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 V1.11

The Forsyth Institute, Cambridge, MA, USA
September 08, 2021

Project ID: FOMC4661

I. Project Summary

Project FOMC4661 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.

 

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 NameRead 2 File Name


S10zr4661_10V1V3_R1.fastq.gzzr4661_10V1V3_R2.fastq.gz


S11zr4661_11V1V3_R1.fastq.gzzr4661_11V1V3_R2.fastq.gz


S12zr4661_12V1V3_R1.fastq.gzzr4661_12V1V3_R2.fastq.gz


S13zr4661_13V1V3_R1.fastq.gzzr4661_13V1V3_R2.fastq.gz


S14zr4661_14V1V3_R1.fastq.gzzr4661_14V1V3_R2.fastq.gz


S15zr4661_15V1V3_R1.fastq.gzzr4661_15V1V3_R2.fastq.gz


S16zr4661_16V1V3_R1.fastq.gzzr4661_16V1V3_R2.fastq.gz


S17zr4661_17V1V3_R1.fastq.gzzr4661_17V1V3_R2.fastq.gz


S18zr4661_18V1V3_R1.fastq.gzzr4661_18V1V3_R2.fastq.gz


S19zr4661_19V1V3_R1.fastq.gzzr4661_19V1V3_R2.fastq.gz


S01zr4661_1V1V3_R1.fastq.gzzr4661_1V1V3_R2.fastq.gz


S20zr4661_20V1V3_R1.fastq.gzzr4661_20V1V3_R2.fastq.gz


S21zr4661_21V1V3_R1.fastq.gzzr4661_21V1V3_R2.fastq.gz


S22zr4661_22V1V3_R1.fastq.gzzr4661_22V1V3_R2.fastq.gz


S23zr4661_23V1V3_R1.fastq.gzzr4661_23V1V3_R2.fastq.gz


S24zr4661_24V1V3_R1.fastq.gzzr4661_24V1V3_R2.fastq.gz


S25zr4661_25V1V3_R1.fastq.gzzr4661_25V1V3_R2.fastq.gz


S26zr4661_26V1V3_R1.fastq.gzzr4661_26V1V3_R2.fastq.gz


S27zr4661_27V1V3_R1.fastq.gzzr4661_27V1V3_R2.fastq.gz


S28zr4661_28V1V3_R1.fastq.gzzr4661_28V1V3_R2.fastq.gz


S29zr4661_29V1V3_R1.fastq.gzzr4661_29V1V3_R2.fastq.gz


S02zr4661_2V1V3_R1.fastq.gzzr4661_2V1V3_R2.fastq.gz


S30zr4661_30V1V3_R1.fastq.gzzr4661_30V1V3_R2.fastq.gz


S31zr4661_31V1V3_R1.fastq.gzzr4661_31V1V3_R2.fastq.gz


S32zr4661_32V1V3_R1.fastq.gzzr4661_32V1V3_R2.fastq.gz


S33zr4661_33V1V3_R1.fastq.gzzr4661_33V1V3_R2.fastq.gz


S34zr4661_34V1V3_R1.fastq.gzzr4661_34V1V3_R2.fastq.gz


S35zr4661_35V1V3_R1.fastq.gzzr4661_35V1V3_R2.fastq.gz


S36zr4661_36V1V3_R1.fastq.gzzr4661_36V1V3_R2.fastq.gz


S37zr4661_37V1V3_R1.fastq.gzzr4661_37V1V3_R2.fastq.gz


S38zr4661_38V1V3_R1.fastq.gzzr4661_38V1V3_R2.fastq.gz


S39zr4661_39V1V3_R1.fastq.gzzr4661_39V1V3_R2.fastq.gz


S03zr4661_3V1V3_R1.fastq.gzzr4661_3V1V3_R2.fastq.gz


S40zr4661_40V1V3_R1.fastq.gzzr4661_40V1V3_R2.fastq.gz


S41zr4661_41V1V3_R1.fastq.gzzr4661_41V1V3_R2.fastq.gz


S42zr4661_42V1V3_R1.fastq.gzzr4661_42V1V3_R2.fastq.gz


S43zr4661_43V1V3_R1.fastq.gzzr4661_43V1V3_R2.fastq.gz


S44zr4661_44V1V3_R1.fastq.gzzr4661_44V1V3_R2.fastq.gz


S45zr4661_45V1V3_R1.fastq.gzzr4661_45V1V3_R2.fastq.gz


S46zr4661_46V1V3_R1.fastq.gzzr4661_46V1V3_R2.fastq.gz


S47zr4661_47V1V3_R1.fastq.gzzr4661_47V1V3_R2.fastq.gz


S48zr4661_48V1V3_R1.fastq.gzzr4661_48V1V3_R2.fastq.gz


S49zr4661_49V1V3_R1.fastq.gzzr4661_49V1V3_R2.fastq.gz


S04zr4661_4V1V3_R1.fastq.gzzr4661_4V1V3_R2.fastq.gz


S50zr4661_50V1V3_R1.fastq.gzzr4661_50V1V3_R2.fastq.gz


S51zr4661_51V1V3_R1.fastq.gzzr4661_51V1V3_R2.fastq.gz


S52zr4661_52V1V3_R1.fastq.gzzr4661_52V1V3_R2.fastq.gz


S53zr4661_53V1V3_R1.fastq.gzzr4661_53V1V3_R2.fastq.gz


S54zr4661_54V1V3_R1.fastq.gzzr4661_54V1V3_R2.fastq.gz


S55zr4661_55V1V3_R1.fastq.gzzr4661_55V1V3_R2.fastq.gz


S56zr4661_56V1V3_R1.fastq.gzzr4661_56V1V3_R2.fastq.gz


S57zr4661_57V1V3_R1.fastq.gzzr4661_57V1V3_R2.fastq.gz


S58zr4661_58V1V3_R1.fastq.gzzr4661_58V1V3_R2.fastq.gz


S59zr4661_59V1V3_R1.fastq.gzzr4661_59V1V3_R2.fastq.gz


S05zr4661_5V1V3_R1.fastq.gzzr4661_5V1V3_R2.fastq.gz


S60zr4661_60V1V3_R1.fastq.gzzr4661_60V1V3_R2.fastq.gz


S06zr4661_6V1V3_R1.fastq.gzzr4661_6V1V3_R2.fastq.gz


S07zr4661_7V1V3_R1.fastq.gzzr4661_7V1V3_R2.fastq.gz


S08zr4661_8V1V3_R1.fastq.gzzr4661_8V1V3_R2.fastq.gz


S09zr4661_9V1V3_R1.fastq.gzzr4661_9V1V3_R2.fastq.gz

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 mergicing 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 analaysis 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
32139.78%46.45%46.60%47.99%48.84%44.42%
31141.66%47.98%48.40%49.35%45.84%32.40%
30141.98%48.20%48.63%44.77%32.84%19.38%
29141.62%47.73%43.51%31.38%19.48%14.18%
28142.43%44.23%31.41%18.87%14.37%5.42%
27138.26%32.05%19.05%13.96%5.27%2.67%

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 IDF4661.S01F4661.S02F4661.S03F4661.S04F4661.S05F4661.S06F4661.S07F4661.S08F4661.S09F4661.S10F4661.S11F4661.S12F4661.S13F4661.S14F4661.S15F4661.S16F4661.S17F4661.S18F4661.S19F4661.S20F4661.S21F4661.S22F4661.S23F4661.S24F4661.S25F4661.S26F4661.S27F4661.S28F4661.S29F4661.S30F4661.S31F4661.S32F4661.S33F4661.S34F4661.S35F4661.S36F4661.S37F4661.S38F4661.S39F4661.S40F4661.S41F4661.S42F4661.S43F4661.S44F4661.S45F4661.S46F4661.S47F4661.S48F4661.S49F4661.S50F4661.S51F4661.S52F4661.S53F4661.S54F4661.S55F4661.S56F4661.S57F4661.S58F4661.S59F4661.S60Row SumPercentage
input42,72769,87758,36256,80251,87754,01959,51463,97845,29351,59946,45050,36546,66246,17253,90555,72242,32055,07045,73751,53846,61650,95554,22149,51738,63452,62043,59950,82042,11449,08644,48151,23350,45759,51452,40161,34661,26965,32162,54963,13347,93650,57350,65252,73752,34959,41256,21157,88142,42050,88845,76752,49748,26148,56852,09353,42739,41348,83753,90360,7853,122,485100.00%
filtered22,87845,71937,91435,94032,73332,45638,23142,24127,98532,16025,36233,11528,56828,13433,19536,36524,90035,45528,20232,80229,27630,69434,47930,77121,54333,10425,91032,30726,06130,22726,74332,36631,58837,83933,85240,00138,90442,01240,73542,02329,26932,14232,38133,60733,16437,95432,93238,75124,52430,89028,26632,54729,27927,44328,08732,79723,05529,37035,23138,2131,944,69262.28%
denoisedF22,05944,24436,42734,58531,63231,35036,91640,54627,31831,13224,91532,53527,95627,61032,01435,06124,30134,49327,56931,66428,76429,89333,60829,66120,83931,78025,24531,48125,09229,64126,08431,02430,11236,43232,94038,20038,02240,59739,84540,44528,85231,34931,15832,59232,13736,93132,10337,80523,84329,85427,22231,42028,52126,83827,47831,66922,50228,35534,02737,1191,885,80760.39%
denoisedR22,35044,65836,63535,14332,03531,48837,36240,67727,45331,53225,00732,48728,01027,82732,27535,41824,60234,94627,83931,99728,73530,21433,85329,94621,04532,20125,39431,76325,39129,71126,42831,32730,52236,61233,09238,77738,29440,87140,12541,16329,14231,64131,47632,90432,46737,10532,39037,95324,07230,21627,44031,87928,70027,13427,51031,85222,53428,69434,32037,6231,902,25760.92%
merged20,28140,40632,29829,33528,41626,65933,08832,95624,55327,53922,87029,54825,11225,83927,73729,95022,10931,74125,01927,98526,58327,83931,58425,17617,84128,33522,53728,91719,98927,44624,61726,10125,93832,02229,19833,38734,73335,50037,03235,73127,79326,05327,13229,84729,16133,43428,84933,15721,47726,38023,12728,41825,78424,17524,20426,66420,19825,09630,01833,8371,678,75153.76%
nonchim10,59220,86517,10113,35815,48114,50418,70816,71614,03415,58116,02313,69914,01113,76614,71314,96912,13115,67111,31015,50115,12316,62817,46414,6349,55115,45411,52214,64911,12514,83311,29713,99314,75615,86214,38318,18019,23217,87620,89818,11621,08712,40113,95415,22914,13517,97115,43515,23511,27013,49712,57515,46915,85913,64712,74313,06610,08411,63614,52816,854890,98528.53%

This table can be downloaded as an Excel table below:

 

5. DADA2 Amplicon Sequence Variants (ASVs). A total of 14342 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 sequence 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 char below:
 
 
 
 

