Table of Content
I.Project Summary
II.Workflow Checklist
III.NGS Sequencing Results
IV.Complete Report Download
V.Raw Sequence Data Download
VI.Analysis - DADA2 Read Processing
VII.Analysis - Read Taxonomy Assignment
 Taxonomy Barplots
VIII.Analysis - Alpha Diversity
IX.Analysis - Beta Diversity
X.Analysis - Differential Abundance
 ANCOM Result
 LEfSe Result
XI.Analysis - Heatmap Profile
XII.Analysis - Network Association
(Click to navigate)

FOMC Service Report

16S rRNA Gene V1V3 Amplicon Sequencing

Version V1.1

The Forsyth Institute, Cambridge, MA, USA
March 25, 2021

Project ID: FOMC4038

I. Project Summary

Project FOMC4038 services include NGS sequencing of the V1V3 region of the 16S rRNA amplicons from the samples. First and foremost, please download this report, as well as the sequence raw data from the download links provided below. These links will expire after 60 days. We cannot guarantee the availability of your data after 60 days.

Full Bioinformatics analysis service was requested. We provide many analyses, starting from the raw sequence quality and noise filtering, pair reads merging, as well as chimera filtering for the sequences, using the DADA2 denosing algorithm and pipeline.

We also provide many downstream analyses such as taxonomy assignment, alpha and beta diversity analyses, and differential abundance analysis.

For taxonomy assignment, most informative would be the taxonomy barplots. We provide an interactive barplots to show the relative abundance of microbes at different taxonomy levels (from Phylum to species) that you can choose.

If you specify which groups of samples you want to compare for differential abundance, we provide both ANCOM and LEfSe differential abundance analysis.

 

II. Workflow Checklist

☑1.Sample Received
☑2.Sample Quality Evaluated
☑3.Sample Prepared for Sequencing
☑4.Next-Gen Sequencing
☑5.Sequence Quality Check
☑6.Absolute Abundance
☑7.Report and Raw Sequence Data Available for Download
☑8.Bioinformatics Analysis - Reads Processing (DADA2 Quality Trimming, Denoising, Paired Reads Merging)
☑9.Bioinformatics Analysis - Reads Taxonomy Assignment
☑10.Bioinformatics Analysis - Alpha Diversity Analysis
☑11.Bioinformatics Analysis - Beta Diversity Analysis
☑12.Bioinformatics Analysis - Differential Abundance Analysis
☑13.Bioinformatics Analysis - Heatmap Profile
☑14.Bioinformatics Analysis - Network Association
 

III. NGS Sequencing

The samples were processed and analyzed with the ZymoBIOMICS® Service: Targeted Metagenomic Sequencing (Zymo Research, Irvine, CA).

DNA Extraction: If DNA extraction was performed, one of three different DNA extraction kits was used depending on the sample type and sample volume and were used according to the manufacturer’s instructions, unless otherwise stated. The kit used in this project is marked below:

☐ZymoBIOMICS® DNA Miniprep Kit (Zymo Research, Irvine, CA)
☐ZymoBIOMICS® DNA Microprep Kit (Zymo Research, Irvine, CA)
☐ZymoBIOMICS®-96 MagBead DNA Kit (Zymo Research, Irvine, CA)
☑N/A (DNA Extraction Not Performed)
Elution Volume: 50µL
Additional Notes: NA

Targeted Library Preparation: The DNA samples were prepared for targeted sequencing with the Quick-16S™ NGS Library Prep Kit (Zymo Research, Irvine, CA). These primers were custom designed by Zymo Research to provide the best coverage of the 16S gene while maintaining high sensitivity. The primer sets used in this project are marked below:

☐Quick-16S™ Primer Set V1-V2 (Zymo Research, Irvine, CA)
☑Quick-16S™ Primer Set V1-V3 (Zymo Research, Irvine, CA)
☐Quick-16S™ Primer Set V3-V4 (Zymo Research, Irvine, CA)
☐Quick-16S™ Primer Set V4 (Zymo Research, Irvine, CA)
☐Quick-16S™ Primer Set V6-V8 (Zymo Research, Irvine, CA)
☐Other: NA
Additional Notes: NA

The sequencing library was prepared using an innovative library preparation process in which PCR reactions were performed in real-time PCR machines to control cycles and therefore limit PCR chimera formation. The final PCR products were quantified with qPCR fluorescence readings and pooled together based on equal molarity. The final pooled library was cleaned up with the Select-a-Size DNA Clean & Concentrator™ (Zymo Research, Irvine, CA), then quantified with TapeStation® (Agilent Technologies, Santa Clara, CA) and Qubit® (Thermo Fisher Scientific, Waltham, WA).

Control Samples: The ZymoBIOMICS® Microbial Community Standard (Zymo Research, Irvine, CA) was used as a positive control for each DNA extraction, if performed. The ZymoBIOMICS® Microbial Community DNA Standard (Zymo Research, Irvine, CA) was used as a positive control for each targeted library preparation. Negative controls (i.e. blank extraction control, blank library preparation control) were included to assess the level of bioburden carried by the wet-lab process.

Sequencing: The final library was sequenced on Illumina® MiSeq™ with a V3 reagent kit (600 cycles). The sequencing was performed with 10% PhiX spike-in.

Absolute Abundance Quantification*: A quantitative real-time PCR was set up with a standard curve. The standard curve was made with plasmid DNA containing one copy of the 16S gene and one copy of the fungal ITS2 region prepared in 10-fold serial dilutions. The primers used were the same as those used in Targeted Library Preparation. The equation generated by the plasmid DNA standard curve was used to calculate the number of gene copies in the reaction for each sample. The PCR input volume (2 µl) was used to calculate the number of gene copies per microliter in each DNA sample.
The number of genome copies per microliter DNA sample was calculated by dividing the gene copy number by an assumed number of gene copies per genome. The value used for 16S copies per genome is 4. The value used for ITS copies per genome is 200. The amount of DNA per microliter DNA sample was calculated using an assumed genome size of 4.64 x 106 bp, the genome size of Escherichia coli, for 16S samples, or an assumed genome size of 1.20 x 107 bp, the genome size of Saccharomyces cerevisiae, for ITS samples. This calculation is shown below:

Calculated Total DNA = Calculated Total Genome Copies × Assumed Genome Size (4.64 × 106 bp) ×
Average Molecular Weight of a DNA bp (660 g/mole/bp) ÷ Avogadro’s Number (6.022 x 1023/mole)


* Absolute Abundance Quantification is only available for 16S and ITS analyses.

The absolute abundance standard curve data can be viewed in Excel here:

The absolute abundance standard curve is shown below:

Absolute Abundance Standard Curve

 

IV. Complete Report Download

The complete report of your project, including all links in this report, can be downloaded by clicking the link provided below. The downloaded file is a compressed ZIP file and once unzipped, open the file “REPORT.html” (may only shown as "REPORT" in your computer) by double clicking it. Your default web browser will open it and you will see the exact content of this report.

Please download and save the file to your computer storage device. The download link will expire after 60 days upon your receiving of this report.

Complete report download link:

To view the report, please follow the following steps:
1.Download the .zip file from the report link above.
2.Extract all the contents of the downloaded .zip file to your desktop.
3.Open the extracted folder and find the "REPORT.html" (may shown as only "REPORT").
4.Open (double-clicking) the REPORT.html file. Your default browser will open the top age of the complete report. Within the report, there are links to view all the analyses performed for the project.

 

V. Raw Sequence Data Download

The raw NGS sequence data is available for download with the link provided below. The data is a compressed ZIP file and can be unzipped to individual sequence files. Since this is a pair-end sequencing, each of your samples is represented by two sequence files, one for READ 1, with the file extension “*_R1.fastq.gz”, another READ 2, with the file extension “*_R1.fastq.gz”. The files are in FASTQ format and are compressed. FASTQ format is a text-based data format for storing both a biological sequence and its corresponding quality scores. Most sequence analysis software will be able to open them. The Sample IDs associated with the R1 and R2 fastq files are listed in the table below:



