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
June 18, 2021

Project ID: FOMC4412

I. Project Summary

Project FOMC4412 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


S100zr4412_100V1V3_R1.fastq.gz26842


S101zr4412_101V1V3_R1.fastq.gz29483


S102zr4412_102V1V3_R1.fastq.gz28194


S103zr4412_103V1V3_R1.fastq.gz27054


S104zr4412_104V1V3_R1.fastq.gz26140


S105zr4412_105V1V3_R1.fastq.gz28709


S106zr4412_106V1V3_R1.fastq.gz23754


S107zr4412_107V1V3_R1.fastq.gz27310


S108zr4412_108V1V3_R1.fastq.gz25061


S109zr4412_109V1V3_R1.fastq.gz34604


S010zr4412_10V1V3_R1.fastq.gz29078


S110zr4412_110V1V3_R1.fastq.gz32198


S111zr4412_111V1V3_R1.fastq.gz26144


S112zr4412_112V1V3_R1.fastq.gz30037


S113zr4412_113V1V3_R1.fastq.gz29398


S114zr4412_114V1V3_R1.fastq.gz26979


S115zr4412_115V1V3_R1.fastq.gz30420


S116zr4412_116V1V3_R1.fastq.gz30388


S117zr4412_117V1V3_R1.fastq.gz26214


S118zr4412_118V1V3_R1.fastq.gz32167


S119zr4412_119V1V3_R1.fastq.gz26115


S011zr4412_11V1V3_R1.fastq.gz28111


S120zr4412_120V1V3_R1.fastq.gz26568


S121zr4412_121V1V3_R1.fastq.gz25786


S122zr4412_122V1V3_R1.fastq.gz23811


S123zr4412_123V1V3_R1.fastq.gz27261


S124zr4412_124V1V3_R1.fastq.gz25377


S125zr4412_125V1V3_R1.fastq.gz28826


S126zr4412_126V1V3_R1.fastq.gz27862


S127zr4412_127V1V3_R1.fastq.gz28407


S128zr4412_128V1V3_R1.fastq.gz27483


S129zr4412_129V1V3_R1.fastq.gz26873


S012zr4412_12V1V3_R1.fastq.gz30830


S130zr4412_130V1V3_R1.fastq.gz27705


S131zr4412_131V1V3_R1.fastq.gz28956


S132zr4412_132V1V3_R1.fastq.gz25523


S133zr4412_133V1V3_R1.fastq.gz29732


S134zr4412_134V1V3_R1.fastq.gz35212


S135zr4412_135V1V3_R1.fastq.gz32049


S013zr4412_13V1V3_R1.fastq.gz30871


S014zr4412_14V1V3_R1.fastq.gz29116


S015zr4412_15V1V3_R1.fastq.gz31136


S016zr4412_16V1V3_R1.fastq.gz29982


S017zr4412_17V1V3_R1.fastq.gz29391


S018zr4412_18V1V3_R1.fastq.gz25529


S019zr4412_19V1V3_R1.fastq.gz27452


S001zr4412_1V1V3_R1.fastq.gz29974


S020zr4412_20V1V3_R1.fastq.gz29909


S021zr4412_21V1V3_R1.fastq.gz28227


S022zr4412_22V1V3_R1.fastq.gz31486


S023zr4412_23V1V3_R1.fastq.gz26881


S024zr4412_24V1V3_R1.fastq.gz30734


S025zr4412_25V1V3_R1.fastq.gz25056


S026zr4412_26V1V3_R1.fastq.gz25278


S027zr4412_27V1V3_R1.fastq.gz23582


S028zr4412_28V1V3_R1.fastq.gz23448


S029zr4412_29V1V3_R1.fastq.gz23446


S002zr4412_2V1V3_R1.fastq.gz26465


S030zr4412_30V1V3_R1.fastq.gz29775


S031zr4412_31V1V3_R1.fastq.gz28196


S032zr4412_32V1V3_R1.fastq.gz30658


S033zr4412_33V1V3_R1.fastq.gz26037


S034zr4412_34V1V3_R1.fastq.gz27459


S035zr4412_35V1V3_R1.fastq.gz24740


S036zr4412_36V1V3_R1.fastq.gz25444


S037zr4412_37V1V3_R1.fastq.gz23224


S038zr4412_38V1V3_R1.fastq.gz27184


S039zr4412_39V1V3_R1.fastq.gz27243


S003zr4412_3V1V3_R1.fastq.gz26289


S040zr4412_40V1V3_R1.fastq.gz31660


S041zr4412_41V1V3_R1.fastq.gz33645


S042zr4412_42V1V3_R1.fastq.gz26575


S043zr4412_43V1V3_R1.fastq.gz29466


S044zr4412_44V1V3_R1.fastq.gz32331


S045zr4412_45V1V3_R1.fastq.gz28677


S046zr4412_46V1V3_R1.fastq.gz25888


S047zr4412_47V1V3_R1.fastq.gz25270


S048zr4412_48V1V3_R1.fastq.gz23803


S049zr4412_49V1V3_R1.fastq.gz19091


S004zr4412_4V1V3_R1.fastq.gz27910


S050zr4412_50V1V3_R1.fastq.gz20628


S051zr4412_51V1V3_R1.fastq.gz18379


S052zr4412_52V1V3_R1.fastq.gz20769


S053zr4412_53V1V3_R1.fastq.gz18923


S054zr4412_54V1V3_R1.fastq.gz22164


S055zr4412_55V1V3_R1.fastq.gz24930


S056zr4412_56V1V3_R1.fastq.gz24582


S057zr4412_57V1V3_R1.fastq.gz26510


S058zr4412_58V1V3_R1.fastq.gz24391


S059zr4412_59V1V3_R1.fastq.gz24709


S005zr4412_5V1V3_R1.fastq.gz25909


S060zr4412_60V1V3_R1.fastq.gz26388


S061zr4412_61V1V3_R1.fastq.gz23831


S062zr4412_62V1V3_R1.fastq.gz25763


S063zr4412_63V1V3_R1.fastq.gz25784


S064zr4412_64V1V3_R1.fastq.gz26671


S065zr4412_65V1V3_R1.fastq.gz24656


S066zr4412_66V1V3_R1.fastq.gz23193


S067zr4412_67V1V3_R1.fastq.gz24907


S068zr4412_68V1V3_R1.fastq.gz24518


S069zr4412_69V1V3_R1.fastq.gz23184


S006zr4412_6V1V3_R1.fastq.gz29505


S070zr4412_70V1V3_R1.fastq.gz29949


S071zr4412_71V1V3_R1.fastq.gz31091


S072zr4412_72V1V3_R1.fastq.gz27277


S073zr4412_73V1V3_R1.fastq.gz24226


S074zr4412_74V1V3_R1.fastq.gz21849


S075zr4412_75V1V3_R1.fastq.gz20856


S076zr4412_76V1V3_R1.fastq.gz24866


S077zr4412_77V1V3_R1.fastq.gz23232


S078zr4412_78V1V3_R1.fastq.gz26035


S079zr4412_79V1V3_R1.fastq.gz29870


S007zr4412_7V1V3_R1.fastq.gz29635


S080zr4412_80V1V3_R1.fastq.gz26076


S081zr4412_81V1V3_R1.fastq.gz26857


S082zr4412_82V1V3_R1.fastq.gz30542


S083zr4412_83V1V3_R1.fastq.gz25690


S084zr4412_84V1V3_R1.fastq.gz29138


S085zr4412_85V1V3_R1.fastq.gz26081


S086zr4412_86V1V3_R1.fastq.gz30348


S087zr4412_87V1V3_R1.fastq.gz32009


S088zr4412_88V1V3_R1.fastq.gz32158


S089zr4412_89V1V3_R1.fastq.gz28304


S008zr4412_8V1V3_R1.fastq.gz31931


S090zr4412_90V1V3_R1.fastq.gz31099


S091zr4412_91V1V3_R1.fastq.gz24270


S092zr4412_92V1V3_R1.fastq.gz32714


S093zr4412_93V1V3_R1.fastq.gz21228


S094zr4412_94V1V3_R1.fastq.gz31706


S095zr4412_95V1V3_R1.fastq.gz27831


S096zr4412_96V1V3_R1.fastq.gz28506


S097zr4412_97V1V3_R1.fastq.gz27420


S098zr4412_98V1V3_R1.fastq.gz24439


S099zr4412_99V1V3_R1.fastq.gz24412


S009zr4412_9V1V3_R1.fastq.gz30161

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
32146.91%51.67%52.36%53.32%54.36%47.28%
31147.33%52.85%53.24%54.28%48.05%39.47%
30147.65%53.17%53.59%47.33%39.45%19.07%
29147.59%53.48%46.76%38.21%18.95%14.34%
28148.40%46.60%38.45%18.68%14.27%7.11%
27142.35%38.77%18.61%13.84%6.93%4.05%

