Table of Content
I.Project Summary
II.Workflow Checklist
III.NGS Sequencing Results
IV.Complete Report Download
V.Raw Sequence Data Download
VI.Analysis - DADA2 Read Processing
 Sample Meta Info
 Read Count by Sample
VII.Analysis - Read Taxonomy Assignment
 Taxonomy Barplots
VIII.Analysis - Alpha Diversity
IX.Analysis - Beta Diversity
X.Analysis - Differential Abundance
 ANCOM Result
 LEfSe Result
XI.Analysis - Heatmap Profile
XII.Analysis - Network Association
XIII.Disclaimer

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FOMC Service Report

16S rRNA Gene V1V3 Amplicon Sequencing

Version V1.43

Version History

The Forsyth Institute, Cambridge, MA, USA
June 09, 2023

Project ID: FOMC10949

I. Project Summary

Project FOMC10949 services include NGS sequencing of the V1V3 region of the 16S rRNA gene amplicons from the samples. First and foremost, please download this report, as well as the sequence raw data from the download links provided below. These links will expire after 60 days. We cannot guarantee the availability of your data after 60 days.

Full Bioinformatics analysis service was requested. We provide many analyses, starting from the raw sequence quality and noise filtering, pair reads merging, as well as chimera filtering for the sequences, using the DADA2 denosing algorithm and pipeline.

We also provide many downstream analyses such as taxonomy assignment, alpha and beta diversity analyses, and differential abundance analysis.

For taxonomy assignment, most informative would be the taxonomy barplots. We provide an interactive barplots to show the relative abundance of microbes at different taxonomy levels (from Phylum to species) that you can choose.

If you specify which groups of samples you want to compare for differential abundance, we provide both ANCOM and LEfSe differential abundance analysis.

 

II. Workflow Checklist

☑1.Sample Received
☑2.Sample Quality Evaluated
☑3.Sample Prepared for Sequencing
☑4.Next-Gen Sequencing
☑5.Sequence Quality Check
☑6.Absolute Abundance
☑7.Report and Raw Sequence Data Available for Download
☑8.Bioinformatics Analysis - Reads Processing (DADA2 Quality Trimming, Denoising, Paired Reads Merging)
☑9.Bioinformatics Analysis - Reads Taxonomy Assignment
☑10.Bioinformatics Analysis - Alpha Diversity Analysis
☑11.Bioinformatics Analysis - Beta Diversity Analysis
☑12.Bioinformatics Analysis - Differential Abundance Analysis
☑13.Bioinformatics Analysis - Heatmap Profile
☑14.Bioinformatics Analysis - Network Association
 

III. NGS Sequencing

The samples were processed and analyzed with the ZymoBIOMICS® Service: Targeted Metagenomic Sequencing (Zymo Research, Irvine, CA).

DNA Extraction: If DNA extraction was performed, one of three different DNA extraction kits was used depending on the sample type and sample volume and were used according to the manufacturer’s instructions, unless otherwise stated. The kit used in this project is marked below:

☐ZymoBIOMICS® DNA Miniprep Kit (Zymo Research, Irvine, CA)
☐ZymoBIOMICS® DNA Microprep Kit (Zymo Research, Irvine, CA)
☐ZymoBIOMICS®-96 MagBead DNA Kit (Zymo Research, Irvine, CA)
☑N/A (DNA Extraction Not Performed)
Elution Volume: 50µL
Additional Notes: NA

Targeted Library Preparation: The DNA samples were prepared for targeted sequencing with the Quick-16S™ NGS Library Prep Kit (Zymo Research, Irvine, CA). These primers were custom designed by Zymo Research to provide the best coverage of the 16S gene while maintaining high sensitivity. The primer sets used in this project are marked below:

☐Quick-16S™ Primer Set V1-V2 (Zymo Research, Irvine, CA)
☑Quick-16S™ Primer Set V1-V3 (Zymo Research, Irvine, CA)
☐Quick-16S™ Primer Set V3-V4 (Zymo Research, Irvine, CA)
☐Quick-16S™ Primer Set V4 (Zymo Research, Irvine, CA)
☐Quick-16S™ Primer Set V6-V8 (Zymo Research, Irvine, CA)
☐Other: NA
Additional Notes: NA

The sequencing library was prepared using an innovative library preparation process in which PCR reactions were performed in real-time PCR machines to control cycles and therefore limit PCR chimera formation. The final PCR products were quantified with qPCR fluorescence readings and pooled together based on equal molarity. The final pooled library was cleaned up with the Select-a-Size DNA Clean & Concentrator™ (Zymo Research, Irvine, CA), then quantified with TapeStation® (Agilent Technologies, Santa Clara, CA) and Qubit® (Thermo Fisher Scientific, Waltham, WA).

Control Samples: The ZymoBIOMICS® Microbial Community Standard (Zymo Research, Irvine, CA) was used as a positive control for each DNA extraction, if performed. The ZymoBIOMICS® Microbial Community DNA Standard (Zymo Research, Irvine, CA) was used as a positive control for each targeted library preparation. Negative controls (i.e. blank extraction control, blank library preparation control) were included to assess the level of bioburden carried by the wet-lab process.

Sequencing: The final library was sequenced on Illumina® MiSeq™ with a V3 reagent kit (600 cycles). The sequencing was performed with 10% PhiX spike-in.

Absolute Abundance Quantification*: A quantitative real-time PCR was set up with a standard curve. The standard curve was made with plasmid DNA containing one copy of the 16S gene and one copy of the fungal ITS2 region prepared in 10-fold serial dilutions. The primers used were the same as those used in Targeted Library Preparation. The equation generated by the plasmid DNA standard curve was used to calculate the number of gene copies in the reaction for each sample. The PCR input volume (2 µl) was used to calculate the number of gene copies per microliter in each DNA sample.
The number of genome copies per microliter DNA sample was calculated by dividing the gene copy number by an assumed number of gene copies per genome. The value used for 16S copies per genome is 4. The value used for ITS copies per genome is 200. The amount of DNA per microliter DNA sample was calculated using an assumed genome size of 4.64 x 106 bp, the genome size of Escherichia coli, for 16S samples, or an assumed genome size of 1.20 x 107 bp, the genome size of Saccharomyces cerevisiae, for ITS samples. This calculation is shown below:

Calculated Total DNA = Calculated Total Genome Copies × Assumed Genome Size (4.64 × 106 bp) ×
Average Molecular Weight of a DNA bp (660 g/mole/bp) ÷ Avogadro’s Number (6.022 x 1023/mole)


* Absolute Abundance Quantification is only available for 16S and ITS analyses.

The absolute abundance standard curve data can be viewed in Excel here:

The absolute abundance standard curve is shown below:

Absolute Abundance Standard Curve

 

IV. Complete Report Download

The complete report of your project, including all links in this report, can be downloaded by clicking the link provided below. The downloaded file is a compressed ZIP file and once unzipped, open the file “REPORT.html” (may only shown as "REPORT" in your computer) by double clicking it. Your default web browser will open it and you will see the exact content of this report.

Please download and save the file to your computer storage device. The download link will expire after 60 days upon your receiving of this report.

Complete report download link:

To view the report, please follow the following steps:
1.Download the .zip file from the report link above.
2.Extract all the contents of the downloaded .zip file to your desktop.
3.Open the extracted folder and find the "REPORT.html" (may shown as only "REPORT").
4.Open (double-clicking) the REPORT.html file. Your default browser will open the top age of the complete report. Within the report, there are links to view all the analyses performed for the project.

 

V. Raw Sequence Data Download

The raw NGS sequence data is available for download with the link provided below. The data is a compressed ZIP file and can be unzipped to individual sequence files. Since this is a pair-end sequencing, each of your samples is represented by two sequence files, one for READ 1, with the file extension “*_R1.fastq.gz”, another READ 2, with the file extension “*_R1.fastq.gz”. The files are in FASTQ format and are compressed. FASTQ format is a text-based data format for storing both a biological sequence and its corresponding quality scores. Most sequence analysis software will be able to open them. The Sample IDs associated with the R1 and R2 fastq files are listed in the table below:



Sample IDOriginal Sample IDRead 1 File NameRead 2 File Name


F10949.S10original sample ID herezr10949_10V1V3_R1.fastq.gzzr10949_10V1V3_R2.fastq.gz


F10949.S11original sample ID herezr10949_11V1V3_R1.fastq.gzzr10949_11V1V3_R2.fastq.gz


F10949.S12original sample ID herezr10949_12V1V3_R1.fastq.gzzr10949_12V1V3_R2.fastq.gz


F10949.S13original sample ID herezr10949_13V1V3_R1.fastq.gzzr10949_13V1V3_R2.fastq.gz


F10949.S14original sample ID herezr10949_14V1V3_R1.fastq.gzzr10949_14V1V3_R2.fastq.gz


F10949.S15original sample ID herezr10949_15V1V3_R1.fastq.gzzr10949_15V1V3_R2.fastq.gz


F10949.S16original sample ID herezr10949_16V1V3_R1.fastq.gzzr10949_16V1V3_R2.fastq.gz


F10949.S17original sample ID herezr10949_17V1V3_R1.fastq.gzzr10949_17V1V3_R2.fastq.gz


F10949.S01original sample ID herezr10949_1V1V3_R1.fastq.gzzr10949_1V1V3_R2.fastq.gz


F10949.S02original sample ID herezr10949_2V1V3_R1.fastq.gzzr10949_2V1V3_R2.fastq.gz


F10949.S03original sample ID herezr10949_3V1V3_R1.fastq.gzzr10949_3V1V3_R2.fastq.gz


F10949.S04original sample ID herezr10949_4V1V3_R1.fastq.gzzr10949_4V1V3_R2.fastq.gz


F10949.S05original sample ID herezr10949_5V1V3_R1.fastq.gzzr10949_5V1V3_R2.fastq.gz


F10949.S06original sample ID herezr10949_6V1V3_R1.fastq.gzzr10949_6V1V3_R2.fastq.gz


F10949.S07original sample ID herezr10949_7V1V3_R1.fastq.gzzr10949_7V1V3_R2.fastq.gz


F10949.S08original sample ID herezr10949_8V1V3_R1.fastq.gzzr10949_8V1V3_R2.fastq.gz


F10949.S09original sample ID herezr10949_9V1V3_R1.fastq.gzzr10949_9V1V3_R2.fastq.gz

Please download and save the file to your computer storage device. The download link will expire after 60 days upon your receiving of this report.

