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

FOMC Service Report

16S rRNA Gene V1V3 Amplicon Sequencing

Version V1.0

The Forsyth Institute, Cambridge, MA, USA
March 18, 2021

Project ID: FOMC4083

I. 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
 

II. 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

 

III. 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.

 

IV. 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


S10zr4083_10V1V3_R1.fastq.gzzr4083_10V1V3_R2.fastq.gz


S11zr4083_11V1V3_R1.fastq.gzzr4083_11V1V3_R2.fastq.gz


S12zr4083_12V1V3_R1.fastq.gzzr4083_12V1V3_R2.fastq.gz


S13zr4083_13V1V3_R1.fastq.gzzr4083_13V1V3_R2.fastq.gz


S14zr4083_14V1V3_R1.fastq.gzzr4083_14V1V3_R2.fastq.gz


S15zr4083_15V1V3_R1.fastq.gzzr4083_15V1V3_R2.fastq.gz


S16zr4083_16V1V3_R1.fastq.gzzr4083_16V1V3_R2.fastq.gz


S17zr4083_17V1V3_R1.fastq.gzzr4083_17V1V3_R2.fastq.gz


S18zr4083_18V1V3_R1.fastq.gzzr4083_18V1V3_R2.fastq.gz


S19zr4083_19V1V3_R1.fastq.gzzr4083_19V1V3_R2.fastq.gz


S01zr4083_1V1V3_R1.fastq.gzzr4083_1V1V3_R2.fastq.gz


S20zr4083_20V1V3_R1.fastq.gzzr4083_20V1V3_R2.fastq.gz


S02zr4083_2V1V3_R1.fastq.gzzr4083_2V1V3_R2.fastq.gz


S03zr4083_3V1V3_R1.fastq.gzzr4083_3V1V3_R2.fastq.gz


S04zr4083_4V1V3_R1.fastq.gzzr4083_4V1V3_R2.fastq.gz


S05zr4083_5V1V3_R1.fastq.gzzr4083_5V1V3_R2.fastq.gz


S06zr4083_6V1V3_R1.fastq.gzzr4083_6V1V3_R2.fastq.gz


S07zr4083_7V1V3_R1.fastq.gzzr4083_7V1V3_R2.fastq.gz


S08zr4083_8V1V3_R1.fastq.gzzr4083_8V1V3_R2.fastq.gz


S09zr4083_9V1V3_R1.fastq.gzzr4083_9V1V3_R2.fastq.gz


S01zr4106_1V1V3_R1.fastq.gzzr4106_1V1V3_R2.fastq.gz


S02zr4106_2V1V3_R1.fastq.gzzr4106_2V1V3_R2.fastq.gz


S03zr4106_3V1V3_R1.fastq.gzzr4106_3V1V3_R2.fastq.gz


S04zr4106_4V1V3_R1.fastq.gzzr4106_4V1V3_R2.fastq.gz


S05zr4106_5V1V3_R1.fastq.gzzr4106_5V1V3_R2.fastq.gz


S06zr4106_6V1V3_R1.fastq.gzzr4106_6V1V3_R2.fastq.gz


S07zr4106_7V1V3_R1.fastq.gzzr4106_7V1V3_R2.fastq.gz


S08zr4106_8V1V3_R1.fastq.gzzr4106_8V1V3_R2.fastq.gz


S09zr4106_9V1V3_R1.fastq.gzzr4106_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:

 

V. 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
32132.55%43.72%45.51%46.47%48.42%44.12%
31133.04%45.27%47.10%48.47%45.65%29.30%
30132.83%45.63%47.16%43.90%29.24%11.86%
29133.39%45.89%43.28%27.76%12.66%11.36%
28134.62%42.35%27.83%12.26%11.94%9.12%
27129.22%28.17%12.23%10.91%9.07%3.26%

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 bulding 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 IDF4083.S01F4083.S02F4083.S03F4083.S04F4083.S05F4083.S06F4083.S07F4083.S08F4083.S09F4083.S10F4083.S11F4083.S12F4083.S13F4083.S14F4083.S15F4083.S16F4083.S17F4083.S18F4083.S19F4083.S20F4106.S01F4106.S02F4106.S03F4106.S04F4106.S05F4106.S06F4106.S07F4106.S08F4106.S09Row SumPercentage
input19,73128,10626,83124,79329,33020,89616,72226,28521,82125,39227,37825,42527,85123,14523,56524,94123,72824,74931,00021,43830,57533,77036,60539,28739,55737,58035,73239,78829,892815,913100.00%
filtered14,55621,72220,64519,27122,67216,07612,75320,54716,50119,69321,19119,32521,70917,81018,10218,88417,75919,08424,02116,36124,09228,17030,38732,32732,23430,76329,24531,55523,056640,51178.50%
denoisedF13,73520,91219,49718,52621,91115,24111,92319,57815,66118,61820,46218,77920,60916,94917,11418,09017,03518,20822,90815,78123,27027,32129,61531,34931,20329,61128,07530,44522,345614,77175.35%
denoisedR13,89521,39819,96418,77922,19215,51112,29620,11915,91419,25020,73019,02421,04817,21717,61718,42617,21118,59423,51715,95923,49727,55729,93731,51731,72829,82028,43130,82722,549624,52476.54%
merged10,62619,16316,68916,15419,44412,6959,86317,12313,18416,59418,60617,40617,75814,63414,36315,90415,28715,82020,58014,21720,47424,48127,27127,42627,73625,05423,05125,21619,262536,08165.70%
nonchim6,1819,1149,1458,5048,5246,7085,3417,4436,9257,0108,3688,2868,9198,2047,7116,9136,5647,2609,6526,9419,75011,28212,78715,05114,24014,59213,52513,13511,241269,31633.01%

This table can be downloaded as an Excel table below:

 

5. DADA2 Amplicon Sequence Variants (ASVs). A total of 5634 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:

 
 
 
 

