FOMC Service Report

16S rRNA Gene V1V3 Amplicon Sequencing

Version V1.41 fork

Version History

The Forsyth Institute, Cambridge, MA, USA
June 28, 2022

Project ID: FOMCX004_arginine


I. Project Summary

Project FOMCX004_arginine services do not include NGS sequencing of the V1V3 region of the 16S rRNA gene amplicons from the samples. First and foremost, please download this report. 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 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

Not available
 

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

Not available
 

VI. Analysis - DADA2 Read Processing

Not available
 

Sample Meta Information

#SampleIDSamplesInoculumTreatmentExperimentGroup
A1A1HealthyControlOneHealthy-Control
A2A2HealthyControlOneHealthy-Control
A3A3HealthyControlOneHealthy-Control
A4A4Healthy2%_ArginineOneHealthy-2%_Arginine
A5A5Healthy2%_ArginineOneHealthy-2%_Arginine
A6A6Healthy2%_ArginineOneHealthy-2%_Arginine
A7A7Healthy4%_ArginineOneHealthy-4%_Arginine
A8A8Healthy4%_ArginineOneHealthy-4%_Arginine
A9A9Healthy4%_ArginineOneHealthy-4%_Arginine
A10A10Healthy8%_ArginineOneHealthy-8%_Arginine
A11A11Healthy8%_ArginineOneHealthy-8%_Arginine
A12A12Healthy8%_ArginineOneHealthy-8%_Arginine
A13A13PerioControlOnePerio-Control
A14A14PerioControlOnePerio-Control
A15A15PerioControlOnePerio-Control
A16A16Perio2%_ArginineOnePerio-2%_Arginine
A17A17Perio2%_ArginineOnePerio-2%_Arginine
A18A18Perio2%_ArginineOnePerio-2%_Arginine
A19A19Perio4%_ArginineOnePerio-4%_Arginine
A20A20Perio4%_ArginineOnePerio-4%_Arginine
A21A21Perio4%_ArginineOnePerio-4%_Arginine
A22A22Perio8%_ArginineOnePerio-8%_Arginine
A23A23Perio8%_ArginineOnePerio-8%_Arginine
A24A24Perio8%_ArginineOnePerio-8%_Arginine
B1B1HealthyControlTwoHealthy-Control
B2B2HealthyControlTwoHealthy-Control
B3B3HealthyControlTwoHealthy-Control
B4B4Healthy2%_ArginineTwoHealthy-2%_Arginine
B5B5Healthy2%_ArginineTwoHealthy-2%_Arginine
B6B6Healthy2%_ArginineTwoHealthy-2%_Arginine
B7B7Healthy4%_ArginineTwoHealthy-4%_Arginine
B8B8Healthy4%_ArginineTwoHealthy-4%_Arginine
B9B9Healthy4%_ArginineTwoHealthy-4%_Arginine
B10B10Healthy8%_ArginineTwoHealthy-8%_Arginine
B11B11Healthy8%_ArginineTwoHealthy-8%_Arginine
B12B12Healthy8%_ArginineTwoHealthy-8%_Arginine
B13B13PerioControlTwoPerio-Control
B14B14PerioControlTwoPerio-Control
B15B15PerioControlTwoPerio-Control
B16B16Perio2%_ArginineTwoPerio-2%_Arginine
B17B17Perio2%_ArginineTwoPerio-2%_Arginine
B18B18Perio2%_ArginineTwoPerio-2%_Arginine
B19B19Perio4%_ArginineTwoPerio-4%_Arginine
B20B20Perio4%_ArginineTwoPerio-4%_Arginine
B21B21Perio4%_ArginineTwoPerio-4%_Arginine
B22B22Perio8%_ArginineTwoPerio-8%_Arginine
B23B23Perio8%_ArginineTwoPerio-8%_Arginine
B24B24Perio8%_ArginineTwoPerio-8%_Arginine
 
 

ASV Read Counts by Samples

#Sample IDRead Count
B51,241
B32,557
B93,431
B104,096
B154,639
B225,250
B185,464
B145,531
A115,589
B15,609
B205,668
B86,024
A156,233
A66,508
B176,603
B26,637
B196,724
B246,810
B67,170
B127,238
A197,258
B167,314
A187,397
B77,520
B47,639
A237,814
B137,951
B118,063
A108,067
A228,082
A28,359
A178,600
A248,858
B238,884
A78,929
A168,941
A89,009
A99,099
B219,193
A149,359
A19,518
A49,683
A1210,002
A1310,368
A310,435
A2010,443
A2111,694
A511,735
 
 
 

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 *

CodeCategoryMPC=0% (>=1 read)MPC=0.01%(>=33 reads)
ATotal reads359,236359,236
BTotal assigned reads338,754338,754
CAssigned reads in species with read count < MPC01,838
DAssigned reads in samples with read count < 50000
ETotal samples4848
FSamples with reads >= 5004848
GSamples with reads < 50000
HTotal assigned reads used for analysis (B-C-D)338,754336,916
IReads assigned to single species217,649216,585
JReads assigned to multiple species117,469117,261
KReads assigned to novel species3,6363,070
LTotal number of species460189
MNumber of single species283125
NNumber of multi-species6636
ONumber of novel species11128
PTotal unassigned reads20,48220,482
QChimeric reads140140
RReads without BLASTN hits00
SOthers: short, low quality, singletons, etc.20,34220,342
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
* MPC = Minimal percent (of all assigned reads) read count per species, species with read count < MPC were removed.
* Samples with reads < 500 were removed from downstream analyses.
* The assignment result from MPC=0.1% was used in the downstream analyses.
 
 
 