Raw sequence data download link:

 

VI. Analysis - DADA2 Read Processing

What is DADA2?

DADA2 is a software package that models and corrects Illumina-sequenced amplicon errors. DADA2 infers sample sequences exactly, without coarse-graining into OTUs, and resolves differences of as little as one nucleotide. DADA2 identified more real variants and output fewer spurious sequences than other methods.

DADA2’s advantage is that it uses more of the data. The DADA2 error model incorporates quality information, which is ignored by all other methods after filtering. The DADA2 error model incorporates quantitative abundances, whereas most other methods use abundance ranks if they use abundance at all. The DADA2 error model identifies the differences between sequences, eg. A->C, whereas other methods merely count the mismatches. DADA2 can parameterize its error model from the data itself, rather than relying on previous datasets that may or may not reflect the PCR and sequencing protocols used in your study.

DADA2 Publication: Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJ, Holmes SP. DADA2: High-resolution sample inference from Illumina amplicon data. Nat Methods. 2016 Jul;13(7):581-3. doi: 10.1038/nmeth.3869. Epub 2016 May 23. PMID: 27214047; PMCID: PMC4927377.

DADA2 Software Package is available as an R package at : https://benjjneb.github.io/dada2/index.html

Analysis Procedures:

DADA2 pipeline includes several tools for read quality control, including quality filtering, trimming, denoising, pair merging and chimera filtering. Below are the major processing steps of DADA2:

Step 1. Read trimming based on sequence quality The quality of NGS Illumina sequences often decreases toward the end of the reads. DADA2 allows to trim off the poor quality read ends in order to improve the error model building and pair mergicing performance.

Step 2. Learn the Error Rates The DADA2 algorithm makes use of a parametric error model (err) and every amplicon dataset has a different set of error rates. The learnErrors method learns this error model from the data, by alternating estimation of the error rates and inference of sample composition until they converge on a jointly consistent solution. As in many machine-learning problems, the algorithm must begin with an initial guess, for which the maximum possible error rates in this data are used (the error rates if only the most abundant sequence is correct and all the rest are errors).

Step 3. Infer amplicon sequence variants (ASVs) based on the error model built in previous step. This step is also called sequence "denoising". The outcome of this step is a list of ASVs that are the equivalent of oligonucleotides.

Step 4. Merge paired reads. If the sequencing products are read pairs, DADA2 will merge the R1 and R2 ASVs into single sequences. Merging is performed by aligning the denoised forward reads with the reverse-complement of the corresponding denoised reverse reads, and then constructing the merged “contig” sequences. By default, merged sequences are only output if the forward and reverse reads overlap by at least 12 bases, and are identical to each other in the overlap region (but these conditions can be changed via function arguments).

Step 5. Remove chimera. The core dada method corrects substitution and indel errors, but chimeras remain. Fortunately, the accuracy of sequence variants after denoising makes identifying chimeric ASVs simpler than when dealing with fuzzy OTUs. Chimeric sequences are identified if they can be exactly reconstructed by combining a left-segment and a right-segment from two more abundant “parent” sequences. The frequency of chimeric sequences varies substantially from dataset to dataset, and depends on on factors including experimental procedures and sample complexity.

Results

1. Read Quality Plots NGS sequence analaysis starts with visualizing the quality of the sequencing. Below are the quality plots of the first sample for the R1 and R2 reads separately. In gray-scale is a heat map of the frequency of each quality score at each base position. The mean quality score at each position is shown by the green line, and the quartiles of the quality score distribution by the orange lines. The forward reads are usually of better quality. It is a common practice to trim the last few nucleotides to avoid less well-controlled errors that can arise there. The trimming affects the downstream steps including error model building, merging and chimera calling. FOMC uses an empirical approach to test many combinations of different trim length in order to achieve best final amplicon sequence variants (ASVs), see the next section “Optimal trim length for ASVs”.

Quality plots for all samples:

2. Optimal trim length for ASVs The final number of merged and chimera-filtered ASVs depends on the quality filtering (hence trimming) in the very beginning of the DADA2 pipeline. In order to achieve highest number of ASVs, an empirical approach was used -

  1. Create a random subset of each sample consisting of 5,000 R1 and 5,000 R2 (to reduce computation time)
  2. Trim 10 bases at a time from the ends of both R1 and R2 up to 50 bases
  3. For each combination of trimmed length (e.g., 300x300, 300x290, 290x290 etc), the trimmed reads are subject to the entire DADA2 pipeline for chimera-filtered merged ASVs
  4. The combination with highest percentage of the input reads becoming final ASVs is selected for the complete set of data

Below is the result of such operation, showing ASV percentages of total reads for all trimming combinations (1st Column = R1 lengths in bases; 1st Row = R2 lengths in bases):

R1/R2281271261251241231
32140.31%40.42%40.67%40.75%34.82%33.43%
31140.22%40.47%41.02%36.82%33.51%32.16%
30138.92%39.58%35.20%33.42%31.60%25.24%
29138.61%34.07%32.72%31.58%25.15%10.10%
28133.80%32.44%31.01%25.63%10.27%7.56%
27132.79%31.59%25.79%10.57%7.73%5.88%

Based on the above result, the trim length combination of R1 = 311 bases and R2 = 261 bases (highlighted red above), was chosen for generating final ASVs for all sequences. This combination generated highest number of merged non-chimeric ASVs and was used for downstream analyses, if requested.

3. Error plots from learning the error rates After DADA2 building the error model for the set of data, it is always worthwhile, as a sanity check if nothing else, to visualize the estimated error rates. The error rates for each possible transition (A→C, A→G, …) are shown below. Points are the observed error rates for each consensus quality score. The black line shows the estimated error rates after convergence of the machine-learning algorithm. The red line shows the error rates expected under the nominal definition of the Q-score. The ideal result would be the estimated error rates (black line) are a good fit to the observed rates (points), and the error rates drop with increased quality as expected.

Forward Read R1 Error Plot


Reverse Read R2 Error Plot

The PDF version of these plots are available here:

 

4. DADA2 Result Summary The table below shows the summary of the DADA2 analysis, tracking paired read counts of each samples for all the steps during DADA2 denoising process - including end-trimming (filtered), denoising (denoisedF, denoisedF), pair merging (merged) and chimera removal (nonchim).

Sample IDF10949.S01F10949.S02F10949.S03F10949.S04F10949.S05F10949.S06F10949.S07F10949.S08F10949.S09F10949.S10F10949.S11F10949.S12F10949.S13F10949.S14F10949.S15F10949.S16F10949.S17Row SumPercentage
input15,97115,54916,38119,63015,91318,95517,29815,62534,47320,02217,35816,70218,51817,95028,01517,31831,877337,555100.00%
filtered11,96811,67512,35314,71511,95114,21213,02511,74626,03114,98613,07212,57013,86213,41521,19813,03224,034253,84575.20%
denoisedF11,28110,75211,60314,23911,45913,67112,40211,16325,58014,61112,73512,04013,48012,84220,36912,61523,956244,79872.52%
denoisedR11,29911,09311,73814,15311,51013,73012,33511,04325,35014,51912,55812,02913,41212,85620,51012,61323,588244,33672.38%
merged9,3778,8069,85812,66210,12012,09610,3989,01623,93913,24811,19110,52512,22611,11118,17511,39022,595216,73364.21%
nonchim4,5225,6925,3139,0865,5376,3056,1385,78512,5148,1757,9787,1026,4837,28811,1457,33817,157133,55839.57%

This table can be downloaded as an Excel table below:

 

5. DADA2 Amplicon Sequence Variants (ASVs). A total of 1922 unique merged and chimera-free ASV sequences were identified, and their corresponding read counts for each sample are available in the "ASV Read Count Table" with rows for the ASV sequences and columns for sample. This read count table can be used for microbial profile comparison among different samples and the sequences provided in the table can be used to taxonomy assignment.

 

The table can be downloaded from this link:

 
 

Sample Meta Information

Download Sample Meta Information
#SampleIDSample NameGroup
F10949.S01WT.Base1WT Normal
F10949.S02WT.Base2WT Normal
F10949.S03WT.Base3WT Normal
F10949.S04WT.HFD1WT HFD
F10949.S05WT.HFD2WT HFD
F10949.S06WT.HFD3WT HFD
F10949.S07SAA3KO.Base1SAA KO Normal
F10949.S08SAA3KO.Base2SAA KO Normal
F10949.S09SAA3KO.Base3SAA KO Normal
F10949.S10SAA3KO.HFD1SAA KO HFD
F10949.S11SAA3KO.HFD2SAA KO HFD
F10949.S12SAA3KO.HFD3SAA KO HFD
F10949.S13SAA3KO.HFD4SAA KO HFD
F10949.S14SAA3KO.BL.HFD1SAA KO BL HFD
F10949.S15SAA3KO.BL.HFD2SAA KO BL HFD
F10949.S16SAA3KO.BL.HFD3SAA KO BL HFD
F10949.S17SAA3KO.BL.HFD4SAA KO BL HFD
 
 

ASV Read Counts by Samples

#Sample IDRead Count
F10949.S014,522
F10949.S035,313
F10949.S055,537
F10949.S025,692
F10949.S085,785
F10949.S076,138
F10949.S066,305
F10949.S136,483
F10949.S127,102
F10949.S147,288
F10949.S167,338
F10949.S117,978
F10949.S108,175
F10949.S049,086
F10949.S1511,145
F10949.S0912,514
F10949.S1717,157
 