Sample IDRead 1 File NameR1 Read Count


S10zr4038_10V1V3_R1.fastq.gz21847


S11zr4038_11V1V3_R1.fastq.gz24362


S12zr4038_12V1V3_R1.fastq.gz26032


S13zr4038_13V1V3_R1.fastq.gz25863


S14zr4038_14V1V3_R1.fastq.gz27704


S15zr4038_15V1V3_R1.fastq.gz25729


S16zr4038_16V1V3_R1.fastq.gz24525


S17zr4038_17V1V3_R1.fastq.gz34453


S18zr4038_18V1V3_R1.fastq.gz33123


S19zr4038_19V1V3_R1.fastq.gz26219


S01zr4038_1V1V3_R1.fastq.gz24835


S20zr4038_20V1V3_R1.fastq.gz31434


S21zr4038_21V1V3_R1.fastq.gz40154


S22zr4038_22V1V3_R1.fastq.gz32032


S23zr4038_23V1V3_R1.fastq.gz32991


S24zr4038_24V1V3_R1.fastq.gz28955


S25zr4038_25V1V3_R1.fastq.gz28979


S26zr4038_26V1V3_R1.fastq.gz30111


S27zr4038_27V1V3_R1.fastq.gz31544


S28zr4038_28V1V3_R1.fastq.gz24284


S29zr4038_29V1V3_R1.fastq.gz36452


S02zr4038_2V1V3_R1.fastq.gz26023


S30zr4038_30V1V3_R1.fastq.gz29563


S31zr4038_31V1V3_R1.fastq.gz37915


S32zr4038_32V1V3_R1.fastq.gz27783


S33zr4038_33V1V3_R1.fastq.gz27480


S34zr4038_34V1V3_R1.fastq.gz27440


S35zr4038_35V1V3_R1.fastq.gz24686


S36zr4038_36V1V3_R1.fastq.gz29049


S37zr4038_37V1V3_R1.fastq.gz40510


S38zr4038_38V1V3_R1.fastq.gz28569


S39zr4038_39V1V3_R1.fastq.gz29432


S03zr4038_3V1V3_R1.fastq.gz22346


S40zr4038_40V1V3_R1.fastq.gz25534


S41zr4038_41V1V3_R1.fastq.gz26940


S42zr4038_42V1V3_R1.fastq.gz24328


S43zr4038_43V1V3_R1.fastq.gz26032


S44zr4038_44V1V3_R1.fastq.gz31576


S45zr4038_45V1V3_R1.fastq.gz46498


S46zr4038_46V1V3_R1.fastq.gz29218


S47zr4038_47V1V3_R1.fastq.gz32764


S48zr4038_48V1V3_R1.fastq.gz24805


S49zr4038_49V1V3_R1.fastq.gz28827


S04zr4038_4V1V3_R1.fastq.gz23991


S50zr4038_50V1V3_R1.fastq.gz22762


S51zr4038_51V1V3_R1.fastq.gz12488


S52zr4038_52V1V3_R1.fastq.gz20212


S53zr4038_53V1V3_R1.fastq.gz29756


S54zr4038_54V1V3_R1.fastq.gz27982


S55zr4038_55V1V3_R1.fastq.gz27158


S56zr4038_56V1V3_R1.fastq.gz23374


S57zr4038_57V1V3_R1.fastq.gz16807


S58zr4038_58V1V3_R1.fastq.gz1324


S59zr4038_59V1V3_R1.fastq.gz23295


S05zr4038_5V1V3_R1.fastq.gz28359


S60zr4038_60V1V3_R1.fastq.gz22703


S61zr4038_61V1V3_R1.fastq.gz25310


S62zr4038_62V1V3_R1.fastq.gz23687


S63zr4038_63V1V3_R1.fastq.gz27268


S64zr4038_64V1V3_R1.fastq.gz30587


S65zr4038_65V1V3_R1.fastq.gz28561


S66zr4038_66V1V3_R1.fastq.gz28417


S67zr4038_67V1V3_R1.fastq.gz28530


S68zr4038_68V1V3_R1.fastq.gz33017


S69zr4038_69V1V3_R1.fastq.gz35383


S06zr4038_6V1V3_R1.fastq.gz24331


S70zr4038_70V1V3_R1.fastq.gz32626


S71zr4038_71V1V3_R1.fastq.gz28484


S72zr4038_72V1V3_R1.fastq.gz28371


S73zr4038_73V1V3_R1.fastq.gz23645


S74zr4038_74V1V3_R1.fastq.gz17486


S75zr4038_75V1V3_R1.fastq.gz16440


S76zr4038_76V1V3_R1.fastq.gz1197


S77zr4038_77V1V3_R1.fastq.gz20999


S78zr4038_78V1V3_R1.fastq.gz18748


S07zr4038_7V1V3_R1.fastq.gz27253


S08zr4038_8V1V3_R1.fastq.gz24716


S09zr4038_9V1V3_R1.fastq.gz25264

Please download and save the file to your computer storage device. The download link will expire after 60 days upon your receiving of this report.

Raw sequence data download link:

 

VI. Analysis - DADA2 Read Processing

What is DADA2?

DADA2 is a software package that models and corrects Illumina-sequenced amplicon errors. DADA2 infers sample sequences exactly, without coarse-graining into OTUs, and resolves differences of as little as one nucleotide. DADA2 identified more real variants and output fewer spurious sequences than other methods.

DADA2’s advantage is that it uses more of the data. The DADA2 error model incorporates quality information, which is ignored by all other methods after filtering. The DADA2 error model incorporates quantitative abundances, whereas most other methods use abundance ranks if they use abundance at all. The DADA2 error model identifies the differences between sequences, eg. A->C, whereas other methods merely count the mismatches. DADA2 can parameterize its error model from the data itself, rather than relying on previous datasets that may or may not reflect the PCR and sequencing protocols used in your study.

DADA2 Publication: Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJ, Holmes SP. DADA2: High-resolution sample inference from Illumina amplicon data. Nat Methods. 2016 Jul;13(7):581-3. doi: 10.1038/nmeth.3869. Epub 2016 May 23. PMID: 27214047; PMCID: PMC4927377.

DADA2 Software Package is available as an R package at : https://benjjneb.github.io/dada2/index.html

Analysis Procedures:

DADA2 pipeline includes several tools for read quality control, including quality filtering, trimming, denoising, pair merging and chimera filtering. Below are the major processing steps of DADA2:

Step 1. Read trimming based on sequence quality The quality of NGS Illumina sequences often decreases toward the end of the reads. DADA2 allows to trim off the poor quality read ends in order to improve the error model building and pair merging performance.

Step 2. Learn the Error Rates The DADA2 algorithm makes use of a parametric error model (err) and every amplicon dataset has a different set of error rates. The learnErrors method learns this error model from the data, by alternating estimation of the error rates and inference of sample composition until they converge on a jointly consistent solution. As in many machine-learning problems, the algorithm must begin with an initial guess, for which the maximum possible error rates in this data are used (the error rates if only the most abundant sequence is correct and all the rest are errors).

Step 3. Infer amplicon sequence variants (ASVs) based on the error model built in previous step. This step is also called sequence "denoising". The outcome of this step is a list of ASVs that are the equivalent of oligonucleotides.

Step 4. Merge paired reads. If the sequencing products are read pairs, DADA2 will merge the R1 and R2 ASVs into single sequences. Merging is performed by aligning the denoised forward reads with the reverse-complement of the corresponding denoised reverse reads, and then constructing the merged “contig” sequences. By default, merged sequences are only output if the forward and reverse reads overlap by at least 12 bases, and are identical to each other in the overlap region (but these conditions can be changed via function arguments).

Step 5. Remove chimera. The core dada method corrects substitution and indel errors, but chimeras remain. Fortunately, the accuracy of sequence variants after denoising makes identifying chimeric ASVs simpler than when dealing with fuzzy OTUs. Chimeric sequences are identified if they can be exactly reconstructed by combining a left-segment and a right-segment from two more abundant “parent” sequences. The frequency of chimeric sequences varies substantially from dataset to dataset, and depends on on factors including experimental procedures and sample complexity.

Results

1. Read Quality Plots NGS sequence analysis starts with visualizing the quality of the sequencing. Below are the quality plots of the first sample for the R1 and R2 reads separately. In gray-scale is a heat map of the frequency of each quality score at each base position. The mean quality score at each position is shown by the green line, and the quartiles of the quality score distribution by the orange lines. The forward reads are usually of better quality. It is a common practice to trim the last few nucleotides to avoid less well-controlled errors that can arise there. The trimming affects the downstream steps including error model building, merging and chimera calling. FOMC uses an empirical approach to test many combinations of different trim length in order to achieve best final amplicon sequence variants (ASVs), see the next section “Optimal trim length for ASVs”.