VI. 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 reads269,316269,316
BTotal assigned reads268,975268,975
CAssigned reads in species with read count < MC05,838
DAssigned reads in samples with read count < 50000
ETotal samples2929
FSamples with reads >= 5002929
GSamples with reads < 50000
HTotal assigned reads used for analysis (B-C-D)268,975263,137
IReads assigned to single species250,594247,089
JReads assigned to multiple species10,83410,544
KReads assigned to novel species7,5475,504
LTotal number of species397200
MNumber of single species256172
NNumber of multi-species136
ONumber of novel species12822
PTotal unassigned reads341341
QChimeric reads1010
RReads without BLASTN hits44
SOthers: short, low quality, singletons, etc.327327
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
F4083.S01CF
F4083.S02SECC
F4083.S03SECC
F4083.S04SECC
F4083.S05SECC
F4083.S06SECC
F4083.S07SECC
F4083.S08SECC
F4083.S09CF
F4083.S10SECC
F4083.S11SECC
F4083.S12SECC
F4083.S13SECC
F4083.S14SECC
F4083.S15SECC
F4083.S16CF
F4083.S17SECC
F4083.S18SECC
F4083.S19CF
F4083.S20CF
F4106.S01SECC
F4106.S02SECC
F4106.S03SECC
F4106.S04CF
F4106.S05CF
F4106.S06SECC
F4106.S07CF
F4106.S08SECC
F4106.S09CF
 
 

Read Taxonomy Assignment - ASV Read Counts by Samples

#Sample IDRead Count
F4083.S075341
F4083.S016181
F4083.S176564
F4083.S066708
F4083.S166913
F4083.S096925
F4083.S206941
F4083.S107010
F4083.S187260
F4083.S087443
F4083.S157711
F4083.S148204
F4083.S128286
F4083.S118368
F4083.S048504
F4083.S058524
F4083.S138919
F4083.S029114
F4083.S039145
F4083.S199652
F4106.S019750
F4106.S0911241
F4106.S0211282
F4106.S0312787
F4106.S0813135
F4106.S0713525
F4106.S0514240
F4106.S0614592
F4106.S0415051
 
 

