FOMC Service Report

16S rRNA Gene V1V3 Amplicon Sequencing

Version V1.41 fork

Version History

The Forsyth Institute, Cambridge, MA, USA
August 07, 2022

Project ID: 20220807_arginine


I. Project Summary

Project 20220807_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%(>=34 reads)
ATotal reads359,236359,236
BTotal assigned reads348,418348,418
CAssigned reads in species with read count < MPC02,095
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)348,418346,323
IReads assigned to single species340,919339,821
JReads assigned to multiple species3,2043,154
KReads assigned to novel species4,2953,348
LTotal number of species464161
MNumber of single species283122
NNumber of multi-species188
ONumber of novel species16331
PTotal unassigned reads10,81810,818
QChimeric reads5656
RReads without BLASTN hits00
SOthers: short, low quality, singletons, etc.10,76210,762
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;Clostridia;Clostridiales;Peptostreptococcaceae_[XI];Eubacterium_[XI][G-7];yurii000026231615162418051462627120000000001000000000000000000000
SP10Bacteria;Bacteroidetes;Bacteroidia;Bacteroidales;Prevotellaceae;Prevotella;pallens78648850229500000119140000010448121072229699574661000010000010001012000000
SP100Bacteria;Firmicutes;Negativicutes;Selenomonadales;Veillonellaceae;Selenomonas;sp._oral_taxon_146106212624010000039000007229501235242301241011200900100260110275
SP101Bacteria;Actinobacteria;Coriobacteriia;Coriobacteriales;Coriobacteriaceae;Atopobium;parvulum161155412401731120171110108431030103225012511301005211
SP102Bacteria;Firmicutes;Negativicutes;Selenomonadales;Veillonellaceae;Selenomonas;sp._Oral_Taxon_G510000000000000000111200000000313782150587270000200
SP103Bacteria;Bacteroidetes;Bacteroidia;Bacteroidales;Prevotellaceae;Prevotella;saccharolytica282547212502012201636480012311151843146303684436618321261513
SP104Bacteria;Firmicutes;Negativicutes;Selenomonadales;Veillonellaceae;Selenomonas;noxia010000000020010000122010201021102001201022801400
SP105Bacteria;Firmicutes;Negativicutes;Selenomonadales;Veillonellaceae;Selenomonas;sp._Oral_Taxon_G55000100001000000011020111103578664331070232401250
SP106Bacteria;Firmicutes;Bacilli;Lactobacillales;Streptococcaceae;Streptococcus;oralis82243200002001600000342015335370101613110201025620000010
SP108Bacteria;Bacteroidetes;Bacteroidia;Bacteroidales;Prevotellaceae;Prevotella;oulorum72910110100220303410134173111414010000000000100001000000
SP11Bacteria;Firmicutes;Bacilli;Lactobacillales;Streptococcaceae;Streptococcus;sp._oral_taxon_064116811291441817201432147107193681769316928413821310624618111000007023041003824446
SP110Bacteria;Firmicutes;Clostridia;Clostridiales;Peptoniphilaceae;Parvimonas;sp._oral_taxon_393000000000010000210000000000040252218131424041301330290000000
SP111Bacteria;Bacteroidetes;Bacteroidia;Bacteroidales;Prevotellaceae;Prevotella;denticola417528400314015500015181303700273822353632489890002830
SP113Bacteria;Firmicutes;Bacilli;Lactobacillales;Streptococcaceae;Streptococcus;anginosus000072110030170330000000000123512150496330000010
SP115Bacteria;Firmicutes;Clostridia;Clostridiales;Lachnospiraceae_[XIV];Lachnoanaerobaculum;umeaense8227250000000500000431442157000000000000000000000000
SP117Bacteria;Firmicutes;Negativicutes;Selenomonadales;Veillonellaceae;Selenomonas;sp._oral_taxon_479601300000000000101030001116000050111020020201221
SP12Bacteria;Bacteroidetes;Bacteroidia;Bacteroidales;Porphyromonadaceae;Porphyromonas;gingivalis00101617128284180119871087829117182717107518208773000020000184870049115581470111887817867042124981571000000