 
 

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%(>=13 reads)
ATotal reads133,558133,558
BTotal assigned reads132,641132,641
CAssigned reads in species with read count < MPC0262
DAssigned reads in samples with read count < 50000
ETotal samples1717
FSamples with reads >= 5001717
GSamples with reads < 50000
HTotal assigned reads used for analysis (B-C-D)132,641132,379
IReads assigned to single species95,90595,883
JReads assigned to multiple species30
KReads assigned to novel species36,73336,496
LTotal number of species329288
MNumber of single species7573
NNumber of multi-species10
ONumber of novel species253215
PTotal unassigned reads917917
QChimeric reads00
RReads without BLASTN hits489489
SOthers: short, low quality, singletons, etc.428428
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.
SPIDTaxonomyF10949.S01F10949.S02F10949.S03F10949.S04F10949.S05F10949.S06F10949.S07F10949.S08F10949.S09F10949.S10F10949.S11F10949.S12F10949.S13F10949.S14F10949.S15F10949.S16F10949.S17
SP1Bacteria;Firmicutes;Bacilli;Lactobacillales;Enterococcaceae;Enterococcus;gallinarum000000000000001400
SP10Bacteria;Proteobacteria;Betaproteobacteria;Neisseriales;Neisseriaceae;Neisseria;shayeganii00000000000000002146
SP11Bacteria;Firmicutes;Erysipelotrichia;Erysipelotrichales;Erysipelotrichaceae;Faecalibaculum;rodentium650550100022696100000000000
SP12Bacteria;Actinobacteria;Coriobacteriia;Eggerthellales;Eggerthellaceae;Adlercreutzia;caecimuris00012404757880134962396479219590
SP13Bacteria;Proteobacteria;Betaproteobacteria;Burkholderiales;Comamonadaceae;Comamonas;sediminis0000000032700000000
SP14Bacteria;Verrucomicrobia;Verrucomicrobiae;Verrucomicrobiales;Akkermansiaceae;Akkermansia;muciniphila6771604216174000004760010400
SP15Bacteria;Proteobacteria;Gammaproteobacteria;Chromatiales;Chromatiaceae;Rheinheimera;sediminis000000005800000000
SP16Bacteria;Proteobacteria;Betaproteobacteria;Burkholderiales;Oxalobacteraceae;Herbaspirillum;huttiense0000000017500000000
SP17Bacteria;Proteobacteria;Alphaproteobacteria;Sphingomonadales;Sphingomonadaceae;Sphingomonas;deserti000000000000000014521
SP18Bacteria;Firmicutes;Clostridia;Eubacteriales;Peptostreptococcaceae;Romboutsia;ilealis0000311100400000000000
SP19Bacteria;Proteobacteria;Epsilonproteobacteria;Campylobacterales;Helicobacteraceae;Helicobacter;hepaticus0000049000018001212400
SP2Bacteria;Proteobacteria;Gammaproteobacteria;Moraxellales;Moraxellaceae;Acinetobacter;radioresistens0000000031000000000
SP20Bacteria;Bacteroidetes;Bacteroidia;Bacteroidales;Bacteroidaceae;Bacteroides;caecimuris0000002323000000000
SP21Bacteria;Firmicutes;Clostridia;Eubacteriales;Peptococcaceae;Peptococcaceae_[G-1];bacterium_MOT-14607007016160140350171250
SP22Bacteria;Bacteroidota;Bacteroidia;Bacteroidales;Muribaculaceae;Duncaniella;freteri000000105125000000000
SP23Bacteria;Firmicutes;Clostridia;Eubacteriales;Oscillospiraceae;Oscillospiraceae_[G-6];bacterium_MOT-15319475900000000000000
SP24Bacteria;Proteobacteria;Alphaproteobacteria;Sphingomonadales;Sphingomonadaceae;Sphingobium;limneticum0000000012600000000
SP25Eukaryota;Streptophyta;Magnoliopsida;Ericales;Actinidiaceae;Actinidia;eriantha00000000738100000000
SP26Bacteria;Firmicutes;Bacilli;Lactobacillales;Streptococcaceae;Lactococcus;cremoris000133086000176196157157114237640
SP27Bacteria;Actinobacteria;Actinomycetia;Micrococcales;Microbacteriaceae;Microbacterium;foliorum000000000146000000
SP28Bacteria;Firmicutes;Bacilli;Lactobacillales;Streptococcaceae;Lactococcus;lactis000591060000154140093011100
SP29Bacteria;Proteobacteria;Gammaproteobacteria;Pseudomonadales;Moraxellaceae;Moraxella;osloensis0000000017100000000
SP3Bacteria;Firmicutes;Bacilli;Lactobacillales;Lactobacillaceae;Ligilactobacillus;murinus00063678455135335501271943252125728513277260
SP30Bacteria;Proteobacteria;Gammaproteobacteria;Enterobacterales;Enterobacteriaceae;Leclercia;adecarboxylata0000000010900000000
SP31Bacteria;Bacteroidetes;Bacteroidia;Bacteroidales;Tannerellaceae;Parabacteroides;distasonis00000018000000150160
SP32Bacteria;Actinobacteria;Coriobacteriia;Eggerthellales;Eggerthellaceae;Adlercreutzia;mucosicola0008500240034105930526100
SP33Bacteria;Proteobacteria;Gammaproteobacteria;Moraxellales;Moraxellaceae;Acinetobacter;lwoffii0000000015500000000
SP34Bacteria;Firmicutes;Bacilli;Lactobacillales;Streptococcaceae;Streptococcus;danieliae180083333838350123113522873158410
SP35Bacteria;Proteobacteria;Betaproteobacteria;Burkholderiales;Burkholderiaceae;Burkholderia;lata000000005100000000
SP36Bacteria;Bacteroidota;Bacteroidia;Bacteroidales;Bacteroidaceae;Phocaeicola;sartorii000000163190032138510360200
SP37Bacteria;Actinobacteria;Coriobacteriia;Eggerthellales;Eggerthellaceae;Adlercreutzia;muris0001359340250333840004000
SP38Bacteria;Proteobacteria;Gammaproteobacteria;Enterobacterales;Erwiniaceae;Erwinia;billingiae0000000013800000000
SP39Bacteria;Proteobacteria;Alphaproteobacteria;Sphingomonadales;Sphingomonadaceae;Sphingomonas;carotinifaciens0000000012200000000
SP4Bacteria;Firmicutes;Bacilli;Lactobacillales;Lactobacillaceae;Limosilactobacillus;reuteri00000021190116126293670857610
SP40Bacteria;Firmicutes;Clostridia;Eubacteriales;Clostridiaceae;Clostridium;disporicum107921030474100000000000
SP41Bacteria;Proteobacteria;Gammaproteobacteria;Enterobacterales;Enterobacteriaceae;Yokenella;regensburgei0000000010300000000
SP42Bacteria;Bacteroidota;Bacteroidia;Bacteroidales;Bacteroidaceae;Phocaeicola;vulgatus0002001109700000000000
SP43Bacteria;Proteobacteria;Gammaproteobacteria;Pseudomonadales;Pseudomonadaceae;Pseudomonas;veronii0000000011200000000
SP44Bacteria;Firmicutes;Clostridia;Eubacteriales;Lachnospiraceae;Lachnospiraceae_[G-14];bacterium_MOT-1851480000000000000000
SP45Bacteria;Firmicutes;Clostridia;Eubacteriales;Eubacteriales_[F-1];Eubacteriales_[G-4];bacterium_MOT-1648605500000000000000
SP46Bacteria;Firmicutes;Clostridia;Eubacteriales;Eubacteriales_[F-1];Eubacteriales_[G-2];bacterium_MOT-16267603100000000000000
SP47Bacteria;Actinobacteria;Actinomycetia;Geodermatophilales;Geodermatophilaceae;Blastococcus;aggregatus000000007700000000
SP48Bacteria;Proteobacteria;Gammaproteobacteria;Moraxellales;Moraxellaceae;Acinetobacter;johnsonii000000007700000000
SP49Bacteria;Proteobacteria;Betaproteobacteria;Burkholderiales;Oxalobacteraceae;Massilia;arenae000000004300000000
SP5Bacteria;Actinobacteria;Actinomycetia;Micrococcales;Micrococcaceae;Kocuria;indica0000000074300000000
SP50Bacteria;Bacteroidetes;Bacteroidia;Bacteroidales;Bacteroidaceae;Bacteroides;acidifaciens000101606414269000913600250
SP51Bacteria;Proteobacteria;Gammaproteobacteria;Pseudomonadales;Pseudomonadaceae;Pseudomonas;cedrina000000002200000000
SP52Bacteria;Proteobacteria;Gammaproteobacteria;Enterobacterales;Enterobacteriaceae;Siccibacter;turicensis000000009300000000
SP53Bacteria;Proteobacteria;Betaproteobacteria;Burkholderiales;Comamonadaceae;Pelomonas;saccharophila0000000019300000000
SP54Bacteria;Proteobacteria;Betaproteobacteria;Burkholderiales;Sutterellaceae;Parasutterella;excrementihominis0002413000000000000
SP55Bacteria;Firmicutes;Clostridia;Eubacteriales;Eubacteriales_[F-1];Eubacteriales_[G-1];bacterium_MOT-1591640000000000000000
SP59Bacteria;Bacteroidetes;Bacteroidia;Bacteroidales;Tannerellaceae;Parabacteroides;goldsteinii0000000000545801510590
SP6Bacteria;Firmicutes;Clostridia;Eubacteriales;Eubacteriales_[F-1];Eubacteriales_[G-3];bacterium_MOT-1634221000000000000000
SP60Bacteria;Bacteroidetes;Sphingobacteriia;Sphingobacteriales;Sphingobacteriaceae;Pedobacter;quisquiliarum0000000011400000000
SP61Bacteria;Proteobacteria;Epsilonproteobacteria;Campylobacterales;Helicobacteraceae;Helicobacter;ganmani0000009410700054008500
SP62Bacteria;Firmicutes;Clostridia;Eubacteriales;Lachnospiraceae;Lachnospiraceae_[G-1];bacterium_MOT-166003800000000000000