Read Taxonomy Assignment - ASV Read Counts Table

SPIDTaxonomyF4083.S01F4083.S02F4083.S03F4083.S04F4083.S05F4083.S06F4083.S07F4083.S08F4083.S09F4083.S10F4083.S11F4083.S12F4083.S13F4083.S14F4083.S15F4083.S16F4083.S17F4083.S18F4083.S19F4083.S20F4106.S01F4106.S02F4106.S03F4106.S04F4106.S05F4106.S06F4106.S07F4106.S08F4106.S09
SP1k__Bacteria;p__Proteobacteria;c__Gammaproteobacteria;o__Pasteurellales;f__Pasteurellaceae;g__Haemophilus;s__parahaemolyticus00203000400033800000540000018066700000
SP10k__Bacteria;p__Fusobacteria;c__Fusobacteriia;o__Fusobacteriales;f__Fusobacteriaceae;g__Fusobacterium;s__nucleatum_subsp._vincentii000108000000000000000000000010900
SP100k__Bacteria;p__Proteobacteria;c__Betaproteobacteria;o__Neisseriales;f__Neisseriaceae;g__Neisseria;s__subflava001670000000004500400000000162860000
SP102k__Bacteria;p__Bacteroidetes;c__Flavobacteriia;o__Flavobacteriales;f__Flavobacteriaceae;g__Capnocytophaga;s__granulosa5100640117000002378035520760103000171016901670
SP104k__Bacteria;p__Proteobacteria;c__Betaproteobacteria;o__Neisseriales;f__Neisseriaceae;g__Kingella;s__oralis1021001359000805915100064430243239396719154021418701240172
SP105k__Bacteria;p__Firmicutes;c__Bacilli;o__Lactobacillales;f__Streptococcaceae;g__Streptococcus;s__oralis_subsp._dentisani_clade_058002207159000000000056060000000000
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_34848311000020133800013914001527222700061821882912568
SP107k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Ruminococcaceae;g__Ruminococcaceae_[G-1];s__bacterium_HMT_075510580025013000036520550023170036004616220
SP109k__Bacteria;p__Actinobacteria;c__Actinobacteria;o__Actinomycetales;f__Actinomycetaceae;g__Actinomyces;s__johnsonii0230000170000000023130000000550330136
SP11k__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_346003600001200009186014203100000001900044
SP110k__Bacteria;p__Actinobacteria;c__Actinobacteria;o__Propionibacteriales;f__Propionibacteriaceae;g__Pseudopropionibacterium;s__propionicum51272362842922515900857101051087800000426210460480
SP111k__Bacteria;p__Bacteroidetes;c__Flavobacteriia;o__Flavobacteriales;f__Flavobacteriaceae;g__Bergeyella;s__sp._HMT_2060026062619239021000070530101300101000100000
SP112k__Bacteria;p__Actinobacteria;c__Actinobacteria;o__Actinomycetales;f__Micrococcaceae;g__Rothia;s__mucilaginosa819442970501162986635519646001607206900249015500040140
SP113k__Bacteria;p__Bacteroidetes;c__Bacteroidia;o__Bacteroidales;f__Prevotellaceae;g__Prevotella;s__jejuni00001890000000041000000014390011000
SP114k__Bacteria;p__Bacteroidetes;c__Bacteroidia;o__Bacteroidales;f__Prevotellaceae;g__Alloprevotella;s__sp._HMT_91200000038037600590401120276400000003000
SP115k__Bacteria;p__Bacteroidetes;c__Bacteroidia;o__Bacteroidales;f__Prevotellaceae;g__Prevotella;s__pallens001250024000001008600021002070996002300
SP116k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Lachnospiraceae_[XIV];g__Lachnoanaerobaculum;s__umeaense1058900700000000580039011752000010359016700
SP118k__Bacteria;p__Proteobacteria;c__Betaproteobacteria;o__Neisseriales;f__Neisseriaceae;g__Kingella;s__sp._Oral_Taxon_C21000000055170600000000000000068290
SP12k__Bacteria;p__Proteobacteria;c__Gammaproteobacteria;o__Pasteurellales;f__Pasteurellaceae;g__Haemophilus;s__parainfluenzae1299042152172561073432712413361921003357133275162210460181371740586721101449560304199
SP120k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Lachnospiraceae_[XIVa];g__Lachnospiraceae_[G];s__sp._Oral_Taxon_B320001100000340000000000000004410110100
SP121k__Bacteria;p__Firmicutes;c__Bacilli;o__Lactobacillales;f__Streptococcaceae;g__Streptococcus;s__parasanguinis_I0000000000000000000035400000000
SP122k__Bacteria;p__Fusobacteria;c__Fusobacteriia;o__Fusobacteriales;f__Leptotrichiaceae;g__Leptotrichia;s__trevisanii02700000120000000000170000793600000
SP123k__Bacteria;p__Proteobacteria;c__Betaproteobacteria;o__Neisseriales;f__Neisseriaceae;g__Neisseria;s__sp._Oral_Taxon_1447590000000001440000000000000061590
SP126k__Bacteria;p__Actinobacteria;c__Actinobacteria;o__Actinomycetales;f__Actinomycetaceae;g__Actinomyces;s__sp._Oral_Taxon_18000033000000280482223000011300000114000
SP127k__Bacteria;p__Firmicutes;c__Bacilli;o__Lactobacillales;f__Streptococcaceae;g__Streptococcus;s__intermedius0310655870121457000000008301301121111565723
SP128k__Bacteria;p__Firmicutes;c__Bacilli;o__Lactobacillales;f__Streptococcaceae;g__Streptococcus;s__mutans00031021076029106480364410061601680148124176080
SP13k__Bacteria;p__Fusobacteria;c__Fusobacteria;o__Fusobacteriales;f__Fusobacteriaceae;g__Fusobacterium;s__nucleatum_ss_polymorphum17717421113501240010100222950110038155000023137457467455306146
SP131k__Bacteria;p__Proteobacteria;c__Gammaproteobacteria;o__Pasteurellales;f__Pasteurellaceae;g__Aggregatibacter;s__segnis580680027000310015009900000001813500500
SP132k__Bacteria;p__Firmicutes;c__Negativicutes;o__Selenomonadales;f__Selenomonadaceae;g__Selenomonas;s__sputigena001800220110000398300101600828300002400
SP135k__Bacteria;p__Fusobacteria;c__Fusobacteriia;o__Fusobacteriales;f__Leptotrichiaceae;g__Leptotrichia;s__sp._HMT_4980052003804264000169875468445800160159085016800118
SP136k__Bacteria;p__Fusobacteria;c__Fusobacteriia;o__Fusobacteriales;f__Leptotrichiaceae;g__Leptotrichia;s__sp._HMT_417004001920000008107206793000032000000
SP138k__Bacteria;p__Proteobacteria;c__Betaproteobacteria;o__Neisseriales;f__Neisseriaceae;g__Neisseria;s__cinerea00928558017011700000330033760259128700962700
SP139k__Bacteria;p__Actinobacteria;c__Actinobacteria;o__Actinomycetales;f__Actinomycetaceae;g__Actinomyces;s__odontolyticus28035005338029000000059600000061000033