Read Taxonomy Assignment - ASV Species-Level Read Counts Table

This table shows the read counts for each sample (columns) and each species identified based on the ASV sequences. The downstream analyses were based on this table.
SPIDTaxonomyA1A10A11A12A13A14A15A16A17A18A19A2A20A21A22A23A24A3A4A5A6A7A8A9B1B10B11B12B13B14B15B16B17B18B19B2B20B21B22B23B24B3B4B5B6B7B8B9
SP1Bacteria;Firmicutes;Bacilli;Lactobacillales;Streptococcaceae;Streptococcus;sanguinis167109222368865371686042114906866464011590388887511489932173614211211615714510667002474336555431637092
SP100Bacteria;Bacteroidetes;Bacteroidia;Bacteroidales;Bacteroidaceae;Bacteroides;heparinolyticus0000000000000000000000000000362829303115508057175105620000000
SP101Bacteria;Firmicutes;Bacilli;Lactobacillales;Streptococcaceae;Streptococcus;cristatus_clade_57814791311135239213312952248259314540285033001316710181511221172411122121002010
SP102Bacteria;Firmicutes;Clostridia;Clostridiales;Veillonellaceae;Selenomonas;sp._Oral_Taxon_F29287101100000006000002113200510693511411002120001051716297
SP103Bacteria;Firmicutes;Bacilli;Lactobacillales;Streptococcaceae;Streptococcus;pseudopneumoniae35670000000200000130545108000000000000000000100000
SP104Bacteria;Bacteroidetes;Bacteroidia;Bacteroidales;Prevotellaceae;Alloprevotella;rava0001251118121236906461610281000000000202641202310669440101237019770000000
SP111Bacteria;Bacteroidetes;Bacteroidia;Bacteroidales;Prevotellaceae;Prevotella;sp. HMT314000042210311417501602840005000010320010100110000000
SP112Bacteria;Firmicutes;Bacilli;Lactobacillales;Aerococcaceae;Abiotrophia;defectiva6509560215097546892316617920000001010000010000000000
SP113Bacteria;Firmicutes;Negativicutes;Selenomonadales;Selenomonadaceae;Selenomonas;sp. HMT481214360001000120010011101659121100003033182417181901525165120000000
SP114Bacteria;Firmicutes;Bacilli;Lactobacillales;Streptococcaceae;Streptococcus;intermedius5305000000040000080111651020000002011001200205811
SP115Bacteria;Fusobacteria;Fusobacteriia;Fusobacteriales;Leptotrichiaceae;Leptotrichia;sp. HMT2155000310017002600000100000000000000000000000089000
SP116Bacteria;Bacteroidetes;Bacteroidia;Bacteroidales;Prevotellaceae;Prevotella;buccae311110001021130024010111100031140100232210000000
SP117Bacteria;Firmicutes;Bacilli;Lactobacillales;Streptococcaceae;Streptococcus;oralis_subsp._tigurinus_clade_070301142109121511403039242414001050063141141011050202544200220
SP119Bacteria;Firmicutes;Negativicutes;Selenomonadales;Selenomonadaceae;Selenomonas;sp. HMT1261000000000000000000100000010658201115284011615011234
SP12Bacteria;Firmicutes;Negativicutes;Selenomonadales;Selenomonadaceae;Selenomonas;dianae41109123522191620192627243530152329162591817119718836333236201614101441172119819186914
SP120Bacteria;Bacteroidetes;Flavobacteriia;Flavobacteriales;Flavobacteriaceae;Capnocytophaga;leadbetteri101000000001000001011001966154000000014100000413020299368
SP122Bacteria;Firmicutes;Bacilli;Lactobacillales;Streptococcaceae;Streptococcus;cristatus00001001000022110000000000001325171141503182300000100
SP125Bacteria;Firmicutes;Clostridia;Clostridiales;Veillonellaceae;Veillonella;sp._Oral_Taxon_G61678821130118012011568268700960002000320000130041713
SP126Bacteria;Firmicutes;Clostridia;Clostridiales;Veillonellaceae;Selenomonas;sp._Oral_Taxon_H23402010220000100011050000513100000003000001003031
SP127Bacteria;Firmicutes;Bacilli;Lactobacillales;Streptococcaceae;Streptococcus;sp._Oral_Taxon_G62000000000000000000000000000000000000112619100000000
SP128Bacteria;Firmicutes;Clostridia;Clostridiales;Veillonellaceae;Selenomonas;sp._Oral_Taxon_G51000020100000010321120100000156786244202431029582016210001200
SP129Bacteria;Firmicutes;Negativicutes;Selenomonadales;Selenomonadaceae;Selenomonas;sp. HMT892400100000003101003110002000020001110205130000000
SP130Bacteria;Bacteroidetes;Bacteroidia;Bacteroidales;Prevotellaceae;Prevotella;oulorum72911110000210303410134143111414010000000000000001000000
SP131Bacteria;Synergistetes;Synergistia;Synergistales;Synergistaceae;Fretibacterium;sp. HMT36000002445324622206994300010002000219814129201682020100200
SP132Bacteria;Firmicutes;Bacilli;Lactobacillales;Streptococcaceae;Streptococcus;sp._Oral_Taxon_B6616030011501073203161155161491727001010060000034522230185
SP133Bacteria;Firmicutes;Clostridia;Clostridiales;Peptoniphilaceae;Parvimonas;sp. HMT393000000000010000210000000000040252218131424041311330290000000
SP134Bacteria;Firmicutes;Bacilli;Lactobacillales;Streptococcaceae;Streptococcus;pneumoniae82730260000001161400035163131234019000010121030022810000000
SP135Bacteria;Actinobacteria;Actinobacteria;Actinomycetales;Actinomycetaceae;Schaalia;sp. HMT180660200000000010200110003410000000004000003300010
SP137Bacteria;Bacteroidetes;Bacteroidia;Bacteroidales;Porphyromonadaceae;Porphyromonas;catoniae44701500100000330000040825992411200000000270000027000001
SP138Bacteria;Bacteroidetes;Bacteroidia;Bacteroidales;Prevotellaceae;Prevotella;oris0201000200110100001100001200312323131136102102040
SP139Bacteria;Actinobacteria;Coriobacteriia;Coriobacteriales;Coriobacteriaceae;Slackia;exigua015230302011022132070030420100100001000120220100001
SP14Bacteria;Firmicutes;Negativicutes;Selenomonadales;Selenomonadaceae;Selenomonas;infelix19411692273010922527811314954671314107188718141610155143401361510
SP143Bacteria;Proteobacteria;Epsilonproteobacteria;Campylobacterales;Campylobacteraceae;Campylobacter;showae44220000001200000103001218471042171217111115817321281128251111
SP144Bacteria;Firmicutes;Negativicutes;Selenomonadales;Selenomonadaceae;Selenomonas;sp. HMT136423400100011010016241317000110120000100000000000
SP146Bacteria;Firmicutes;Clostridia;Clostridiales;Veillonellaceae;Selenomonas;sp._Oral_Taxon_G67141104510433171212256134200519695558151081592101334
SP147Bacteria;Firmicutes;Clostridia;Clostridiales;Veillonellaceae;Selenomonas;sp._Oral_Taxon_F210000000000000000011000004210533292041252040336355731263215851
SP15Bacteria;Bacteroidetes;Bacteroidia;Bacteroidales;Bacteroidales_[F-2];Bacteroidales_[G-2];bacterium HMT274187996227599944028553991115416082208169144120661032541556126715063119142391091712281017810187169876911064775590343
SP150Bacteria;Firmicutes;Bacilli;Lactobacillales;Streptococcaceae;Streptococcus;danieliae941133111423424511310031102000000000004000000102211