SP63Bacteria;Actinobacteria;Coriobacteriia;Coriobacteriales;Coriobacteriaceae;Parvibacter;caecicola000320000020240003900
SP64Bacteria;Actinobacteria;Actinomycetia;Geodermatophilales;Geodermatophilaceae;Blastococcus;saxobsidens0000000010300000000
SP66Bacteria;Proteobacteria;Alphaproteobacteria;Sphingomonadales;Sphingomonadaceae;Sphingomonas;echinoides0000000017800000000
SP67Bacteria;Proteobacteria;Gammaproteobacteria;Pseudomonadales;Pseudomonadaceae;Pseudomonas;helleri000000009400000000
SP68Bacteria;Proteobacteria;Alphaproteobacteria;Rhizobiales;Bradyrhizobiaceae;Afipia;broomeae000000004700000000
SP69Bacteria;Firmicutes;Clostridia;Eubacteriales;Lachnospiraceae;Lachnospiraceae_[G-3];bacterium_MOT-168000000000016260788600
SP7Bacteria;Firmicutes;Erysipelotrichia;Erysipelotrichales;Erysipelotrichaceae;Erysipelatoclostridium;[Clostridium] cocleatum0005701152020810001917326333506443167360220150
SP70Bacteria;Proteobacteria;Gammaproteobacteria;Pseudomonadales;Pseudomonadaceae;Pseudomonas;extremaustralis000000004100000000
SP71Bacteria;Proteobacteria;Gammaproteobacteria;Enterobacterales;Enterobacteriaceae;Klebsiella;michiganensis000000004000000000
SP72Bacteria;Actinobacteria;Actinomycetia;Micrococcales;Microbacteriaceae;Microbacterium;kitamiense00000000095000000
SP73Bacteria;Proteobacteria;Gammaproteobacteria;Enterobacterales;Enterobacteriaceae;Enterobacter;cloacae000000009300000000
SP74Bacteria;Firmicutes;Clostridia;Eubacteriales;Oscillospiraceae;Oscillospiraceae_[G-7];bacterium_MOT-154801300000000000000
SP75Bacteria;Proteobacteria;Gammaproteobacteria;Chromatiales;Chromatiaceae;Pararheinheimera;mesophila000000004700000000
SP76Bacteria;Proteobacteria;Gammaproteobacteria;Enterobacterales;Enterobacteriaceae;Enterobacter;hormaechei000000007100000000
SP77Bacteria;Firmicutes;Bacilli;Lactobacillales;Streptococcaceae;Streptococcus;sanguinis000000008800000000
SP8Bacteria;Firmicutes;Bacilli;Lactobacillales;Lactobacillaceae;Lactobacillus;johnsonii1451085620340144042002849177002511383155228150
SP9Bacteria;Firmicutes;Bacilli;Lactobacillales;Lactobacillaceae;Lactobacillus;gasseri355704400157780287193027101683230
SPN1Bacteria;Firmicutes;Clostridia;Eubacteriales;Lachnospiraceae;Lachnospiraceae_[G-6];bacterium_MOT-171_nov_93.307%010327300000000000000
SPN10Bacteria;Firmicutes;Clostridia;Eubacteriales;Oscillospiraceae;Oscillibacter;valericigenes_nov_93.654%911000000000000000
SPN100Bacteria;Firmicutes;Clostridia;Eubacteriales;Lachnospiraceae;Lachnospiraceae_[G-1];bacterium_MOT-166_nov_95.661%00000096139000000000
SPN101Bacteria;Firmicutes;Clostridia;Eubacteriales;Lachnospiraceae;Lachnospiraceae_[G-11];bacterium_MOT-176_nov_95.543%00008713905000000000
SPN102Bacteria;Bacteroidetes;Bacteroidetes_[C-1];Bacteroidetes_[O-1];Bacteroidetes_[F-1];Bacteroidetes_[G-3];bacterium HMT436 nov_85.575%0000476700000005316450
SPN103Bacteria;Firmicutes;Clostridia;Eubacteriales;Lachnospiraceae;Lachnospiraceae_[G-3];bacterium_MOT-168_nov_94.059%0003300000002838012100
SPN104Bacteria;Firmicutes;Clostridia;Eubacteriales;Lachnospiraceae;Lachnospiraceae_[G-14];bacterium_MOT-185_nov_92.105%791221300000000000000
SPN105Bacteria;Firmicutes;Clostridia;Eubacteriales;Lachnospiraceae;Lachnospiraceae_[G-6];bacterium_MOT-171_nov_95.644%63786900000000000000
SPN106Bacteria;Bacteroidetes;Bacteroidia;Bacteroidales;Prevotellaceae;Prevotella;shahii_nov_87.242%000000491275000000000
SPN107Bacteria;Firmicutes;Clostridia;Eubacteriales;Lachnospiraceae;Faecalicatena;orotica_nov_94.553%0000000000091106700
SPN108Bacteria;Firmicutes;Erysipelotrichia;Erysipelotrichales;Erysipelotrichaceae;Faecalibaculum;rodentium_nov_96.571%0002149731451654300004280000
SPN109Bacteria;Firmicutes;Clostridia;Eubacteriales;Clostridiaceae;Clostridium;collagenovorans_nov_80.952%00000082127000000000
SPN11Bacteria;Firmicutes;Clostridia;Eubacteriales;Lachnospiraceae;Lachnospiraceae_[G-11];bacterium_MOT-178_nov_96.332%094000000000000000
SPN110Bacteria;Firmicutes;Erysipelotrichia;Erysipelotrichales;Turicibacteraceae;Turicibacter;sanguinis_nov_95.817%12184000000000000000
SPN111Bacteria;Firmicutes;Clostridia;Eubacteriales;Lachnospiraceae;Bariatricus;massiliensis_nov_93.037%0604500454902200037000
SPN112Bacteria;Firmicutes;Clostridia;Clostridiales;Eubacteriaceae_[XV];Eubacterium;ventriosum_nov_92.843%0000000000001990000
SPN113Bacteria;Proteobacteria;Deltaproteobacteria;Desulfovibrionales;Desulfovibrionaceae;Desulfovibrio;fairfieldensis_nov_89.168%0004624394049000000000
SPN114Bacteria;Firmicutes;Clostridia;Eubacteriales;Lachnospiraceae;Lachnospiraceae_[G-14];bacterium_MOT-182_nov_89.784%55697200000000000000
SPN115Bacteria;Firmicutes;Erysipelotrichia;Erysipelotrichales;Erysipelotrichaceae;Longibaculum;muris_nov_91.211%00000000000001230670
SPN116Bacteria;Firmicutes;Clostridia;Eubacteriales;Lachnospiraceae;Bariatricus;massiliensis_nov_93.230%000000000954601303500
SPN117Bacteria;Firmicutes;Clostridia;Clostridiales;Eubacteriaceae_[XV];Eubacterium;ramulus_nov_91.296%37147000000000000000
SPN118Bacteria;Firmicutes;Clostridia;Eubacteriales;Oscillospiraceae;Lawsonibacter;asaccharolyticus_nov_91.571%52125000000000000000
SPN119Bacteria;Firmicutes;Clostridia;Eubacteriales;Oscillospiraceae;Hydrogenoanaerobacterium;saccharovorans_nov_88.395%0002462000000000000
SPN12Bacteria;Firmicutes;Clostridia;Eubacteriales;Lachnospiraceae;Kineothrix;alysoides_nov_87.352%000000019000000000
SPN120Bacteria;Deferribacteres;Deferribacteres;Deferribacterales;Mucispirillaceae;Mucispirillum;schaedleri_nov_93.307%00000002201253912005040600
SPN121Bacteria;Actinobacteria;Coriobacteriia;Coriobacteriales;Atopobiaceae;Lancefieldella;parvula_nov_91.552%36489200000000000000
SPN122Bacteria;Actinobacteria;Coriobacteriia;Eggerthellales;Eggerthellaceae;Adlercreutzia;caecimuris_nov_94.000%0002300000163236006900
SPN123Bacteria;Firmicutes;Clostridia;Eubacteriales;Lachnospiraceae;Lachnospiraceae_[G-3];bacterium_MOT-168_nov_94.653%06096000019000000000
SPN124Bacteria;Firmicutes;Clostridia;Eubacteriales;Oscillospiraceae;Oscillospiraceae_[G-2];bacterium_MOT-149_nov_93.861%700000000332583024000
SPN125Bacteria;Firmicutes;Clostridia;Eubacteriales;Lachnospiraceae;Anaerotignum;lactatifermentans_nov_95.769%000102100000051076600
SPN126Bacteria;Firmicutes;Clostridia;Eubacteriales;Oscillospiraceae;Anaerotruncus;rubiinfantis_nov_91.506%000000000002601082800
SPN127Bacteria;Firmicutes;Clostridia;Eubacteriales;Oscillospiraceae;Sporobacter;termitidis_nov_82.970%525400003124000000000
SPN128Bacteria;Firmicutes;Clostridia;Eubacteriales;Lachnospiraceae;Lachnospiraceae_[G-14];bacterium_MOT-185_nov_92.353%0000006694000000000
SPN129Bacteria;Actinobacteria;Actinomycetia;Micrococcales;Microbacteriaceae;Leifsonia;kafniensis_nov_84.158%13719000000000000000
SPN13Bacteria;Bacteroidetes;Bacteroidia;Bacteroidales;Muribaculaceae;Muribaculaceae_[G-1];bacterium_MOT-129_nov_86.590%632471830000250119000000000
SPN130Bacteria;Firmicutes;Clostridia;Eubacteriales;Lachnospiraceae;Lachnospiraceae_[G-12];bacterium_MOT-179_nov_94.737%01600003136000000000
SPN131Bacteria;Proteobacteria;Deltaproteobacteria;Desulfovibrionales;Desulfovibrionaceae;Mailhella;massiliensis_nov_90.377%0000000006932377020649140
SPN132Bacteria;Firmicutes;Clostridia;Eubacteriales;Lachnospiraceae;Lachnospiraceae_[G-7];bacterium_MOT-172_nov_91.945%0786700000000000000
SPN133Bacteria;Firmicutes;Clostridia;Eubacteriales;Lachnospiraceae;Lachnospiraceae_[G-7];bacterium_MOT-172_nov_93.204%04000000105000000000
SPN134Bacteria;Bacteroidota;Bacteroidia;Bacteroidales;Muribaculaceae;Duncaniella;freteri_nov_90.152%0000007861000000000
SPN135Bacteria;Bacteroidota;Bacteroidia;Bacteroidales;Muribaculaceae;Duncaniella;freteri_nov_87.896%0000007356000000000