SP14k__Bacteria;p__Firmicutes;c__Bacilli;o__Lactobacillales;f__Streptococcaceae;g__Streptococcus;s__oralis4344122382449383115382179589888015412785219176813176672281129328048985023685225
SP141k__Bacteria;p__Actinobacteria;c__Actinobacteria;o__Actinomycetales;f__Actinomycetaceae;g__Actinomyces;s__sp._HMT_17505801480000006004400001030000008087000
SP142k__Bacteria;p__Firmicutes;c__Bacilli;o__Lactobacillales;f__Streptococcaceae;g__Streptococcus;s__sp._str._C15009000001000004700000000000000040
SP144k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Lachnospiraceae_[XIVa];g__Catonella;s__morbi56036340230001500000000900000320371433
SP145k__Bacteria;p__Bacteroidetes;c__Bacteroidia;o__Bacteroidales;f__Porphyromonadaceae;g__Porphyromonas;s__sp._HMT_275230046000000000038000000004000000
SP146k__Bacteria;p__Actinobacteria;c__Actinobacteria;o__Actinomycetales;f__Actinomycetaceae;g__Actinomyces;s__massiliensis01000000000000001603400000762720000
SP147k__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_870000115363200190000432691128201508444960001270075
SP148k__Bacteria;p__Firmicutes;c__Bacilli;o__Lactobacillales;f__Streptococcaceae;g__Streptococcus;s__constellatus0016000018000015000000015500000000
SP149k__Bacteria;p__Bacteroidetes;c__Bacteroidia;o__Bacteroidales;f__Prevotellaceae;g__Prevotella;s__micans0025000008000000110000000014800019
SP15k__Bacteria;p__Bacteroidetes;c__Bacteroidia;o__Bacteroidales;f__Prevotellaceae;g__Prevotella;s__loescheii205800000480000005100019134400169162690000
SP150k__Bacteria;p__Fusobacteria;c__Fusobacteriia;o__Fusobacteriales;f__Fusobacteriaceae;g__Fusobacterium;s__sp._HMT_2480012000017000000003100970000000000
SP151k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Lachnospiraceae;g__Lachnoanaerobaculum;s__orale0000004800510450310005500581141254100000
SP153k__Bacteria;p__Actinobacteria;c__Actinobacteria;o__Corynebacteriales;f__Corynebacteriaceae;g__Corynebacterium;s__durum02721239300000172900000065000071154040510
SP154k__Bacteria;p__Proteobacteria;c__Gammaproteobacteria;o__Pasteurellales;f__Pasteurellaceae;g__Aggregatibacter;s__sp._HMT_949000000000000810490000000012100096286
SP157k__Bacteria;p__Firmicutes;c__Bacilli;o__Lactobacillales;f__Streptococcaceae;g__Streptococcus;s__sp._Oral_Taxon_710000000000000005056000000000000
SP158k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Peptostreptococcaceae;g__Peptoanaerobacter;s__yurii002400000000320000002809000001101233
SP159k__Bacteria;p__Fusobacteria;c__Fusobacteriia;o__Fusobacteriales;f__Leptotrichiaceae;g__Leptotrichia;s__sp._HMT_225240000000000000000115300003505011000
SP16k__Bacteria;p__Bacteroidetes;c__Bacteroidia;o__Bacteroidales;f__Porphyromonadaceae;g__Porphyromonas;s__pasteri178029614724922217511346115181367737721012019012012311512402502550252456295506
SP160k__Bacteria;p__Firmicutes;c__Bacilli;o__Lactobacillales;f__Streptococcaceae;g__Streptococcus;s__infantis_clade_43100000037004200019000000123750000000
SP161k__Bacteria;p__Bacteroidetes;c__Bacteroidia;o__Bacteroidales;f__Porphyromonadaceae;g__Porphyromonas;s__sp._HMT_278038000000000000510100000000008800
SP163k__Bacteria;p__Firmicutes;c__Bacilli;o__Lactobacillales;f__Streptococcaceae;g__Streptococcus;s__anginosus0000077007120003400100000000027000
SP165k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Lachnospiraceae_[XIV];g__Oribacterium;s__sinus2700120013000033000000006200000000
SP166k__Bacteria;p__Bacteroidetes;c__Bacteroidia;o__Bacteroidales;f__Prevotellaceae;g__Prevotella;s__nanceiensis1900430011402790000048000000087004100
SP167k__Bacteria;p__Bacteroidetes;c__Bacteroidia;o__Bacteroidales;f__Porphyromonadaceae;g__Porphyromonas;s__sp._HMT_28406700000000000000000000000001510
SP17k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Veillonellaceae;g__Veillonella;s__parvula2332483243262394533268243141316461829293542591846084866331058023748819169059801017582302
SP171k__Bacteria;p__Proteobacteria;c__Betaproteobacteria;o__Neisseriales;f__Neisseriaceae;g__Neisseria;s__elongata00394583920390170390000000000673230411130
SP172k__Bacteria;p__Proteobacteria;c__Gammaproteobacteria;o__Pasteurellales;f__Pasteurellaceae;g__Aggregatibacter;s__sp._HMT_89800290530000009200000000000136200440
SP173k__Bacteria;p__Bacteroidetes;c__Flavobacteriia;o__Flavobacteriales;f__Flavobacteriaceae;g__Bergeyella;s__sp._HMT_9002000160000000029002100022000000013634
SP174k__Bacteria;p__Proteobacteria;c__Betaproteobacteria;o__Neisseriales;f__Neisseriaceae;g__Kingella;s__sp._HMT_012070050000000001330000980000061000
SP175k__Bacteria;p__Bacteroidetes;c__Bacteroides;o__Bacteroidales;f__Porphyromonadaceae;g__Porphyromonas;s__sp._Oral_Taxon_B4300460004900160000745000000000000880
SP176k__Bacteria;p__Bacteroidetes;c__Bacteroidia;o__Bacteroidales;f__Prevotellaceae;g__Alloprevotella;s__sp._HMT_3080037403400019350001080274400069008800000
SP178k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Lachnospiraceae_[XIV];g__Lachnospiraceae_[G-3];s__bacterium_HMT_1006043247100000000420000190000055460129027
SP18k__Bacteria;p__Fusobacteria;c__Fusobacteria;o__Fusobacteriales;f__Leptotrichiaceae;g__Leptotrichia;s__sp._AF189244.10374188733213002201230364250011320400013657988401011065100322476
SP180k__Bacteria;p__Bacteroidetes;c__Bacteroidia;o__Bacteroidales;f__Prevotellaceae;g__Alloprevotella;s__tannerae19033003922000005400000007600000000
SP181k__Bacteria;p__Actinobacteria;c__Actinobacteria;o__Actinomycetales;f__Actinomycetaceae;g__Actinomyces;s__sp._str._ChDCB197760000000040000000000001370017607200