Below is the link to a PDF file for viewing the quality plots for all samples:

2. Optimal trim length for ASVs The final number of merged and chimera-filtered ASVs depends on the quality filtering (hence trimming) in the very beginning of the DADA2 pipeline. In order to achieve highest number of ASVs, an empirical approach was used -

  1. Create a random subset of each sample consisting of 5,000 R1 and 5,000 R2 (to reduce computation time)
  2. Trim 10 bases at a time from the ends of both R1 and R2 up to 50 bases
  3. For each combination of trimmed length (e.g., 300x300, 300x290, 290x290 etc), the trimmed reads are subject to the entire DADA2 pipeline for chimera-filtered merged ASVs
  4. The combination with highest percentage of the input reads becoming final ASVs is selected for the complete set of data

Below is the result of such operation, showing ASV percentages of total reads for all trimming combinations (1st Column = R1 lengths in bases; 1st Row = R2 lengths in bases):

R1/R2281271261251241231
32131.02%41.32%41.86%42.65%43.40%39.58%
31134.37%45.49%45.98%46.56%43.95%34.08%
30134.96%45.82%46.41%42.90%34.03%21.86%
29134.86%45.53%42.34%32.96%21.99%16.04%
28134.82%41.74%33.08%21.36%16.00%8.20%
27131.72%32.97%21.34%15.72%8.28%5.02%

Based on the above result, the trim length combination of R1 = 311 bases and R2 = 251 bases (highlighted red above), was chosen for generating final ASVs for all sequences. This combination generated highest number of merged non-chimeric ASVs and was used for downstream analyses, if requested.

3. Error plots from learning the error rates After DADA2 building the error model for the set of data, it is always worthwhile, as a sanity check if nothing else, to visualize the estimated error rates. The error rates for each possible transition (A→C, A→G, …) are shown below. Points are the observed error rates for each consensus quality score. The black line shows the estimated error rates after convergence of the machine-learning algorithm. The red line shows the error rates expected under the nominal definition of the Q-score. The ideal result would be the estimated error rates (black line) are a good fit to the observed rates (points), and the error rates drop with increased quality as expected.

Forward Read R1 Error Plot


Reverse Read R2 Error Plot

The PDF version of these plots are available here:

 

4. DADA2 Result Summary The table below shows the summary of the DADA2 analysis, tracking paired read counts of each samples for all the steps during DADA2 denoising process - including end-trimming (filtered), denoising (denoisedF, denoisedF), pair merging (merged) and chimera removal (nonchim).

Sample IDF4038.S01F4038.S02F4038.S03F4038.S04F4038.S05F4038.S06F4038.S07F4038.S08F4038.S09F4038.S10F4038.S11F4038.S12F4038.S13F4038.S14F4038.S15F4038.S16F4038.S17F4038.S18F4038.S19F4038.S20F4038.S21F4038.S22F4038.S23F4038.S24F4038.S25F4038.S26F4038.S27F4038.S28F4038.S29F4038.S30F4038.S31F4038.S32F4038.S33F4038.S34F4038.S35F4038.S36F4038.S37F4038.S38F4038.S39F4038.S40F4038.S41F4038.S42F4038.S43F4038.S44F4038.S45F4038.S46F4038.S47F4038.S48F4038.S49F4038.S50F4038.S51F4038.S52F4038.S53F4038.S54F4038.S55F4038.S56F4038.S57F4038.S58F4038.S59F4038.S60F4038.S61F4038.S62F4038.S63F4038.S64F4038.S65F4038.S66F4038.S67F4038.S68F4038.S69F4038.S70F4038.S71F4038.S72F4038.S73F4038.S74F4038.S75F4038.S76F4038.S77F4038.S78Row SumPercentage
input24,83526,02322,34623,99128,35924,33127,25324,71625,26421,84724,36226,03225,86327,70425,72924,52534,45333,12326,21931,43440,15432,03232,99128,95528,97930,11131,54424,28436,45229,56337,91527,78327,48027,44024,68629,04940,51028,56929,43225,53426,94024,32826,03231,57646,49829,21832,76424,80528,82722,76212,48820,21229,75627,98227,15823,37416,8071,32423,29522,70325,31023,68727,26830,58728,56128,41728,53033,01735,38332,62628,48428,37123,64517,48616,4401,19720,99918,7482,089,477100.00%
filtered18,70920,10716,36918,39021,79217,90320,62919,10918,63616,22018,06419,95519,77720,53118,70618,54726,21726,46519,44522,37131,41724,69025,92422,01622,12423,44324,15318,77227,56122,65229,41821,46920,58920,91718,35322,01729,86821,15421,32619,26920,30918,71619,99522,62735,83222,05424,94619,36021,70817,27279615,34723,17519,88120,12818,13810,77117018,16817,23820,09017,37920,33723,50121,26222,48322,31925,28427,34825,10321,88921,52217,88613,33012,03020716,37814,1161,574,16975.34%
denoisedF18,04719,18715,76317,53620,95617,25319,98518,02217,81515,49117,44719,13118,84119,51317,93317,86225,42725,45418,87021,77230,52123,56224,99921,47121,16422,44323,25217,67226,49321,30028,21920,44219,55020,41117,49521,53629,13820,21120,44818,55719,61418,11318,96421,90334,60821,09723,88218,67220,56816,06060814,54321,99519,23119,67217,24310,24215917,16516,72419,25916,66819,59722,86320,52821,79821,47324,65026,38124,21221,11020,57216,94712,73811,65713115,41613,5201,511,77272.35%
denoisedR18,34119,42815,85517,80820,97117,39020,08018,26618,04215,66617,49719,39318,99019,96918,21018,01325,67625,71019,01421,84630,76323,73825,28821,74421,16522,87623,58218,05826,72321,79128,63920,87220,00620,29817,92121,67429,58620,46920,34918,75219,72318,06119,50722,22934,85121,08324,17418,88720,83916,47070814,04722,50819,44919,91617,26010,05415917,32016,88219,38016,90919,89123,14120,71021,89421,58324,94826,77124,36721,31720,88917,38213,01311,82514015,58913,5341,527,86973.12%
merged16,02717,46413,05715,42818,53215,13517,63015,20015,64113,38415,00916,51215,49917,25414,73215,59023,24021,78016,48919,07327,28719,79321,54119,88117,97519,68119,88513,92222,78317,56024,24618,22316,33518,22115,24120,16627,52317,97017,04616,45917,38816,45516,48119,59931,44417,84520,33315,67217,07513,71556611,25417,89015,90218,59513,0117,529014,60015,16516,65914,57517,45921,70218,29519,44818,59222,99023,90221,14318,55218,05915,32511,72410,97912912,92211,5171,318,90563.12%
nonchim9,1529,7457,3857,8549,8218,3308,4448,1948,6617,3918,3628,5139,3949,3787,3236,36410,6829,3337,8089,81413,4879,1259,2889,56110,5579,85610,4437,91513,8229,74011,7739,4488,7557,3868,4979,71216,96610,2719,5318,2467,9568,5158,01210,52517,2379,60612,4647,5129,7739,0873385,7297,3627,68511,5515,5954,14608,8317,04510,7996,7569,89010,35611,4849,1488,93114,85711,32310,5898,4168,9617,4325,1624,1721297,2716,586687,55832.91%

This table can be downloaded as an Excel table below:

 

5. DADA2 Amplicon Sequence Variants (ASVs). A total of 15440 unique merged and chimera-free ASV sequences were identified, and their corresponding read counts for each sample are available in the "ASV Read Count Table" with rows for the ASV sequences and columns for sample. This read count table can be used for microbial profile comparison among different samples and the sequences provided in the table can be used to taxonomy assignment.

 

The table can be downloaded from this link:

 
 
 
 

VII. Analysis - Read Taxonomy Assignment

Read Taxonomy Assignment - Methods

 

The species-level, open-reference 16S rRNA NGS reads taxonomy assignment pipeline

Version 20210310
 

1. Raw sequences reads in FASTA format were BLASTN-searched against a combined set of 16S rRNA reference sequences. It consists of MOMD (version 0.1), the HOMD (version 15.2 http://www.homd.org/index.php?name=seqDownload&file&type=R ), HOMD 16S rRNA RefSeq Extended Version 1.1 (EXT), GreenGene Gold (GG) (http://greengenes.lbl.gov/Download/Sequence_Data/Fasta_data_files/gold_strains_gg16S_aligned.fasta.gz) , and the NCBI 16S rRNA reference sequence set (https://ftp.ncbi.nlm.nih.gov/blast/db/16S_ribosomal_RNA.tar.gz). These sequences were screened and combined to remove short sequences (<1000nt), chimera, duplicated and sub-sequences, as well as sequences with poor taxonomy annotation (e.g., without species information). This process resulted in 1,015 from HOMD V15.22, 495 from EXT, 3,940 from GG and 18,044 from NCBI, a total of 25,120 sequences. Altogether these sequences represent a total of 15,601 oral and non-oral microbial species.

The NCBI BLASTN version 2.7.1+ (Zhang et al, 2000) was used with the default parameters. Reads with ≥ 98% sequence identity to the matched reference and ≥ 90% alignment length (i.e., ≥ 90% of the read length that was aligned to the reference and was used to calculate the sequence percent identity) were classified based on the taxonomy of the reference sequence with highest sequence identity. If a read matched with reference sequences representing more than one species with equal percent identity and alignment length, it was subject to chimera checking with USEARCH program version v8.1.1861 (Edgar 2010). Non-chimeric reads with multi-species best hits were considered valid and were assigned with a unique species notation (e.g., spp) denoting unresolvable multiple species.

