs) based on their taxonomic classifications. Group diversity (beta diversity) metrics assess the similarity and dissimilarity of OTU composition between groups of samples. Due to varying read counts between samples, all read counts were normalized to match the sample with the lowest read count, via subsampling without replacement, prior to statistical analysis. Diversity heatmaps and bar charts were generated using the total read count per sample.

Principal Component Analysis

Principal Component Analysis (PCA) was done using normalized read counts as described above. Groups were predefined on the sample submission form, and colored accordingly. Mock samples were also included for this analysis and separated as an independent group. A PCA was done to reduce the dimensions of the final OTU table, and the first two principal components (PC1 and PC2) are plotted below using the plotly R package^[3].

Phylum Level OTU Heatmap

Samples were clustered based on phylum level OTUs and the heatmap is shown below. OTU read counts were log10 .transformed and infinite values were set to zero. The sample and OTU dendrograms were ordered using optimal leaf ordering (OLO) from the heatmaply R package^[10] and the heatmap was visualized using the plotly R package^[3]. Values are shown as log10(total reads) and infinite values were set to zero.

AMOVA

A Statistics section will only be included if there is more than one group for the project.

The Analysis of Molecular Variance (AMOVA) is used to detect the variance of molecular markers between two groups. The groups used for these analyses have been predefined on the sample submission form. Normalized read counts from genus level OTUs were used for this section. Mothur uses an asterisk to denote significance (p-Value < 0.01). All the tables in this section were generated using the kableExtra R package^[1].

Bar Charts

Visualization of group diversity and taxonomic classification with bar charts. The Mock Communities were assessed for accurate classification and compared to the expected read counts from the BEI data sheet. The phylum level composition and the genus level composition are provided for all submitted samples. All bar graphs were generated using the ggplot2 and plotly R packages^{[2, 3]}.

V3 Composition

V4 Composition

V6 Composition

Sample Diversity

Sample Diversity (alpha diversity) summarizes the composition of the microbial community within a sample using measurements for its richness (number of taxonomic groups) and/or evenness (distribution of abundances of the groups). The calculations for Sample Diversity were done using normalized read counts as described in the Group Diversity section.

Rarefaction Curves

Rarefaction and rarefaction curves are used to measure OTU richness within a sample. The number of OTUs are plotted against the number of sequences and the resulting curves suggest the richness of each sample. Sequences were subsampled 100 times to generate the rarefaction curves below. Rarefaction curves typically flatten after a steep incline for samples that have been sufficiently sequenced. Low diversity samples will reach their peak and flatten out much earlier. Very high diversity samples or samples that have not been sequenced completely will continue to rise without flattening.

Diversity Index

Sample diversity can also be assessed using the Simpson’s Diversity Index and the Shannon’s Diversity Index. Simpson’s Diversity Index measures the probability that any two individuals drawn at random from a community belong to different species/OTUs (dominance and richness). Shannon’s Diversity Index describes the diversity and species/OTU richness of a sample in one metric. All the tables in this section were generated using the kableExtra R package^[1].

Taxonomic Classification

Sunburst Plots

The sunburst plots for the groups and individual samples were generated using the plotly R package^[3].

V3 Classification

V4 Classification

V6 Classification

Phylum Level Pie Charts

Pie charts showing the phylum level composition for the groups defined on the submission sheet as well as individual samples. These plots were generated using the plotly R package^[3].

V3 Pie Charts

V4 Pie Charts

V6 Pie Charts

Differential OTUs

Differntial OTU tables were generated using LEfSe. The LDA Effect Size (LEfSe) is an algorithm for High-Dimensional microbiome biomarker discovery. LEfSe uses the Kruskal-Wallis test, Wilcoxon-Rank Sum test, and Linear Discriminant Analysis to find biomarkers of groups^[11].

Classification Table

Tables with read counts and full taxonomic breakdowns. Regions are separated into tabs and the link for the csv can be found at the bottom of each table. These tables were created using the reactable R package^[12].