SP185k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Peptostreptococcaceae_[XIII];g__Parvimonas;s__sp._Oral_Taxon_11000000000000000000001200000013300
SP186k__Bacteria;p__Firmicutes;c__Negativicutes;o__Selenomonadales;f__Selenomonadaceae;g__Selenomonas;s__infelix003124000000000000028000000550000
SP187k__Bacteria;p__Fusobacteria;c__Fusobacteriia;o__Fusobacteriales;f__Leptotrichiaceae;g__Leptotrichia;s__goodfellowii000000000000000000000001101190000
SP19k__Bacteria;p__Bacteroidetes;c__Bacteroidia;o__Bacteroidales;f__Porphyromonadaceae;g__Tannerella;s__sp._HMT_286662650752887332655000671246038561500003041291162419421
SP190k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Lachnospiraceae_[XIV];g__Stomatobaculum;s__longum018000210000002045000000125025000000
SP192k__Bacteria;p__Firmicutes;c__Negativicutes;o__Selenomonadales;f__Selenomonadaceae;g__Selenomonas;s__flueggei00000000000003200000000000101000
SP194k__Bacteria;p__Bacteroidetes;c__Flavobacteriia;o__Flavobacteriales;f__Flavobacteriaceae;g__Capnocytophaga;s__sp._HMT_326830000000000000000000000322031000
SP195k__Bacteria;p__Fusobacteria;c__Fusobacteriia;o__Fusobacteriales;f__Fusobacteriaceae;g__Fusobacterium;s__sp._HMT_20400000000000000002200000001220000
SP196k__Bacteria;p__Proteobacteria;c__Gammaproteobacteria;o__Pseudomonadales;f__Moraxellaceae;g__Moraxella;s__sp._Oral_Taxon_B07007000510000000051000000150000930
SP197k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Lachnospiraceae_[XIV];g__Lachnospiraceae_[G-2];s__bacterium_HMT_0960000000000000000000016400000000
SP198k__Bacteria;p__Actinobacteria;c__Actinobacteria;o__Actinomycetales;f__Actinomycetaceae;g__Actinomyces;s__dentalis0060000000004100000000000062000
SP199k__Bacteria;p__Proteobacteria;c__Gammaproteobacteria;o__Pasteurellales;f__Pasteurellaceae;g__Haemophilus;s__influenzae00000013000000000000000006373000
SP2k__Bacteria;p__Bacteroidetes;c__Bacteroidia;o__Bacteroidales;f__Prevotellaceae;g__Prevotella;s__oulorum001000350090180296400000093147090253000
SP20k__Bacteria;p__Firmicutes;c__Bacilli;o__Lactobacillales;f__Streptococcaceae;g__Streptococcus;s__sp._str._2136FAA000051000000000158644600070000270000
SP200k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Lachnospiraceae_[XIV];g__Johnsonella;s__ignava270000000000000000000000411030000
SP202k__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_957004727000000005900970000000000000
SP203k__Bacteria;p__Actinobacteria;c__Actinobacteria;o__Actinomycetales;f__Actinomycetaceae;g__Actinomyces;s__naeslundii0000000000000000032018500001390000
SP21k__Bacteria;p__Bacteroidetes;c__Flavobacteriia;o__Flavobacteriales;f__Flavobacteriaceae;g__Capnocytophaga;s__leadbetteri216590660135554127000430000480360001050121154181107
SP210k__Bacteria;p__Fusobacteria;c__Fusobacteriia;o__Fusobacteriales;f__Leptotrichiaceae;g__Leptotrichia;s__sp._HMT_2191427000000000000000000000053015400
SP212k__Bacteria;p__Actinobacteria;c__Actinobacteria;o__Propionibacteriales;f__Propionibacteriaceae;g__Arachnia;s__rubra0000000000000000000000000013100
SP215k__Bacteria;p__Bacteroidetes;c__Bacteroidia;o__Bacteroidales;f__Prevotellaceae;g__Prevotella;s__sp._Oral_Taxon_2990012800000000000280000250000510000
SP216k__Bacteria;p__Bacteroidetes;c__Bacteroidia;o__Bacteroidales;f__Prevotellaceae;g__Prevotella;s__denticola0000076000000000000006100000000
SP217k__Bacteria;p__Proteobacteria;c__Gammaproteobacteria;o__Cardiobacteriales;f__Cardiobacteriaceae;g__Cardiobacterium;s__valvarum360001700000000000000710000920000
SP22k__Bacteria;p__Firmicutes;c__Bacilli;o__Lactobacillales;f__Streptococcaceae;g__Streptococcus;s__mitis3695224491403882213943105876994921835559133304273728888511454315997675137926335571027541
SP220k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Veillonellaceae;g__Megasphaera;s__micronuciformis000000000000030900000110910000000
SP222k__Bacteria;p__Actinobacteria;c__Actinobacteria;o__Actinomycetales;f__Corynebacteriaceae;g__Corynebacterium;s__sp._Oral_Taxon_A160000002530000000000000170025000027
SP223k__Bacteria;p__Firmicutes;c__Erysipelotrichi;o__Erysipelotrichales;f__Erysipelotrichaceae;g__Solobacterium;s__moorei004100100000001352000000000000000
SP226k__Bacteria;p__Actinobacteria;c__Actinobacteria;o__Actinomycetales;f__Actinomycetaceae;g__Actinomyces;s__sp._Oral_Taxon_848000000020000000000000000501250000
SP23k__Bacteria;p__Firmicutes;c__Bacilli;o__Lactobacillales;f__Streptococcaceae;g__Streptococcus;s__cristatus8284195135344160128117166218130353726354722711923290853331191001082819253258369739
SP235k__Bacteria;p__Firmicutes;c__Negativicutes;o__Selenomonadales;f__Selenomonadaceae;g__Selenomonas;s__noxia0000000000000000000000000077360
SP236k__Bacteria;p__Firmicutes;c__Bacilli;o__Lactobacillales;f__Streptococcaceae;g__Streptococcus;s__sp._HMT_0640000000000000000000001090000000
SP239k__Bacteria;p__Proteobacteria;c__Betaproteobacteria;o__Neisseriales;f__Neisseriaceae;g__Neisseria;s__sp._HMT_01800000000000160000000000000020900
SP24k__Bacteria;p__Fusobacteria;c__Fusobacteriia;o__Fusobacteriales;f__Leptotrichiaceae;g__Leptotrichia;s__wadei00102005400001920161248127135104000019402730230000
SP25k__Bacteria;p__Proteobacteria;c__Gammaproteobacteria;o__Pasteurellales;f__Pasteurellaceae;g__Haemophilus;s__sputorum001900014281040260000004400000004500
SP26k__Bacteria;p__Firmicutes;c__Bacilli;o__Bacillales;f__NA;g__Gemella;s__haemolysans226016335142504918713511015434124139654296017866176702742431751340126192
SP27k__Bacteria;p__Firmicutes;c__Bacilli;o__Lactobacillales;f__Streptococcaceae;g__Streptococcus;s__gordonii270371081330121469017800100410301481647346214169024000037