2. Unassigned reads (i.e., reads with < 98% identity or < 90% alignment length) were pooled together and reads < 200 bases were removed. The remaining reads were subject to the de novo operational taxonomy unit (OTU) calling and chimera checking using the USEARCH program version v8.1.1861 (Edgar 2010). The de novo OTU calling and chimera checking was done using 98% as the sequence identity cutoff, i.e., the species-level OTU. The output of this step produced species-level de novo clustered OTUs with 98% identity. Representative reads from each of the OTUs/species were then BLASTN-searched against the same reference sequence set again to determine the closest species for these potential novel species. These potential novel species were pooled together with the reads that were signed to specie-level in the previous step, for down-stream analyses.

Reference:
Edgar RC. Search and clustering orders of magnitude faster than BLAST. Bioinformatics. 2010 Oct 1;26(19):2460-1. doi: 10.1093/bioinformatics/btq461. Epub 2010 Aug 12. PubMed PMID: 20709691.

3. Designations used in the taxonomy:

	1) Taxonomy levels are indicated by these prefixes:
	
	   k__: domain/kingdom
	   p__: phylum
	   c__: class
	   o__: order
	   f__: family
	   g__: genus  
	   s__: species
	
	   Example: 
	
	   k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Lachnospiraceae;g__Blautia;s__faecis
		
	2) Unique level identified – known species:
	   
	   k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Lachnospiraceae;g__Roseburia;s__hominis
	
	   The above example shows some reads match to a single species (all levels are unique)
	
	3) Non-unique level identified – known species:

	   k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Lachnospiraceae;g__Roseburia;s__multispecies_spp123_3
	   
	   The above example “s__multispecies_spp123_3” indicates certain reads equally match to 3 species of the 
	   genus Roseburia; the “spp123” is a temporally assigned species ID.
	
	   k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Lachnospiraceae;g__multigenus;s__multispecies_spp234_5
	   
	   The above example indicates certain reads match equally to 5 different species, which belong to multiple genera.; 
	   the “spp234” is a temporally assigned species ID.
	
	4) Unique level identified – unknown species, potential novel species:
	   
	   k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Lachnospiraceae;g__Roseburia;s__ hominis_nov_97%
	   
	   The above example indicates that some reads have no match to any of the reference sequences with 
	   sequence identity ≥ 98% and percent coverage (alignment length)  ≥ 98% as well. However this groups 
	   of reads (actually the representative read from a de novo  OTU) has 96% percent identity to 
	   Roseburia hominis, thus this is a potential novel species, closest to Roseburia hominis. 
	   (But they are not the same species).
	
	5) Multiple level identified – unknown species, potential novel species:
	   k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Lachnospiraceae;g__Roseburia;s__ multispecies_sppn123_3_nov_96%
	
	   The above example indicates that some reads have no match to any of the reference sequences 
	   with sequence identity ≥ 98% and percent coverage (alignment length)  ≥ 98% as well. 
	   However this groups of reads (actually the representative read from a de novo  OTU) 
	   has 96% percent identity equally to 3 species in Roseburia. Thus this is no single 
	   closest species, instead this group of reads match equally to multiple species at 96%. 
	   Since they have passed chimera check so they represent a novel species. “sppn123” is a 
	   temporary ID for this potential novel species.

 
4. The taxonomy assignment algorithm is illustrated in this flow chart below:
 
 
 
 

Read Taxonomy Assignment - Result Summary

CodeCategoryRead Count (MC=1)*Read Count (MC=100)*
ATotal reads687,558687,558
BTotal assigned reads685,541685,541
CAssigned reads in species with read count < MC011,645
DAssigned reads in samples with read count < 500467467
ETotal samples7777
FSamples with reads >= 5007575
GSamples with reads < 50022
HTotal assigned reads used for analysis (B-C-D)685,074673,429
IReads assigned to single species655,452649,782
JReads assigned to multiple species17,81716,452
KReads assigned to novel species11,8057,195
LTotal number of species737364
MNumber of single species458316
NNumber of multi-species4415
ONumber of novel species23533
PTotal unassigned reads2,0172,017
QChimeric reads262262
RReads without BLASTN hits2323
SOthers: short, low quality, singletons, etc.1,7321,732
A=B+P=C+D+H+Q+R+S
E=F+G
B=C+D+H
H=I+J+K
L=M+N+O
P=Q+R+S
* MC = Minimal Count per species, species with total read count < MC were removed.
* The assignment result from MC=100 was used in the downstream analyses.
 
 

Read Taxonomy Assignment - Sample Meta Information

#SampleIDSample_namevisiticam_changevcam_changeperio
F4038.S011001.BS.1Visit 1DecreaseDecreaseYes
F4038.S0215.BS.2Visit 1IncreaseIncreaseNo
F4038.S0321.BS.3Visit 1DecreaseIncreaseNo
F4038.S04266.BS.4Visit 1IncreaseIncreaseNo
F4038.S0543.BS.5Visit 1DecreaseDecreaseYes
F4038.S0652.BS.6Visit 1IncreaseIncreaseYes
F4038.S0759.BS.7Visit 1DecreaseDecreaseNo
F4038.S08106.BS.8Visit 1DecreaseDecreaseYes
F4038.S09126.BS.9Visit 1DecreaseIncreaseYes
F4038.S10144.BS.10Visit 1IncreaseDecreaseNo
F4038.S11188.BS.11Visit 1IncreaseDecreaseNo
F4038.S12873.BS.12Visit 1DecreaseIncreaseYes
F4038.S131027.BS.13Visit 1DecreaseDecreaseYes
F4038.S14908.BS.14Visit 1DecreaseDecreaseYes
F4038.S15938.BS.15Visit 1DecreaseIncreaseNo
F4038.S16729.BS.16Visit 1IncreaseIncreaseYes
F4038.S17601.BS.17Visit 1DecreaseIncreaseNo
F4038.S18629.BS.18Visit 1DecreaseDecreaseYes
F4038.S1963.BS.19Visit 1DecreaseIncreaseNo
F4038.S20582.BS.20Visit 1IncreaseIncreaseNo
F4038.S21452.BS.21Visit 1IncreaseIncreaseNo
F4038.S22508.BS.23Visit 1IncreaseIncreaseNo
F4038.S23564.BS.24Visit 1DecreaseDecreaseYes
F4038.S24238.BS.25Visit 1IncreaseIncreaseNo
F4038.S25618.BS.26Visit 1IncreaseIncreaseNo
F4038.S26680.BS.28Visit 1DecreaseIncreaseYes
F4038.S27691.BS.29Visit 1IncreaseIncreaseYes
F4038.S28696.BS.30Visit 1IncreaseIncreaseYes
F4038.S29717.BS.31Visit 1IncreaseIncreaseYes
F4038.S30790.BS.32Visit 1DecreaseDecreaseYes
F4038.S31803.BS.33Visit 1DecreaseDecreaseYes
F4038.S32807.BS.34Visit 1IncreaseIncreaseNo
F4038.S33824.BS.35Visit 1IncreaseIncreaseYes
F4038.S34831.BS.36Visit 1IncreaseIncreaseNo
F4038.S35870.BS.37Visit 1IncreaseDecreaseYes
F4038.S36282.BS.38Visit 1IncreaseIncreaseNo
F4038.S37317.BS.39Visit 1DecreaseDecreaseNo
F4038.S38403.BS.40Visit 1DecreaseDecreaseNo
F4038.S39433.BS.41Visit 1DecreaseDecreaseNo
F4038.S401001.V2.42Visit 2DecreaseDecreaseYes
F4038.S4115.V2.43Visit 2IncreaseIncreaseNo
F4038.S4243.V2.44Visit 2DecreaseDecreaseYes
F4038.S43266.V2.45Visit 2IncreaseIncreaseNo
F4038.S4452.V2.46Visit 2IncreaseIncreaseYes
F4038.S45106.V2.47Visit 2DecreaseDecreaseYes
F4038.S4621.V2.48Visit 2DecreaseIncreaseNo
F4038.S47188.V2.49Visit 2IncreaseDecreaseNo
F4038.S4859.V2.50Visit 2DecreaseDecreaseNo
F4038.S49126.V2.51Visit 2DecreaseIncreaseYes
F4038.S50144.V2.52Visit 2IncreaseDecreaseNo
F4038.S51282.V2.53Visit 2IncreaseIncreaseNo
F4038.S52403.V2.54Visit 2DecreaseDecreaseNo
F4038.S53452.V2.55Visit 2IncreaseIncreaseNo
F4038.S54582.V2.56Visit 2IncreaseIncreaseNo
F4038.S55317.V2.57Visit 2DecreaseDecreaseNo
F4038.S56508.V2.58Visit 2IncreaseIncreaseNo
F4038.S57564.V2.59Visit 2DecreaseDecreaseYes
F4038.S58601.V2.60Visit 2DecreaseIncreaseNo
F4038.S59629.V2.62Visit 2DecreaseDecreaseYes
F4038.S60680.V2.63Visit 2DecreaseIncreaseYes
F4038.S61696.V2.64Visit 2IncreaseIncreaseYes
F4038.S62717.V2.65Visit 2IncreaseIncreaseYes
F4038.S63790.V2.66Visit 2DecreaseDecreaseYes
F4038.S64803.V2.67Visit 2DecreaseDecreaseYes
F4038.S65618.V2.68Visit 2IncreaseIncreaseNo
F4038.S66831.V2.69Visit 2IncreaseIncreaseNo
F4038.S67691.V2.70Visit 2IncreaseIncreaseYes
F4038.S68873.V2.71Visit 2DecreaseIncreaseYes
F4038.S69908.V2.72Visit 2DecreaseDecreaseYes
F4038.S70938.V2.73Visit 2DecreaseIncreaseNo
F4038.S71807.V2.74Visit 2IncreaseIncreaseNo
F4038.S72729.V2.75Visit 2IncreaseIncreaseYes
F4038.S731027.V2.76Visit 2DecreaseDecreaseYes
F4038.S74870.V2.77Visit 2IncreaseDecreaseYes
F4038.S7563.V2.78Visit 2DecreaseIncreaseNo
F4038.S76433.V2.79Visit 2DecreaseDecreaseNo
F4038.S77824.V2.80Visit 2IncreaseIncreaseYes
F4038.S78238.V2.81Visit 2IncreaseIncreaseNo
 