Based on the above result, the trim length combination of R1 = 321 bases and R2 = 241 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 IDF4412.S001F4412.S002F4412.S003F4412.S004F4412.S005F4412.S006F4412.S007F4412.S008F4412.S009F4412.S010F4412.S011F4412.S012F4412.S013F4412.S014F4412.S015F4412.S016F4412.S017F4412.S018F4412.S019F4412.S020F4412.S021F4412.S022F4412.S023F4412.S024F4412.S025F4412.S026F4412.S027F4412.S028F4412.S029F4412.S030F4412.S031F4412.S032F4412.S033F4412.S034F4412.S035F4412.S036F4412.S037F4412.S038F4412.S039F4412.S040F4412.S041F4412.S042F4412.S043F4412.S044F4412.S045F4412.S046F4412.S047F4412.S048F4412.S049F4412.S050F4412.S051F4412.S052F4412.S053F4412.S054F4412.S055F4412.S056F4412.S057F4412.S058F4412.S059F4412.S060F4412.S061F4412.S062F4412.S063F4412.S064F4412.S065F4412.S066F4412.S067F4412.S068F4412.S069F4412.S070F4412.S071F4412.S072F4412.S073F4412.S074F4412.S075F4412.S076F4412.S077F4412.S078F4412.S079F4412.S080F4412.S081F4412.S082F4412.S083F4412.S084F4412.S085F4412.S086F4412.S087F4412.S088F4412.S089F4412.S090F4412.S091F4412.S092F4412.S093F4412.S094F4412.S095F4412.S096F4412.S097F4412.S098F4412.S099F4412.S100F4412.S101F4412.S102F4412.S103F4412.S104F4412.S105F4412.S106F4412.S107F4412.S108F4412.S109F4412.S110F4412.S111F4412.S112F4412.S113F4412.S114F4412.S115F4412.S116F4412.S117F4412.S118F4412.S119F4412.S120F4412.S121F4412.S122F4412.S123F4412.S124F4412.S125F4412.S126F4412.S127F4412.S128F4412.S129F4412.S130F4412.S131F4412.S132F4412.S133F4412.S134F4412.S135Row SumPercentage
input29,97426,46526,28927,91025,90929,50529,63531,93130,16129,07828,11130,83030,87129,11631,13629,98229,39125,52927,45229,90928,22731,48626,88130,73425,05625,27823,58223,44823,44629,77528,19630,65826,03727,45924,74025,44423,22427,18427,24331,66033,64526,57529,46632,33128,67725,88825,27023,80319,09120,62818,37920,76918,92322,16424,93024,58226,51024,39124,70926,38823,83125,76325,78426,67124,65623,19324,90724,51823,18429,94931,09127,27724,22621,84920,85624,86623,23226,03529,87026,07626,85730,54225,69029,13826,08130,34832,00932,15828,30431,09924,27032,71421,22831,70627,83128,50627,42024,43924,41226,84229,48328,19427,05426,14028,70923,75427,31025,06134,60432,19826,14430,03729,39826,97930,42030,38826,21432,16726,11526,56825,78623,81127,26125,37728,82627,86228,40727,48326,87327,70528,95625,52329,73235,21232,0493,673,289100.00%
filtered24,72122,10021,37322,20921,11924,49524,46326,71324,25924,23622,79525,77424,77424,22026,00225,11523,87621,03522,13624,70522,68526,17322,36825,37520,12221,01118,94319,49718,56724,73823,61525,22020,70522,49819,60220,61018,49022,34422,31225,80427,27221,88423,95826,83923,26421,68021,14919,91015,95217,28515,38617,56215,85518,60120,86220,56521,58219,85219,93621,76519,56721,00421,23321,67720,07119,05120,22020,30418,77424,87925,82522,44419,65618,03116,60420,57317,73421,59024,77821,67022,00925,31820,72124,37521,27425,30826,53526,69922,76025,35519,26827,45315,18926,52521,05423,68422,63920,11320,22522,32824,46723,73121,48821,77323,83019,77322,78120,76428,80726,27020,60225,11124,47322,50525,20725,15016,33926,84720,88322,14121,48719,57022,46720,92523,86823,25422,22722,86422,22823,01224,02121,16424,43829,47324,7773,011,19281.98%
denoisedF23,80921,32220,52121,13920,08423,49123,48425,68523,19723,14521,72224,65123,70923,15224,98223,93122,99820,16021,44923,97521,83925,40221,41924,70219,50720,32718,27518,90717,96923,89522,42723,95319,80621,27318,43219,45117,47421,11521,11224,53726,15820,76922,78625,47022,18620,34719,95418,71515,15416,56814,60416,86415,11917,82920,10619,75120,71119,25019,05420,76518,75520,29920,43520,76219,30218,21819,37619,56118,03923,96524,81521,60418,81317,24015,85319,62516,83420,57623,70820,72120,98124,19119,67723,23120,26024,05925,40125,58721,55924,14718,63326,57614,56825,66920,15622,80821,84519,45519,58621,54323,63722,73120,76720,99223,11319,17522,02420,06528,10325,59220,00424,42623,77321,75424,50224,51715,79126,15520,12121,45020,65018,65021,63420,28722,98622,49721,43022,06821,48922,32223,25720,38523,49428,50923,9472,891,31878.71%