SP120Bacteria;Bacteroidetes;Bacteroidia;Bacteroidales;Porphyromonadaceae;Porphyromonas;sp._Oral_Taxon_F92000014044720326320000000000000000000000000000000
SP122Bacteria;Firmicutes;Bacilli;Lactobacillales;Streptococcaceae;Streptococcus;sp._str._C300103310000012000001001332314400000005000000311312
SP123Bacteria;Bacteroidetes;Bacteroidia;Bacteroidales;Prevotellaceae;Prevotella;maculosa9551091082471546154473210781241410134001200050010026074159
SP125Bacteria;Bacteroidetes;Flavobacteriia;Flavobacteriales;Flavobacteriaceae;Capnocytophaga;sp._oral_taxon_8646103100011020000122123254001000000001000000101000
SP127Bacteria;Firmicutes;Negativicutes;Selenomonadales;Veillonellaceae;Selenomonas;infelix194116922730109225278113131054671314107188718141610155153401361510
SP128Bacteria;Firmicutes;Negativicutes;Selenomonadales;Veillonellaceae;Selenomonas;sp._oral_taxon_920000000000000001000000000000095741470355420000000
SP13Bacteria;Bacteroidetes;Bacteroidia;Bacteroidales;Prevotellaceae;Prevotella;baroniae0000110004302410100000000000124644255463440051241120000000
SP132Bacteria;Firmicutes;Bacilli;Lactobacillales;Streptococcaceae;Streptococcus;infantis040010000001000007351732674100000000000000000000000
SP133Bacteria;Bacteroidetes;Bacteroidia;Bacteroidales;Prevotellaceae;Prevotella;oralis1200410010216501308230011001100000000000000101103
SP137Bacteria;Firmicutes;Clostridia;Clostridiales;Lachnospiraceae_[XIV];Oribacterium;sp._oral_taxon_078042046267441151715923400110000000000000000000000000
SP14Bacteria;Bacteroidetes;Bacteroidia;Bacteroidales;Prevotellaceae;Prevotella;marshii10003632818391724069619669330000000000031430000201021000000
SP142Bacteria;Firmicutes;Negativicutes;Selenomonadales;Veillonellaceae;Selenomonas;flueggei1010111302050140013000001210000111005010001303042
SP144Bacteria;Firmicutes;Bacilli;Lactobacillales;Streptococcaceae;Streptococcus;sp._Oral_Taxon_B6616030011501073203161155161491727001010060000034522229185
SP146Bacteria;Firmicutes;Negativicutes;Selenomonadales;Veillonellaceae;Selenomonas;sp._oral_taxon_136422400100011010016241217000110120000100000000000
SP148Bacteria;Fusobacteria;Fusobacteriia;Fusobacteriales;Fusobacteriaceae;Fusobacterium;sp._Oral_Taxon_H27100110000100001000010420385100053239101650161419771000000
SP149Bacteria;Bacteroidetes;Bacteroidia;Bacteroidales;Bacteroidaceae;Bacteroidaceae_[G-1];sp._oral_taxon_272000012076320651563000000000021143010012100000000
SP15Bacteria;Bacteroidetes;Bacteroidia;Bacteroidales;Porphyromonadaceae;Porphyromonas;sp._oral_taxon_27852402310624191240279413283211652282541572744231532000011000001100000010000
SP156Bacteria;Firmicutes;Bacilli;Lactobacillales;Streptococcaceae;Streptococcus;constellatus200074642350111371190110100002001000001001000001000
SP157Bacteria;Firmicutes;Negativicutes;Selenomonadales;Veillonellaceae;Veillonella;atypica00000001000000000000000000222000000000000511021114
SP159Bacteria;Firmicutes;Negativicutes;Selenomonadales;Veillonellaceae;Selenomonas;sp._Oral_Taxon_E8300000020010000001000000032100010000160111005080114
SP16Bacteria;Bacteroidetes;Bacteroidia;Bacteroidales;Prevotellaceae;Alloprevotella;sp._oral_taxon_47301001181107865189277269553801129593002631741000000012961111240113757039178775014351554838138141148201721261132
SP166Bacteria;Firmicutes;Bacilli;Lactobacillales;Streptococcaceae;Streptococcus;intermedius5305000000040000090111651020000002011001200205811