 

Read Taxonomy Assignment - ASV Read Counts by Samples

#Sample IDRead Count
F4038.S76129
F4038.S51338
F4038.S574146
F4038.S754172
F4038.S745162
F4038.S565595
F4038.S525729
F4038.S166364
F4038.S786586
F4038.S626756
F4038.S607045
F4038.S777271
F4038.S157323
F4038.S537362
F4038.S037385
F4038.S347386
F4038.S107391
F4038.S737432
F4038.S487512
F4038.S547685
F4038.S197808
F4038.S047854
F4038.S287915
F4038.S417956
F4038.S438012
F4038.S088194
F4038.S408246
F4038.S068330
F4038.S118362
F4038.S718416
F4038.S078444
F4038.S358497
F4038.S128513
F4038.S428515
F4038.S098661
F4038.S338755
F4038.S598831
F4038.S678931
F4038.S728961
F4038.S509087
F4038.S229125
F4038.S669148
F4038.S019152
F4038.S239288
F4038.S189333
F4038.S149378
F4038.S139394
F4038.S329448
F4038.S399531
F4038.S249561
F4038.S469606
F4038.S369712
F4038.S309740
F4038.S029745
F4038.S499773
F4038.S209814
F4038.S059821
F4038.S269856
F4038.S639890
F4038.S3810271
F4038.S6410356
F4038.S2710443
F4038.S4410525
F4038.S2510557
F4038.S7010589
F4038.S1710682
F4038.S6110799
F4038.S6911323
F4038.S6511484
F4038.S5511551
F4038.S3111773
F4038.S4712464
F4038.S2113487
F4038.S2913822
F4038.S6814857
F4038.S3716966
F4038.S4517237
 
 