SP28k__Bacteria;p__Bacteroidetes;c__Bacteroidia;o__Bacteroidales;f__Prevotellaceae;g__Alloprevotella;s__sp._HMT_473240466023112142337270911030032683669581163601790191090219392144157244
SP29k__Bacteria;p__Bacteroidetes;c__Bacteroidia;o__Bacteroidales;f__Prevotellaceae;g__Prevotella;s__sp._HMT_3170016021209400980000099674600000072140307250573163
SP3k__Bacteria;p__Fusobacteria;c__Fusobacteriia;o__Fusobacteriales;f__Leptotrichiaceae;g__Leptotrichia;s__sp._HMT_221069000000014400143650000000205000383000
SP30k__Bacteria;p__Proteobacteria;c__Betaproteobacteria;o__Neisseriales;f__Neisseriaceae;g__Kingella;s__denitrificans7008711910085251703369106430120003300709011312242339187
SP31k__Bacteria;p__Firmicutes;c__Bacilli;o__Lactobacillales;f__Streptococcaceae;g__Streptococcus;s__sp._HMT_05613671567430040170600000000027000014000090
SP32k__Bacteria;p__Fusobacteria;c__Fusobacteria;o__Fusobacteriales;f__Leptotrichiaceae;g__Leptotrichia;s__hofstadii021010786600481030360019600038000069040200340155
SP33k__Bacteria;p__Firmicutes;c__Bacilli;o__Lactobacillales;f__Streptococcaceae;g__Streptococcus;s__sanguinis99166262322205131022562883910134202816020413937016325460209445717634272517469109
SP34k__Bacteria;p__Firmicutes;c__Bacilli;o__Lactobacillales;f__Aerococcaceae;g__Abiotrophia;s__defectiva4672691481730812301072407710090161432115513518701940411191161369813355
SP36k__Bacteria;p__Firmicutes;c__Bacilli;o__Bacillales;f__Staphylococcaceae;g__Gemella;s__moribillum00450780056430000300000291600101000000
SP37k__Bacteria;p__Firmicutes;c__Bacilli;o__Lactobacillales;f__Streptococcaceae;g__Streptococcus;s__salivarius000430013200080003113014400016700720101000
SP38k__Bacteria;p__Firmicutes;c__Negativicutes;o__Veillonellales;f__Veillonellaceae;g__Veillonella;s__sp._HMT_78000128022211801458201340901831400256716616630150380208130087258
SP39k__Bacteria;p__Fusobacteria;c__Fusobacteriia;o__Fusobacteriales;f__Leptotrichiaceae;g__Leptotrichia;s__sp._HMT_39215013110400079400000750658904310500001000032819955
SP4k__Bacteria;p__Proteobacteria;c__Betaproteobacteria;o__Burkholderiales;f__Burkholderiaceae;g__Lautropia;s__mirabilis54684976730011116097128500802223220000178240111689315756
SP40k__Bacteria;p__Proteobacteria;c__Betaproteobacteria;o__Neisseriales;f__Neisseriaceae;g__Neisseria;s__flavescens|subflava65420561041041772807101910310110323101050017801331331290
SP41k__Bacteria;p__Bacteroidetes;c__Bacteroidia;o__Bacteroidales;f__Prevotellaceae;g__Alloprevotella;s__sp._HMT_9140010500282540000471703710145080110013500000198
SP42k__Bacteria;p__Bacteroidetes;c__Bacteroides;o__Bacteroidales;f__Porphyromonadaceae;g__Porphyromonas;s__sp._Oral_Taxon_C344204000008755003100769800000000000089
SP43k__Bacteria;p__Bacteroidetes;c__Bacteroidia;o__Bacteroidales;f__Porphyromonadaceae;g__Porphyromonas;s__sp._HMT_9300012801123200800001591830037049000140054000102
SP46k__Bacteria;p__Bacteroidetes;c__Bacteroidia;o__Bacteroidales;f__Prevotellaceae;g__Prevotella;s__melaninogenica079170139873699875000851242361300312787531640961702310272178123259
SP47k__Bacteria;p__Bacteroidetes;c__Bacteroidia;o__Bacteroidales;f__Prevotellaceae;g__Prevotella;s__sp._HMT_3060054072000090930004800222000000021
SP48k__Bacteria;p__Proteobacteria;c__Betaproteobacteria;o__Neisseriales;f__Neisseriaceae;g__Eikenella;s__corrodens03300829000000018380800540037006414900
SP49k__Bacteria;p__Fusobacteria;c__Fusobacteriia;o__Fusobacteriales;f__Fusobacteriaceae;g__Fusobacterium;s__nucleatum14910447600188011116801590001191150341761059600303312436615315137
SP5k__Bacteria;p__Proteobacteria;c__Betaproteobacteria;o__Neisseriales;f__Neisseriaceae;g__Neisseria;s__mucosa48001760000024026324560002021081781025621100110782011236683
SP52k__Bacteria;p__Firmicutes;c__Bacilli;o__Lactobacillales;f__Streptococcaceae;g__Streptococcus;s__infantis56005148020001100018540001769600000165320012400
SP53k__Bacteria;p__Firmicutes;c__Bacilli;o__Lactobacillales;f__Streptococcaceae;g__Streptococcus;s__oralis_subsp._tigurinus_clade_07000140013000000049460132210270000000000
SP55k__Bacteria;p__Actinobacteria;c__Actinobacteria;o__Actinomycetales;f__Micrococcaceae;g__Rothia;s__dentocariosa018250368347241639537122815754598625051147346631643975145114282110170
SP56k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Peptostreptococcaceae_[XI];g__Eubacterium_[XI][G-7];s__yurii05401200000000120000000000491160000
SP57k__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_3473102253000001010600240001700000150104000
SP58k__Bacteria;p__Bacteroidetes;c__Flavobacteriia;o__Flavobacteriales;f__Flavobacteriaceae;g__Bergeyella;s__sp._HMT_3225090370241826690291511301715442759000042771531112510792
SP59k__Bacteria;p__Bacteroidetes;c__Flavobacteriia;o__Flavobacteriales;f__Flavobacteriaceae;g__Bergeyella;s__sp._HMT_93100200000000000032000490037000300180
SP6k__Bacteria;p__Proteobacteria;c__Gammaproteobacteria;o__Pasteurellales;f__Pasteurellaceae;g__Aggregatibacter;s__aphrophilus519800003700150000055072000002900000
SP60k__Bacteria;p__Actinobacteria;c__Actinobacteria;o__Actinomycetales;f__Actinomycetaceae;g__Actinomyces;s__sp._HMT_4480000000000001001140000000000000300
SP61k__Bacteria;p__Firmicutes;c__Bacilli;o__Lactobacillales;f__Streptococcaceae;g__Streptococcus;s__parasanguinis_II00000460001316901150302717800101059907101530049
SP62k__Bacteria;p__Proteobacteria;c__Gammaproteobacteria;o__Pasteurellales;f__Pasteurellaceae;g__Haemophilus;s__haemolyticus000003310545168131053738297630740016702111491170920185
SP63k__Bacteria;p__Proteobacteria;c__Gammaproteobacteria;o__Pasteurellales;f__Pasteurellaceae;g__Haemophilus;s__sp._HMT_9080378108663085009900873301036340000000001280