SPN136Bacteria;Proteobacteria;Betaproteobacteria;Burkholderiales;Sutterellaceae;Parasutterella;excrementihominis_nov_94.584%00000070400000017000
SPN137Bacteria;Firmicutes;Clostridia;Eubacteriales;Oscillospiraceae;Phocea;massiliensis_nov_90.297%34603200000000000000
SPN138Bacteria;Firmicutes;Clostridia;Eubacteriales;Oscillospiraceae;Pseudoflavonifractor;phocaeensis_nov_95.761%0502724290000028001200
SPN139Bacteria;Firmicutes;Clostridia;Eubacteriales;Lachnospiraceae;Anaerotignum;aminivorans_nov_93.173%0665600000000000000
SPN14Bacteria;Firmicutes;Clostridia;Eubacteriales;Lachnospiraceae;Faecalicatena;fissicatena_nov_93.580%181019400000000000000
SPN140Bacteria;Bacteroidetes;Bacteroidia;Bacteroidales;Muribaculaceae;Muribaculaceae_[G-1];bacterium_MOT-129_nov_87.308%00000033230002700000
SPN141Bacteria;Firmicutes;Clostridia;Eubacteriales;Lachnospiraceae;Lachnospiraceae_[G-9];bacterium_MOT-174_nov_86.957%0000120000000000000
SPN142Bacteria;Firmicutes;Clostridia;Eubacteriales;Lachnospiraceae;Lachnospiraceae_[G-11];bacterium_MOT-176_nov_92.885%0000003783000000000
SPN143Bacteria;Firmicutes;Clostridia;Eubacteriales;Eubacteriales Family XIII. Incertae Sedis;Ihubacter;massiliensis_nov_94.767%001200000004238047594860
SPN144Bacteria;Firmicutes;Clostridia;Eubacteriales;Lachnospiraceae;Tyzzerella;[Clostridium] colinum_nov_88.494%0118000000000000000
SPN145Bacteria;Firmicutes;Bacilli;Bacillales;Planococcaceae;Planococcus;massiliensis_nov_96.992%0000000011500000000
SPN146Bacteria;Firmicutes;Clostridia;Eubacteriales;Lachnospiraceae;Lachnospiraceae_[G-7];bacterium_MOT-172_nov_86.508%0115000000000000000
SPN147Bacteria;Firmicutes;Clostridia;Eubacteriales;Lachnospiraceae;Lachnospiraceae_[G-12];bacterium_MOT-180_nov_89.942%027150003141000000000
SPN148Bacteria;Firmicutes;Clostridia;Clostridiales;Eubacteriaceae_[XV];Eubacterium;coprostanoligenes_nov_91.892%5405700000000000000
SPN149Bacteria;Bacteroidota;Bacteroidia;Bacteroidales;Muribaculaceae;Duncaniella;freteri_nov_87.759%0000007733000000000
SPN15Bacteria;Firmicutes;Clostridia;Eubacteriales;Oscillospiraceae;Oscillospiraceae_[G-2];bacterium_MOT-149_nov_93.241%019000000000000000
SPN150Bacteria;Firmicutes;Clostridia;Eubacteriales;Lachnospiraceae;Lachnospiraceae_[G-7];bacterium_MOT-172_nov_92.843%00250005926000000000
SPN151Bacteria;Firmicutes;Clostridia;Eubacteriales;Lachnospiraceae;Lachnoclostridium;[Clostridium] polysaccharolyticum_nov_93.050%0000002360000000000
SPN152Bacteria;Firmicutes;Clostridia;Eubacteriales;Lachnospiraceae;Lachnospiraceae_[G-7];bacterium_MOT-172_nov_86.047%18702100000000000000
SPN153Bacteria;Firmicutes;Clostridia;Eubacteriales;Lachnospiraceae;Mediterraneibacter;[Ruminococcus] torques_nov_95.200%0004662000000000000
SPN154Bacteria;Firmicutes;Clostridia;Eubacteriales;Lachnospiraceae;Lachnospiraceae_[G-2];bacterium_MOT-167_nov_89.349%0792900000000000000
SPN155Bacteria;Firmicutes;Clostridia;Eubacteriales;Oscillospiraceae;Caproicibacter;fermentans_nov_89.824%0000000690129881084073215230
SPN156Bacteria;Firmicutes;Clostridia;Eubacteriales;Lachnospiraceae;Fusicatenibacter;saccharivorans_nov_90.514%00000003900025004200
SPN157Bacteria;Proteobacteria;Gammaproteobacteria;Enterobacterales;Erwiniaceae;Erwinia;billingiae_nov_97.519%0000000010600000000
SPN158Bacteria;Firmicutes;Clostridia;Eubacteriales;Lachnospiraceae;Lachnospiraceae_[G-3];bacterium_MOT-168_nov_95.059%0000003373000000000
SPN159Bacteria;Firmicutes;Clostridia;Eubacteriales;Lachnospiraceae;Lachnoclostridium;[Clostridium] scindens_nov_89.827%0091000014000000000
SPN16Bacteria;Firmicutes;Clostridia;Eubacteriales;Lachnospiraceae;Lachnospiraceae_[G-11];bacterium_MOT-178_nov_94.220%001900000000000000
SPN160Bacteria;Firmicutes;Clostridia;Eubacteriales;Lachnospiraceae;Lachnospiraceae_[G-14];bacterium_MOT-182_nov_89.200%0000000000000010500
SPN161Bacteria;Firmicutes;Clostridia;Clostridiales;Eubacteriaceae_[XV];Eubacterium;oxidoreducens_nov_88.846%23334800000000000000
SPN162Bacteria;Firmicutes;Clostridia;Eubacteriales;Lachnospiraceae;Lachnospiraceae_[G-11];bacterium_MOT-176_nov_94.798%000740009200003613416100
SPN163Bacteria;Firmicutes;Bacilli;Bacillales;Paenibacillaceae;Saccharibacillus;kuerlensis_nov_95.336%0000000010400000000
SPN164Bacteria;Firmicutes;Clostridia;Eubacteriales;Oscillospiraceae;Acutalibacter;muris_nov_94.264%31304200000000000000
SPN165Bacteria;Firmicutes;Clostridia;Eubacteriales;Oscillospiraceae;Phocea;massiliensis_nov_90.239%51203200000000000000
SPN166Bacteria;Bacteroidota;Bacteroidia;Bacteroidales;Muribaculaceae;Duncaniella;freteri_nov_89.077%0000004953000000000
SPN167Bacteria;Firmicutes;Clostridia;Eubacteriales;Lachnospiraceae;Fusicatenibacter;saccharivorans_nov_91.018%5302900000000000000
SPN168Bacteria;Firmicutes;Clostridia;Eubacteriales;Lachnospiraceae;Lachnospiraceae_[G-9];bacterium_MOT-174_nov_90.234%13402800000000000000
SPN169Bacteria;Actinobacteria;Coriobacteriia;Eggerthellales;Eggerthellaceae;Adlercreutzia;caecimuris_nov_95.382%000000000800000000
SPN17Bacteria;Firmicutes;Erysipelotrichia;Erysipelotrichales;Erysipelotrichaceae;Breznakia;pachnodae_nov_81.284%000180000000000000
SPN170Bacteria;Firmicutes;Clostridia;Eubacteriales;Oscillospiraceae;Oscillospiraceae_[G-4];bacterium_MOT-151_nov_93.491%0000000001106800000
SPN171Bacteria;Firmicutes;Clostridia;Eubacteriales;Lachnospiraceae;Lachnospiraceae_[G-11];bacterium_MOT-176_nov_95.174%0000000000000304800
SPN172Bacteria;Firmicutes;Clostridia;Eubacteriales;Lachnospiraceae;Faecalicatena;fissicatena_nov_94.521%95750281200036000006828100
SPN173Bacteria;Firmicutes;Clostridia;Eubacteriales;Lachnospiraceae;Lachnoclostridium;[Clostridium] polysaccharolyticum_nov_92.664%076000000000000000
SPN174Bacteria;Firmicutes;Bacilli;Bacillales;Paenibacillaceae;Saccharibacillus;kuerlensis_nov_95.709%000000007300000000
SPN175Bacteria;Firmicutes;Clostridia;Eubacteriales;Clostridiaceae;Clostridium;collagenovorans_nov_83.466%1953000000000000000
SPN176Bacteria;Firmicutes;Clostridia;Eubacteriales;Lachnospiraceae;Lachnospiraceae_[G-13];bacterium_MOT-181_nov_91.634%03900001815000000000
SPN177Bacteria;Firmicutes;Tissierellia;Tissierellales;Thermohalobacteraceae;Sporosalibacterium;tautonense_nov_82.659%000040067000000000
SPN178Bacteria;Firmicutes;Erysipelotrichia;Erysipelotrichales;Erysipelotrichaceae;Absiella;tortuosum_nov_88.725%0000002941000000000
SPN179Bacteria;Actinobacteria;Coriobacteriia;Eggerthellales;Eggerthellaceae;Adlercreutzia;caecimuris_nov_92.277%221500001815000000000
SPN18Bacteria;Firmicutes;Clostridia;Eubacteriales;Oscillospiraceae;Lawsonibacter;asaccharolyticus_nov_92.514%000000017000000000
SPN180Bacteria;Firmicutes;Clostridia;Eubacteriales;Oscillospiraceae;Oscillibacter;valericigenes_nov_95.164%069000000000000000
SPN181Bacteria;Firmicutes;Clostridia;Eubacteriales;Lachnospiraceae;Lachnospiraceae_[G-14];bacterium_MOT-183_nov_97.967%0000003830000000000
SPN182Bacteria;Firmicutes;Erysipelotrichia;Erysipelotrichales;Erysipelotrichaceae;Breznakia;pachnodae_nov_83.181%28132400000000000000
SPN183Bacteria;Firmicutes;Clostridia;Eubacteriales;Lachnospiraceae;Lachnoclostridium;[Clostridium] polysaccharolyticum_nov_88.462%43400190000000000000
SPN184Bacteria;Firmicutes;Clostridia;Eubacteriales;Lachnospiraceae;Lachnospiraceae_[G-3];bacterium_MOT-168_nov_95.050%000650000000000000
SPN185Bacteria;Bacteroidetes;Bacteroidia;Bacteroidales;Tannerellaceae;Parabacteroides;merdae_nov_93.182%0000004420000000000
SPN186Bacteria;Firmicutes;Clostridia;Eubacteriales;Clostridiaceae;Hathewaya;proteolytica_nov_84.970%064000000000000000
SPN187Bacteria;Firmicutes;Clostridia;Clostridiales;Eubacteriaceae_[XV];Eubacterium;ramulus_nov_89.362%063000000000000000
SPN188Bacteria;Firmicutes;Clostridia;Eubacteriales;Lachnospiraceae;Eisenbergiella;massiliensis_nov_86.127%0392300000000000000