SP167Bacteria;Firmicutes;Negativicutes;Selenomonadales;Veillonellaceae;Selenomonas;sp._Oral_Taxon_G67610000000101000001111111001001211111142440001034
SP17Bacteria;Firmicutes;Clostridia;Clostridiales;Peptostreptococcaceae_[XI];Peptostreptococcaceae_[XI][G-7];[Eubacterium]_yurii_subsp._yurii_&_margaretiae10071271421180157224230240113516434310314279334971124610015326043142429132013648352676016510720
SP171Bacteria;Firmicutes;Negativicutes;Selenomonadales;Veillonellaceae;Selenomonas;sp._oral_taxon_91910001158353002534200000001013000000010000025043141
SP175Bacteria;Bacteroidetes;Flavobacteriia;Flavobacteriales;Flavobacteriaceae;Bergeyella;sp._oral_taxon_9070000000000000000000000001924100000005000002123233
SP18Bacteria;Bacteroidetes;Bacteroidia;Bacteroidales;Porphyromonadaceae;Porphyromonas;sp._oral_taxon_27941130115908633853794945146643555477755639374922092148566275130108125252332137353229506642001231
SP184Bacteria;Firmicutes;Clostridia;Clostridiales;Peptostreptococcaceae_[XI];Peptostreptococcaceae_[XI][G-9];[Eubacterium]_brachy0000016100000000200000000000007273040200100000100
SP188Bacteria;Firmicutes;Negativicutes;Selenomonadales;Veillonellaceae;Selenomonas;sp._Oral_Taxon_F830000000000000000000000000000392310201110220000010
SP19Bacteria;Firmicutes;Clostridia;Clostridiales;Lachnospiraceae_[XIV];Catonella;morbi000085676471944186010014310812512300000001356831221171181048895529618910712871757621542290380021241
SP190Bacteria;Firmicutes;Negativicutes;Selenomonadales;Veillonellaceae;Megasphaera;micronuciformis60000100015180100245000000000000000000000000000000
SP2Bacteria;Fusobacteria;Fusobacteriia;Fusobacteriales;Fusobacteriaceae;Fusobacterium;sp._HOT_20429316644375122626702550816353054543810154524000310000000000000000000
SP20Bacteria;Firmicutes;Bacilli;Lactobacillales;Carnobacteriaceae;Granulicatella;adiacens12516018530438122817259261032434466074286249277136315276269991267996844442162926133273928531730229041019286467398259
SP21Bacteria;Firmicutes;Negativicutes;Selenomonadales;Veillonellaceae;Dialister;invisus1108176315572030391513954151720159028650330571310292926810426730685501001180
SP22Bacteria;Firmicutes;Bacilli;Lactobacillales;Streptococcaceae;Streptococcus;dentisani185231318224021211229299025742204182394288028582300169024516415511762313446627441352019452919346035768322610693646283911
SP23Bacteria;Fusobacteria;Fusobacteriia;Fusobacteriales;Fusobacteriaceae;Fusobacterium;nucleatum_subsp._vincentii8711699499378136646022519962163544210195205369721121114156206194110645951610724014115962546547796292110160091001
SP24Bacteria;Fusobacteria;Fusobacteriia;Fusobacteriales;Fusobacteriaceae;Fusobacterium;naviforme9513930994120126216423002971648058112859001605020061229005400000002
SP25Bacteria;Bacteroidetes;Bacteroidia;Bacteroidales;Prevotellaceae;Prevotella;intermedia0000411314131614180193430331300000100000103727712491009114098096408303394801346922580000020
SP26Bacteria;Firmicutes;Bacilli;Lactobacillales;Streptococcaceae;Streptococcus;sp._oral_taxon_4231382272813554414522937112727611919478263453820582142373029932900214928441421865755040692813246449226324437116206267328
SP27Bacteria;Fusobacteria;Fusobacteriia;Fusobacteriales;Leptotrichiaceae;Leptotrichia;sp._oral_taxon_2155000310017002600000100000000000000000000000089000
SP28Bacteria;Fusobacteria;Fusobacteriia;Fusobacteriales;Fusobacteriaceae;Fusobacterium;nucleatum_subsp._animalis6141366723112823917104550264732413224222529152011726199609698296618335349507163658784789559410000035