SP151Bacteria;Firmicutes;Clostridia;Clostridiales;Lachnospiraceae_[XIV];Lachnoanaerobaculum;umeaense002619000000000000000001700000000000000000000000000
SP156Bacteria;Bacteroidetes;Flavobacteriia;Flavobacteriales;Flavobacteriaceae;Capnocytophaga;sp. HMT8646103100011020000122123254001000000001000000101000
SP160Bacteria;Firmicutes;Negativicutes;Veillonellales;Veillonellaceae;Megasphaera;micronuciformis60000100015180100245000000000000000000000000000000
SP167Bacteria;Firmicutes;Bacilli;Lactobacillales;Streptococcaceae;Streptococcus;anginosus000072110030170330000000000123512150496330000010
SP168Bacteria;Firmicutes;Clostridia;Clostridiales;Peptostreptococcaceae_[XI];Peptostreptococcaceae_[XI][G-9];[Eubacterium]_brachy0000016100000000200000000000007273040200100000100
SP169Bacteria;Firmicutes;Bacilli;Lactobacillales;Streptococcaceae;Streptococcus;sp._Oral_Taxon_F530000108224310922202680000000000000000000000000000000
SP17Bacteria;Firmicutes;Bacilli;Lactobacillales;Streptococcaceae;Streptococcus;sp. HMT05621273213500000001400000194391012052213000000000000000010000000
SP170Bacteria;Bacteroidetes;Flavobacteriia;Flavobacteriales;Flavobacteriaceae;Bergeyella;sp. HMT9070000000000000000000000001924100000005000002123233
SP173Bacteria;Firmicutes;Clostridia;Eubacteriales;Eubacteriales Family XIII. Incertae Sedis;Mogibacterium;diversum0000000001700364200000000000002031198812043084110693290000000
SP175Bacteria;Firmicutes;Clostridia;Clostridiales;Lachnospiraceae_[XIVa];Lachnospiraceae_[G];sp._Oral_Taxon_A610000000000000000000000000000011553701592230000000
SP177Bacteria;Bacteroidetes;Bacteroidia;Bacteroidales;Prevotellaceae;Prevotella;sp. HMT31520100001000200000001810000000693347301530300000000
SP183Bacteria;Proteobacteria;Betaproteobacteria;Neisseriales;Neisseriaceae;Neisseria;elongata00000000000000000001100039321000000005000004806553
SP185Bacteria;Bacteroidetes;Bacteroidia;Bacteroidales;Prevotellaceae;Prevotella;oralis1200410010216501308230011001100000000000000101103
SP188Bacteria;Firmicutes;Negativicutes;Selenomonadales;Selenomonadaceae;Selenomonas;flueggei1010111302050140013000000110000111005010000301042
SP189Bacteria;Firmicutes;Negativicutes;Selenomonadales;Selenomonadaceae;Selenomonas;artemidis00000101000110011002100010852000000013000000917589
SP19Bacteria;Firmicutes;Bacilli;Lactobacillales;Carnobacteriaceae;Granulicatella;adiacens121157185298242131119814101142223454426424226312928326925935910039282444115282313312772544112919281548106171160103
SP198Bacteria;Synergistetes;Synergistia;Synergistales;Synergistaceae;Fretibacterium;fastidiosum040013000030110102001000000022142120130000000000
SP199Bacteria;Firmicutes;Bacilli;Lactobacillales;Streptococcaceae;Streptococcus;constellatus200074642350111371190110100002001000001001000001000
SP2Bacteria;Proteobacteria;Epsilonproteobacteria;Campylobacterales;Campylobacteraceae;Campylobacter;concisus372111111616666483169902231545141419948612922815719171125711840611454210428634202217246100
SP20Bacteria;Fusobacteria;Fusobacteriia;Fusobacteriales;Fusobacteriaceae;Fusobacterium;nucleatum_subsp._vincentii1847811040510111701211220210441000211143021011120000000
SP212Bacteria;Firmicutes;Negativicutes;Veillonellales;Veillonellaceae;Veillonella;atypica00000001000000000000000000222000000000000511021114
SP213Bacteria;Firmicutes;Negativicutes;Selenomonadales;Selenomonadaceae;Selenomonas;sp. HMT1491000000001001000021000000100334147211410520003220
SP215Bacteria;Firmicutes;Mollicutes;Mycoplasmatales;Mycoplasmataceae;Mycoplasma;salivarium010031000000306210000011131300000100020000001110
SP22Bacteria;Bacteroidetes;Bacteroidia;Bacteroidales;Prevotellaceae;Alloprevotella;sp. HMT47301001182107965189277269653901130593002631741000000012961111241113757039278775014351554838138141148201721261132
SP228Bacteria;Firmicutes;Clostridia;Clostridiales;Lachnospiraceae_[XIV];Stomatobaculum;sp. HMT09700015000000000000000000000000200100000100000000015
SP23Bacteria;Fusobacteria;Fusobacteriia;Fusobacteriales;Fusobacteriaceae;Fusobacterium;sp. HMT20429416944376122626702550816353254573810154824000310000000000000000000
SP232Bacteria;Firmicutes;Clostridia;Clostridiales;Veillonellaceae;Centipeda;sp._Oral_Taxon_D18602500000005000002260024000000000000000000100010
SP24Bacteria;Firmicutes;Clostridia;Clostridiales;Lachnospiraceae_[XIV];Oribacterium;sinus6700387121621252423282162038341817201917582425441322523126182324151313051
SP243Bacteria;Firmicutes;Clostridia;Clostridiales;Veillonellaceae;Selenomonas;sp._Oral_Taxon_E8300000020010000001000000032100010000160111005080114
SP25Bacteria;Firmicutes;Bacilli;Bacillales;Gemellaceae;Gemella;morbillorum9372204388325224291832402433156362614545534130937336501785134325403930293139106321132515351710226994151022131136334
SP26Bacteria;Fusobacteria;Fusobacteriia;Fusobacteriales;Fusobacteriaceae;Fusobacterium;nucleatum0101001000010000001504300151000000204200101060000000
SP268Bacteria;Firmicutes;Clostridia;Clostridiales;Veillonellaceae;Selenomonas;sp._Oral_Taxon_F830000000000000000000000000000392310201110220000010
SP27Bacteria;Firmicutes;Bacilli;Lactobacillales;Streptococcaceae;Streptococcus;mitis169201218211111285422104914341633243024283740000000220000292315177157
SP28Bacteria;Firmicutes;Erysipelotrichia;Erysipelotrichales;Erysipelotrichaceae;Solobacterium;moorei720182119026232867640837355382706984901604903611459550692741136333371236733932163209221115231311821732692234826363101502634164
SP29Bacteria;Fusobacteria;Fusobacteriia;Fusobacteriales;Fusobacteriaceae;Fusobacterium;hwasookii1413210110211237313161707711617103435200113627130002411
SP30Bacteria;Bacteroidetes;Bacteroidia;Bacteroidales;Prevotellaceae;Prevotella;intermedia0000421314131614180193430331300000100000104428012561019115098696908353414806348322720000020
SP33Bacteria;Firmicutes;Bacilli;Lactobacillales;Streptococcaceae;Streptococcus;sp. HMT0648543551121717201332147441436717657376110859931731810500007023041003724443
SP34Bacteria;Bacteroidetes;Bacteroidia;Bacteroidales;Prevotellaceae;Prevotella;pallens79249150429600000119180000010518171078231703576668000010000010001012000000
SP36Bacteria;Firmicutes;Bacilli;Lactobacillales;Streptococcaceae;Streptococcus;oralis_subsp._dentisani_clade_398100010100010101300000000000042291273010838160110000