denoisedR24,31421,61920,93821,56420,48723,92423,79425,95623,67623,57722,13425,08224,21823,57425,30324,32723,31020,48621,58424,01622,23825,65321,90624,80419,69520,43418,54219,11718,11024,24522,76624,27919,70721,79018,82719,95617,79821,40921,52324,84426,63721,05423,20025,88922,42520,85620,27319,01515,46416,66914,90017,00715,37017,97720,29919,90020,80519,31919,39920,97619,03320,32720,70221,13919,55618,38619,63019,73118,22424,33625,25021,94619,02917,53016,15919,92217,18720,82624,18421,15321,52324,70520,15323,77520,73024,75225,61325,97822,16324,70218,82626,84314,77625,93220,47123,31022,07619,70119,83821,97023,99723,30320,97821,25123,40319,26522,21420,38928,32325,80920,21324,42024,09021,97924,81024,83016,04126,32820,36821,74520,72519,07621,68220,37823,33022,67221,73922,35221,78822,56623,36020,76023,91428,86924,0712,932,08579.82%
merged21,21818,75918,15118,93817,84121,03221,20823,16821,11020,86719,77822,25521,44320,87122,46618,53320,07616,97918,65820,88318,50421,82718,46621,00616,79717,40816,01716,47915,06820,86219,88021,36516,91718,41116,04817,35915,21118,54218,83822,14623,14218,06220,54622,41819,26316,62316,15615,07912,84613,88512,46913,98212,31114,91517,08816,80917,08316,59915,84217,31115,82917,17517,65217,03116,03715,09015,71116,21414,43220,78021,66518,14514,86514,05113,05816,29413,91016,93921,24318,19718,70321,14917,30421,16617,64021,74722,45422,55019,25521,31816,21123,99512,63522,47617,34720,31719,29617,18717,19319,29421,00020,30218,29918,44220,49616,72219,18017,38825,06722,97217,75221,67621,61419,27422,13322,33013,70723,46717,83319,14218,00916,27118,66217,45420,50919,40919,08519,88619,17919,80820,32518,09221,13725,47821,1202,520,58968.62%
nonchim11,71710,50210,24012,97312,53814,87214,96815,78215,29015,14914,01315,82615,02715,04115,59311,04714,15210,77413,43514,67813,32315,18512,69714,49912,13811,83311,47811,79810,79014,41912,40013,16310,39811,07410,02110,4658,96310,85011,37213,71613,40410,65712,60313,07211,5938,9778,6498,5007,7637,8968,2579,1608,0309,99610,96411,27811,2479,94310,19311,0029,51510,10010,51411,13610,6349,87110,42210,2599,85213,52114,25812,1309,2579,4878,78910,0128,5319,86212,99710,80811,43212,38310,91112,83810,88212,67713,12113,6939,81911,94610,81816,7828,93815,83612,13913,71413,86312,56912,58113,78914,47214,31313,32512,56114,56612,36314,04212,83517,83216,62313,18915,68815,43614,15216,05115,97410,65017,27412,71813,74812,04710,15111,96611,25413,30312,59912,88613,42412,72212,68412,65711,87014,20916,11314,2491,660,01545.19%

This table can be downloaded as an Excel table below:

 

5. DADA2 Amplicon Sequence Variants (ASVs). A total of 15733 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 reads1,660,0151,660,015
BTotal assigned reads1,657,7131,657,713
CAssigned reads in species with read count < MC05,910
DAssigned reads in samples with read count < 50000
ETotal samples135135
FSamples with reads >= 500135135
GSamples with reads < 50000
HTotal assigned reads used for analysis (B-C-D)1,657,7131,651,803
IReads assigned to single species1,599,2871,596,570
JReads assigned to multiple species42,88042,332
KReads assigned to novel species15,54612,901
LTotal number of species568321
MNumber of single species351275
NNumber of multi-species2615
ONumber of novel species19131
PTotal unassigned reads2,3022,302
QChimeric reads5757
RReads without BLASTN hits1818
SOthers: short, low quality, singletons, etc.2,2272,227
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