Read Taxonomy Assignment - Result Summary

CodeCategoryRead Count (MC=1)*Read Count (MC=100)*
ATotal reads890,985890,985
BTotal assigned reads885,930885,930
CAssigned reads in species with read count < MC08,453
DAssigned reads in samples with read count < 50000
ETotal samples6060
FSamples with reads >= 5006060
GSamples with reads < 50000
HTotal assigned reads used for analysis (B-C-D)885,930877,477
IReads assigned to single species864,888859,913
JReads assigned to multiple species7,1686,648
KReads assigned to novel species13,87410,916
LTotal number of species548307
MNumber of single species392269
NNumber of multi-species2310
ONumber of novel species13328
PTotal unassigned reads5,0555,055
QChimeric reads314314
RReads without BLASTN hits2,4252,425
SOthers: short, low quality, singletons, etc.2,3162,316
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 NameVisitGroup1Group2Group
F4661.S03AL03.V1Visit1ControlBaseline Baseline Control
F4661.S04AL04.V1Visit1ControlBaseline Baseline Control
F4661.S05AL05.V1Visit1ControlBaseline Baseline Control
F4661.S07AL07.V1Visit1ControlBaseline Baseline Control
F4661.S10AL10.V1Visit1ControlBaseline Baseline Control
F4661.S12AL12.V1Visit1ControlBaseline Baseline Control
F4661.S15AL15.V1Visit1ControlBaseline Baseline Control
F4661.S17AL17.V1Visit1ControlBaseline Baseline Control
F4661.S18AL18.V1Visit1ControlBaseline Baseline Control
F4661.S21AL21.V1Visit1ControlBaseline Baseline Control
F4661.S22AL22.V1Visit1ControlBaseline Baseline Control
F4661.S23AL23.V1Visit1ControlBaseline Baseline Control
F4661.S26AL26.V1Visit1ControlBaseline Baseline Control
F4661.S27AL27.V1Visit1ControlBaseline Baseline Control
F4661.S28AL28.V1Visit1ControlBaseline Baseline Control
F4661.S01AL01.V1Visit1TestBaseline Baseline Test
F4661.S02AL02.V1Visit1TestBaseline Baseline Test
F4661.S06AL06.V1Visit1TestBaseline Baseline Test
F4661.S08AL08.V1Visit1TestBaseline Baseline Test
F4661.S09AL09.V1Visit1TestBaseline Baseline Test
F4661.S11AL11.V1Visit1TestBaseline Baseline Test
F4661.S13AL13.V1Visit1TestBaseline Baseline Test
F4661.S14AL14.V1Visit1TestBaseline Baseline Test
F4661.S16AL16.V1Visit1TestBaseline Baseline Test
F4661.S19AL19.V1Visit1TestBaseline Baseline Test
F4661.S20AL20.V1Visit1TestBaseline Baseline Test
F4661.S24AL24.V1Visit1TestBaseline Baseline Test
F4661.S25AL25.V1Visit1TestBaseline Baseline Test
F4661.S29AL29.V1Visit1TestBaseline Baseline Test
F4661.S30AL30.V1Visit1TestBaseline Baseline Test
F4661.S33AL03.V3Visit3ControlWeek 6Week 6 Control
F4661.S34AL04.V3Visit3ControlWeek 6Week 6 Control
F4661.S35AL05.V3Visit3ControlWeek 6Week 6 Control
F4661.S37AL07.V3Visit3ControlWeek 6Week 6 Control
F4661.S40AL10.V3Visit3ControlWeek 6Week 6 Control
F4661.S42AL12.V3Visit3ControlWeek 6Week 6 Control
F4661.S45AL15.V3Visit3ControlWeek 6Week 6 Control
F4661.S47AL17.V3Visit3ControlWeek 6Week 6 Control
F4661.S48AL18.V3Visit3ControlWeek 6Week 6 Control
F4661.S51AL21.V3Visit3ControlWeek 6Week 6 Control
F4661.S52AL22.V3Visit3ControlWeek 6Week 6 Control
F4661.S53AL23.V3Visit3ControlWeek 6Week 6 Control
F4661.S56AL26.V3Visit3ControlWeek 6Week 6 Control
F4661.S57AL27.V3Visit3ControlWeek 6Week 6 Control
F4661.S58AL28.V3Visit3ControlWeek 6Week 6 Control
F4661.S31AL01.V3Visit3TestWeek 6Week 6 Test
F4661.S32AL02.V3Visit3TestWeek 6Week 6 Test
F4661.S36AL06.V3Visit3TestWeek 6Week 6 Test
F4661.S38AL08.V3Visit3TestWeek 6Week 6 Test
F4661.S39AL09.V3Visit3TestWeek 6Week 6 Test
F4661.S41AL11.V3Visit3TestWeek 6Week 6 Test
F4661.S43AL13.V3Visit3TestWeek 6Week 6 Test
F4661.S44AL14.V3Visit3TestWeek 6Week 6 Test
F4661.S46AL16.V3Visit3TestWeek 6Week 6 Test
F4661.S49AL19.V3Visit3TestWeek 6Week 6 Test
F4661.S50AL20.V3Visit3TestWeek 6Week 6 Test
F4661.S54AL24.V3Visit3TestWeek 6Week 6 Test
F4661.S55AL25.V3Visit3TestWeek 6Week 6 Test
F4661.S59AL29.V3Visit3TestWeek 6Week 6 Test
F4661.S60AL30.V3Visit3TestWeek 6Week 6 Test
 
 