SP37Bacteria;Firmicutes;Clostridia;Clostridiales;Lachnospiraceae_[XIV];Catonella;morbi000085676471944186010014310912512400000001356831221171181048995529618910812971777721543290480121241
SP38Bacteria;Firmicutes;Bacilli;Lactobacillales;Streptococcaceae;Streptococcus;oralis_subsp._tigurinus_clade_071538495680465344541302615343882939544937644425677427761901892311043329197221042356779339120000011
SP39Bacteria;Firmicutes;Clostridia;Clostridiales;Peptoniphilaceae;Parvimonas;micra455413846001022105204383506514334802048483881762433694377623710142022071016221701044114815
SP41Bacteria;Firmicutes;Clostridia;Clostridiales;Lachnospiraceae_[XIV];Lachnospiraceae_[G-8];bacterium HMT500000052121190751217110000000000053426271741031812685370000000
SP43Bacteria;Firmicutes;Bacilli;Lactobacillales;Streptococcaceae;Streptococcus;sp._str._C30048471653887292619631335537251119222360356010328209317281129124147522442010100020132300572063190178222282
SP44Bacteria;Bacteroidetes;Bacteroidia;Bacteroidales;Porphyromonadaceae;Porphyromonas;sp. HMT27852403310624191240279413281211652282541572741230430000012000001100000010000
SP45Bacteria;Firmicutes;Negativicutes;Veillonellales;Veillonellaceae;Veillonella;dispar000054054413965162000010000001112010010200001201
SP47Bacteria;Firmicutes;Negativicutes;Veillonellales;Veillonellaceae;Veillonella;parvula20169104918142029382712314923251959101611221213791055981110929102193221016962246146625238
SP48Bacteria;Bacteroidetes;Bacteroidia;Bacteroidales;Porphyromonadaceae;Porphyromonas;endodontalis00007462092821011263571201161401129482210000000053526116787102128400314040930000000
SP49Bacteria;Bacteroidetes;Bacteroidia;Bacteroidales;Prevotellaceae;Alloprevotella;sp. HMT30819910547735160771108615412372579232166177152107588219936293139201101711321012777806514
SP5Bacteria;Firmicutes;Negativicutes;Veillonellales;Veillonellaceae;Dialister;pneumosintes0000562273877404595707378000000000002613152491478442103764844137336500000000
SP50Bacteria;Firmicutes;Clostridia;Clostridiales;Peptostreptococcaceae_[XI];Peptostreptococcaceae_[XI][G-7];[Eubacterium]_yurii_subsps._yurii_&_margaretiae1007111115777549681114113168137102135126334871124710015326043142429132013548352676016510720
SP51Bacteria;Firmicutes;Negativicutes;Veillonellales;Veillonellaceae;Anaeroglobus;geminatus5110473235448911000306101121140001111652836252211021433713000000
SP54Bacteria;Firmicutes;Clostridia;Clostridiales;Peptostreptococcaceae_[XI];Peptostreptococcaceae_[XI][G-7];bacterium HMT08100000000000000000000000000004028307339425905145241790000000
SP57Bacteria;Bacteroidetes;Bacteroidia;Bacteroidales;Prevotellaceae;Prevotella;sp. HMT3178510225959512167112018176114715476527145482184243169333543161571842
SP58Bacteria;Proteobacteria;Betaproteobacteria;Neisseriales;Neisseriaceae;Eikenella;corrodens4716423817629222431393222225481736479577612153155180140946471263979315441112111320104052126543484660441
SP59Bacteria;Bacteroidetes;Bacteroidia;Bacteroidales;Prevotellaceae;Prevotella;denticola427528400314015501015181303700273822353632489890002830
SP6Bacteria;Bacteroidetes;Bacteroidia;Bacteroidales;Porphyromonadaceae;Porphyromonas;gingivalis0000144272227142402491199753112751710000000001784621453144811721018733066750287153240000000
SP60Bacteria;Firmicutes;Clostridia;Clostridiales;Peptoniphilaceae;Parvimonas;sp. HMT110100049824911431841786838405335594056319422100101065343325100101000151021158220010
SP61Bacteria;Bacteroidetes;Bacteroidia;Bacteroidales;Bacteroidales_[F-2];Bacteroidales_[G-2];sp._Oral_Taxon_2741617651180000000360000092174721308039100010010131113000000000
SP62Bacteria;Firmicutes;Clostridia;Clostridiales;Lachnospiraceae_[XIV];Oribacterium;sp. HMT078042046267441151715923400110000000000000000000000000
SP65Bacteria;Firmicutes;Negativicutes;Selenomonadales;Selenomonadaceae;Selenomonas;sputigena6000231053201521018291334213132011200140001100000
SP66Bacteria;Fusobacteria;Fusobacteria;Fusobacteriales;Fusobacteriaceae;Fusobacterium;sp._Oral_Taxon_D95000030572516171017047433534520000001000019558451104319180000000
SP67Bacteria;Firmicutes;Negativicutes;Selenomonadales;Selenomonadaceae;Selenomonas;sp. HMT91910001158353002534200000001013000000010000025043141
SP7Bacteria;Bacteroidetes;Bacteroides;Bacteroidales;Porphyromonadaceae;Porphyromonas;sp._Oral_Taxon_F92000023301126210118215200000000000045213114100722700000000
SP70Bacteria;Bacteroidetes;Bacteroidia;Bacteroidales;Prevotellaceae;Prevotella;maculosa9551091082471546154473210781251410134001200050010026094159
SP71Bacteria;Bacteroidetes;Bacteroidia;Bacteroidales;Prevotellaceae;Prevotella;melaninogenica24231511790301000033418011034014439198851501110000313017542027235727234742401000000
SP72Bacteria;Firmicutes;Bacilli;Lactobacillales;Streptococcaceae;Streptococcus;sp._Oral_Taxon_712001000000000000021113207227500000004000002100122
SP73Bacteria;Fusobacteria;Fusobacteriia;Fusobacteriales;Fusobacteriaceae;Fusobacterium;periodonticum00019400411023001104010000005091033640315711511333321461004101
SP74Bacteria;Bacteroidetes;Flavobacteriia;Flavobacteriales;Flavobacteriaceae;Capnocytophaga;gingivalis181212111224910631978463261515131114614172723211100120306211724322211314
SP77Bacteria;Fusobacteria;Fusobacteriia;Fusobacteriales;Fusobacteriaceae;Fusobacterium;canifelinum200020000000001002120010100012551143603366180000000
SP78Bacteria;Proteobacteria;Gammaproteobacteria;Pasteurellales;Pasteurellaceae;Haemophilus;parainfluenzae7777059203071181903304156118152143147151951133015002023037023150148
SP79Bacteria;Firmicutes;Bacilli;Lactobacillales;Streptococcaceae;Streptococcus;chosunense3889141941307332287191095773047413519264413882341576254699000001306011120200101100
SP8Bacteria;Bacteroidetes;Bacteroidia;Bacteroidales;Porphyromonadaceae;Porphyromonas;pasteri611011592867389382496518670265587825583977552209114858623601010912827233213783229606624001230
SP80Bacteria;Firmicutes;Bacilli;Lactobacillales;Streptococcaceae;Streptococcus;oralis_subsp._dentisani_clade_05810211011001601533301122061231747911200000101000125645392
SP81Bacteria;Firmicutes;Negativicutes;Selenomonadales;Selenomonadaceae;Selenomonas;sp. HMT146107222624010000040000007329501235242301241011200900100260110275
SP83Bacteria;Firmicutes;Negativicutes;Selenomonadales;Selenomonadaceae;Selenomonas;noxia010000000020010000122010201031100001201022801400