Read Taxonomy Assignment - ASV Read Counts Table

SPIDTaxonomyF4038.S01F4038.S02F4038.S03F4038.S04F4038.S05F4038.S06F4038.S07F4038.S08F4038.S09F4038.S10F4038.S11F4038.S12F4038.S13F4038.S14F4038.S15F4038.S16F4038.S17F4038.S18F4038.S19F4038.S20F4038.S21F4038.S22F4038.S23F4038.S24F4038.S25F4038.S26F4038.S27F4038.S28F4038.S29F4038.S30F4038.S31F4038.S32F4038.S33F4038.S34F4038.S35F4038.S36F4038.S37F4038.S38F4038.S39F4038.S40F4038.S41F4038.S42F4038.S43F4038.S44F4038.S45F4038.S46F4038.S47F4038.S48F4038.S49F4038.S50F4038.S51F4038.S52F4038.S53F4038.S54F4038.S55F4038.S56F4038.S57F4038.S59F4038.S60F4038.S61F4038.S62F4038.S63F4038.S64F4038.S65F4038.S66F4038.S67F4038.S68F4038.S69F4038.S70F4038.S71F4038.S72F4038.S73F4038.S74F4038.S75F4038.S76F4038.S77F4038.S78
SP1k__Bacteria;p__Actinobacteria;c__Actinobacteria;o__Actinomycetales;f__Actinomycetaceae;g__Actinomyces;s__sp._Oral_Taxon_180003803949300145000000000001620846400000000000000000000001108500000000007572002526000106000300160072
SP100k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Veillonellaceae;g__Selenomonas;s__dianae0000000000000000000000003300310000000000000000930000000000003005600000000000000000
SP101k__Bacteria;p__Actinobacteria;c__Actinobacteria;o__Actinomycetales;f__Actinomycetaceae;g__Actinomyces;s__dentalis41007163075290034800000048000000001350560000550000003190037000121002318000060075280084336600000000000
SP102k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Lachnospiraceae_[XIV];g__Lachnospiraceae_[G-2];s__bacterium_HMT_0880000000000000130000000020000390000000000000000000450000000000000000009800000000000
SP103k__Bacteria;p__Bacteroidetes;c__Bacteroidia;o__Bacteroidales;f__Prevotellaceae;g__Prevotella;s__denticola001390188000320000000750000000000630000000000480037500017300640011002000080000560000000323200136109
SP104k__Bacteria;p__Bacteroidetes;c__Flavobacteriia;o__Flavobacteriales;f__Flavobacteriaceae;g__Capnocytophaga;s__sp._HMT_38003421202228000000000008000000084000880320005500000006102419000005602500000000000000000000000
SP105k__Bacteria;p__Bacteroidetes;c__Bacteroidia;o__Bacteroidales;f__Bacteroidales_[F-2];g__Bacteroidales_[G-2];s__sp._Oral_Taxon_27400480000000024292830020015200007800000068700000770015700010702124009210102100000113000000278013037500000000
SP106k__Bacteria;p__Firmicutes;c__Bacilli;o__Lactobacillales;f__Streptococcaceae;g__Streptococcus;s__parasanguinis_I000000500000000000001500000000000000000000110000000000000000000000000000081330000
SP107k__Bacteria;p__Actinobacteria;c__Actinobacteria;o__Actinomycetales;f__Actinomycetaceae;g__Actinomyces;s__sp._HMT_4489202900048500029740001000017000000000000000000000000052801580000001750000000000000000000290000
SP108k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Peptostreptococcaceae_[XI];g__Peptostreptococcaceae_[XI][G-1];s__bacterium_HMT_383015600000000000140000000000000000000000011000000000000000000000000000000000000000
SP109k__Bacteria;p__Fusobacteria;c__Fusobacteria;o__Fusobacteriales;f__Fusobacteriaceae;g__Fusobacterium;s__nucleatum_ss_nucleatum2100000000000027100000000014000000084430000000000000000000000000413000000026811500000000000
SP11k__Bacteria;p__Actinobacteria;c__Coriobacteriia;o__Coriobacteriales;f__Coriobacteriaceae;g__Slackia;s__exigua036000002000000000530000000150023000000000222200730000000000000000047000000000021016002320
SP110k__Bacteria;p__Actinobacteria;c__Actinobacteria;o__Actinomycetales;f__Micrococcaceae;g__Rothia;s__mucilaginosa0000000000000005000000000330006200000041000005500000000000000002927325668000103000590350636914
SP111k__Bacteria;p__Fusobacteria;c__Fusobacteriia;o__Fusobacteriales;f__Leptotrichiaceae;g__Leptotrichia;s__sp._HMT_21200011200000116440782000057000150000151000196344100005568009307702644006500008701110021400000000001970000000
SP112k__Bacteria;p__Firmicutes;c__Bacilli;o__Lactobacillales;f__Streptococcaceae;g__Streptococcus;s__sp._str._C30088312418811635591542241854529788341136000535293852488810419602150248029963903302141210486602805416722533927523299430096467496181572536014121501179910272446965613334442468114477000
SP113k__Bacteria;p__Bacteroidetes;c__Flavobacteriia;o__Flavobacteriales;f__Flavobacteriaceae;g__Capnocytophaga;s__leadbetteri0000000009811610822510958540212118001921350860119062108135992214790007904030000256214153820137000000218000827704725488016211531656000000
SP114k__Bacteria;p__Fusobacteria;c__Fusobacteria;o__Fusobacteria_[O];f__Fusobacteria_[F];g__Fusobacteria_[G-1];s__sp._Oral_Taxon_A71200000000000003100000000000000000000000000000000000000000001800000000000000000500
SP115k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Veillonellaceae;g__Veillonella;s__parvula103103482322937019120268217812719654141104760121778638337504748133259505180939201330033102000057732106747839718647111220729400258510624144036064530873791549458038437013350613212441601621265
SP116k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Peptostreptococcaceae;g__Peptoanaerobacter;s__yurii800000002547007943103005500000044001630096005300026000000800000001600002110600000243860256900000000
SP117k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Lachnospiraceae_[XIV];g__Lachnospiraceae_[G-3];s__bacterium_HMT_100000310037012130140991360130286000179008701348017121402511954801985102652350001261451141051860000000019100500015241760048030000000
SP118k__Bacteria;p__Proteobacteria;c__Betaproteobacteria;o__Neisseriales;f__Neisseriaceae;g__Neisseria;s__sicca00180000000000000016019371320825953396064046000620148000000000670000000000241190588203500948916510000055000
SP119k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Peptococcaceae;g__Peptococcus;s__sp._HMT_16700000000000000000000004600000000000000158000460054240003800000005704800000054825400000000
SP12k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Peptostreptococcaceae_[XI];g__Peptostreptococcaceae_[XI][G-9];s__[Eubacterium]_brachy48224002405225563490420033737900000620120430029002001203310792100400026100910550000001357002000011007065209065083000141
SP120k__Bacteria;p__Spirochaetes;c__Spirochaetes;o__Spirochaetales;f__Spirochaetaceae;g__Treponema;s__denticola01150000026900000230067000005200004000370000001210001100156000000000000510470000000041000000150
SP121k__Bacteria;p__Spirochaetes;c__Spirochaetia;o__Spirochaetales;f__Spirochaetaceae;g__Treponema;s__vincentii000000197200000000000000000000000014000000000003300000000000000100000000000000000
SP122k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Peptoniphilaceae;g__Parvimonas;s__sp._HMT_11000000000000000003280000000000000086000712000000000130001900000000000000008200031000000
SP125k__Bacteria;p__Bacteroidetes;c__Bacteroidia;o__Bacteroidales;f__Prevotellaceae;g__Prevotella;s__oris2201211370133452390540285300000000000237740002706142120120078638107918000000420000000005400078370400000561100108238
SP126k__Bacteria;p__Bacteroidetes;c__Flavobacteriia;o__Flavobacteriales;f__Flavobacteriaceae;g__Capnocytophaga;s__sp._HMT_326000001339200120089001950160001152472000010401783600001180037016415929430256010689152210000000870026200930220098513413054000
SP127k__Bacteria;p__Proteobacteria;c__Betaproteobacteria;o__Neisseriales;f__Neisseriaceae;g__Neisseria;s__elongata0000000003513107188402900270005100231021191920261572764031000395000000024696021000000230932370091020127893423000342900000
SP128k__Bacteria;p__Firmicutes;c__Bacilli;o__Lactobacillales;f__Streptococcaceae;g__Streptococcus;s__intermedius43115706021400481472782901810000050100000007400698700608000560000010450000074000004305000001620000300000
SP129k__Bacteria;p__Firmicutes;c__Negativicutes;o__Selenomonadales;f__Selenomonadaceae;g__Selenomonas;s__artemidis000000000074000019000000000000000007000001837000146034144040000000000000010620150000000740
SP13k__Bacteria;p__Fusobacteria;c__Fusobacteria;o__Fusobacteriales;f__Leptotrichiaceae;g__Leptotrichia;s__buccalis00090001000000012098000834022095001650003216445501670179004592084011900131297873158900011801797014400000001810055780000000
SP130k__Bacteria;p__Actinobacteria;c__Actinobacteria;o__Actinomycetales;f__Actinomycetaceae;g__Actinomyces;s__sp._HMT_89700033000300000021700000002419502000112025003200000000229001600107027000002707202200000880381700000000
SP131k__Bacteria;p__Bacteroidetes;c__Bacteroidia;o__Bacteroidales;f__Porphyromonadaceae;g__Porphyromonas;s__sp._HMT_277000000000000012000000000000000560000000120000000563500380000000000020000000000000000
SP132k__Bacteria;p__Firmicutes;c__Bacilli;o__Lactobacillales;f__Carnobacteriaceae;g__Granulicatella;s__elegans000000000000000000080000000000002080000000000000000000002300000008769000000151000001130
SP133k__Bacteria;p__Proteobacteria;c__Gammaproteobacteria;o__Pasteurellales;f__Pasteurellaceae;g__Haemophilus;s__sp._HMT_03600000000000000000000000000000000000000000000000000000000000000000029700000000120
SP134k__Bacteria;p__Bacteroidetes;c__Bacteroidia;o__Bacteroidales;f__Prevotellaceae;g__Prevotella;s__oulorum000031101170003620000000904500000002232001708000000000900150000441000000000059000000001900710
SP135k__Bacteria;p__Firmicutes;c__Negativicutes;o__Selenomonadales;f__Selenomonadaceae;g__Selenomonas;s__sp._HMT_134089000440290000000021000000014000590000000004249000002100000000000000100000000700000000022
SP136k__Bacteria;p__Actinobacteria;c__Actinobacteria;o__Actinomycetales;f__Actinomycetaceae;g__Actinomyces;s__johnsonii000512809101161048511300014100420108353000029015000012940000011902800079623200060001300000000000000010000074
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SP14k__Bacteria;p__Firmicutes;c__Bacilli;o__Lactobacillales;f__Streptococcaceae;g__Streptococcus;s__gordonii69900010220621061212192230014383035000383836013020911102644244680013502810234002018000194197446100284766001202400710161001600538025410586191478715050133000
SP140k__Bacteria;p__Bacteroidetes;c__Bacteroidia;o__Bacteroidales;f__Prevotellaceae;g__Alloprevotella;s__tannerae01050018667724100021190001080000000364660372461000000001560013100291150027002670000010404173000004840009000001080
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SP167k__Bacteria;p__Actinobacteria;c__Actinobacteria;o__Actinomycetales;f__Micrococcaceae;g__Rothia;s__aeria0201431041657005347211186000702401444817900001400177940126604035002965034000502640073001600107091070009770830213543571593890418722655090000
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SP177k__Bacteria;p__Absconditabacteria_(SR1);c__Absconditabacteria_(SR1)_[C-1];o__Absconditabacteria_(SR1)_[O-1];f__Absconditabacteria_(SR1)_[F-1];g__Absconditabacteria_(SR1)_[G-1];s__bacterium_HMT_345025000000000000007000000000074058640000000000018000000390000000340640000000130000000220
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SPN60k__Bacteria;p__Bacteroidetes;c__Bacteroidia;o__Bacteroidales;f__Porphyromonadaceae;g__Porphyromonas;s__catoniae00000000000000000000000000000000000001300000000000001070000000000000000000000000
SPN61k__Bacteria;p__Bacteroidetes;c__Bacteroidia;o__Bacteroidales;f__Prevotellaceae;g__Prevotella;s__sp._HMT_3170000000000000000000009200000000000000027000000000000000000000000000000000000000
SPN62k__Bacteria;p__Proteobacteria;c__Gammaproteobacteria;o__Pasteurellales;f__Pasteurellaceae;g__Haemophilus;s__paraphrohaemolyticus0000090000000000000000000000000000000000000620000000000460000000000000000000000
SPN63k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Peptostreptococcaceae_[XI];g__Peptostreptococcaceae_[XI][G-2];s__bacterium_HMT_0910000000000000000000000000000000000000000000000000000000000000000000000109000000
SPN64k__Bacteria;p__Firmicutes;c__Bacilli;o__Lactobacillales;f__Streptococcaceae;g__Streptococcus;s__gordonii00000000003200000000110000000000000027000004660000023000000000000000000000000000000
SPN66k__Bacteria;p__Firmicutes;c__Bacilli;o__Lactobacillales;f__Lactobacillaceae;g__Lactobacillus;s__crispatus00000000036000000000000000000560000000000000000000000160000000000000000000000000
SPN67k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Peptostreptococcaceae;g__Peptostreptococcaceae_[G];s__sp._Oral_Taxon_B6100003300000000000000000000000000000000000019000000000000000000000000000000000051
SPN68k__Bacteria;p__Proteobacteria;c__Betaproteobacteria;o__Neisseriales;f__Neisseriaceae;g__Kingella;s__oralis00006420000000000000000000000000000000000001800000000000000000000000000000000000
SPN69k__Bacteria;p__Proteobacteria;c__Gammaproteobacteria;o__Cardiobacteriales;f__Cardiobacteriaceae;g__Cardiobacterium;s__hominis00000000000000000014000000000000000510000000000000360000000000000000000000000000
SPN77k__Bacteria;p__Saccharibacteria_(TM7);c__Saccharibacteria_(TM7)_[C-1];o__Saccharibacteria_(TM7)_[O-1];f__Saccharibacteria_(TM7)_[F-1];g__Saccharibacteria_(TM7)_[G-1];s__bacterium_HMT_957000000000000000000001560000000540000000000000000002800000000000000000000012800044000
SPN88k__Bacteria;p__Proteobacteria;c__Betaproteobacteria;o__Neisseriales;f__Neisseriaceae;g__Neisseria;s__elongata0000000000000000000000389000000000000000000000000007000000000000000000000000000
SPN99k__Bacteria;p__Bacteroidetes;c__Flavobacteriia;o__Flavobacteriales;f__Flavobacteriaceae;g__Capnocytophaga;s__sputigena00000037000000000085000000000001500000000000000048000000014000000000000000000000000
SPP14k__Bacteria;p__Firmicutes;c__Bacilli;o__Lactobacillales;f__Streptococcaceae;g__Streptococcus;s__multispecies_spp14_20000000003200000000000000000660000251000000000000000000000000002500000000000000000
SPP18k__Bacteria;p__Firmicutes;c__Bacilli;o__Lactobacillales;f__Carnobacteriaceae;g__Granulicatella;s__multispecies_spp18_2106000063105029000006400380033001090517200000340135000000005168007000000310000800000730000840010466440660
SPP2k__Bacteria;p__Firmicutes;c__Bacilli;o__Lactobacillales;f__Streptococcaceae;g__Streptococcus;s__multispecies_spp2_3000000000000500000000000000001510000000000590000133000350000000000000000000000000000
SPP20k__Bacteria;p__Fusobacteria;c__Fusobacteria;o__Fusobacteriales;f__Fusobacteriaceae;g__Fusobacterium;s__multispecies_spp20_203200000200000670000695340000005100000000000008300130001260000000000000013900000007700111036001270
SPP21k__Bacteria;p__Bacteroidetes;c__Bacteroidia;o__Bacteroidales;f__Porphyromonadaceae;g__Porphyromonas;s__multispecies_spp21_2000000000000000000000264100000002700000000000000000000000033000000000000000000000
SPP25k__Bacteria;p__Firmicutes;c__Bacilli;o__Lactobacillales;f__Streptococcaceae;g__Streptococcus;s__multispecies_spp25_20000088000000000000000000000000000000000001208346000000000020000000000000000000000
SPP26k__Bacteria;p__Bacteroidetes;c__Flavobacteriia;o__Flavobacteriales;f__Flavobacteriaceae;g__Capnocytophaga;s__multispecies_spp26_20000000000000000000000000057000000000000000000000000000000000081000000000000000
SPP30k__Bacteria;p__Fusobacteria;c__Fusobacteriia;o__Fusobacteriales;f__Fusobacteriaceae;g__Fusobacterium;s__multispecies_spp30_2000000000630000247058300000000000400000000000000400000000000000000000000000000000000
SPP31k__Bacteria;p__Bacteroidetes;c__Flavobacteriia;o__Flavobacteriales;f__Flavobacteriaceae;g__Capnocytophaga;s__multispecies_spp31_2000000000000000000005900000000019000000000880000000000000000000013000000000000000
SPP32k__Bacteria;p__Firmicutes;c__Bacilli;o__Lactobacillales;f__Lactobacillaceae;g__Lactobacillus;s__multispecies_spp32_2000000000000000009200053475800000000000000000000000000000000000000000000000000000
SPP35k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__multifamily;g__multigenus;s__multispecies_spp35_2000000000000089000000000000000090000007000000000000000000000000000006000000000
SPP36k__Bacteria;p__Fusobacteria;c__Fusobacteria;o__Fusobacteriales;f__Fusobacteriaceae;g__Fusobacterium;s__multispecies_spp36_22272297401542950008416801282300000000004030001150001180013502301619502172520681286670000010900000169081002223260035600000360119293
SPP4k__Bacteria;p__Proteobacteria;c__Betaproteobacteria;o__Neisseriales;f__Neisseriaceae;g__Neisseria;s__multispecies_spp4_20000000014100000000000000000000000000000000000000000000000000000000000000000000
SPP44k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__multifamily;g__Lachnoanaerobaculum;s__saburreum8200153005400061040000005601201010000014111313700260350004677130315702600035000000000001170000000000000000
SPP6k__Bacteria;p__Firmicutes;c__Bacilli;o__Lactobacillales;f__Streptococcaceae;g__Streptococcus;s__multispecies_spp6_2000000003400000930000770000000001010075088000000000000010067000217000037000523055431470000829660000
SPPN1k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__multifamily;g__multigenus;s__multispecies_sppn1_2_nov_96.923%000000001600000000000000000000000023000000000000000000000000000000270006500000000
 