Read Taxonomy Assignment - ASV Read Counts by Samples

#Sample IDRead Count
F4661.S259551
F4661.S5710084
F4661.S0110592
F4661.S2911125
F4661.S4911270
F4661.S3111297
F4661.S1911310
F4661.S2711522
F4661.S5811636
F4661.S1712131
F4661.S4212401
F4661.S5112575
F4661.S5512743
F4661.S5613066
F4661.S0413358
F4661.S5013497
F4661.S5413647
F4661.S1213699
F4661.S1413766
F4661.S4313954
F4661.S3213993
F4661.S1314011
F4661.S0914034
F4661.S4514135
F4661.S3514383
F4661.S0614504
F4661.S5914528
F4661.S2414634
F4661.S2814649
F4661.S1514713
F4661.S3314756
F4661.S3014833
F4661.S1614969
F4661.S2115123
F4661.S4415229
F4661.S4815235
F4661.S4715435
F4661.S2615454
F4661.S5215469
F4661.S0515481
F4661.S2015501
F4661.S1015581
F4661.S1815671
F4661.S5315859
F4661.S3415862
F4661.S1116023
F4661.S2216628
F4661.S0816716
F4661.S6016854
F4661.S0317101
F4661.S2317464
F4661.S3817876
F4661.S4617971
F4661.S4018116
F4661.S3618180
F4661.S0718708
F4661.S3719232
F4661.S0220865
F4661.S3920898
F4661.S4121087
 
 