SP88Bacteria;Bacteroidetes;Bacteroidia;Bacteroidales;Prevotellaceae;Prevotella;saccharolytica282547212502012201636480012311151843146303684436618321261513
SP89Bacteria;Bacteroidetes;Bacteroidia;Bacteroidales;Porphyromonadaceae;Porphyromonas;sp. HMT2750001451601091294154090000323790000712763310321130000000
SP9Bacteria;Bacteroidetes;Bacteroidia;Bacteroidales;Prevotellaceae;Prevotella;marshii10003632818391724069619670340000000000031430000201021000000
SP90Bacteria;Bacteroidetes;Bacteroidia;Bacteroidales;Prevotellaceae;Alloprevotella;sp. HMT9140000000000000000000000000000241246106405910127440000000
SP91Bacteria;Fusobacteria;Fusobacteria;Fusobacteriales;Fusobacteriaceae;Fusobacterium;sp._Oral_Taxon_H271001200001000011002517223124100064140101650161419781000000
SP93Bacteria;Firmicutes;Clostridia;Clostridiales;Veillonellaceae;Dialister;invisus1208176315572130391513954151720159028750330571310302926810426730695701001180
SP94Bacteria;Bacteroidetes;Bacteroidia;Bacteroidales;Prevotellaceae;Alloprevotella;tannerae44211330721984727192111611590439152571554115047000048331438171931018312430000000
SP95Bacteria;Firmicutes;Clostridia;Clostridiales;Veillonellaceae;Selenomonas;sp._Oral_Taxon_G55000100001000000011020111103588664331070232401250
SP96Bacteria;Bacteroidetes;Flavobacteriia;Flavobacteriales;Flavobacteriaceae;Capnocytophaga;sputigena22329423301417162121051849310511100000002000000316161
SP97Bacteria;Firmicutes;Bacilli;Lactobacillales;Streptococcaceae;Streptococcus;gordonii13683117772216215798392451331776312129581328010277115112213506511103000
SP98Bacteria;Bacteroidetes;Bacteroidia;Bacteroidales;Prevotellaceae;Prevotella;baroniae0000110004302410100000000000124644255463440051241120000000
SPN16Bacteria;Firmicutes;Clostridia;Clostridiales;Lachnospiraceae_[XIVa];Lachnospiraceae_[G];sp._Oral_Taxon_A61_nov_97.473%0000301000109941360000000000000014020008300000000
SPN18Bacteria;Firmicutes;Clostridia;Clostridiales;Veillonellaceae;Selenomonas;sp._Oral_Taxon_F30_nov_97.857%0000217825460312230000000000000000000000000000000
SPN19Bacteria;Firmicutes;Clostridia;Eubacteriales;Lachnospiraceae;Lachnoanaerobaculum;gingivalis_nov_97.865%3315000000060000053192290000000000000000000000000
SPN2Bacteria;Fusobacteria;Fusobacteria;Fusobacteriales;Fusobacteriaceae;Fusobacterium;sp._Oral_Taxon_H27_nov_97.865%40000000000000000000501342100000000060000029000000
SPN20Bacteria;Bacteroidetes;Bacteroidia;Bacteroidales;Porphyromonadaceae;Porphyromonas;endodontalis_nov_97.872%0000000000000000000000000000257214910210030000000
SPN21Bacteria;Bacteroidetes;Flavobacteriia;Flavobacteriales;Flavobacteriaceae;Bergeyella;sp. HMT900 nov_97.500%000000000200000000000100000043194231183220400001
SPN22Bacteria;Bacteroidetes;Bacteroidia;Bacteroidales;Bacteroidaceae;Bacteroides;heparinolyticus_nov_95.745%00004120354622162103965503467000000000002525111823122004203427290000000
SPN23Bacteria;Bacteroidetes;Bacteroidia;Bacteroidales;Prevotellaceae;Prevotella;melaninogenica_nov_97.865%652000000009000009282212000000000000000000000000
SPN24Bacteria;Proteobacteria;Epsilonproteobacteria;Campylobacterales;Campylobacteraceae;Campylobacter;sp._Oral_Taxon_G43_nov_96.809%0000000000000000000000001334700000000000000000020
SPN25Bacteria;Firmicutes;Bacilli;Lactobacillales;Streptococcaceae;Streptococcus;sp._Oral_Taxon_E05_nov_97.509%000000000000000000000000000071040060453880000000
SPN26Bacteria;Firmicutes;Bacilli;Lactobacillales;Streptococcaceae;Streptococcus;oralis_subsp._dentisani_clade_058_nov_97.857%530100000004000008432335000000000000000000000000
SPN27Bacteria;Fusobacteria;Fusobacteriia;Fusobacteriales;Fusobacteriaceae;Fusobacterium;sp. HMT204 nov_97.849%1510015900110003001000130801000000000000000001000000
SPN28Bacteria;Firmicutes;Bacilli;Lactobacillales;Streptococcaceae;Streptococcus;oralis_subsp._dentisani_clade_058_nov_97.872%0000000000000000000000000000610730140530100000000
SPN29Bacteria;Bacteroidetes;Bacteroidia;Bacteroidales;Prevotellaceae;Prevotella;intermedia_nov_90.210%000000000000000000000000000042732530380200000000
SPN30Bacteria;Firmicutes;Negativicutes;Selenomonadales;Selenomonadaceae;Selenomonas;sp. HMT481 nov_96.786%000074603340331120000000000000000000000000000000
SPN31Bacteria;Firmicutes;Negativicutes;Selenomonadales;Selenomonadaceae;Selenomonas;dianae_nov_97.527%00001256334405114130000000010000000001000000302111
SPN32Bacteria;Bacteroidetes;Bacteroidia;Bacteroidales;Prevotellaceae;Alloprevotella;sp. HMT473 nov_96.820%0000000000000000000000000000210101100830380000000
SPN33Bacteria;Firmicutes;Bacilli;Bacillales;Gemellaceae;Gemella;morbillorum_nov_95.053%000000000000000000000000302200000007000004811711
SPN34Bacteria;Firmicutes;Erysipelotrichia;Erysipelotrichales;Erysipelotrichaceae;Solobacterium;moorei_nov_97.143%000014010120334460000000010000001000010000000020
SPN35Bacteria;Firmicutes;Bacilli;Lactobacillales;Carnobacteriaceae;Granulicatella;adiacens_nov_97.857%221600000003000002240200110000000000000000002231
SPN47Bacteria;Firmicutes;Clostridia;Clostridiales;Lachnospiraceae_[XIV];Oribacterium;sp. HMT102 nov_95.035%00000000000000000000000000004036222731181701223162160000000
SPN55Bacteria;Bacteroidetes;Bacteroidia;Bacteroidales;Prevotellaceae;Alloprevotella;sp. HMT473 nov_96.797%00001221620602542200000006400001000001300000015000134
SPN66Bacteria;Bacteroidetes;Bacteroidia;Bacteroidales;Prevotellaceae;Prevotella;intermedia_nov_97.143%000000000000000000000000000031414121051608141112140000000
SPN77Bacteria;Firmicutes;Clostridia;Clostridiales;Veillonellaceae;Selenomonas;sp._Oral_Taxon_F87_nov_97.857%10002727104215121001001007517100000000200000150117011
SPN88Bacteria;Firmicutes;Clostridia;Clostridiales;Veillonellaceae;Selenomonas;sp._Oral_Taxon_F29_nov_97.857%0100000000000000001000000000611715514707419120000000
SPP1Bacteria;Firmicutes;Bacilli;Lactobacillales;Streptococcaceae;Streptococcus;multispecies_spp1_2000010363224079311600001000122105212195611511150930936563401
SPP11Bacteria;Proteobacteria;Gammaproteobacteria;Pasteurellales;Pasteurellaceae;Aggregatibacter;multispecies_spp11_20000321413401731150000000000000000010000000000000
SPP12Bacteria;Firmicutes;multiclass;multiorder;Selenomonadaceae;Selenomonas;multispecies_spp12_2000077925670813138110000000000000000000000000000000