SP29Bacteria;Bacteroidetes;Bacteroidia;Bacteroidales;Bacteroidales_[F-2];Bacteroidales_[G-2];sp._oral_taxon_274203275663919994402854389115141608220801611561678713333319461367148630201423910102012261118119187159866911060773590342
SP3Bacteria;Firmicutes;Negativicutes;Selenomonadales;Veillonellaceae;Veillonella;parvula_group7143422803201244441707079774978510568675479946344522139540649455429937975369312129464063270674765776514038110467360427360
SP30Bacteria;Proteobacteria;Betaproteobacteria;Neisseriales;Neisseriaceae;Eikenella;corrodens4716423717629222431393222225481736379577612053153180140946451262979315441112111320104051826543483659439
SP31Bacteria;Firmicutes;Bacilli;Lactobacillales;Streptococcaceae;Streptococcus;tigurinus5394946804785456513932183938742627582586494296824287659038921033693732222425154987611111256173100134
SP32Bacteria;Proteobacteria;Epsilonproteobacteria;Campylobacterales;Campylobacteraceae;Campylobacter;concisus372111111616666483169902231545141419948512922815719171125711840611454210428634201216246100
SP33Bacteria;Bacteroidetes;Bacteroidia;Bacteroidales;Prevotellaceae;Alloprevotella;tannerae44111330721984727192111611590438152551554114847000048331438171931018312430000000
SP34Bacteria;Firmicutes;Clostridia;Clostridiales;Peptoniphilaceae;Parvimonas;sp._oral_taxon_11017001501249115218419280383153456140863394262410292921321065343324100101000151021158220110
SP35Bacteria;Firmicutes;Negativicutes;Selenomonadales;Veillonellaceae;Selenomonas;artemidis30201123000120012107100014983000000016000001911051110
SP36Bacteria;Bacteroidetes;Bacteroidia;Bacteroidales;Prevotellaceae;Alloprevotella;sp._oral_taxon_30819910547735160771108615412372579232166176152107588219936293139201101701321012777806514
SP37Bacteria;Firmicutes;Negativicutes;Selenomonadales;Veillonellaceae;Selenomonas;sp._oral_taxon_89240019710366739141612123110102000155655536192426027491814170001000
SP38Bacteria;Firmicutes;Bacilli;Lactobacillales;Streptococcaceae;Streptococcus;mitis2923143652356279541331226231671913483275457356380602402103410121030022811212101
SP4Bacteria;Firmicutes;Bacilli;Lactobacillales;Streptococcaceae;Streptococcus;sanguinis165109222367865370675741113886863454011490381887411289932153604191211615714510667002474336555331536892
SP40Bacteria;Firmicutes;Negativicutes;Selenomonadales;Veillonellaceae;Selenomonas;sp._oral_taxon_1491000000001001000032000003194335147314420543218951
SP41Bacteria;Proteobacteria;Gammaproteobacteria;Pasteurellales;Pasteurellaceae;Haemophilus;parainfluenzae7777059203071181903304156118152143147151951133015002023037023150148
SP42Bacteria;Actinobacteria;Actinobacteria;Actinomycetales;Actinomycetaceae;Actinomyces;sp._oral_taxon_180660221203010052300110103410000000004000003300010
SP43Bacteria;Bacteroidetes;Bacteroidia;Bacteroidales;Prevotellaceae;Prevotella;melaninogenica23831311689301000033218011033414338894841461090000312917522027235727234341401000000
SP44Bacteria;Firmicutes;Negativicutes;Selenomonadales;Veillonellaceae;Selenomonas;sp._oral_taxon_1261000000000000000000100000010658201114284011615011234
SP45Bacteria;Bacteroidetes;Bacteroidia;Bacteroidales;Prevotellaceae;Alloprevotella;sp._oral_taxon_9140000000000000000000000000000241246106405910127440000000
SP46Bacteria;Firmicutes;Clostridia;Clostridiales;Peptoniphilaceae;Parvimonas;micra455413846001022105204383486514234802048473861762433684367623700142002070016221701044114815