#SampleIDGroup
F4412.S001Random1
F4412.S002Random1
F4412.S003Random1
F4412.S004Random1
F4412.S005Random1
F4412.S006Random1
F4412.S007Random1
F4412.S008Random1
F4412.S009Random1
F4412.S010Random1
F4412.S011Random1
F4412.S012Random1
F4412.S013Random1
F4412.S014Random1
F4412.S015Random1
F4412.S016Random1
F4412.S017Random1
F4412.S018Random1
F4412.S019Random1
F4412.S020Random1
F4412.S021Random1
F4412.S022Random1
F4412.S023Random1
F4412.S024Random1
F4412.S025Random1
F4412.S026Random1
F4412.S027Random1
F4412.S028Random1
F4412.S029Random2
F4412.S030Random2
F4412.S031Random2
F4412.S032Random2
F4412.S033Random2
F4412.S034Random2
F4412.S035Random2
F4412.S036Random2
F4412.S037Random2
F4412.S038Random2
F4412.S039Random2
F4412.S040Random2
F4412.S041Random2
F4412.S042Random2
F4412.S043Random2
F4412.S044Random2
F4412.S045Random2
F4412.S046Random2
F4412.S047Random2
F4412.S048Random2
F4412.S049Random2
F4412.S050Random2
F4412.S051Random2
F4412.S052Random2
F4412.S053Random2
F4412.S054Random2
F4412.S055Random2
F4412.S056Random2
F4412.S057Random2
F4412.S058Random2
F4412.S059Random2
F4412.S060Random2
F4412.S061Random2
F4412.S062Random2
F4412.S063Random2
F4412.S064Random2
F4412.S065Random2
F4412.S066Random2
F4412.S067Random2
F4412.S068Random2
F4412.S069Random2
F4412.S070Random2
F4412.S071Random2
F4412.S072Random2
F4412.S073Random2
F4412.S074Random2
F4412.S075Random2
F4412.S076Random3
F4412.S077Random3
F4412.S078Random3
F4412.S079Random3
F4412.S080Random3
F4412.S081Random3
F4412.S082Random3
F4412.S083Random3
F4412.S084Random3
F4412.S085Random3
F4412.S086Random3
F4412.S087Random3
F4412.S088Random3
F4412.S089Random3
F4412.S090Random3
F4412.S091Random3
F4412.S092Random3
F4412.S093Random3
F4412.S094Random3
F4412.S095Random3
F4412.S096Random3
F4412.S097Random3
F4412.S098Random3
F4412.S099Random3
F4412.S100Random3
F4412.S101Random3
F4412.S102Random3
F4412.S103Random3
F4412.S104Random3
F4412.S105Random3
F4412.S106Random3
F4412.S107Random3
F4412.S108Random3
F4412.S109Random3
F4412.S110Random3
F4412.S111Random3
F4412.S112Random3
F4412.S113Random3
F4412.S114Random3
F4412.S115Random3
F4412.S116Random3
F4412.S117Random3
F4412.S118Random3
F4412.S119Random3
F4412.S120Random3
F4412.S121Random3
F4412.S122Random3
F4412.S123Random3
F4412.S124Random3
F4412.S125Random3
F4412.S126Random3
F4412.S127Random3
F4412.S128Random3
F4412.S129Random3
F4412.S130Random3
F4412.S131Random3
F4412.S132Random3
F4412.S133Random3
F4412.S134Random3
F4412.S135Random3
 
 

Read Taxonomy Assignment - ASV Read Counts by Samples

#Sample IDRead Count
F4412.S0497763
F4412.S0507896
F4412.S0538030
F4412.S0518257
F4412.S0488500
F4412.S0778531
F4412.S0478649
F4412.S0758789
F4412.S0938938
F4412.S0378963
F4412.S0468977
F4412.S0529160
F4412.S0739257
F4412.S0749487
F4412.S0619515
F4412.S0899819
F4412.S0699852
F4412.S0789862
F4412.S0669871
F4412.S0589943
F4412.S0549996
F4412.S07610012
F4412.S03510021
F4412.S06210100
F4412.S12210151
F4412.S05910193
F4412.S00310240
F4412.S06810259
F4412.S03310398
F4412.S06710422
F4412.S03610465
F4412.S00210502
F4412.S06310514
F4412.S06510634
F4412.S11710650
F4412.S04210657
F4412.S01810774
F4412.S02910790
F4412.S08010808
F4412.S09110818
F4412.S03810850
F4412.S08510882
F4412.S08310911
F4412.S05510964
F4412.S06011002
F4412.S01611047
F4412.S03411074
F4412.S06411136
F4412.S05711247
F4412.S12411254
F4412.S05611278
F4412.S03911372
F4412.S08111432
F4412.S02711478
F4412.S04511593
F4412.S00111717
F4412.S02811798
F4412.S02611833
F4412.S13211870
F4412.S09011946
F4412.S12311966
F4412.S12112047
F4412.S07212130
F4412.S02512138
F4412.S09512139
F4412.S10612363
F4412.S08212383
F4412.S03112400
F4412.S00512538
F4412.S10412561
F4412.S09812569
F4412.S09912581
F4412.S12612599
F4412.S04312603
F4412.S13112657
F4412.S08612677
F4412.S13012684
F4412.S02312697
F4412.S11912718
F4412.S12912722
F4412.S10812835
F4412.S08412838
F4412.S12712886
F4412.S00412973
F4412.S07912997
F4412.S04413072
F4412.S08713121
F4412.S03213163
F4412.S11113189
F4412.S12513303
F4412.S02113323
F4412.S10313325
F4412.S04113404
F4412.S12813424
F4412.S01913435
F4412.S07013521
F4412.S08813693
F4412.S09613714
F4412.S04013716
F4412.S12013748
F4412.S10013789
F4412.S09713863
F4412.S01114013
F4412.S10714042
F4412.S11414152
F4412.S01714152
F4412.S13314209
F4412.S13514249
F4412.S07114258
F4412.S10214313
F4412.S03014419
F4412.S10114472
F4412.S02414499
F4412.S10514566
F4412.S02014678
F4412.S00614872
F4412.S00714968
F4412.S01315027
F4412.S01415041
F4412.S01015149
F4412.S02215185
F4412.S00915290
F4412.S11315436
F4412.S01515593
F4412.S11215688
F4412.S00815782
F4412.S01215826
F4412.S09415836
F4412.S11615974
F4412.S11516051
F4412.S13416113
F4412.S11016623
F4412.S09216782
F4412.S11817274
F4412.S10917832
 
 