SPN189Bacteria;Firmicutes;Clostridia;Eubacteriales;Lachnospiraceae;Lachnoclostridium;[Clostridium] polysaccharolyticum_nov_90.751%275059640042430343500507300
SPN19Bacteria;Firmicutes;Clostridia;Eubacteriales;Lachnospiraceae;Blautia;producta_nov_95.164%000017000000000000
SPN190Bacteria;Firmicutes;Clostridia;Eubacteriales;Lachnospiraceae;Lachnospiraceae_[G-7];bacterium_MOT-172_nov_94.831%000000061000000000
SPN191Bacteria;Proteobacteria;Betaproteobacteria;Rhodocyclales;Azonexaceae;Dechloromonas;denitrificans_nov_97.446%000000006100000000
SPN192Bacteria;Firmicutes;Clostridia;Eubacteriales;Lachnospiraceae;Blautia;producta_nov_96.132%0000000000000380220
SPN193Bacteria;Firmicutes;Clostridia;Eubacteriales;Lachnospiraceae;Lachnospiraceae_[G-3];bacterium_MOT-168_nov_96.252%057000000000000000
SPN194Bacteria;Bacteroidetes;Bacteroidia;Bacteroidales;Muribaculaceae;Muribaculaceae_[G-2];bacterium_MOT-104_nov_89.905%00000049350513000000
SPN195Bacteria;Firmicutes;Clostridia;Eubacteriales;Lachnospiraceae;Lachnoclostridium;[Clostridium] polysaccharolyticum_nov_92.636%0000000000042130000
SPN196Bacteria;Bacteroidota;Bacteroidia;Bacteroidales;Muribaculaceae;Duncaniella;freteri_nov_88.598%000550000000000000
SPN197Bacteria;Actinobacteria;Coriobacteriia;Eggerthellales;Eggerthellaceae;Adlercreutzia;caecimuris_nov_92.644%0000001441000000000
SPN198Bacteria;Firmicutes;Clostridia;Eubacteriales;Lachnospiraceae;Lachnoclostridium;[Clostridium] scindens_nov_90.211%000000000000005500
SPN199Bacteria;Firmicutes;Clostridia;Eubacteriales;Lachnospiraceae;Lachnospiraceae_[G-11];bacterium_MOT-178_nov_95.358%0292500000000000000
SPN2Bacteria;Firmicutes;Clostridia;Eubacteriales;Lachnospiraceae;Lachnospiraceae_[G-6];bacterium_MOT-171_nov_93.254%022000000000000000
SPN20Bacteria;Firmicutes;Clostridia;Eubacteriales;Lachnospiraceae;Roseburia;inulinivorans_nov_93.786%016000000000000000
SPN200Bacteria;Firmicutes;Clostridia;Eubacteriales;Lachnospiraceae;Faecalicatena;fissicatena_nov_95.499%005400000000000000
SPN201Bacteria;Firmicutes;Clostridia;Eubacteriales;Lachnospiraceae;Lachnospiraceae_[G-6];bacterium_MOT-171_nov_94.083%0000003457031501250547800
SPN202Bacteria;Firmicutes;Clostridia;Eubacteriales;Lachnospiraceae;Faecalicatena;fissicatena_nov_93.810%000000000000005400
SPN203Bacteria;Firmicutes;Clostridia;Eubacteriales;Lachnospiraceae;Lachnospiraceae_[G-14];bacterium_MOT-185_nov_94.726%0000002429000000000
SPN204Bacteria;Firmicutes;Clostridia;Eubacteriales;Oscillospiraceae;Hydrogenoanaerobacterium;saccharovorans_nov_90.522%000000000004300900
SPN205Bacteria;Firmicutes;Clostridia;Eubacteriales;Lachnospiraceae;Anaerotignum;aminivorans_nov_92.585%0000003169000000000
SPN206Bacteria;Firmicutes;Clostridia;Eubacteriales;Oscillospiraceae;Pseudoflavonifractor;phocaeensis_nov_86.756%1834000000000000000
SPN207Bacteria;Firmicutes;Clostridia;Eubacteriales;Oscillospiraceae;Phocea;massiliensis_nov_90.060%0000001338000000000
SPN208Bacteria;Firmicutes;Clostridia;Eubacteriales;Lachnospiraceae;Lachnoclostridium;phytofermentans_nov_89.057%510000000000000000
SPN209Bacteria;Firmicutes;Clostridia;Clostridiales;Eubacteriaceae_[XV];Eubacterium;coprostanoligenes_nov_90.476%20150160000000000000
SPN21Bacteria;Firmicutes;Clostridia;Eubacteriales;Oscillospiraceae;Oscillospiraceae_[G-2];bacterium_MOT-149_nov_93.097%016000000000000000
SPN210Bacteria;Bacteroidota;Bacteroidia;Bacteroidales;Muribaculaceae;Duncaniella;freteri_nov_89.013%0000003120000000000
SPN211Bacteria;Firmicutes;Clostridia;Clostridiales;Clostridiales_[F-1];Clostridiales_[F-1][G-1];bacterium HMT093 nov_91.892%2821000000000000000
SPN212Bacteria;Firmicutes;Clostridia;Clostridiales;Clostridiales_[F-1];Clostridiales_[F-1][G-1];bacterium HMT093 nov_84.298%490000000000000000
SPN213Bacteria;Firmicutes;Clostridia;Eubacteriales;Lachnospiraceae;Lachnoclostridium;[Clostridium] aminophilum_nov_89.961%0003185000058331214736000
SPN214Bacteria;Firmicutes;Clostridia;Eubacteriales;Oscillospiraceae;Neglectibacter;timonensis_nov_94.318%00000001801701100000
SPN215Bacteria;Firmicutes;Clostridia;Eubacteriales;Lachnospiraceae;Lachnospiraceae_[G-9];bacterium_MOT-174_nov_92.216%046000000000000000
SPN216Bacteria;Firmicutes;Clostridia;Eubacteriales;Lachnospiraceae;Enterocloster;clostridioformis_nov_94.380%000032002600000182200
SPN217Bacteria;Saccharibacteria_(TM7);Saccharibacteria_(TM7)_[C-1];Saccharibacteria_(TM7)_[O-1];Saccharibacteria_(TM7)_[F-1];Saccharibacteria_(TM7)_[G-3];bacterium HMT351 nov_95.551%0000002916000000000
SPN218Bacteria;Bacteroidota;Bacteroidia;Bacteroidales;Muribaculaceae;Duncaniella;freteri_nov_87.453%0000002322000000000
SPN219Bacteria;Firmicutes;Clostridia;Eubacteriales;Lachnospiraceae;Butyrivibrio;sp. HMT455 nov_83.556%044000000000000000
SPN22Bacteria;Firmicutes;Clostridia;Eubacteriales;Lachnospiraceae;Lachnospiraceae_[G-14];bacterium_MOT-182_nov_92.480%000000000000009300
SPN220Bacteria;Bacteroidetes;Bacteroidia;Bacteroidales;Muribaculaceae;Muribaculaceae_[G-2];bacterium_MOT-104_nov_89.544%000000440000000000
SPN221Bacteria;Firmicutes;Clostridia;Eubacteriales;Oscillospiraceae;Oscillospiraceae_[G-2];bacterium_MOT-149_nov_96.640%000000043000000000
SPN222Bacteria;Firmicutes;Clostridia;Eubacteriales;Oscillospiraceae;Sporobacter;termitidis_nov_88.294%11101800000000000000
SPN223Bacteria;Firmicutes;Clostridia;Eubacteriales;Lachnospiraceae;Lacrimispora;xylanolytica_nov_93.969%00038626000020011312690130
SPN224Bacteria;Firmicutes;Clostridia;Eubacteriales;Eubacteriales_[F-1];Eubacteriales_[G-1];bacterium_MOT-159_nov_88.550%2001800000000000000
SPN225Bacteria;Proteobacteria;Betaproteobacteria;Burkholderiales;Comamonadaceae;Acidovorax;defluvii_nov_95.946%000000009700000000
SPN226Bacteria;Firmicutes;Clostridia;Eubacteriales;Oscillospiraceae;Oscillospiraceae_[G-3];bacterium_MOT-150_nov_91.585%000000000000003700
SPN227Bacteria;Firmicutes;Clostridia;Eubacteriales;Oscillospiraceae;Oscillospiraceae_[G-2];bacterium_MOT-149_nov_95.276%370000000000000000
SPN228Bacteria;Firmicutes;Clostridia;Eubacteriales;Oscillospiraceae;Oscillospiraceae_[G-2];bacterium_MOT-149_nov_93.452%037000000000000000
SPN229Bacteria;Actinobacteria;Coriobacteriia;Eggerthellales;Eggerthellaceae;Adlercreutzia;equolifaciens_nov_94.389%003600000000000000
SPN23Bacteria;Actinobacteria;Coriobacteriia;Eggerthellales;Eggerthellaceae;Adlercreutzia;equolifaciens_nov_96.593%000000000000016000
SPN230Bacteria;Firmicutes;Clostridia;Eubacteriales;Eubacteriales_[F-1];Eubacteriales_[G-4];bacterium_MOT-164_nov_97.980%036000000000000000
SPN231Bacteria;Firmicutes;Clostridia;Eubacteriales;Lachnospiraceae;Lachnospiraceae_[G-11];bacterium_MOT-176_nov_89.981%034000000000000000
SPN232Bacteria;Firmicutes;Clostridia;Eubacteriales;Lachnospiraceae;Lachnoclostridium;[Clostridium] polysaccharolyticum_nov_89.443%032000000000000000
SPN233Bacteria;Firmicutes;Mollicutes;Mollicutes_[O-2];Mollicutes_[F-2];Mollicutes_[G-2];bacterium_MOT-187_nov_93.284%265000000000000000
SPN234Bacteria;Firmicutes;Clostridia;Eubacteriales;Oscillospiraceae;Oscillospiraceae_[G-4];bacterium_MOT-151_nov_91.732%1515000000000000000
SPN235Bacteria;Firmicutes;Clostridia;Eubacteriales;Oscillospiraceae;Oscillospiraceae_[G-4];bacterium_MOT-151_nov_94.477%000824700000077285382170
SPN236Bacteria;Firmicutes;Erysipelotrichia;Erysipelotrichales;Erysipelotrichaceae;Faecalibaculum;rodentium_nov_96.571%0000015000000820000
SPN237Bacteria;Bacteroidota;Bacteroidia;Bacteroidales;Muribaculaceae;Duncaniella;freteri_nov_83.895%000000000000003000
SPN238Bacteria;Firmicutes;Erysipelotrichia;Erysipelotrichales;Erysipelotrichaceae;Longibaculum;muris_nov_90.392%000000090000000210
SPN239Bacteria;Actinobacteria;Coriobacteriia;Eggerthellales;Eggerthellaceae;Adlercreutzia;muris_nov_89.506%0001800100000000000