SP47Bacteria;Firmicutes;Bacilli;Bacillales;Gemellaceae;Gemella;morbillorum9372204288325124291832402433156362614545534130937326471782133525403830293239106221129515351709226294150422071131330
SP48Bacteria;Firmicutes;Negativicutes;Selenomonadales;Veillonellaceae;Anaeroglobus;geminatus5110473235448911000306101121140001111652836252211021423713000000
SP49Bacteria;Fusobacteria;Fusobacteriia;Fusobacteriales;Fusobacteriaceae;Fusobacterium;canifelinum00000000000000000010000000001135743403324140000000
SP5Bacteria;Bacteroidetes;Bacteroidia;Bacteroidales;Porphyromonadaceae;Porphyromonas;sp._oral_taxon_2750001451601081274134090000320790000712763310321130000000
SP50Bacteria;Firmicutes;Bacilli;Lactobacillales;Streptococcaceae;Streptococcus;cristatus14791311238301241571291211716142593145402950330122443511285621354963457132624210376241111
SP52Bacteria;Firmicutes;Negativicutes;Selenomonadales;Veillonellaceae;Dialister;pneumosintes0000562273877404595707378000000000002613152491478442003744814127286460000000
SP53Bacteria;Firmicutes;Clostridia;Clostridiales;Lachnospiraceae_[XIV];Oribacterium;sinus67003871216222524232821620383418141411917582425441322523126182324151313051
SP54Bacteria;Bacteroidetes;Bacteroidia;Bacteroidales;Bacteroidaceae;Bacteroides;heparinolyticus0000000000000000000000000000362729303115508057174104610000000
SP55Bacteria;Bacteroidetes;Flavobacteriia;Flavobacteriales;Flavobacteriaceae;Capnocytophaga;sputigena22329423301417162121051849310511100000002000000316161
SP56Bacteria;Firmicutes;Bacilli;Lactobacillales;Streptococcaceae;Streptococcus;gordonii133831176723821149819153911766312027571267860277114112213506521011000
SP57Bacteria;Actinobacteria;Coriobacteriia;Coriobacteriales;Coriobacteriaceae;Slackia;exigua015230302011022132070030420100100001000120220100001
SP58Bacteria;Firmicutes;Negativicutes;Selenomonadales;Veillonellaceae;Selenomonas;sp._Oral_Taxon_F21000000000000000000000000121133291941251939036345731240000120
SP59Bacteria;Bacteroidetes;Bacteroidia;Bacteroidales;Prevotellaceae;Prevotella;sp._oral_taxon_314000042210311417501602840005000010320010100110000000
SP6Bacteria;Fusobacteria;Fusobacteriia;Fusobacteriales;Fusobacteriaceae;Fusobacterium;nucleatum_subsp._polymorphum875400258517702689503339273265331959263326267196327134310171361153731944470469733217234471633131114100918272073717611224403241366155
SP60Bacteria;Synergistetes;Synergistia;Synergistales;Synergistaceae;Fretibacterium;sp._oral_taxon_36000002444324622206994300010002000219814129201682020100200
SP62Bacteria;Bacteroidetes;Bacteroidia;Bacteroidales;Prevotellaceae;Prevotella;sp._oral_taxon_3178510225959512167112018176114715476527145482184243169333543161571842
SP63Bacteria;Actinobacteria;Coriobacteriia;Coriobacteriales;Coriobacteriaceae;Atopobium;rimae53113384834162621202832189315354744172111672729103551120167622111157186162094400320
SP64Bacteria;Firmicutes;Bacilli;Lactobacillales;Aerococcaceae;Abiotrophia;defectiva6509560215097546892316617920000001010000010000000000
SP65Bacteria;Proteobacteria;Epsilonproteobacteria;Campylobacterales;Campylobacteraceae;Campylobacter;showae4422011100121000010300122181413482014191613152219351391171923391212
SP66Bacteria;Bacteroidetes;Flavobacteriia;Flavobacteriales;Flavobacteriaceae;Capnocytophaga;gingivalis181212111224910631978463261515131114617193024311101122407211727331231316
SP69Bacteria;Firmicutes;Negativicutes;Selenomonadales;Veillonellaceae;Selenomonas;dianae41109123522191620192627243530152329162591817119718835333236201614101441172119819186814