 
Download Read Count Tables at Different Taxonomy Levels
domain
phylum
class
order
family
genus
species
;
 

Sample Taxonomy Bar Plots

 

VIII. Analysis - Alpha Diversity

 

In ecology, alpha diversity (α-diversity) is the mean species diversity in sites or habitats at a local scale. The term was introduced by R. H. Whittaker[1][2] together with the terms beta diversity (β-diversity) and gamma diversity (γ-diversity). Whittaker's idea was that the total species diversity in a landscape (gamma diversity) is determined by two different things, the mean species diversity in sites or habitats at a more local scale (alpha diversity) and the differentiation among those habitats (beta diversity).

References:
Whittaker, R. H. (1960) Vegetation of the Siskiyou Mountains, Oregon and California. Ecological Monographs, 30, 279–338. doi:10.2307/1943563
Whittaker, R. H. (1972). Evolution and Measurement of Species Diversity. Taxon, 21, 213-251. doi:10.2307/1218190

 

Boxplot of Alpha-diversity indices

The two main factors taken into account when measuring diversity are richness and evenness. Richness is a measure of the number of different kinds of organisms present in a particular area. Evenness compares the similarity of the population size of each of the species present. There are many different ways to measure the richness and evenness. These measurements are called "estimators" or "indices". Below is a diversity of 3 commonly used indices showing the values for all the samples (dots) and in groups (boxes).

 
 
 
 
 

Alpha diversity analysis by rarefaction

Diversity measures are affected by the sampling depth. Rarefaction is a technique to assess species richness from the results of sampling. Rarefaction allows the calculation of species richness for a given number of individual samples, based on the construction of so-called rarefaction curves. This curve is a plot of the number of species as a function of the number of samples. Rarefaction curves generally grow rapidly at first, as the most common species are found, but the curves plateau as only the rarest species remain to be sampled.

References:
Willis AD. Rarefaction, Alpha Diversity, and Statistics. Front Microbiol. 2019 Oct 23;10:2407. doi: 10.3389/fmicb.2019.02407. PMID: 31708888; PMCID: PMC6819366.

 
 

IX. Analysis - Beta Diversity

 

NMDS and PCoA Plots

Beta diversity compares the similarity (or dissimilarity) of microbial profiles between different groups of samples. There are many different similarity/dissimilarity metrics. In general, they can be quantitative (using sequence abundance, e.g., Bray-Curtis or weighted UniFrac) or binary (considering only presence-absence of sequences, e.g., binary Jaccard or unweighted UniFrac). They can be even based on phylogeny (e.g., UniFrac metrics) or not (non-UniFrac metrics, such as Bray-Curtis, etc.).

For microbiome studies, species profiles of samples can be compared with the Bray-Curtis dissimilarity, which is based on the count data type. The pair-wise Bray-Curtis dissimilarity matrix of all samples can then be subject to either multi-dimensional scaling (MDS, also known as PCoA) or non-metric MDS (NMDS).

MDS/PCoA is a scaling or ordination method that starts with a matrix of similarities or dissimilarities between a set of samples and aims to produce a low-dimensional graphical plot of the data in such a way that distances between points in the plot are close to original dissimilarities.

NMDS is similar to MDS, however it does not use the dissimilarities data, instead it converts them into the ranks and use these ranks in the calculation.

In our beta diversity analysis, Bray-Curtis dissimilarity matrix was first calculated and then plotted by the PCoA and NMDS separately. The results are shown below:

 
 
 
 
 

The above PCoA and NMDS plots are based on count data. The count data can also be transformed into centered log ratio (CLR) for each species. The CLR data is no longer count data and cannot be used in Bray-Curtis dissimilarity calculation. Instead CLR can be compared with Euclidean distances. When CLR data are compared by Euclidean distance, the distance is also called Aitchison distance.