SPN24Bacteria;Firmicutes;Clostridia;Eubacteriales;Lachnospiraceae;Cuneatibacter;caecimuris_nov_92.486%07000000000000900
SPN240Bacteria;Firmicutes;Clostridia;Eubacteriales;Lachnospiraceae;Roseburia;hominis_nov_91.715%270000000000000000
SPN241Bacteria;Firmicutes;Clostridia;Eubacteriales;Lachnospiraceae;Lachnospiraceae_[G-2];bacterium_MOT-167_nov_97.018%002700000000000000
SPN242Bacteria;Firmicutes;Clostridia;Eubacteriales;Oscillospiraceae;Acetivibrio;cellulolyticus_nov_83.644%1100000015000000000
SPN243Bacteria;Proteobacteria;Deltaproteobacteria;Desulfovibrionales;Desulfovibrionaceae;Mailhella;massiliensis_nov_89.888%000000900001006000
SPN244Bacteria;Firmicutes;Clostridia;Eubacteriales;Lachnospiraceae;Faecalimonas;umbilicata_nov_92.692%024000000000000000
SPN245Bacteria;Firmicutes;Clostridia;Eubacteriales;Lachnospiraceae;Anaerotignum;aminivorans_nov_92.600%024000000000000000
SPN246Bacteria;Firmicutes;Clostridia;Eubacteriales;Lachnospiraceae;Blautia;producta_nov_95.174%240000000000000000
SPN247Bacteria;Firmicutes;Clostridia;Eubacteriales;Oscillospiraceae;Pseudoflavonifractor;phocaeensis_nov_92.131%00190000000000621400
SPN25Bacteria;Firmicutes;Clostridia;Eubacteriales;Oscillospiraceae;Acetivibrio;cellulolyticus_nov_84.058%016000000000000000
SPN26Bacteria;Firmicutes;Clostridia;Eubacteriales;Lachnospiraceae;Blautia;faecicola_nov_89.709%0826600023190000000000
SPN27Bacteria;Firmicutes;Clostridia;Eubacteriales;Lachnospiraceae;Eisenbergiella;massiliensis_nov_92.969%000000000001500000
SPN28Bacteria;Firmicutes;Clostridia;Eubacteriales;Lachnospiraceae;Lachnospiraceae_[G-14];bacterium_MOT-185_nov_93.333%00000000006000900
SPN29Bacteria;Firmicutes;Clostridia;Eubacteriales;Lachnospiraceae;Lachnospiraceae_[G-14];bacterium_MOT-182_nov_87.160%014000000000000000
SPN3Bacteria;Bacteroidetes;Bacteroidia;Bacteroidales;Prevotellaceae;Alloprevotella;sp. HMT473 nov_90.177%000000000022000000
SPN30Bacteria;Firmicutes;Bacilli;Lactobacillales;Lactobacillaceae;Lactobacillus;gasseri_nov_93.345%000000000140000000
SPN31Bacteria;Firmicutes;Clostridia;Eubacteriales;Eubacteriales_[F-1];Eubacteriales_[G-1];bacterium_MOT-159_nov_93.976%000000130000000000
SPN32Bacteria;Firmicutes;Clostridia;Eubacteriales;Lachnospiraceae;Lachnospiraceae_[G-5];bacterium_MOT-170_nov_97.614%000130000000000000
SPN33Bacteria;Firmicutes;Clostridia;Eubacteriales;Lachnospiraceae;Eisenbergiella;massiliensis_nov_87.669%000920000000000000
SPN34Bacteria;Firmicutes;Clostridia;Eubacteriales;Oscillospiraceae;Oscillospiraceae_[G-4];bacterium_MOT-151_nov_95.858%130000000000000000
SPN38Bacteria;Firmicutes;Clostridia;Eubacteriales;Lachnospiraceae;Lacrimispora;xylanolytica_nov_94.314%27104790004593000000000
SPN4Bacteria;Firmicutes;Clostridia;Eubacteriales;Lachnospiraceae;Lachnospiraceae_[G-11];bacterium_MOT-178_nov_91.715%002200000000000000
SPN43Bacteria;Bacteroidetes;Bacteroidia;Bacteroidales;Muribaculaceae;Muribaculaceae_[G-1];bacterium_MOT-129_nov_89.768%000000346280000000000
SPN49Bacteria;Bacteroidetes;Bacteroidia;Bacteroidales;Bacteroidaceae;Bacteroides;uniformis_nov_95.594%000000103144047350001700
SPN5Bacteria;Firmicutes;Mollicutes;Mollicutes_[O-2];Mollicutes_[F-2];Mollicutes_[G-2];bacterium_MOT-187_nov_90.841%130900000000000000
SPN54Bacteria;Firmicutes;Clostridia;Eubacteriales;Lachnospiraceae;Lachnospiraceae_[G-11];bacterium_MOT-178_nov_93.064%0494300000000000000
SPN6Bacteria;Firmicutes;Clostridia;Eubacteriales;Lachnospiraceae;Eisenbergiella;massiliensis_nov_87.308%021000000000000000
SPN60Bacteria;Firmicutes;Clostridia;Eubacteriales;Lachnospiraceae;Lacrimispora;xylanolytica_nov_91.992%00000000053371490753200
SPN64Bacteria;Proteobacteria;Betaproteobacteria;Burkholderiales;Oxalobacteraceae;Massilia;arenae_nov_97.885%000000009200000000
SPN7Bacteria;Firmicutes;Clostridia;Eubacteriales;Oscillospiraceae;Oscillospiraceae_[G-2];bacterium_MOT-149_nov_94.235%021000000000000000
SPN72Bacteria;Firmicutes;Clostridia;Eubacteriales;Lachnospiraceae;Eisenbergiella;massiliensis_nov_90.805%38172930002418000000000
SPN75Bacteria;Firmicutes;Clostridia;Eubacteriales;Lachnospiraceae;Lachnoclostridium;[Clostridium] aminophilum_nov_87.476%3705300000000000000
SPN79Bacteria;Firmicutes;Clostridia;Eubacteriales;Lachnospiraceae;Lachnospiraceae_[G-6];bacterium_MOT-171_nov_94.643%06711800044102000000000
SPN8Bacteria;Firmicutes;Clostridia;Eubacteriales;Lachnospiraceae;Lacrimispora;xylanolytica_nov_92.308%000000020000000000
SPN80Bacteria;Bacteroidota;Flavobacteriia;Flavobacteriales;Flavobacteriaceae;Flavobacterium;branchiicola_nov_96.282%0000000032200000000
SPN81Bacteria;Firmicutes;Clostridia;Eubacteriales;Oscillospiraceae;Oscillospiraceae_[G-2];bacterium_MOT-149_nov_95.050%86823202003700005700000
SPN82Bacteria;Tenericutes;Mollicutes;Anaeroplasmatales;Anaeroplasmataceae;Anaeroplasma;abactoclasticum_nov_87.352%23076000000000000000
SPN83Bacteria;Firmicutes;Clostridia;Eubacteriales;Lachnospiraceae;Faecalicatena;orotica_nov_92.218%6210916102002800001020176124150
SPN84Bacteria;Firmicutes;Clostridia;Eubacteriales;Lachnospiraceae;Lachnoclostridium;[Clostridium] scindens_nov_88.247%0310271600000274806673180
SPN85Bacteria;Firmicutes;Clostridia;Eubacteriales;Oscillospiraceae;Oscillospiraceae_[G-3];bacterium_MOT-150_nov_93.910%01500000000124001498600
SPN86Bacteria;Firmicutes;Clostridia;Eubacteriales;Lachnospiraceae;Lachnospiraceae_[G-14];bacterium_MOT-185_nov_93.517%0000000000055034000
SPN87Bacteria;Firmicutes;Clostridia;Eubacteriales;Lachnospiraceae;Lachnospiraceae_[G-6];bacterium_MOT-171_nov_95.050%254411000561340000042400
SPN88Bacteria;Actinobacteria;Coriobacteriia;Coriobacteriales;Atopobiaceae;Olsenella;phocaeensis_nov_92.172%000091163000000260000
SPN89Bacteria;Firmicutes;Clostridia;Eubacteriales;Lachnospiraceae;Eisenbergiella;massiliensis_nov_88.292%000044000023083166036170
SPN9Bacteria;Firmicutes;Clostridia;Eubacteriales;Lachnospiraceae;Muricomes;intestini_nov_89.921%020000000000000000
SPN90Bacteria;Firmicutes;Clostridia;Eubacteriales;Lachnospiraceae;Lachnoclostridium;[Clostridium] polysaccharolyticum_nov_87.692%99177000000000000000
SPN91Bacteria;Firmicutes;Clostridia;Eubacteriales;Lachnospiraceae;Lachnospiraceae_[G-11];bacterium_MOT-177_nov_96.267%074000077110000000000
SPN92Bacteria;Firmicutes;Erysipelotrichia;Erysipelotrichales;Erysipelotrichaceae;Longibaculum;muris_nov_93.910%000162603700000000000
SPN93Bacteria;Firmicutes;Clostridia;Eubacteriales;Lachnospiraceae;Roseburia;hominis_nov_92.471%00000099160000000000
SPN94Bacteria;Firmicutes;Clostridia;Eubacteriales;Lachnospiraceae;Eisenbergiella;massiliensis_nov_88.697%0000170000057921230151132490
SPN95Bacteria;Bacteroidetes;Bacteroidia;Bacteroidales;Tannerellaceae;Parabacteroides;distasonis_nov_97.514%00097896400000000000
SPN96Bacteria;Firmicutes;Clostridia;Eubacteriales;Oscillospiraceae;Lawsonibacter;asaccharolyticus_nov_90.421%4204500000000000000
SPN97Bacteria;Firmicutes;Clostridia;Clostridiales;Eubacteriaceae_[XV];Eubacterium;xylanophilum_nov_91.149%00000016581000000000
SPN98Bacteria;Firmicutes;Clostridia;Eubacteriales;Lachnospiraceae;Lachnospiraceae_[G-14];bacterium_MOT-182_nov_92.245%00000000000770016800
SPN99Bacteria;Bacteroidetes;Bacteroidia;Bacteroidales;Tannerellaceae;Parabacteroides;distasonis_nov_97.323%0000000130114189082000
SPPN3Bacteria;Bacteroidetes;Bacteroidia;Bacteroidales;Prevotellaceae;Prevotella;multispecies_sppn3_2_nov_89.792%000000129150000000000
SPPN4Bacteria;Proteobacteria;Deltaproteobacteria;Desulfovibrionales;Desulfovibrionaceae;Desulfovibrio;multispecies_sppn4_2_nov_96.275%000000000280810202500
SPPN5Bacteria;Proteobacteria;Gammaproteobacteria;Pseudomonadales;Pseudomonadaceae;Pseudomonas;multispecies_sppn5_2_nov_97.461%000000004200000000
SPPN6Bacteria;Firmicutes;Clostridia;Eubacteriales;Lachnospiraceae;Blautia;multispecies_sppn6_2_nov_96.132%000000000000040000
 