SP7Bacteria;Firmicutes;Erysipelotrichia;Erysipelotrichales;Erysipelotrichaceae;Solobacterium;moorei710182119026232867340837155282705983900604900611459550692741136333371236733932163209220114231301811732662224826263101502634164
SP70Bacteria;Firmicutes;Clostridia;Clostridiales;Lachnospiraceae_[XIV];Lachnospiraceae_[G-8];sp._oral_taxon_5000000521211907512161100000000000545303019470328635101390000000
SP74Bacteria;Firmicutes;Bacilli;Lactobacillales;Streptococcaceae;Streptococcus;sp._oral_taxon_05834468614169520012029835633332614342701713153218000001070101210114881813
SP75Bacteria;Bacteroidetes;Bacteroidia;Bacteroidales;Prevotellaceae;Prevotella;oris0201000200110100001100001200312323131136102102040
SP77Bacteria;Bacteroidetes;Bacteroidia;Bacteroidales;Prevotellaceae;Prevotella;buccae311110001021130024010111100031140100232210000000
SP79Bacteria;Firmicutes;Clostridia;Clostridiales;Peptostreptococcaceae_[XI];Peptostreptococcaceae_[XI][G-7];sp._oral_taxon_081000000000000000000000000000074645017769101120098823928120000000
SP8Bacteria;Bacteroidetes;Bacteroidia;Bacteroidales;Porphyromonadaceae;Porphyromonas;endodontalis00007442092811011263561201151401129472210000000053526116687102128400314040930000000
SP80Bacteria;Firmicutes;Bacilli;Lactobacillales;Streptococcaceae;Streptococcus;sp._oral_taxon_070301142109121511403039242414001050063141141011050202544200220
SP82Bacteria;Firmicutes;Clostridia;Clostridiales;Lachnospiraceae_[XIV];Oribacterium;asaccharolyticum001410423035061813112100007910000000000000041100000000
SP84Bacteria;Bacteroidetes;Flavobacteriia;Flavobacteriales;Flavobacteriaceae;Capnocytophaga;leadbetteri101000000001000001011001946154000000014100000413020299368
SP85Bacteria;Bacteroidetes;Bacteroidia;Bacteroidales;Prevotellaceae;Alloprevotella;rava0001251118121236906461610281000000000202641202310668440101237019770000000
SP86Bacteria;Firmicutes;Clostridia;Clostridiales;Peptostreptococcaceae_[XI];Mogibacterium;diversum0000000001700364200000000000002031198812043084110493290000000
SP87Bacteria;Firmicutes;Negativicutes;Selenomonadales;Veillonellaceae;Selenomonas;sp._oral_taxon_481214360001000120010011101659121100003033182317161901525165120000000
SP9Bacteria;Fusobacteria;Fusobacteriia;Fusobacteriales;Fusobacteriaceae;Fusobacterium;periodonticum00109200000003000001000000005271003540115610511333301460000000
SP90Bacteria;Bacteroidetes;Bacteroidia;Bacteroidales;Prevotellaceae;Prevotella;sp._oral_taxon_31520100001000200000001810000000693347301530300000000
SP91Bacteria;Firmicutes;Negativicutes;Selenomonadales;Veillonellaceae;Selenomonas;sp._Oral_Taxon_F29287101100000006000002113200510693511411002120001051616296
SP92Bacteria;Firmicutes;Bacilli;Lactobacillales;Streptococcaceae;Streptococcus;sp._oral_taxon_05621273213500000001400000194391012052213000000000000000010000000
SP93Bacteria;Firmicutes;Negativicutes;Selenomonadales;Veillonellaceae;Selenomonas;sputigena3000231053201511017171113112122011200140001000000
SP94Bacteria;Bacteroidetes;Bacteroidia;Bacteroidales;Porphyromonadaceae;Porphyromonas;catoniae01010010000060000010405591200000000000000009000000
SP95Bacteria;Firmicutes;Bacilli;Lactobacillales;Streptococcaceae;Streptococcus;sp._Oral_Taxon_H214001010804402082524032372000000000000000000102000
SP96Bacteria;Fusobacteria;Fusobacteriia;Fusobacteriales;Fusobacteriaceae;Fusobacterium;nucleatum_subsp._nucleatum10000001000000003000001010000533214151310606150300000000
SP97Bacteria;Synergistetes;Synergistia;Synergistales;Synergistaceae;Fretibacterium;fastidiosum040013000030110102001000000021142120130000000000