Below are the NMDS and PCoA plots of the Aitchison distances of the samples:

 
 
 
 
 

Interactive 3D PCoA Plots - Bray-Curtis Dissimilarity

 
 
 

Interactive 3D PCoA Plots - Euclidean Distance

 
 
 

Interactive 3D PCoA Plots - Correlation Coefficients

 
 
 

X. Analysis - Differential Abundance

16S rRNA next generation sequencing (NGS) generates a fixed number of reads that reflect the proportion of different species in a sample, i.e., the relative abundance of species, instead of the absolute abundance. In Mathematics, measurements involving probabilities, proportions, percentages, and ppm can all be thought of as compositional data. This makes the microbiome read count data “compositional” (Gloor et al, 2017). In general, compositional data represent parts of a whole which only carry relative information (http://www.compositionaldata.com/).

The problem of microbiome data being compositional arises when comparing two groups of samples for identifying “differentially abundant” species. A species with the same absolute abundance between two conditions, its relative abundances in the two conditions (e.g., percent abundance) can become different if the relative abundance of other species change greatly. This problem can lead to incorrect conclusion in terms of differential abundance for microbial species in the samples.

When studying differential abundance (DA), the current better approach is to transform the read count data into log ratio data. The ratios are calculated between read counts of all species in a sample to a “reference” count (e.g., mean read count of the sample). The log ratio data allow the detection of DA species without being affected by percentage bias mentioned above

In this report, a compositional DA analysis tool “ANCOM” (analysis of composition of microbiomes) was used. ANCOM transforms the count data into log-ratios and thus is more suitable for comparing the composition of microbiomes in two or more populations

References:

Gloor GB, Macklaim JM, Pawlowsky-Glahn V, Egozcue JJ. Microbiome Datasets Are Compositional: And This Is Not Optional. Front Microbiol. 2017 Nov 15;8:2224. doi: 10.3389/fmicb.2017.02224. PMID: 29187837; PMCID: PMC5695134.

Mandal S, Van Treuren W, White RA, Eggesbø M, Knight R, Peddada SD. Analysis of composition of microbiomes: a novel method for studying microbial composition. Microb Ecol Health Dis. 2015 May 29;26:27663. doi: 10.3402/mehd.v26.27663. PMID: 26028277; PMCID: PMC4450248.

 

ANCOM differential abundance analysis

 
View ANCOM results
Comparison No.Comparison Name
Comparison 1.No PD/Increase ICAM BL vs V2
Comparison 2.No PD/decrease ICAM BL vs V2
Comparison 3.No PD/Increase VCAM BL vs V2
Comparison 4.No PD/decrease VCAM BL vs V2
Comparison 5.PD/Increase ICAM BL vs V2
Comparison 6.PD/decrease ICAM BL vs V2
Comparison 7.PD/Increase VCAM BL vs V2
Comparison 8.PD/decrease VCAM BL vs V2
Comparison 9.Increased ICAM baseline; no PD vs PD
Comparison 10.Increased ICAM Visit 2; no PD vs PD
Comparison 11.decreased ICAM baseline; no PD vs PD
Comparison 12.decreased ICAM Visit 2; no PD vs PD
Comparison 13.Increased VCAM baseline; no PD vs PD
Comparison 14.Increased VCAM Visit 2; no PD vs PD
Comparison 15.decreased VCAM baseline; no PD vs PD
Comparison 16.Decreased VCAM Visit 2; no PD vs PD
Comparison 17.No PD; increase vs decrease ICAM baseline;
Comparison 18.no PD; increase vs decrease VCAM baseline;
Comparison 19.PD; increase vs decrease ICAM baseline;
Comparison 20.PD; increase vs decrease VCAM baseline;
Comparison 21.No PD; increase vs decrease ICAM Visit 2
Comparison 22.No PD; increase vs decrease VCAM Visit 2
Comparison 23.PD; increase vs decrease ICAM Visit 2
Comparison 24.PD; increase vs decrease VCAM Visit 2
 
 
 

LEfSe - Linear Discriminant Analysis Effect Size

LEfSe (Linear Discriminant Analysis Effect Size) is an alternative method to find "organisms, genes, or pathways that consistently explain the differences between two or more microbial communities" (Segata et al., 2011). Specifically, LEfSe uses rank-based Kruskal-Wallis (KW) sum-rank test to detect features with significant differential (relative) abundance with respect to the class of interest. Since it is rank-based, instead of proportional based, the differential species identified among the comparison groups is less biased (than percent abundance based).

Reference:

Segata N, Izard J, Waldron L, Gevers D, Miropolsky L, Garrett WS, Huttenhower C. Metagenomic biomarker discovery and explanation. Genome Biol. 2011 Jun 24;12(6):R60. doi: 10.1186/gb-2011-12-6-r60. PMID: 21702898; PMCID: PMC3218848.

 
No PD/Increase ICAM BL vs V2
 
 
 
 
 
 
 
All LEfSe Comparisons
Comparison No.Comparison Name
Comparison 1.No PD/Increase ICAM BL vs V2
Comparison 2.No PD/decrease ICAM BL vs V2
Comparison 3.No PD/Increase VCAM BL vs V2
Comparison 4.No PD/decrease VCAM BL vs V2
Comparison 5.PD/Increase ICAM BL vs V2
Comparison 6.PD/decrease ICAM BL vs V2
Comparison 7.PD/Increase VCAM BL vs V2
Comparison 8.PD/decrease VCAM BL vs V2
Comparison 9.Increased ICAM baseline; no PD vs PD
Comparison 10.Increased ICAM Visit 2; no PD vs PD
Comparison 11.decreased ICAM baseline; no PD vs PD
Comparison 12.decreased ICAM Visit 2; no PD vs PD
Comparison 13.Increased VCAM baseline; no PD vs PD
Comparison 14.Increased VCAM Visit 2; no PD vs PD
Comparison 15.decreased VCAM baseline; no PD vs PD
Comparison 16.Decreased VCAM Visit 2; no PD vs PD
Comparison 17.No PD; increase vs decrease ICAM baseline;
Comparison 18.no PD; increase vs decrease VCAM baseline;
Comparison 19.PD; increase vs decrease ICAM baseline;
Comparison 20.PD; increase vs decrease VCAM baseline;
Comparison 21.No PD; increase vs decrease ICAM Visit 2
Comparison 22.No PD; increase vs decrease VCAM Visit 2
Comparison 23.PD; increase vs decrease ICAM Visit 2
Comparison 24.PD; increase vs decrease VCAM Visit 2
 
 

XI. Analysis - Heatmap Profile

 

Species vs sample abundance heatmap

 
 
 

XII. Analysis - Network Association

To analyze the co-occurrence or co-exclusion between microbial species among different samples, network correlation analysis tools are usually used for this purpose. However, microbiome count data are compositional. If count data are normalized to the total number of counts in the sample, the data become not independent and traditional statistical metrics (e.g., correlation) for the detection of specie-species relationships can lead to spurious results. In addition, sequencing-based studies typically measure hundreds of OTUs (species) on few samples; thus, inference of OTU-OTU association networks is severely under-powered. Here we use SPIEC-EASI (SParse InversE Covariance Estimation for Ecological Association Inference), a statistical method for the inference of microbial ecological networks from amplicon sequencing datasets that addresses both of these issues (Kurtz et al., 2015). SPIEC-EASI combines data transformations developed for compositional data analysis with a graphical model inference framework that assumes the underlying ecological association network is sparse. SPIEC-EASI provides two algorithms for network inferencing – 1) Meinshausen-Bühlmann's neighborhood selection (MB method) and inverse covariance selection (GLASSO method, i.e., graphical least absolute shrinkage and selection operator). This is fundamentally distinct from SparCC, which essentially estimate pairwise correlations. In addition to these two methods, we provide the results of a third method - SparCC (Sparse Correlations for Compositional Data)(Friedman & Alm 2012), which is also a method for inferring correlations from compositional data. SparCC estimates the linear Pearson correlations between the log-transformed components.

References:

Kurtz ZD, Müller CL, Miraldi ER, Littman DR, Blaser MJ, Bonneau RA. Sparse and compositionally robust inference of microbial ecological networks. PLoS Comput Biol. 2015 May 7;11(5):e1004226. doi: 10.1371/journal.pcbi.1004226. PMID: 25950956; PMCID: PMC4423992.

Friedman J, Alm EJ. Inferring correlation networks from genomic survey data. PLoS Comput Biol. 2012;8(9):e1002687. doi: 10.1371/journal.pcbi.1002687. Epub 2012 Sep 20. PMID: 23028285; PMCID: PMC3447976.

 

SPIEC-EASI network inference by neighborhood selection (MB method)

 
 

 

SPIEC-EASI network inference by inverse covariance selection (GLASSO method)

 
 

 

Association network inference by SparCC

 
 

 
 
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