Read Taxonomy Assignment - ASV Read Counts Table

This table shows the read counts for each sample (columns) and each species identified based on the ASV sequences. The downstream analyses were based on this table.
SPIDTaxonomyF4661.S01F4661.S02F4661.S03F4661.S04F4661.S05F4661.S06F4661.S07F4661.S08F4661.S09F4661.S10F4661.S11F4661.S12F4661.S13F4661.S14F4661.S15F4661.S16F4661.S17F4661.S18F4661.S19F4661.S20F4661.S21F4661.S22F4661.S23F4661.S24F4661.S25F4661.S26F4661.S27F4661.S28F4661.S29F4661.S30F4661.S31F4661.S32F4661.S33F4661.S34F4661.S35F4661.S36F4661.S37F4661.S38F4661.S39F4661.S40F4661.S41F4661.S42F4661.S43F4661.S44F4661.S45F4661.S46F4661.S47F4661.S48F4661.S49F4661.S50F4661.S51F4661.S52F4661.S53F4661.S54F4661.S55F4661.S56F4661.S57F4661.S58F4661.S59F4661.S60
SP1k__Bacteria;p__Actinobacteria;c__Actinobacteria;o__Actinomycetales;f__Actinomycetaceae;g__Actinomyces;s__sp._HMT_175183960019111302301521514921704021792716852812322161250041591076499237414140126342402299116820031351381066153413538947218930524716014170819308648740
SP10k__Bacteria;p__Firmicutes;c__Bacilli;o__Lactobacillales;f__Streptococcaceae;g__Streptococcus;s__sanguinis11965650496443617647639165011861563299106429262741263175626710441966159531967167249681719938691086112867827151516196241953967730436740320631603229831754227107814502242346175559663953183650417614757455268260
SP100k__Bacteria;p__Firmicutes;c__Bacilli;o__Lactobacillales;f__Carnobacteriaceae;g__Granulicatella;s__paradiacens04413231105129811091009614269660460104696012777236838122823111903600000611196818717901514610677461705710845114560001580500
SP101k__Bacteria;p__Firmicutes;c__Negativicutes;o__Selenomonadales;f__Selenomonadaceae;g__Selenomonas;s__sputigena1511416069335116014705909270000000041093011000208861523104916001100000000019000000100148880
SP102k__Bacteria;p__Firmicutes;c__Bacilli;o__Lactobacillales;f__Streptococcaceae;g__Streptococcus;s__oralis_subsp._dentisani_clade_05800052000003200000000363500000000220000000110047000000021503121000004929180
SP103k__Bacteria;p__Actinobacteria;c__Actinobacteria;o__Actinomycetales;f__Actinomycetaceae;g__Actinomyces;s__naeslundii0445163697421214111243411413144458088241018019126646024456512013203589410803782075893142712371273973110430250115441660292418220118219923123330231
SP105k__Bacteria;p__Actinobacteria;c__Actinobacteria;o__Actinomycetales;f__Micrococcaceae;g__Rothia;s__mucilaginosa11300000000014000054092000000000000030310000000003000730000007400490400029
SP106k__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_869054000011490380000000000000045000000000000000000000430000026013400000
SP107k__Bacteria;p__Proteobacteria;c__Betaproteobacteria;o__Neisseriales;f__Neisseriaceae;g__Neisseria;s__subflava00100000800016000026000040000017000000421025060000133806000027471000003500
SP108k__Bacteria;p__Proteobacteria;c__Epsilonproteobacteria;o__Campylobacterales;f__Campylobacteraceae;g__Campylobacter;s__concisus522086275600730032000290000390001806120000171230024107097017150010922220006400018026051240
SP109k__Bacteria;p__Spirochaetes;c__Spirochaetia;o__Spirochaetales;f__Spirochaetaceae;g__Treponema;s__sp._HMT_231000000155900000000000000000000000000076711110000000000000000000000
SP11k__Bacteria;p__Firmicutes;c__Bacilli;o__Lactobacillales;f__Streptococcaceae;g__Streptococcus;s__mitis313414410900150763553000686002317626411314440027980911606351241613907350701302948817698018076580285129197557494021319513456910145105
SP110k__Bacteria;p__Actinobacteria;c__Actinobacteria;o__Actinomycetales;f__Actinomycetaceae;g__Actinomyces;s__johnsonii00436283011124100004001962160001898013088173000002911210011513711830006799116000003578001788000910
SP111k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Peptostreptococcaceae_[XI];g__Peptostreptococcaceae_[XI][G-1];s__[Eubacterium]_infirmum029206000001100000090000000000000000001500090027000260000000007000
SP112k__Bacteria;p__Actinobacteria;c__Actinobacteria;o__Actinomycetales;f__Actinomycetaceae;g__Actinomyces;s__sp._HMT_525001020025346001403800000440045000000202027000000058004500000048019600000020300660
SP113k__Bacteria;p__Bacteroidetes;c__Bacteroidia;o__Bacteroidales;f__Prevotellaceae;g__Prevotella;s__intermedia0039400900260710000000000000000000000148129106900099000000000005000014001670
SP114k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Ruminococcaceae;g__Ruminococcaceae_[G-1];s__bacterium_HMT_0750617120000040180000000000000800001000700900123029028095000220300000017061490
SP115k__Bacteria;p__Firmicutes;c__Bacilli;o__Lactobacillales;f__Streptococcaceae;g__Streptococcus;s__parasanguinis_II86900000000069400002100000000400000140330013000000008310000000000000017
SP116k__Bacteria;p__Proteobacteria;c__Gammaproteobacteria;o__Pasteurellales;f__Pasteurellaceae;g__Haemophilus;s__haemolyticus000000000000000000000066000280000000000000000002500025001700000021
SP117k__Bacteria;p__Bacteroidetes;c__Bacteroidia;o__Bacteroidales;f__Porphyromonadaceae;g__Tannerella;s__sp._HMT_9160010218000000000000000000000000000002300000000000000000000000000
SP118k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Lachnospiraceae;g__Lachnoanaerobaculum;s__orale0691300390000001200680340320210490006800004600400190380008607100012201280000049470
SP119k__Bacteria;p__Fusobacteria;c__Fusobacteriia;o__Fusobacteriales;f__Leptotrichiaceae;g__Leptotrichia;s__sp._HMT_49800023720000000000000000000030800000002561001888000000000000000000037200