SP98Bacteria;Proteobacteria;Betaproteobacteria;Neisseriales;Neisseriaceae;Neisseria;elongata00000000000000000001100039321000000005000004806553
SP99Bacteria;Firmicutes;Clostridia;Clostridiales;Lachnospiraceae_[XIV];Stomatobaculum;sp._oral_taxon_09700015000000000000000000000000200100000100000000015
SPN10Bacteria;Fusobacteria;Fusobacteriia;Fusobacteriales;Fusobacteriaceae;Fusobacterium;nucleatum_subsp._nucleatum_nov_93.031%665398254349353341814141953203201000016000001105248
SPN100Bacteria;Firmicutes;Bacilli;Lactobacillales;Streptococcaceae;Streptococcus;sp._oral_taxon_056_nov_93.286%521122119233222112000015900000000001000000101010
SPN122Bacteria;Firmicutes;Negativicutes;Selenomonadales;Veillonellaceae;Selenomonas;sp._oral_taxon_479_nov_97.173%0000000000000000000000004344000000080000021016168
SPN128Bacteria;Bacteroidetes;Flavobacteriia;Flavobacteriales;Flavobacteriaceae;Bergeyella;sp._oral_taxon_900_nov_97.500%000000000200000000000100000043194231183220400001
SPN129Bacteria;Bacteroidetes;Bacteroidia;Bacteroidales;Prevotellaceae;Prevotella;melaninogenica_nov_97.865%652000000009000009282212000000000000000000000000
SPN130Bacteria;Firmicutes;Bacilli;Lactobacillales;Streptococcaceae;Streptococcus;sp._oral_taxon_058_nov_97.509%000000000000000000000000000071040060453880000000
SPN131Bacteria;Fusobacteria;Fusobacteriia;Fusobacteriales;Fusobacteriaceae;Fusobacterium;sp._HOT_204_nov_97.849%1520016000110003001000130801000000000000000001000000
SPN132Bacteria;Firmicutes;Bacilli;Lactobacillales;Carnobacteriaceae;Granulicatella;adiacens_nov_97.857%212600000003000002240330110000000000000000202341
SPN133Bacteria;Firmicutes;Clostridia;Clostridiales;Lachnospiraceae_[XIV];Lachnoanaerobaculum;saburreum_nov_97.500%3214000000060000042101270000000000000000000000000
SPN134Bacteria;Fusobacteria;Fusobacteriia;Fusobacteriales;Fusobacteriaceae;Fusobacterium;nucleatum_subsp._polymorphum_nov_97.491%050300000000120001201004000203123110201150000000
SPN136Bacteria;Firmicutes;Bacilli;Lactobacillales;Streptococcaceae;Streptococcus;sp._oral_taxon_058_nov_97.872%0000000000000000000000000000610730140530100000000
SPN137Bacteria;Bacteroidetes;Bacteroidia;Bacteroidales;Prevotellaceae;Prevotella;intermedia_nov_90.210%000000000000000000000000000042732530380200000000
SPN138Bacteria;Firmicutes;Negativicutes;Selenomonadales;Veillonellaceae;Selenomonas;sp._oral_taxon_481_nov_96.786%000074603340331120000000000000000000000000000000
SPN139Bacteria;Bacteroidetes;Bacteroidia;Bacteroidales;Prevotellaceae;Alloprevotella;sp._oral_taxon_473_nov_96.820%0000000000000000000000000000210101100830380000000
SPN140Bacteria;Fusobacteria;Fusobacteriia;Fusobacteriales;Fusobacteriaceae;Fusobacterium;nucleatum_subsp._nucleatum_nov_91.289%201111001211001122262630001000000000000000000000
SPN141Bacteria;Firmicutes;Bacilli;Bacillales;Gemellaceae;Gemella;morbillorum_nov_95.053%000000000000000000000000302200000007000004811711
SPN142Bacteria;Proteobacteria;Epsilonproteobacteria;Campylobacterales;Campylobacteraceae;Campylobacter;rectus_nov_96.797%0000000000000000000000000033300000000000000000000
SPN143Bacteria;Firmicutes;Clostridia;Clostridiales;Peptostreptococcaceae_[XI];Peptostreptococcaceae_[XI][G-7];[Eubacterium]_yurii_subsp._schtitka_nov_96.071%000027192920181034012231238340000000000000000000000000000000
SPN144Bacteria;Firmicutes;Bacilli;Lactobacillales;Streptococcaceae;Streptococcus;sp._oral_taxon_056_nov_97.857%430100000003000006332333000000000000000000000000