Read Taxonomy Assignment - ASV Read Counts Table

SPIDTaxonomyF4412.S001F4412.S002F4412.S003F4412.S004F4412.S005F4412.S006F4412.S007F4412.S008F4412.S009F4412.S010F4412.S011F4412.S012F4412.S013F4412.S014F4412.S015F4412.S016F4412.S017F4412.S018F4412.S019F4412.S020F4412.S021F4412.S022F4412.S023F4412.S024F4412.S025F4412.S026F4412.S027F4412.S028F4412.S029F4412.S030F4412.S031F4412.S032F4412.S033F4412.S034F4412.S035F4412.S036F4412.S037F4412.S038F4412.S039F4412.S040F4412.S041F4412.S042F4412.S043F4412.S044F4412.S045F4412.S046F4412.S047F4412.S048F4412.S049F4412.S050F4412.S051F4412.S052F4412.S053F4412.S054F4412.S055F4412.S056F4412.S057F4412.S058F4412.S059F4412.S060F4412.S061F4412.S062F4412.S063F4412.S064F4412.S065F4412.S066F4412.S067F4412.S068F4412.S069F4412.S070F4412.S071F4412.S072F4412.S073F4412.S074F4412.S075F4412.S076F4412.S077F4412.S078F4412.S079F4412.S080F4412.S081F4412.S082F4412.S083F4412.S084F4412.S085F4412.S086F4412.S087F4412.S088F4412.S089F4412.S090F4412.S091F4412.S092F4412.S093F4412.S094F4412.S095F4412.S096F4412.S097F4412.S098F4412.S099F4412.S100F4412.S101F4412.S102F4412.S103F4412.S104F4412.S105F4412.S106F4412.S107F4412.S108F4412.S109F4412.S110F4412.S111F4412.S112F4412.S113F4412.S114F4412.S115F4412.S116F4412.S117F4412.S118F4412.S119F4412.S120F4412.S121F4412.S122F4412.S123F4412.S124F4412.S125F4412.S126F4412.S127F4412.S128F4412.S129F4412.S130F4412.S131F4412.S132F4412.S133F4412.S134F4412.S135
SP1k__Bacteria;p__Proteobacteria;c__Gammaproteobacteria;o__Oceanospirillales;f__Halomonadaceae;g__Halomonas;s__alkaliantarctica000114586743051460575755490000000000000000000000002220000160000000000000000000000000000000002124152803035033200000000000000000000031015120000018000006000000014000
SP10k__Bacteria;p__Firmicutes;c__Bacilli;o__Lactobacillales;f__Streptococcaceae;g__Streptococcus;s__cristatus06836157167881743452519830219715497241000000000000000798863965854284272734997833583326452420002004553305402645000000000000000282241463394256375387393426395520694262101612540019405020026000390200008009000001190106117122186120146105313162164255262146204217
SP100k__Bacteria;p__Spirochaetes;c__Spirochaetia;o__Spirochaetales;f__Spirochaetaceae;g__Treponema;s__socranskii00000000000000000000000000000013718912210181838572911080116123911060000000000000000000000000000003531331514025300122985356828000000000000000000000000000000281934000000900000
SP101k__Bacteria;p__Fusobacteria;c__Fusobacteria;o__Fusobacteriales;f__Fusobacteriaceae;g__Fusobacterium;s__nucleatum_ss_polymorphum128000000000000000000000000000000001621160017400237007400010100010301440110001171460000000000000003071292042839626031113320142840113018517328600000000000000000000000000000019772312352147228277241174180260267265350334
SP102k__Bacteria;p__Bacteroidetes;c__Bacteroidia;o__Bacteroidales;f__Porphyromonadaceae;g__Tannerella;s__sp._HMT_80800000000000000000000000000000016314316776811379011997970711151409900000000000000600000000000000047744601928245223283963385731000000000000000000000000000000000000000000000
SP103k__Bacteria;p__Proteobacteria;c__Epsilonproteobacteria;o__Campylobacterales;f__Campylobacteraceae;g__Campylobacter;s__gracilis000000000000000000000000000000542439989700679673118914562120000000000000000000000000000000806880162909910910212494165371529572000000000000000000000000000000109921441141515113514172154171115150127154
SP104k__Bacteria;p__Firmicutes;c__Bacilli;o__Lactobacillales;f__Streptococcaceae;g__Streptococcus;s__sp._HMT_0660000000000000000000000000000000000000000000001380232514524034355052212640151534374036433026444548283642000000000000000000000000000000000000000000000000000000000000
SP105k__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_347000000000001700000000000000000046390720570834149300813974000000000000000000000000000000005991454853628035820470034460182502503015025002300000000000000073006147745412976906760529551
SP106k__Bacteria;p__Bacteroidetes;c__Bacteroidia;o__Bacteroidales;f__Prevotellaceae;g__Alloprevotella;s__sp._HMT_308436401027812043889480103100901027329642357614153475851406330373200000000080000017622820861010447578442810535723183259183220004500520190190000120000000110004701388711011801756117752100731039399861181281161731121459425021314200917220160031130031240
SP107k__Bacteria;p__Proteobacteria;c__Betaproteobacteria;o__Neisseriales;f__Neisseriaceae;g__Neisseria;s__sicca0000000545663604700660000000000000000000001014008000000000000000000000000000000000003728358774655158444070074370000530036061431836033350520037000025005900247755044135685467951224613099122
SP108k__Bacteria;p__Saccharibacteria_(TM7);c__Saccharibacteria_(TM7)_[C-1];o__Saccharibacteria_(TM7)_[O-1];f__Saccharibacteria_(TM7)_[F-1];g__Saccharibacteria_(TM7)_[G-6];s__bacterium_HMT_87000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000025360240222302913262124200000000001500000127110384082605607142042767363
SP109k__Bacteria;p__Proteobacteria;c__Gammaproteobacteria;o__Pasteurellales;f__Pasteurellaceae;g__Aggregatibacter;s__sp._HMT_458000018234247350004014270000000000000005580398157891211123311312627016416810500003500001200000000000000000000704999070150001216300190000000027000160000000000014000004347391046133684750759970818865
SP11k__Bacteria;p__Bacteroidetes;c__Bacteroidia;o__Bacteroidales;f__Prevotellaceae;g__Prevotella;s__jejuni00085281049923987477866440935814845127891164910476061111117771520000000000000003372682192216038302969237023004134834355611251973971141165952548350000000000000000000000000000000013000000000000000000000000000
SP110k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Lachnospiraceae_[XIV];g__Stomatobaculum;s__sp._HMT_09730303066056570088650825263640801347216712811611412112876127124101000000000000000000000000000000000000000292600000000008800000000000213704040410520423600465105405229048028684900000000004600000
SP111k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Lachnospiraceae_[XIVa];g__Catonella;s__morbi000384000025210403012334877296648446055445530275133534364653236322487334903755394834504706000212222221920038344357515435605779103614343342824012000000044158840631074553304241335340585443304951503857573541413839384157506051375723191321182026022222324