SP12k__Bacteria;p__Actinobacteria;c__Actinobacteria;o__Corynebacteriales;f__Corynebacteriaceae;g__Corynebacterium;s__durum051012869701391552755112851508002110575016215324396226850001219209701510018449317164102541217053193286283013523798390246118002137134
SP120k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Peptostreptococcaceae_[XI];g__Mogibacterium;s__timidum05656000000000000000000000000000040114400000000000000000000000000
SP121k__Bacteria;p__Actinobacteria;c__Actinobacteria;o__Actinomycetales;f__Actinomycetaceae;g__Actinomyces;s__georgiae00000231100000003801600000000000000000006115700000360170000000000000
SP122k__Bacteria;p__Fusobacteria;c__Fusobacteria;o__Fusobacteriales;f__Fusobacteriaceae;g__Fusobacterium;s__nucleatum_ss_animalis01548600001290156005511922000000001204900770011100840103001060023412700002200000000000
SP123k__Bacteria;p__Actinobacteria;c__Actinobacteria;o__Actinomycetales;f__Actinomycetaceae;g__Actinomyces;s__sp._Oral_Taxon_18000080059300084045004500000000003440000000028850380199044015875000105000000230069137
SP126k__Bacteria;p__Gracilibacteria_(GN02);c__Gracilibacteria_(GN02)_[C-1];o__Gracilibacteria_(GN02)_[O-1];f__Gracilibacteria_(GN02)_[F-1];g__Gracilibacteria_(GN02)_[G-1];s__bacterium_HMT_8720122600000178400001030000000000900920096500000202600025142901110003200000000
SP127k__Bacteria;p__Firmicutes;c__Bacilli;o__Lactobacillales;f__Streptococcaceae;g__Streptococcus;s__intermedius1000117113508905009051900320060000803102101000003106123800058293045001107006315000
SP128k__Bacteria;p__Bacteroidetes;c__Bacteroidia;o__Bacteroidales;f__Prevotellaceae;g__Prevotella;s__melaninogenica016500278900000674535000830000001314464000001020074020000055811113900072033000250000
SP129k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Lachnospiraceae_[XIVa];g__Catonella;s__morbi015039305113630001000240000001317003000000598744230180000011100034000035000800011912
SP13k__Bacteria;p__Bacteroidetes;c__Flavobacteriia;o__Flavobacteriales;f__Flavobacteriaceae;g__Capnocytophaga;s__sp._HMT_3360000000430740000790000033001530000150000000000410000000760000001311106800
SP130k__Bacteria;p__Proteobacteria;c__Gammaproteobacteria;o__Pasteurellales;f__Pasteurellaceae;g__Aggregatibacter;s__aphrophilus011423102816700134174000003480000576110003577088009200143002930000011758500000130000800022
SP131k__Bacteria;p__Fusobacteria;c__Fusobacteriia;o__Fusobacteriales;f__Leptotrichiaceae;g__Leptotrichia;s__sp._HMT_219000170004400000001010000000005000150000810263758240008007429000434700029002829
SP132k__Bacteria;p__Proteobacteria;c__Betaproteobacteria;o__Neisseriales;f__Neisseriaceae;g__Neisseria;s__elongata012616912303012112070157000018877646014438917070601511500013904381857118260958614808049109456537500193109105006179000
SP133k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Lachnospiraceae_[XIVa];g__Oribacterium;s__sp._Oral_Taxon_780200081005205100014000002900011405200000000430000000101270016800000000006300
SP134k__Bacteria;p__Bacteroidetes;c__Bacteroidia;o__Bacteroidales;f__Prevotellaceae;g__Prevotella;s__oulorum3120003700003101900490260000001390000001659001800260370001340070000000000099060
SP135k__Bacteria;p__Actinobacteria;c__Actinobacteria;o__Actinomycetales;f__Actinomycetaceae;g__Actinomyces;s__dentalis0261185016613204802203182065300000000530360000023020700001740000950120000580000421253714600
SP136k__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_34902299816102000000000230000000000108000003130178024680000049000000000000102001120
SP137k__Bacteria;p__Bacteroidetes;c__Bacteroidetes_[C-1];o__Bacteroidetes_[O-1];f__Bacteroidetes_[F-1];g__Bacteroidetes_[G-5];s__bacterium_HMT_511033180000000000000000000000000000911000575400000000000000000000180
SP138k__Bacteria;p__Bacteroidetes;c__Bacteroidia;o__Bacteroidales;f__Prevotellaceae;g__Alloprevotella;s__tannerae01113014702001600000000000000000013000002442096070100000000014000000111120380
SP139k__Bacteria;p__Firmicutes;c__Negativicutes;o__Selenomonadales;f__Selenomonadaceae;g__Selenomonas;s__infelix05823571502210804800001330000000019000000020456200069000000220000000270002228260
SP14k__Bacteria;p__Firmicutes;c__Bacilli;o__Lactobacillales;f__Streptococcaceae;g__Streptococcus;s__sp._str._C30066105518315897231624333525215729001190371325227923289250414591329124969861772742513692771277143434129011234014012533651968243618284082409981672064249191801107123966101562380305529
SP140k__Bacteria;p__Actinobacteria;c__Actinobacteria;o__Actinomycetales;f__Actinomycetaceae;g__Actinomyces;s__sp._Oral_Taxon_171000080120026000142002852000110025059000091001035018270310005500074823107723039200190000
SP142k__Bacteria;p__Firmicutes;c__Bacilli;o__Lactobacillales;f__Streptococcaceae;g__Streptococcus;s__sp._Oral_Taxon_7106890000000000047000000000164000210895710165014000000000000000000390000039
SP143k__Bacteria;p__Proteobacteria;c__Betaproteobacteria;o__Neisseriales;f__Neisseriaceae;g__Neisseria;s__sp._Oral_Taxon_1400982700363023000000678020904910499000115330013327000000163670420000019200010610915800004720086
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SP78k__Bacteria;p__Actinobacteria;c__Actinobacteria;o__Actinomycetales;f__Actinomycetaceae;g__Actinomyces;s__sp._Oral_Taxon_1780596301110661605113125301077100019000270109000005368230330206012101141201389013017170020078035210
SP79k__Bacteria;p__Bacteroidetes;c__Flavobacteriia;o__Flavobacteriales;f__Flavobacteriaceae;g__Capnocytophaga;s__sp._HMT_9020000000000000001340090000000000000000000000000004400000000000000