SP64k__Bacteria;p__Bacteroidetes;c__Bacteroidia;o__Bacteroidales;f__Prevotellaceae;g__Alloprevotella;s__sp._HMT_91320011701447017000271320104352602100000023533096
SP65k__Bacteria;p__Actinobacteria;c__Actinobacteria;o__Actinomycetales;f__Micrococcaceae;g__Rothia;s__aeria30195848700606742946811803391208983240601056430841410
SP67k__Bacteria;p__Fusobacteria;c__Fusobacteriia;o__Fusobacteriales;f__Leptotrichiaceae;g__Leptotrichia;s__sp._HMT_21500187720498400500165000000000011200015300
SP68k__Bacteria;p__Fusobacteria;c__Fusobacteria;o__Fusobacteriales;f__Leptotrichiaceae;g__Leptotrichia;s__buccalis28910111300932160000029201355701733090000710487615870
SP69k__Bacteria;p__Bacteroidetes;c__Bacteroidia;o__Bacteroidales;f__Prevotellaceae;g__Prevotella;s__sp._HMT_9420000160021520000610000016000000000
SP7k__Bacteria;p__Firmicutes;c__Negativicutes;o__Veillonellales;f__Veillonellaceae;g__Veillonella;s__atypica00000142000032081191200430006446480002940072
SP70k__Bacteria;p__Bacteroidetes;c__Flavobacteriia;o__Flavobacteriales;f__Flavobacteriaceae;g__Capnocytophaga;s__sp._HMT_332570011001100000000370000000000000
SP71k__Bacteria;p__Proteobacteria;c__Gammaproteobacteria;o__Cardiobacteriales;f__Cardiobacteriaceae;g__Cardiobacterium;s__hominis27960380014490131523151900000660003046618124211380
SP72k__Bacteria;p__Bacteroidetes;c__Bacteroidia;o__Bacteroidales;f__Porphyromonadaceae;g__Porphyromonas;s__catoniae77078494102258230027610390053106130002711835793209179
SP73k__Bacteria;p__Actinobacteria;c__Actinobacteria;o__Actinomycetales;f__Actinomycetaceae;g__Actinomyces;s__sp._Oral_Taxon_178000000000000000000000001069600025
SP74k__Bacteria;p__Proteobacteria;c__Gammaproteobacteria;o__Pasteurellales;f__Pasteurellaceae;g__Aggregatibacter;s__sp._HMT_458300136171434901526000063065444222530000507179193139365171
SP75k__Bacteria;p__Proteobacteria;c__Gammaproteobacteria;o__Pasteurellales;f__Pasteurellaceae;g__Haemophilus;s__paraphrohaemolyticus1679063138580104370900590002164068223000214111018414500
SP77k__Bacteria;p__Firmicutes;c__Bacilli;o__Bacillales;f__NA;g__Gemella;s__morbillorum6493887300205800006438528427168139145000162637102960
SP78k__Bacteria;p__Firmicutes;c__Bacilli;o__Lactobacillales;f__Carnobacteriaceae;g__Granulicatella;s__elegans069349163208120020624071164021423326825401313701613092476240428231236380312
SP79k__Bacteria;p__Fusobacteria;c__Fusobacteriia;o__Fusobacteriales;f__Leptotrichiaceae;g__Leptotrichia;s__shahii01750371412440530000115111279618304700000183316095167
SP8k__Bacteria;p__Firmicutes;c__Bacilli;o__Lactobacillales;f__Carnobacteriaceae;g__Granulicatella;s__paradiacens0099765112715293010217207823017500710031786560167013178
SP80k__Bacteria;p__Bacteroidetes;c__Bacteroidia;o__Bacteroidales;f__Prevotellaceae;g__Prevotella;s__sp._HMT_31400000440000000784800000780000003700
SP81k__Bacteria;p__Firmicutes;c__Bacilli;o__Lactobacillales;f__Streptococcaceae;g__Streptococcus;s__sp._Oral_Taxon_B660011558000000257000680016811000000000059
SP82k__Bacteria;p__Firmicutes;c__Bacilli;o__Lactobacillales;f__Carnobacteriaceae;g__Granulicatella;s__adiacens00540526605015030680660069012000193108840000460
SP83k__Bacteria;p__Bacteroidetes;c__Bacteroidia;o__Bacteroidales;f__Prevotellaceae;g__Prevotella;s__histicola0002904300007609064024240001139000021300106
SP84k__Bacteria;p__Proteobacteria;c__Betaproteobacteria;o__Neisseriales;f__Neisseriaceae;g__Neisseria;s__sicca2442571411914736312527377027107710228117114307742508864300283184333222
SP85k__Bacteria;p__Proteobacteria;c__Gammaproteobacteria;o__Pasteurellales;f__Pasteurellaceae;g__Aggregatibacter;s__sp._HMT_51397660039617100260042079000000000085000
SP86k__Bacteria;p__Actinobacteria;c__Actinobacteria;o__Actinomycetales;f__Actinomycetaceae;g__Actinomyces;s__oris26000000000000000000000000154000
SP87k__Bacteria;p__Firmicutes;c__Bacilli;o__Lactobacillales;f__Streptococcaceae;g__Streptococcus;s__australis0000001030033068000000000005500000
SP89k__Bacteria;p__Fusobacteria;c__Fusobacteriia;o__Fusobacteriales;f__Leptotrichiaceae;g__Leptotrichia;s__sp._HMT_2129813462054930058405271570071791540000343114293026600
SP9k__Bacteria;p__Firmicutes;c__Bacilli;o__Lactobacillales;f__Streptococcaceae;g__Streptococcus;s__sp._str._C300158219801767491580612256111624693079105826661487501731694654327213688380398446430
SP90k__Bacteria;p__Fusobacteria;c__Fusobacteria;o__Fusobacteriales;f__Fusobacteriaceae;g__Fusobacterium;s__periodonticum128019700269909001110094125752160001610001270196
SP92k__Bacteria;p__Bacteroidetes;c__Flavobacteriia;o__Flavobacteriales;f__Flavobacteriaceae;g__Capnocytophaga;s__sputigena12312301144702738003160026340011122000484927698919150
SP93k__Bacteria;p__Proteobacteria;c__Gammaproteobacteria;o__Pasteurellales;f__Pasteurellaceae;g__Haemophilus;s__sp._HMT_03633076273301137500007259442100124200067000202082
SP94k__Bacteria;p__Bacteroidetes;c__Bacteroidia;o__Bacteroidales;f__Prevotellaceae;g__Prevotella;s__oris0000230000000670000005745270000106031
SP95k__Bacteria;p__Actinobacteria;c__Actinobacteria;o__Actinomycetales;f__Corynebacteriaceae;g__Corynebacterium;s__matruchotii1833969640274722510003619042046210000110325793988212
SP97k__Bacteria;p__Bacteroidetes;c__Bacteroidia;o__Bacteroidales;f__Prevotellaceae;g__Prevotella;s__veroralis0000000280097017000000001040000000
SP98k__Bacteria;p__Bacteroidetes;c__Flavobacteriia;o__Flavobacteriales;f__Flavobacteriaceae;g__Capnocytophaga;s__gingivalis416859001306923000000000068000049291771200
SP99k__Bacteria;p__Bacteroidetes;c__Bacteroidia;o__Bacteroidales;f__Prevotellaceae;g__Prevotella;s__salivae00250000000005249000000119261000327000