SPP13Bacteria;Firmicutes;Negativicutes;Selenomonadales;Selenomonadaceae;Selenomonas;multispecies_spp13_23120011000020002030130005260302011292015171162247103102211
SPP14Bacteria;Firmicutes;Negativicutes;Selenomonadales;Selenomonadaceae;Selenomonas;multispecies_spp14_3000010100100000100000000000082934976615143036932327360000000
SPP17Bacteria;Firmicutes;Negativicutes;Selenomonadales;Selenomonadaceae;Selenomonas;multispecies_spp17_2000011000000000002011211115400010200120000401300
SPP18Bacteria;Firmicutes;Bacilli;Lactobacillales;Streptococcaceae;Streptococcus;multispecies_spp18_2030010000001000006312522514100000000000000000000000
SPP20Bacteria;Proteobacteria;Epsilonproteobacteria;Campylobacterales;Campylobacteraceae;Campylobacter;multispecies_spp20_2000000100000000000000000347362223001122110580287111
SPP25Bacteria;Firmicutes;Negativicutes;Veillonellales;Veillonellaceae;Veillonella;multispecies_spp25_2000100000001000000000201112410000211100200010041310
SPP26Bacteria;Bacteroidetes;Bacteroidia;Bacteroidales;Bacteroidaceae;multigenus;multispecies_spp26_2000011055210531461000000000021141010001100000000
SPP29Bacteria;Firmicutes;Bacilli;Lactobacillales;Carnobacteriaceae;Granulicatella;multispecies_spp29_24325111014570122101019142518517422610568151513630112302411496124223310164267213138
SPP30Bacteria;Fusobacteria;Fusobacteriia;Fusobacteriales;Fusobacteriaceae;Fusobacterium;multispecies_spp30_2000014339122903131100000040113200070823235582024524341232952393624603890000000
SPP31Bacteria;Fusobacteria;Fusobacteriia;Fusobacteriales;Fusobacteriaceae;Fusobacterium;multispecies_spp31_3100020101010031000000000000082411140221350000000
SPP33Bacteria;Fusobacteria;Fusobacteriia;Fusobacteriales;Fusobacteriaceae;Fusobacterium;multispecies_spp33_420812016139102110017803000350265631141049204241158272915023464473840000000
SPP34Bacteria;Fusobacteria;Fusobacteriia;Fusobacteriales;Fusobacteriaceae;Fusobacterium;multispecies_spp34_261103783864601124435173480541364034587516728871866990182173276164104117108121104161291865003435140051001
SPP37Bacteria;Fusobacteria;Fusobacteria;Fusobacteriales;Fusobacteriaceae;Fusobacterium;multispecies_spp37_42756151871062011332261544336046724246308130000000020000013000100000
SPP38Bacteria;Fusobacteria;Fusobacteriia;Fusobacteriales;Fusobacteriaceae;Fusobacterium;multispecies_spp38_35102881733666296294593002472352965172372892421792957667447571054035582704406298153233813231381393012221162614810424390223333149
SPP39Bacteria;Fusobacteria;Fusobacteriia;Fusobacteriales;Fusobacteriaceae;Fusobacterium;multispecies_spp39_2801242387412012421412200283138685472550001505020061121003400000001
SPP4Bacteria;Actinobacteria;Coriobacteriia;Coriobacteriales;multifamily;Atopobium;rimae489835433211242118242816841405369346410262262794521100167611710127076112064300320
SPP44Bacteria;Firmicutes;Bacilli;Lactobacillales;Streptococcaceae;Streptococcus;multispecies_spp44_318232215519951158282924312089170910272927252176158423051542363113522283234261514351611273323665237138172719224
SPP47Bacteria;Firmicutes;Bacilli;Lactobacillales;Streptococcaceae;Streptococcus;multispecies_spp47_21940724252300420214201102252164912127300000000000001302113
SPP5Bacteria;Firmicutes;Clostridia;Clostridiales;Lachnospiraceae_[XIV];Oribacterium;multispecies_spp5_200221041102405189716000010220030700000003003110050305434
SPP51Bacteria;Firmicutes;Bacilli;Lactobacillales;Streptococcaceae;Streptococcus;multispecies_spp51_2000493551021412811020000000004321635708501350000001
SPP52Bacteria;Firmicutes;Bacilli;Lactobacillales;Streptococcaceae;Streptococcus;multispecies_spp52_261732900002001200000291914327340100313010201014620000010
SPP53Bacteria;Firmicutes;Clostridia;Clostridiales;Peptostreptococcaceae_[XIII];Parvimonas;multispecies_spp53_2160013080111101123205689362321020000000000000000000000000
SPP54Bacteria;Actinobacteria;Coriobacteriia;Coriobacteriales;multifamily;multigenus;multispecies_spp54_2158422103004311001318010643100010222501248300002171
SPP55Bacteria;Firmicutes;Bacilli;Lactobacillales;Streptococcaceae;Streptococcus;multispecies_spp55_277713539632355146174715441023153268915871473232520711810163513802037556902612124800502638723274146
SPP56Bacteria;Firmicutes;multiclass;Veillonellales;Veillonellaceae;Veillonella;multispecies_spp56_362729123827050152335312328420333426322066841438819032635344641119925555751187183021311644125445354932869369210322275
SPP57Bacteria;Firmicutes;Negativicutes;Veillonellales;Veillonellaceae;Veillonella;multispecies_spp57_251008122757061095370141020176282611101241130001018003865
SPP59Bacteria;Firmicutes;Clostridia;multiorder;multifamily;multigenus;multispecies_spp59_30000000000000000000000000000333319902552540453212830000000
SPP6Bacteria;Fusobacteria;Fusobacteria;Fusobacteriales;Fusobacteriaceae;Fusobacterium;multispecies_spp6_25036754552558177973936232326332511013320383172507729419492342104124162120522436140418310000029
SPP60Bacteria;Firmicutes;Clostridia;multiorder;multifamily;multigenus;multispecies_spp60_2000016827010510813716113303613132131831590100000001000000000100000000000
SPP62Bacteria;Bacteroidetes;Bacteroides;Bacteroidales;Porphyromonadaceae;Porphyromonas;multispecies_spp62_20010152279158166617399615220142324879861586321200002000005974381012618112311061821153111141000000
SPP63Bacteria;Fusobacteria;Fusobacteriia;Fusobacteriales;Fusobacteriaceae;Fusobacterium;multispecies_spp63_200000001000000001000000000000171416115030373120000000
SPP65Bacteria;Firmicutes;Negativicutes;Selenomonadales;Selenomonadaceae;Selenomonas;multispecies_spp65_200001915632860232130000000000100000000000000000000
SPP8Bacteria;Fusobacteria;Fusobacteriia;Fusobacteriales;Fusobacteriaceae;Fusobacterium;multispecies_spp8_2000242010333901705071323557000024043517219618818227203682973873753380000000
SPPN2Bacteria;Firmicutes;Bacilli;Lactobacillales;Streptococcaceae;Streptococcus;multispecies_sppn2_3_nov_97.500%000063002030951190000000000000000000000000000000
SPPN3Bacteria;Fusobacteria;Fusobacteria;Fusobacteriales;Fusobacteriaceae;Fusobacterium;multispecies_sppn3_3_nov_97.436%302112002211001122252530001000000000000000000000
SPPN4Bacteria;Firmicutes;Clostridia;multiorder;multifamily;multigenus;multispecies_sppn4_2_nov_96.071%000027192920181034012231238340000000000000000000000000000000
 