SP8k__Bacteria;p__Proteobacteria;c__Epsilonproteobacteria;o__Campylobacterales;f__Campylobacteraceae;g__Campylobacter;s__showae14775667068053010403843030950100014243310242590257600094470100229201210228513031776155101007545230226007356184
SP80k__Bacteria;p__Fusobacteria;c__Fusobacteria;o__Fusobacteriales;f__Fusobacteriaceae;g__Fusobacterium;s__nucleatum_ss_polymorphum02331541861825601300177861841070314355010101221350015910714801201541360125944969432603160001361991982793313948897512230067024556093309155
SP81k__Bacteria;p__Firmicutes;c__Negativicutes;o__Selenomonadales;f__Selenomonadaceae;g__Selenomonas;s__noxia01061310168100146247018044721027775210462000139432340114001719899050107562590700216100149044155076310021003304312229
SP82k__Bacteria;p__Actinobacteria;c__Actinobacteria;o__Actinomycetales;f__Corynebacteriaceae;g__Corynebacterium;s__sp._Oral_Taxon_A1642002200150585700025005083701316003500001731293202200420583500063102042955610272013573002723063
SP83k__Bacteria;p__Bacteroidetes;c__Bacteroidia;o__Bacteroidales;f__Prevotellaceae;g__Prevotella;s__denticola5801150050111003923319640400000000000000000500320227012001285500003300000000000
SP84k__Bacteria;p__Spirochaetes;c__Spirochaetia;o__Spirochaetales;f__Spirochaetaceae;g__Treponema;s__maltophilum00270000901000000000000000000000000590003100000000000000000140000
SP86k__Bacteria;p__Fusobacteria;c__Fusobacteriia;o__Fusobacteriales;f__Leptotrichiaceae;g__Leptotrichia;s__sp._HMT_22300000051009900000000000000000000000000000104008000027500000000000280
SP87k__Bacteria;p__Proteobacteria;c__Gammaproteobacteria;o__Cardiobacteriales;f__Cardiobacteriaceae;g__Cardiobacterium;s__hominis19001604831017611083046700191186061105351111124379000261751192167771710597217109126108079220109851544926241090152220591550235
SP89k__Bacteria;p__Bacteroidetes;c__Flavobacteriia;o__Flavobacteriales;f__Flavobacteriaceae;g__Capnocytophaga;s__sputigena10690039002441791582761960374109716603220208248330280433322808612592989089540322618318146420051018335110146476610223700176257065689
SP9k__Bacteria;p__Firmicutes;c__Bacilli;o__Lactobacillales;f__Streptococcaceae;g__Streptococcus;s__sp._str._2136FAA00000039018000734200001110000391260050004000000003939007813200000000043000600
SP90k__Bacteria;p__Bacteroidetes;c__Bacteroidia;o__Bacteroidales;f__Prevotellaceae;g__Prevotella;s__loescheii02130001680001460082025300650000059113000000000000600182009500837600510904009203901740
SP91k__Bacteria;p__Firmicutes;c__Bacilli;o__Lactobacillales;f__Streptococcaceae;g__Streptococcus;s__sp._HMT_0560000000500000016000000000000001600000000420000067000006000000002469
SP92k__Bacteria;p__Actinobacteria;c__Actinobacteria;o__Actinomycetales;f__Actinomycetaceae;g__Actinomyces;s__sp._HMT_448189070707571290000532950049281711199806500003724043400002504318014662356000000023260000360000220911221030000
SP95k__Bacteria;p__Firmicutes;c__Tissierellia;o__Tissierellales;f__Peptoniphilaceae;g__Parvimonas;s__micra101781430503501280660027000000000000451000001517611011515310900013400084000007000224000
SP97k__Bacteria;p__Fusobacteria;c__Fusobacteriia;o__Fusobacteriales;f__Leptotrichiaceae;g__Leptotrichia;s__sp._HMT_2250792621000000000000000000000290990003500095000000560035049169088080026001120
SP98k__Bacteria;p__Fusobacteria;c__Fusobacteriia;o__Fusobacteriales;f__Leptotrichiaceae;g__Leptotrichia;s__sp._HMT_392065143113000560840000019900000000015800061020382667200640000820036961158001670000130002720
SP99k__Bacteria;p__Firmicutes;c__Bacilli;o__Lactobacillales;f__Streptococcaceae;g__Streptococcus;s__gordonii182387404181241483812751286019501402937146611801963101961651681890174817813849163332074215004001991442400116125000109145044161120001740
SPN107k__Bacteria;p__Actinobacteria;c__Actinobacteria;o__Actinomycetales;f__Actinomycetaceae;g__Actinomyces;s__sp._str._ChDCB197058000002800000010016100000009200007800610000000003400000000000830000540
SPN118k__Bacteria;p__Actinobacteria;c__Actinobacteria;o__Actinomycetales;f__Actinomycetaceae;g__Actinomyces;s__israelii_nov_97.584%07513728078000000000000000000000000010977820730000000000000000000004200
SPN130k__Bacteria;p__Actinobacteria;c__Actinobacteria;o__Actinomycetales;f__Actinomycetaceae;g__Actinomyces;s__viscosus_nov_96.234%00788560000113000017100000000002600000090013000026000055000000000000000
SPN22k__Bacteria;p__Bacteroidetes;c__Bacteroidia;o__Bacteroidales;f__Prevotellaceae;g__Prevotella;s__sp._HMT_29200000000007700054350000000000000001400000000790007664000000062000000
SPN27k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Veillonellaceae;g__Selenomonas;s__sp._Oral_Taxon_1370029000007300000000000000000021000071005300126000190082000000360000000
SPN45k__Bacteria;p__Proteobacteria;c__Betaproteobacteria;o__Neisseriales;f__Neisseriaceae;g__Neisseria;s__bacilliformis0000066019002552014880000000001070540660004600012208304487547613890000000003220133453000
SPN47k__Bacteria;p__Actinobacteria;c__Actinobacteria;o__Actinomycetales;f__Actinomycetaceae;g__Actinomyces;s__massiliensis0001840000000460000000000000104000000000000000520000074000000000000
SPN56k__Bacteria;p__Actinobacteria;c__Actinobacteria;o__Actinomycetales;f__Actinomycetaceae;g__Actinomyces;s__sp._HMT_4140002300000000000000003853000001908500000049000000002640000790000000540
SPN57k__Bacteria;p__Bacteroidetes;c__Flavobacteriia;o__Flavobacteriales;f__Flavobacteriaceae;g__Bergeyella;s__zoohelcum0930000282700017000000000000000014001300007600007000123000190000000000