SP112k__Bacteria;p__Bacteroidetes;c__Bacteroidia;o__Bacteroidales;f__Porphyromonadaceae;g__Porphyromonas;s__catoniae001009000000100000000000000000000626114217992689188167381613175700130000008111314015000000000000000002821262624220263631145442221040250007005000000000000000000000000000050070
SP113k__Bacteria;p__Saccharibacteria(TM7);c__TM7_[C];o__TM7_[O];f__TM7_[F];g__TM7_[G];s__sp._Oral_Taxon_A560000036253127261927252137010024221717232523013221935000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000
SP114k__Bacteria;p__Proteobacteria;c__Gammaproteobacteria;o__Pasteurellales;f__Pasteurellaceae;g__Aggregatibacter;s__segnis000900000000000000000000000000001270000119481300000057095125186140346761641751653080000000000400000655778381318000007000000000000000000000000000000000000000000000000
SP116k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Lachnospiraceae_[XIV];g__Lachnospiraceae_[G-3];s__bacterium_HMT_1000700000000000000400000000500002723220202115261719002100000000000000000000000000000000270021100102226002416161800000000000000000000000000000017111980001408011000
SP117k__Bacteria;p__Firmicutes;c__Bacilli;o__Lactobacillales;f__Streptococcaceae;g__Streptococcus;s__infantis_clade_431602441491152897122299001317401591630000004400005200610000000000000000125087136124126103833311011449123118008151120039804358147796181600000000000000000003805750000000580000000000000000000000000000000
SP118k__Bacteria;p__Spirochaetes;c__Spirochaetia;o__Spirochaetales;f__Spirochaetaceae;g__Treponema;s__sp._HMT_247000000010000000000000000000000092963522000254155004074400000000000000000000000000000000000000000025000000000000000000000000000000000000000000000000
SP119k__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_35239584231023300404341640382513727811006044415405204138054000000000000000122124487357741271141569112410453155140127123128118159801551267312516988012739000000000000000437542382821262331333626263028101801701623091312011170000000000000000
SP12k__Bacteria;p__Fusobacteria;c__Fusobacteria;o__Fusobacteriales;f__Fusobacteriaceae;g__Fusobacterium;s__periodonticum2197235821484825444826035797396776076686144455553428198819821487144214221642141915861089683107110741074157800000000000000072965668318713032120928124228217617623429933736330342335139537144824138946443938648531338800000000000000021009381911140132771091448880102287373353458420311379383321325363255316251323000000000000000
SP120k__Bacteria;p__Firmicutes;c__Bacilli;o__Lactobacillales;f__Streptococcaceae;g__Streptococcus;s__sp._Oral_Taxon_B6600000000000000000000000000000000000000000000015000000002900000000000000000000410012024001017002200000000000000000000000000000000513140000301000000
SP121k__Bacteria;p__Firmicutes;c__Negativicutes;o__Selenomonadales;f__Selenomonadaceae;g__Selenomonas;s__artemidis00000000000000000000000000000014001929130000003516190000000000000000000000000000000000000000000000000000000000000000000000000005053490182201100110101815
SP122k__Bacteria;p__Bacteroidetes;c__Bacteroidia;o__Bacteroidales;f__Porphyromonadaceae;g__Porphyromonas;s__sp._HMT_2770000000000000000000000000000000005100000036000000000000000000000000000000000001702601941213040320491947000000000000000000000000000000000000000000000
SP123k__Bacteria;p__Proteobacteria;c__Betaproteobacteria;o__Neisseriales;f__Neisseriaceae;g__Neisseria;s__flavescens|subflava7265214481339129815201856170917761593171616991643151415612815362858219238111112868106178483197276185284800000000000000001102017215231692333572924351358682356574181221380272753742600000000000000085194120875602679939859962941121510247496448401327161420182640318720972734260223032423267515532358187520484083744237269956271111793782986807130210171102735
SP124k__Bacteria;p__Proteobacteria;c__Gammaproteobacteria;o__Enterobacteriales;f__Enterobacteriaceae;g__Enterobacter;s__cloacae00000000000000000000000000000000000000000000000048610100000000168300000000000000000000000000000000000000000000000000000000000000000000000000000
SP125k__Bacteria;p__Firmicutes;c__Bacilli;o__Lactobacillales;f__Streptococcaceae;g__Streptococcus;s__sp._HMT_074556302651272892263502202742272972032831801373172144183744614043803064405663552422954640000000000000000000000000000000000000000000000000000000000000018918383921195183197171762531320455840225119606945533035000000000000000
SP126k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Lachnospiraceae_[XIV];g__Stomatobaculum;s__longum000049006566000000013100310292939000004001400000120000000127012564818677741431441581599613710913718517319910212115823517131525495662042066547787805405410607315532467039040045000490500050370059000033045000390607948360540400037285837
SP127k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Peptostreptococcaceae_[XI];g__Peptostreptococcaceae_[XI][G-1];s__[Eubacterium]_infirmum0000000014000000000000000000000767672545068490008397884510000000000000000000000000000000612600120000560430000000000000000000000000000000000000000000000
SP128k__Bacteria;p__Bacteroidetes;c__Bacteroidia;o__Bacteroidales;f__Porphyromonadaceae;g__Porphyromonas;s__gingivalis0003003037075406006300000000000000000001239221329043184460200015112622418000000000000000000000000000000000000000000149000000000000000000000000000000000000000000000000
SP129k__Bacteria;p__Bacteroidetes;c__Bacteroidia;o__Bacteroidales;f__Prevotellaceae;g__Prevotella;s__loescheii00000000000000000000000000000085007900080000099156000000000000000000000000000000000000000000943000000000000000000000000000000000000000000000000
SP13k__Bacteria;p__Firmicutes;c__Bacilli;o__Lactobacillales;f__Carnobacteriaceae;g__Granulicatella;s__paradiacens1027162234285392223334200390313297360191368751631952303542874483142623583923542201613121260064004500570006401541231296920312922286242196303205155228225044183212953472832791971972973943293632454922147105308030058877205389112713182378575327956198195156906026306339509373774445104725834884063903525216843956344515298707810101000499200002878
SP130k__Bacteria;p__Actinobacteria;c__Actinobacteria;o__Actinomycetales;f__Actinomycetaceae;g__Actinomyces;s__sp._Oral_Taxon_178000000000000000000000000000000000000000000000000000000000000000000000000000291625210000000000100000000000000000000000000000009131935003000005250
SP131k__Bacteria;p__Firmicutes;c__Bacilli;o__Lactobacillales;f__Streptococcaceae;g__Streptococcus;s__sp._str._2136FAA00000000000000000000000000000000000000001531500001691714033010202326021000000000000000067721281561611541571211048416801495158000000000000000000000000000000000000000000000