SPN145Bacteria;Firmicutes;Erysipelotrichia;Erysipelotrichales;Erysipelotrichaceae;Solobacterium;moorei_nov_97.143%000014010120334460000000010000001000010000000020
SPN155Bacteria;Firmicutes;Clostridia;Clostridiales;Lachnospiraceae_[XIV];Oribacterium;sp._oral_taxon_102_nov_94.681%00000000000000000000000000004036222731181701223162160000000
SPN23Bacteria;Bacteroidetes;Bacteroidia;Bacteroidales;Prevotellaceae;Alloprevotella;sp._oral_taxon_473_nov_96.797%00001221620602542200000006400001000001300000015000134
SPN35Bacteria;Bacteroidetes;Bacteroidia;Bacteroidales;Prevotellaceae;Prevotella;intermedia_nov_96.786%000000000000000000000000000031414121051608141112140000000
SPN44Bacteria;Firmicutes;Negativicutes;Selenomonadales;Veillonellaceae;Selenomonas;dianae_nov_97.527%00001256334405114130000000010000000001000000302111
SPN47Bacteria;Firmicutes;Negativicutes;Selenomonadales;Veillonellaceae;Selenomonas;dianae_nov_97.500%10002727104215121001001007517100000000200000150117011
SPN58Bacteria;Firmicutes;Negativicutes;Selenomonadales;Veillonellaceae;Selenomonas;sp._oral_taxon_146_nov_96.454%0100000000000000001000000000611715514707419120000000
SPN74Bacteria;Bacteroidetes;Bacteroidia;Bacteroidales;Bacteroidaceae;Bacteroides;heparinolyticus_nov_95.745%00004120354622162103965503467000000000002525111823122004203427290000000
SPN78Bacteria;Firmicutes;Clostridia;Clostridiales;Lachnospiraceae_[XIV];Lachnospiraceae_[G-8];sp._oral_taxon_500_nov_96.786%0000301000109941360000000000000014020008300000000
SPP1Bacteria;Firmicutes;Negativicutes;Selenomonadales;Veillonellaceae;Selenomonas;multispecies_spp1_200001915632860232130000000000100000000000000000000
SPP10Bacteria;Bacteroidetes;Bacteroidia;Bacteroidales;Porphyromonadaceae;Porphyromonas;multispecies_spp10_261012000000013000003021401000000000000000000000000
SPP11Bacteria;Fusobacteria;Fusobacteriia;Fusobacteriales;Fusobacteriaceae;Fusobacterium;multispecies_spp11_2208120161392655281514815178404132304735026563114204920584520143034240274848811000000000
SPP16Bacteria;Proteobacteria;Gammaproteobacteria;Pasteurellales;Pasteurellaceae;Aggregatibacter;multispecies_spp16_20000321413401731150000000000000000010000000000000
SPP17Bacteria;Firmicutes;Clostridia;Clostridiales;Lachnospiraceae_[XIV];Oribacterium;multispecies_spp17_200101000000001000000003410030700000003002010050305434
SPP2Bacteria;Firmicutes;Negativicutes;Selenomonadales;Veillonellaceae;Selenomonas;multispecies_spp2_2000011000000000002011211115400010200120000401300
SPP3Bacteria;Firmicutes;Negativicutes;Selenomonadales;Veillonellaceae;Selenomonas;multispecies_spp3_3000010100100000100000000000082934976615143036932327360000000
SPP4Bacteria;Firmicutes;Negativicutes;Selenomonadales;Veillonellaceae;Selenomonas;multispecies_spp4_23120011000020002030130005260302011292015171162247103102211
SPPN3Bacteria;Fusobacteria;Fusobacteriia;Fusobacteriales;Fusobacteriaceae;Fusobacterium;multispecies_sppn3_2_nov_96.797%40000000000000000000501342100000000060000029000000
SPPN4Bacteria;Firmicutes;Negativicutes;Selenomonadales;Veillonellaceae;Selenomonas;multispecies_sppn4_3_nov_97.500%0000217825460312230000000000000000000000000000000
SPPN5Bacteria;Firmicutes;Bacilli;Lactobacillales;Streptococcaceae;Streptococcus;multispecies_sppn5_2_nov_97.509%00001540633505140110000000000000000000000000000000
 
 
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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