SPN64k__Bacteria;p__Actinobacteria;c__Actinobacteria;o__Corynebacteriales;f__Corynebacteriaceae;g__Corynebacterium;s__matruchotii0000000000000000000000000000000000001080000017300000000000000000
SPN75k__Bacteria;p__Firmicutes;c__Negativicutes;o__Veillonellales;f__Veillonellaceae;g__Anaeroglobus;s__geminatus0000000000000000000000000200000005200000000000000000000001960000
SPN8k__Bacteria;p__Firmicutes;c__Bacilli;o__Lactobacillales;f__Streptococcaceae;g__Streptococcus;s__vestibularis00000000000000000170730000000000004200000000000000449000000000000
SPN81k__Bacteria;p__Firmicutes;c__Bacilli;o__Bacillales;f__Paenibacillaceae;g__Saccharibacillus;s__kuerlensis00057000003700000000000017000000000799000006000000000000200000000
SPN82k__Bacteria;p__Bacteroidetes;c__Flavobacteriia;o__Flavobacteriales;f__Flavobacteriaceae;g__Capnocytophaga;s__gingivalis0000000720000000000000000000000000000014700000000000001500000000
SPN83k__Bacteria;p__Firmicutes;c__Negativicutes;o__Veillonellales;f__Veillonellaceae;g__Veillonellaceae_[G-1];s__bacterium_HMT_15000011001223000000000000100038360000000027000000000000000000007100000
SPN84k__Bacteria;p__Bacteroidetes;c__Bacteroidia;o__Bacteroidales;f__Prevotellaceae;g__Prevotella;s__denticola000000062000460000000000000000000000000000000000000000000000550
SPN85k__Bacteria;p__Actinobacteria;c__Actinobacteria;o__Propionibacteriales;f__Propionibacteriaceae;g__Pseudopropionibacterium;s__propionicum000000000007000000000000000000000000044000000000000000000001100
SPN86k__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_347000055000000000000000000000000000000000000000000180870000000000
SPN87k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Lachnospiraceae_[XIVa];g__Eubacterium_[XIVa][G-1];s__saburreum0000038000000000000000000000000000008200000180002000000000000000
SPN88k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Lachnospiraceae;g__Lachnoanaerobaculum;s__saburreum00000000000000000000000007200000000000000000000000000000790000
SPN90k__Bacteria;p__Proteobacteria;c__Deltaproteobacteria;o__Desulfovibrionales;f__Desulfovibrionaceae;g__Desulfovibrio;s__legallii00000009600000000000000000000000000000049000000000000000000000
SPN91k__Bacteria;p__Firmicutes;c__Bacilli;o__Lactobacillales;f__Streptococcaceae;g__Streptococcus;s__mitis0000000000000036000000000000000001900000000000460000029000000000
SPN92k__Bacteria;p__Fusobacteria;c__Fusobacteriia;o__Fusobacteriales;f__Leptotrichiaceae;g__Leptotrichia;s__sp._HMT_2150410000000000000000000000021000000000000017000000000000000000340
SPN93k__Bacteria;p__Firmicutes;c__Negativicutes;o__Selenomonadales;f__Selenomonadaceae;g__Selenomonas;s__sp._HMT_12600560000000000000000000000000000005300000000000000000000000000
SPN94k__Bacteria;p__Actinobacteria;c__Actinobacteria;o__Actinomycetales;f__Corynebacteriaceae;g__Corynebacterium;s__sp._Oral_Taxon_A1600610000000000000000000000000000047000000000000000000000000000
SPN95k__Bacteria;p__Firmicutes;c__Firmicutes_[C];o__Firmicutes_[O];f__Firmicutes_[F];g__Firmicutes_[G];s__sp._Oral_Taxon_C6800000036000000890000000420000000000004005670000012300000000000000000
SPN96k__Bacteria;p__Firmicutes;c__Bacilli;o__Lactobacillales;f__Enterococcaceae;g__Vagococcus;s__penaei00000000000000000000000000025000000000000000000000007700000000
SPP1k__Bacteria;p__Firmicutes;c__Bacilli;o__Lactobacillales;f__Carnobacteriaceae;g__Granulicatella;s__multispecies_spp1_25198059000031000353800038181340098486101627506208900058047185122690692509600270659111002300230
SPP11k__Bacteria;p__Bacteroidetes;c__Bacteroidia;o__Bacteroidales;f__Prevotellaceae;g__Prevotella;s__multispecies_spp11_200000003000000000000000000000000000000790000000000000000000000
SPP14k__Bacteria;p__Firmicutes;c__Bacilli;o__Lactobacillales;f__Streptococcaceae;g__Streptococcus;s__multispecies_spp14_2000000300000000000000001910000000000000000000000980000004800000027
SPP17k__Bacteria;p__Fusobacteria;c__Fusobacteriia;o__Fusobacteriales;f__Leptotrichiaceae;g__Leptotrichia;s__multispecies_spp17_20000000000000000000000000000000000000000000003902121200000000000
SPP18k__Bacteria;p__Firmicutes;c__multiclass;o__multiorder;f__Selenomonadaceae;g__Mitsuokella;s__multispecies_spp18_200000000000000000000000000000000000000000000000000000000013500
SPP22k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__multifamily;g__Eubacterium_[XIVa][G-1];s__saburreum270007206800100017500218000109430001390004300015600680015000074006654008300048000012600
SPP3k__Bacteria;p__Fusobacteria;c__Fusobacteria;o__Fusobacteriales;f__Fusobacteriaceae;g__Fusobacterium;s__multispecies_spp3_200017272017112500000059064000000360430000000560089000000000330000000000112000
SPP4k__Bacteria;p__Bacteroidetes;c__Flavobacteriia;o__Flavobacteriales;f__Flavobacteriaceae;g__Capnocytophaga;s__multispecies_spp4_200000000000000000000000000000000000000000000000000000018400000
SPP5k__Bacteria;p__Fusobacteria;c__Fusobacteria;o__Fusobacteriales;f__Fusobacteriaceae;g__Fusobacterium;s__multispecies_spp5_2115000000000000000000000000000000000004700000000000000000000000
SPP6k__Bacteria;p__Fusobacteria;c__Fusobacteria;o__Fusobacteriales;f__Fusobacteriaceae;g__Fusobacterium;s__multispecies_spp6_20000000000000000000000000000000002020030000001300000000000000000
SPPN1k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__multifamily;g__multigenus;s__multispecies_sppn1_2_nov_97.303%000000000000002600000000006600000000000000000000000060000110000
 