SP132k__Bacteria;p__Proteobacteria;c__Betaproteobacteria;o__Burkholderiales;f__Burkholderiaceae;g__Lautropia;s__mirabilis30272340018424425640513829430000000000000000256000900910300050000000100150140000000000000000003172922731071068416213513389112012793726117077188120149160107213196230233198100144486747583232453341283233623638306300418308312712355606579323400265302436608
SP133k__Bacteria;p__Fusobacteria;c__Fusobacteriia;o__Fusobacteriales;f__Leptotrichiaceae;g__Leptotrichia;s__wadei0300010700001481420106000001211881117775001390012613477840000024043930502400002102700240000001131251121050000521182201560756619243228577598011779106116792014919115044012699006008600000000008010800560001098827441231927309210283382262682320167269
SP134k__Bacteria;p__Bacteroidetes;c__Bacteroidia;o__Bacteroidales;f__Tannerellaceae;g__Tannerella;s__forsythia0000101012141718121213372400000000000000051584241387984628810223694255767030000000070000000000000000000181701716092820597518760000000000000000000000000000000000000000000000
SP135k__Bacteria;p__Firmicutes;c__Bacilli;o__Lactobacillales;f__Carnobacteriaceae;g__Granulicatella;s__adiacens0000000008000000000000000000000119106000050540456735000000000000000000000000000000001201381371327261566766844208507400004000000400000000000000000061135145571035485776252424614194104
SP136k__Bacteria;p__Bacteroidetes;c__Flavobacteriia;o__Flavobacteriales;f__Flavobacteriaceae;g__Capnocytophaga;s__leadbetteri200120000001617000012040004000000097768355998480760593250601405900000000000000000000000000000000230460360300276637018157174111131891696429501093006894036000132300000080199186181229172139135144163222320271207165185
SP137k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Peptostreptococcaceae_[XI];g__Eubacterium_[XI][G-7];s__yurii00000000000000000000000000000042531500193114153203519027000000000000000000000000000000000500000002500000000000000000000000000000000013131300000019180151313
SP138k__Bacteria;p__Firmicutes;c__Bacilli;o__Lactobacillales;f__Streptococcaceae;g__Streptococcus;s__sp._str._C300868711697424191235313353320133129154283315444242100000411446505700062567722621648115729917520051113216012037238915390168203229135235207199245933342302543390003600009259000088117027167001356198161100138067012925601281081341266255115152180155991491201128668527171635459610363233147588816996150148571117519372105145115
SP139k__Bacteria;p__Actinobacteria;c__Actinobacteria;o__Propionibacteriales;f__Propionibacteriaceae;g__Pseudopropionibacterium;s__propionicum000090000000000000000000000000383013163728532906298102546761620000000000000000000000000000000001030895609351951384235390000000000000000000000000000002618210016000000000
SP14k__Bacteria;p__Firmicutes;c__Bacilli;o__Lactobacillales;f__Streptococcaceae;g__Streptococcus;s__parasanguinis_II004924628641538235632645132631725046126715332323942533933342739742838270530946828251400000000000000015016017231330837944935539543252147637143845723925326761360957158858753378895175050961353800000000000000019929915540043445235339726233127330934536630724723523139036231026333331651742130245027632700000000038001005
SP140k__Bacteria;p__Proteobacteria;c__Epsilonproteobacteria;o__Campylobacterales;f__Campylobacteraceae;g__Campylobacter;s__sp._HMT_044006500025000025013012783501210000000000000000000000000041534000000000000044360000000000000000000000000000000000000000000000000000000000000000000000000
SP141k__Bacteria;p__Actinobacteria;c__Actinobacteria;o__Actinomycetales;f__Actinomycetaceae;g__Actinomyces;s__graevenitzii00000019003200000044032819516738618532341375297239239277000000000000000000564968680769513194000004670132107893812360111152654891000000000000000131500000000000000001500000000000000000000000000
SP142k__Bacteria;p__Bacteroidetes;c__Bacteroidia;o__Bacteroidales;f__Prevotellaceae;g__Prevotella;s__denticola0000230205100043016952000000000000000221260115219178190228632492491125729029816311597106000000000003100000000000000000010001120000206920000000000000000000000000000000066866083129156907583481006613966123
SP143k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Peptostreptococcaceae_[XIII];g__Parvimonas;s__sp._Oral_Taxon_110000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000138801040025040340253634350
SP144k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Peptostreptococcaceae_[XI];g__Peptostreptococcaceae_[XI][G-4];s__bacterium_HMT_369000000000000000000000000000000302731500001214001000000000000000000000000000000000006000000008090000000000000000000000000000000000000000000000
SP145k__Bacteria;p__Actinobacteria;c__Actinobacteria;o__Actinomycetales;f__Propionibacteriaceae;g__Propionibacterium;s__acnes0003400210223268390200000000000000000000000000000000000000000000000000000000000000000000000000609000000000000000000000000000000000000000000000
SP147k__Bacteria;p__Proteobacteria;c__Gammaproteobacteria;o__Pasteurellales;f__Pasteurellaceae;g__Aggregatibacter;s__actinomycetemcomitans0002036960488537016933540000000000000000004049833689628312200110159450000000000000000000000000000000000000000060000000000000000000000000000000000000000000000000
SP15k__Bacteria;p__Firmicutes;c__Bacilli;o__Lactobacillales;f__Streptococcaceae;g__Streptococcus;s__anginosus000421064245260625500026000000000000004421382660102210114512141816801101430434940055471729000000000000008066507745673876554432389345364000000000000000000000000000000000000000000000
SP150k__Bacteria;p__Actinobacteria;c__Actinobacteria;o__Actinomycetales;f__Actinomycetaceae;g__Actinomyces;s__georgiae00000000000000000000000000000037410210000292500230000000000000000000000000000000000120000000000000000000000000000000000000000000000000000270000
SP151k__Bacteria;p__Proteobacteria;c__Gammaproteobacteria;o__Pasteurellales;f__Pasteurellaceae;g__Haemophilus;s__haemolyticus13811511900000000000230000000000000000012800000000000000000000000000000000000000000000000000000000000070000000009000080011018000000000000000000000
SP152k__Bacteria;p__Bacteroidetes;c__Bacteroidia;o__Bacteroidales;f__Prevotellaceae;g__Prevotella;s__sp._HMT_315000000000000000000000000000000000090045000120013403839000000000000000000000000000000000000000000000000000000000000000000000000000000000000000