 
Download OTU Tables at Different Taxonomy Levels
PhylumCount*: Relative**: CLR***:
ClassCount*: Relative**: CLR***:
OrderCount*: Relative**: CLR***:
FamilyCount*: Relative**: CLR***:
GenusCount*: Relative**: CLR***:
SpeciesCount*: Relative**: CLR***:
* Read count
** Relative abundance (count/total sample count)
*** Centered log ratio transformed abundance
;
 
The species listed in the table has full taxonomy and a dynamically assigned species ID specific to this report. When some reads match with the reference sequences of more than one species equally (i.e., same percent identiy and alignmnet coverage), they can't be assigned to a particular species. Instead, they are assigned to multiple species with the species notaton "s__multispecies_spp2_2". In this notation, spp2 is the dynamic ID assigned to these reads that hit multiple sequences and the "_2" at the end of the notation means there are two species in the spp2.

You can look up which species are included in the multi-species assignment, in this table below:
 
 
 
 
Another type of notation is "s__multispecies_sppn2_2", in which the "n" in the sppn2 means it's a potential novel species because all the reads in this species have < 98% idenity to any of the reference sequences. They were grouped together based on de novo OTU clustering at 98% identity cutoff. And then a representative sequence was chosed to BLASTN search against the reference database to find the closest match (but will still be < 98%). This representative sequence also matched equally to more than one species, hence the "spp" was given in the label.
 
 

Taxonomy Bar Plots for All Samples

 
 

Taxonomy Bar Plots for Individual Comparison Groups

 
 
Comparison No.Comparison NameFamiliesGeneraSpecies
Comparison 1Healthy-2%_Arginine vs Healthy-ControlPDFSVGPDFSVGPDFSVG
Comparison 2Healthy-4%_Arginine vs Healthy-ControlPDFSVGPDFSVGPDFSVG
Comparison 3Healthy-8%_Arginine vs Healthy-ControlPDFSVGPDFSVGPDFSVG
Comparison 4Perio-2%_Arginine vs Perio-ControlPDFSVGPDFSVGPDFSVG
Comparison 5Perio-4%_Arginine vs Perio-ControlPDFSVGPDFSVGPDFSVG
Comparison 6Perio-8%_Arginine vs Perio-ControlPDFSVGPDFSVGPDFSVG
 
 

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

 

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.

 
 
 

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 Box Plots for All Groups
 
 
 
 
 
 
 
Alpha Diversity Box Plots for Individual Comparisons
 
Comparison 1Healthy-2%_Arginine vs Healthy-ControlView in PDFView in SVG
Comparison 2Healthy-4%_Arginine vs Healthy-ControlView in PDFView in SVG
Comparison 3Healthy-8%_Arginine vs Healthy-ControlView in PDFView in SVG
Comparison 4Perio-2%_Arginine vs Perio-ControlView in PDFView in SVG
Comparison 5Perio-4%_Arginine vs Perio-ControlView in PDFView in SVG
Comparison 6Perio-8%_Arginine vs Perio-ControlView in PDFView in SVG
 
 
 

Group Significance of Alpha-diversity Indices

To test whether the alpha diversity among different comparison groups are different statisticall, we use the Kruskal Wallis H test provided the "alpha-group-significance" fucntion in the QIIME 2 diversity package. Kruskal Wallis H test is the non parametric alternative to the One Way ANOVA. Non parametric means that the test doesn’t assume your data comes from a particular distribution. The H test is used when the assumptions for ANOVA aren’t met (like the assumption of normality). It is sometimes called the one-way ANOVA on ranks, as the ranks of the data values are used in the test rather than the actual data points. The test determines whether the medians of two or more groups are different.

Below are the Kruskal Wallis H test results for each comparison based on three different alpha diversity measures: 1) Observed species (features), 2) Shannon index, and 3) Simpson index.

 
 
Comparison 1.Healthy-2%_Arginine vs Healthy-ControlObserved FeaturesShannon IndexSimpson Index
Comparison 2.Healthy-4%_Arginine vs Healthy-ControlObserved FeaturesShannon IndexSimpson Index
Comparison 3.Healthy-8%_Arginine vs Healthy-ControlObserved FeaturesShannon IndexSimpson Index
Comparison 4.Perio-2%_Arginine vs Perio-ControlObserved FeaturesShannon IndexSimpson Index
Comparison 5.Perio-4%_Arginine vs Perio-ControlObserved FeaturesShannon IndexSimpson Index
Comparison 6.Perio-8%_Arginine vs Perio-ControlObserved FeaturesShannon IndexSimpson Index
 
 

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. Below are beta diveristy results for all groups together:

 
 
NMDS and PCoA Plots for All Groups
 
 
 
 
 

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:

 
 
 
 
 
 
 
NMDS and PCoA Plots for Individual Comparisons
 
 
Comparison No.Comparison NameNMDAPCoA
Bray-CurtisCLR EuclideanBray-CurtisCLR Euclidean
Comparison 1Healthy-2%_Arginine vs Healthy-ControlPDFSVGPDFSVGPDFSVGPDFSVG
Comparison 2Healthy-4%_Arginine vs Healthy-ControlPDFSVGPDFSVGPDFSVGPDFSVG
Comparison 3Healthy-8%_Arginine vs Healthy-ControlPDFSVGPDFSVGPDFSVGPDFSVG
Comparison 4Perio-2%_Arginine vs Perio-ControlPDFSVGPDFSVGPDFSVGPDFSVG
Comparison 5Perio-4%_Arginine vs Perio-ControlPDFSVGPDFSVGPDFSVGPDFSVG
Comparison 6Perio-8%_Arginine vs Perio-ControlPDFSVGPDFSVGPDFSVGPDFSVG
 
 
 
 
 

Interactive 3D PCoA Plots - Bray-Curtis Dissimilarity

 
 
 

Interactive 3D PCoA Plots - Euclidean Distance

 
 
 

Interactive 3D PCoA Plots - Correlation Coefficients

 
 
 

Group Significance of Beta-diversity Indices

To test whether the between-group dissimilarities are significantly greater than the within-group dissimilarities, the "beta-group-significance" function provided in the QIIME 2 "diversity" package was used with PERMANOVA (permutational multivariate analysis of variance) chosen s the group significan testing method.