SPN105k__Bacteria;p__Proteobacteria;c__Gammaproteobacteria;o__Cardiobacteriales;f__Cardiobacteriaceae;g__Cardiobacterium;s__hominis_nov_97.505%0022000003900000000342300002087702400
SPN117k__Bacteria;p__Fusobacteria;c__Fusobacteria;o__Fusobacteriales;f__Fusobacteriaceae;g__Fusobacterium;s__nucleatum_ss_polymorphum_nov_97.536%001300000000001003900085000000000145
SPN15k__Bacteria;p__Firmicutes;c__Negativicutes;o__Selenomonadales;f__Selenomonadaceae;g__Selenomonas;s__sputigena0013017500000000000006500000022000
SPN27k__Bacteria;p__Saccharibacteria_(TM7);c__Saccharibacteria_(TM7)_[C-1];o__Saccharibacteria_(TM7)_[O-1];f__Saccharibacteria_(TM7)_[F-1];g__Saccharibacteria_(TM7)_[G-7];s__bacterium_HMT_98608700000000000000000000000001190
SPN3k__Bacteria;p__Bacteroidetes;c__Bacteroidia;o__Bacteroidales;f__Porphyromonadaceae;g__Porphyromonas;s__catoniae00156800000000110000000000000045161
SPN33k__Bacteria;p__Proteobacteria;c__Gammaproteobacteria;o__Pasteurellales;f__Pasteurellaceae;g__Haemophilus;s__sputorum0031029000000004048000000000002500
SPN46k__Bacteria;p__Actinobacteria;c__Actinobacteria;o__Actinomycetales;f__Actinomycetaceae;g__Actinomyces;s__sp._Oral_Taxon_17800000000760000102800860630000001830890
SPN49k__Bacteria;p__Bacteroidetes;c__Bacteroides;o__Bacteroidales;f__Porphyromonadaceae;g__Porphyromonas;s__sp._Oral_Taxon_B43000472800000000350000000000000054
SPN57k__Bacteria;p__Bacteroidetes;c__Bacteroidia;o__Bacteroidales;f__Prevotellaceae;g__Prevotella;s__histicola00001620039000000000000000000000
SPN61k__Bacteria;p__Bacteroidetes;c__Bacteroidia;o__Bacteroidales;f__Prevotellaceae;g__Prevotella;s__veroralis0000000000000000000005300099000
SPN64k__Bacteria;p__Saccharibacteria(TM7);c__TM7_[C];o__TM7_[O];f__TM7_[F];g__TM7_[G];s__sp._Oral_Taxon_A5600000000000000000000000105450000
SPN65k__Bacteria;p__Bacteroidetes;c__Cytophagia;o__Cytophagales;f__Cyclobacteriaceae;g__Indibacter;s__alkaliphilus002500016000000005400510000000000
SPN66k__Bacteria;p__Fusobacteria;c__Fusobacteriia;o__Fusobacteriales;f__Leptotrichiaceae;g__Leptotrichia;s__sp._HMT_21500000000004900000020000000760000
SPN67k__Bacteria;p__Bacteroidetes;c__Bacteroidia;o__Bacteroidales;f__Prevotellaceae;g__Prevotella;s__sp._Oral_Taxon_2990000000000000000000013300000000
SPN68k__Bacteria;p__Fusobacteria;c__Fusobacteriia;o__Fusobacteriales;f__Leptotrichiaceae;g__Leptotrichia;s__sp._HMT_3920000000000000000000000013100000
SPN69k__Bacteria;p__Bacteroidetes;c__Bacteroidia;o__Bacteroidales;f__Prevotellaceae;g__Alloprevotella;s__sp._HMT_91400470000000046000000000032000000
SPN70k__Bacteria;p__Proteobacteria;c__Betaproteobacteria;o__Neisseriales;f__Neisseriaceae;g__Neisseria;s__sp._HMT_499000000038540000000000000000390000
SPN71k__Bacteria;p__Actinobacteria;c__Actinobacteria;o__Actinomycetales;f__Actinomycetaceae;g__Actinomyces;s__israelii0000000000000000003900000000070
SPN72k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Veillonellaceae;g__Selenomonas;s__infelix00000000000001100000004900043000
SPN74k__Bacteria;p__Bacteroidetes;c__Bacteroidia;o__Bacteroidales;f__Prevotellaceae;g__Prevotella;s__sp._HMT_306000000033000000200030170000000000
SPN82k__Bacteria;p__Proteobacteria;c__Gammaproteobacteria;o__Oceanospirillales;f__Oceanospirillaceae;g__Oceanospirillum;s__beijerinckii420800107210000000007113060000000580230
SPN93k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Lachnospiraceae;g__Lachnoclostridium;s__sphenoides000000032620000000001220016400000066
SPP10k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__multifamily;g__Eubacterium_[XIVa][G-1];s__saburreum026117139057043720460617431043380331203101551399800159
SPP11k__Bacteria;p__Proteobacteria;c__Betaproteobacteria;o__Neisseriales;f__Neisseriaceae;g__Neisseria;s__multispecies_spp11_20000000002080000000000000000000
SPP13k__Bacteria;p__Firmicutes;c__Bacilli;o__Lactobacillales;f__Carnobacteriaceae;g__Granulicatella;s__multispecies_spp13_2058005400005513613500007706114000112850060430
SPP7k__Bacteria;p__Bacteroidetes;c__Bacteroides;o__Bacteroidales;f__Porphyromonadaceae;g__Porphyromonas;s__multispecies_spp7_2000490357620400000000001830100648900000160
SPP8k__Bacteria;p__Firmicutes;c__Bacilli;o__Lactobacillales;f__Streptococcaceae;g__Streptococcus;s__multispecies_spp8_2054708536213649232364175234009791010011837493608108360335800
SPP9k__Bacteria;p__Firmicutes;c__Bacilli;o__Lactobacillales;f__Streptococcaceae;g__Streptococcus;s__multispecies_spp9_300480635391006504388000127035890290002241921300
 
 
Download Read Count Tables at Different Taxonomy Levels
domain
phylum
class
order
family
genus
species
;
 

Sample Taxonomy Bar Plots

 

VII. 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.

 
 

VIII. 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-dimentional 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 seperately. 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:

 
 
 
 
 

Interative 3D PCoA Plots - Bray-Curtis Dissimilarity

 
 
 

Interative 3D PCoA Plots - Euclidean Distance

 
 
 

Interative 3D PCoA Plots - Correlation Coefficients

 
 
 

IX. 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 differetial abundance analysis

 
 
 
 

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.

 
 
 
 
 
 
 
 

X. Analysis - Heatmap Profile

 

Species vs sample abundance heatmap

 
 
 

XI. Analysis - Network Association

To analyze the co-occurrence or co-exclusion between microbial species among different samples, network correlation analysis tools are ususally 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., grahical 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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