 
Download Read Count Tables at Different Taxonomy Levels
domain
phylum
class
order
family
genus
species
;
 
The species listed in the table has full taxonomy and a dynamically assigned species ID specific to this report. When some reads match with the reference sequences of more than one species equally (i.e., same percent identiy and alignmnet coverage), they can't be assigned to a particular species. Instead, they are assigned to multiple species with the species notaton "s__multispecies_spp2_2". In this notation, spp2 is the dynamic ID assigned to these reads that hit multiple sequences and the "_2" at the end of the notation means there are two species in the spp2. You can look up which species are included in the multi-species assignment, in this table below. Another type of notation is "s__multispecies_sppn2_2", in which the "n" in the sppn2 means it's a potential novel species because all the reads in this species have < 98% idenity to any of the reference sequences. They were grouped together based on de novo OTU clustering at 98% identity cutoff. And then a representative sequence was chosed to BLASTN search against the reference database to find the closest match (but will still be < 98%). This representative sequence also matched equally to more than one species, hence the "spp" was given in the label.
 

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.Baseline Test vs Baseline Control
Comparison 2.Week 6 Test vs Week 6 Control
Comparison 3.Baseline Test vs Week 6 Test
Comparison 4.Baseline Control vs Week 6 Control
 
 
 

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.

 
Baseline Test vs Baseline Control
 
 
 
 
 
 
 
All LEfSe Comparisons
Comparison No.Comparison Name
Comparison 1.Baseline Test vs Baseline Control
Comparison 2.Week 6 Test vs Week 6 Control
Comparison 3.Baseline Test vs Week 6 Test
Comparison 4.Baseline Control vs Week 6 Control
 
 

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)

 
 

 

Association network inference by SparCC

 
 

 
 
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