SP153k__Bacteria;p__Proteobacteria;c__Betaproteobacteria;o__Neisseriales;f__Neisseriaceae;g__Kingella;s__sp._Oral_Taxon_C21000000000000000000000000000000000041001105001531000000000000000000000000000000000000000000025000000000000000000000000000000000000000000000000
SP156k__Bacteria;p__Firmicutes;c__Bacilli;o__Lactobacillales;f__Streptococcaceae;g__Streptococcus;s__intermedius0000203605008500000000000000005484774444153875802963395527000000000000000000000000000000069759912484735886931028086143486100000400010015000000000000000013016216346526490562334380737473
SP157k__Bacteria;p__Firmicutes;c__Negativicutes;o__Selenomonadales;f__Selenomonadaceae;g__Selenomonas;s__dianae0000000000000000000000000000004081400042003660042010000000000000000000000000000000000000000000083000000000000000000000000000000000000000000000000
SP158k__Bacteria;p__Actinobacteria;c__Actinobacteria;o__Actinomycetales;f__Actinomycetaceae;g__Schaalia;s__cardiffensis00000000000000000000000000000017235100000021080117000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000
SP159k__Bacteria;p__Bacteroidetes;c__Bacteroidia;o__Bacteroidales;f__Bacteroidales_[F-2];g__Bacteroidales_[G-2];s__sp._Oral_Taxon_2740000000111060010131700000000000000012615512810068110986698852591167728700000000000000000000000000000014117081117132312668516010000000000000000000000000000000000000000000000
SP16k__Bacteria;p__Bacteroidetes;c__Bacteroidia;o__Bacteroidales;f__Prevotellaceae;g__Prevotella;s__oris10064040490000730431690000000000000000295241353367341260239401326345146329418286289480580210001120000594900000000000000052303966419360468496054298400114000006000003001108000000000004019923615514219620215318217920215590166253231
SP160k__Bacteria;p__Bacteroidetes;c__Bacteroidia;o__Bacteroidales;f__Prevotellaceae;g__Prevotella;s__sp._HMT_313000000000000000000000000000000000000000000000114807100000000000012915415100000020290000000000000000000000000000000000000000000000000000000000000000
SP161k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Lachnospiraceae_[XIV];g__Lachnospiraceae_[G-2];s__bacterium_HMT_0960000000000000000000000000000000000000000000000170000000000028030006841485172567210792526951000000000000000000000000000000000000000000000000000000000000
SP162k__Bacteria;p__Synergistetes;c__Synergistia;o__Synergistales;f__Synergistaceae;g__Fretibacterium;s__sp._HMT_36000000000000000000000000000000046525728142921200170290222800000000000000000000000000000000000000002233000000000000000000000000000000000000000000000000
SP163k__Bacteria;p__Proteobacteria;c__Betaproteobacteria;o__Neisseriales;f__Neisseriaceae;g__Neisseria;s__elongata0000000000000000000000000000000035000000000000000000000000000007000000000000000180000046000029662939625613882111291051537150744048255845255652392900400476104810119829913415010017015818415186
SP164k__Bacteria;p__Firmicutes;c__Bacilli;o__Bacillales;f__NA;g__Gemella;s__haemolysans138114352051311428812111310968961971342270251100010191900000136155000770000000510000200000483500006000000000000000000250000210000000170139137109621195007491780762606710347580375351401737052250362125000000000000
SP166k__Bacteria;p__Fusobacteria;c__Fusobacteria;o__Fusobacteriales;f__Leptotrichiaceae;g__Leptotrichia;s__sp._AF189244.100003004103403500404800000000000000059550281618290241430519143728000000000000000000000000000000262178260175219135209153158201154020281200027024182600019382600300220360002200000021429358449728758814722866657605855611726864907
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SP91k__Bacteria;p__Actinobacteria;c__Actinobacteria;o__Actinomycetales;f__Actinomycetaceae;g__Schaalia;s__sp._HMT_1720000766401641220054123000633112940085144514507400650000000000000000000847487050425000000161125344209900100585585546400000000000000007500000000000000380000000000000000000000000000
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SP93k__Bacteria;p__Firmicutes;c__Bacilli;o__Bacillales;f__Staphylococcaceae;g__Gemella;s__moribillum0000000000000000000000000000000010200700007700008500000000000000000000000000000000570035000007152000000000000000011000000000000000497179599389125840141981041180139
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SPN16k__Bacteria;p__Bacteroidetes;c__Bacteroidia;o__Bacteroidales;f__Prevotellaceae;g__Prevotella;s__intermedia08000000000140000182400100901680000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000
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SPN39k__Bacteria;p__Bacteroidetes;c__Bacteroidia;o__Bacteroidales;f__Porphyromonadaceae;g__Porphyromonas;s__pasteri00065616375576710180965746750000000000000000001600091720260011000000000000000000000000000000000016120171219017340013000000000000000000190000162300008000000007000050
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SPP8k__Bacteria;p__Fusobacteria;c__Fusobacteria;o__Fusobacteriales;f__Fusobacteriaceae;g__Fusobacterium;s__multispecies_spp8_2000000000000096000000000000000015047797412011567132131111861321110511600000540000000000000000000000000000000519000134325340000000000000000000000000000000000000000000000
SPP9k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Lachnospiraceae_[XIV];g__Oribacterium;s__multispecies_spp9_27875072163123149731527118914317711512823420416115116114419117211411098921591141870000000000000000000066315806789680684555828966751101011221368010610213811924000000000000000000392732044333519234718423100220001921231202500000000000000000
SPPN1k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__multifamily;g__Lachnoanaerobaculum;s__saburreum00000000000000000000000000000025280000140211800172322000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000
SPPN2k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__multifamily;g__Eubacterium_[XIVa][G-1];s__saburreum000000000000000024000000170000610000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000
SPPN4k__Bacteria;p__Fusobacteria;c__Fusobacteriia;o__Fusobacteriales;f__Fusobacteriaceae;g__Fusobacterium;s__multispecies_sppn4_2_nov_97.416%000000015000000000000000000000018231100132300380097270000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000
 
 
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.Random
 
 
 

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.

 
 
 
 
 
 
 
 

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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