Three beta diversity matrics were used: 1) Bray–Curtis dissimilarity 2) Correlation coefficient matrix , and 3) Aitchison distance (Euclidean distance between clr-transformed compositions).

 
 
Comparison 1.Healthy-2%_Arginine vs Healthy-ControlBray–CurtisCorrelationAitchison
Comparison 2.Healthy-4%_Arginine vs Healthy-ControlBray–CurtisCorrelationAitchison
Comparison 3.Healthy-8%_Arginine vs Healthy-ControlBray–CurtisCorrelationAitchison
Comparison 4.Perio-2%_Arginine vs Perio-ControlBray–CurtisCorrelationAitchison
Comparison 5.Perio-4%_Arginine vs Perio-ControlBray–CurtisCorrelationAitchison
Comparison 6.Perio-8%_Arginine vs Perio-ControlBray–CurtisCorrelationAitchison
 
 
 

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. "ANCOM" generates a table of features with W-statistics and whether the null hypothesis is rejected. The “W” is the W-statistic, or number of features that a single feature is tested to be significantly different against. Hence the higher the "W" the more statistical sifgnificane that a feature/species is differentially abundant.


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.

Lin H, Peddada SD. Analysis of compositions of microbiomes with bias correction. Nat Commun. 2020 Jul 14;11(1):3514. doi: 10.1038/s41467-020-17041-7. PMID: 32665548; PMCID: PMC7360769.

 
 

ANCOM Differential Abundance Analysis

 
ANCOM Results for Individual Comparisons
Comparison No.Comparison Name
Comparison 1.Healthy-2%_Arginine vs Healthy-Control
Comparison 2.Healthy-4%_Arginine vs Healthy-Control
Comparison 3.Healthy-8%_Arginine vs Healthy-Control
Comparison 4.Perio-2%_Arginine vs Perio-Control
Comparison 5.Perio-4%_Arginine vs Perio-Control
Comparison 6.Perio-8%_Arginine vs Perio-Control
 
 

ANCOM-BC Differential Abundance Analysis

 

Starting with version V1.2, we also include the results of ANCOM-BC (Analysis of Compositions of Microbiomes with Bias Correction) (Lin and Peddada 2020). ANCOM-BC is an updated version of "ANCOM" that: (a) provides statistically valid test with appropriate p-values, (b) provides confidence intervals for differential abundance of each taxon, (c) controls the False Discovery Rate (FDR), (d) maintains adequate power, and (e) is computationally simple to implement. The bias correction (BC) addresses a challenging problem of the bias introduced by differences in the sampling fractions across samples. This bias has been a major hurdle in performing DA analysis of microbiome data. ANCOM-BC estimates the unknown sampling fractions and corrects the bias induced by their differences among samples. The absolute abundance data are modeled using a linear regression framework.

References:

Lin H, Peddada SD. Analysis of compositions of microbiomes with bias correction. Nat Commun. 2020 Jul 14;11(1):3514. doi: 10.1038/s41467-020-17041-7. PMID: 32665548; PMCID: PMC7360769.

 
 
ANCOM-BC Results for Individual Comparisons
 
Comparison No.Comparison Name
Comparison 1.Healthy-2%_Arginine vs Healthy-Control
Comparison 2.Healthy-4%_Arginine vs Healthy-Control
Comparison 3.Healthy-8%_Arginine vs Healthy-Control
Comparison 4.Perio-2%_Arginine vs Perio-Control
Comparison 5.Perio-4%_Arginine vs Perio-Control
Comparison 6.Perio-8%_Arginine vs Perio-Control
 
 
 

LEfSe - Linear Discriminant Analysis Effect Size

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

Reference:

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

 
Healthy-2%_Arginine vs Healthy-Control
 
 
 
 
 
 
 
LEfSe Results for All Comparisons
 
Comparison No.Comparison Name
Comparison 1.Healthy-2%_Arginine vs Healthy-Control
Comparison 2.Healthy-4%_Arginine vs Healthy-Control
Comparison 3.Healthy-8%_Arginine vs Healthy-Control
Comparison 4.Perio-2%_Arginine vs Perio-Control
Comparison 5.Perio-4%_Arginine vs Perio-Control
Comparison 6.Perio-8%_Arginine vs Perio-Control
 
 

XI. Analysis - Heatmap Profile

 

Species vs Sample Abundance Heatmap for All Samples

 
 
 

Heatmaps for Individual Comparisons

 
A) Two-way clustering - clustered on both columns (Samples) and rows (organism)
Comparison No.Comparison NameFamily LevelGenus LevelSpecies Level
Comparison 1Healthy-2%_Arginine vs Healthy-ControlPDFSVGPDFSVGPDFSVG
Comparison 2Healthy-4%_Arginine vs Healthy-ControlPDFSVGPDFSVGPDFSVG
Comparison 3Healthy-8%_Arginine vs Healthy-ControlPDFSVGPDFSVGPDFSVG
Comparison 4Perio-2%_Arginine vs Perio-ControlPDFSVGPDFSVGPDFSVG
Comparison 5Perio-4%_Arginine vs Perio-ControlPDFSVGPDFSVGPDFSVG
Comparison 6Perio-8%_Arginine vs Perio-ControlPDFSVGPDFSVGPDFSVG
 
 
B) One-way clustering - clustered on rows (organism) only
Comparison No.Comparison NameFamily LevelGenus LevelSpecies Level
Comparison 1Healthy-2%_Arginine vs Healthy-ControlPDFSVGPDFSVGPDFSVG
Comparison 2Healthy-4%_Arginine vs Healthy-ControlPDFSVGPDFSVGPDFSVG
Comparison 3Healthy-8%_Arginine vs Healthy-ControlPDFSVGPDFSVGPDFSVG
Comparison 4Perio-2%_Arginine vs Perio-ControlPDFSVGPDFSVGPDFSVG
Comparison 5Perio-4%_Arginine vs Perio-ControlPDFSVGPDFSVGPDFSVG
Comparison 6Perio-8%_Arginine vs Perio-ControlPDFSVGPDFSVGPDFSVG
 
 
C) No clustering
Comparison No.Comparison NameFamily LevelGenus LevelSpecies Level
Comparison 1Healthy-2%_Arginine vs Healthy-ControlPDFSVGPDFSVGPDFSVG
Comparison 2Healthy-4%_Arginine vs Healthy-ControlPDFSVGPDFSVGPDFSVG
Comparison 3Healthy-8%_Arginine vs Healthy-ControlPDFSVGPDFSVGPDFSVG
Comparison 4Perio-2%_Arginine vs Perio-ControlPDFSVGPDFSVGPDFSVG
Comparison 5Perio-4%_Arginine vs Perio-ControlPDFSVGPDFSVGPDFSVG
Comparison 6Perio-8%_Arginine vs Perio-ControlPDFSVGPDFSVGPDFSVG
 
 

XII. Analysis - Network Association

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


References:

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

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

 

SPIEC-EASI Network Inference by Neighborhood Selection (MB Method)

 

 

 

Association Network Inference by SparCC

 

 

 
 

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