 
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 1WT Normal vs WT HFD vs SAA KO Normal vs SAA KO HFD vs SAA KO BL HFDPDFSVGPDFSVGPDFSVG
Comparison 2WT Normal vs WT HFDPDFSVGPDFSVGPDFSVG
Comparison 3SAA KO Normal vs SAA KO HFDPDFSVGPDFSVGPDFSVG
Comparison 4SAA KO Normal vs SAA KO BL HFDPDFSVGPDFSVGPDFSVG
Comparison 5SAA KO Normal vs SAA KO BL HFDPDFSVGPDFSVGPDFSVG
 
 

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 1WT Normal vs WT HFD vs SAA KO Normal vs SAA KO HFD vs SAA KO BL HFDView in PDFView in SVG
Comparison 2WT Normal vs WT HFDView in PDFView in SVG
Comparison 3SAA KO Normal vs SAA KO HFDView in PDFView in SVG
Comparison 4SAA KO Normal vs SAA KO BL HFDView in PDFView in SVG
Comparison 5SAA KO Normal vs SAA KO BL HFDView in PDFView in SVG
 
 
 

Group Significance of Alpha-diversity Indices

To test whether the alpha diversity among different comparison groups are different statistically, 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 H 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.WT Normal vs WT HFD vs SAA KO Normal vs SAA KO HFD vs SAA KO BL HFDObserved FeaturesShannon IndexSimpson Index
Comparison 2.WT Normal vs WT HFDObserved FeaturesShannon IndexSimpson Index
Comparison 3.SAA KO Normal vs SAA KO HFDObserved FeaturesShannon IndexSimpson Index
Comparison 4.SAA KO Normal vs SAA KO BL HFDObserved FeaturesShannon IndexSimpson Index
Comparison 5.SAA KO Normal vs SAA KO BL HFDObserved 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 1WT Normal vs WT HFD vs SAA KO Normal vs SAA KO HFD vs SAA KO BL HFDPDFSVGPDFSVGPDFSVGPDFSVG
Comparison 2WT Normal vs WT HFDPDFSVGPDFSVGPDFSVGPDFSVG
Comparison 3SAA KO Normal vs SAA KO HFDPDFSVGPDFSVGPDFSVGPDFSVG
Comparison 4SAA KO Normal vs SAA KO BL HFDPDFSVGPDFSVGPDFSVGPDFSVG
Comparison 5SAA KO Normal vs SAA KO BL HFDPDFSVGPDFSVGPDFSVGPDFSVG
 
 
 
 
 

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) as the group significant 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.WT Normal vs WT HFD vs SAA KO Normal vs SAA KO HFD vs SAA KO BL HFDBray–CurtisCorrelationAitchison
Comparison 2.WT Normal vs WT HFDBray–CurtisCorrelationAitchison
Comparison 3.SAA KO Normal vs SAA KO HFDBray–CurtisCorrelationAitchison
Comparison 4.SAA KO Normal vs SAA KO BL HFDBray–CurtisCorrelationAitchison
Comparison 5.SAA KO Normal vs SAA KO BL HFDBray–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 sifgnificant 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.WT Normal vs WT HFD vs SAA KO Normal vs SAA KO HFD vs SAA KO BL HFD
Comparison 2.WT Normal vs WT HFD
Comparison 3.SAA KO Normal vs SAA KO HFD
Comparison 4.SAA KO Normal vs SAA KO BL HFD
Comparison 5.SAA KO Normal vs SAA KO BL HFD
 
 

ANCOM-BC2 Differential Abundance Analysis

 

Starting with version V1.2, we 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.

Starting with version V1.43, ANCOM-BC2 is used instead of ANCOM-BC, So that multiple pairwise directional test can be performed (if there are more than two gorups in a comparison). When performning pairwise directional test, the mixed directional false discover rate (mdFDR) is taken into account. The mdFDR is the combination of false discovery rate due to multiple testing, multiple pairwise comparisons, and directional tests within each pairwise comparison. The mdFDR is adopted from (Guo, Sarkar, and Peddada 2010; Grandhi, Guo, and Peddada 2016). For more detail explanation and additional features of ANCOM-BC2 please see author's documentation.

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.

Guo W, Sarkar SK, Peddada SD. Controlling false discoveries in multidimensional directional decisions, with applications to gene expression data on ordered categories. Biometrics. 2010 Jun;66(2):485-92. doi: 10.1111/j.1541-0420.2009.01292.x. Epub 2009 Jul 23. PMID: 19645703; PMCID: PMC2895927.

Grandhi A, Guo W, Peddada SD. A multiple testing procedure for multi-dimensional pairwise comparisons with application to gene expression studies. BMC Bioinformatics. 2016 Feb 25;17:104. doi: 10.1186/s12859-016-0937-5. PMID: 26917217; PMCID: PMC4768411.

 
 
ANCOM-BC Results for Individual Comparisons
 
Comparison No.Comparison Name
Comparison 1.WT Normal vs WT HFD vs SAA KO Normal vs SAA KO HFD vs SAA KO BL HFD
Comparison 2.WT Normal vs WT HFD
Comparison 3.SAA KO Normal vs SAA KO HFD
Comparison 4.SAA KO Normal vs SAA KO BL HFD
Comparison 5.SAA KO Normal vs SAA KO BL HFD
 
 
 

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.

 
WT Normal vs WT HFD vs SAA KO Normal vs SAA KO HFD vs SAA KO BL HFD
 
 
 
 
 
 
 
LEfSe Results for All Comparisons
 
Comparison No.Comparison Name
Comparison 1.WT Normal vs WT HFD vs SAA KO Normal vs SAA KO HFD vs SAA KO BL HFD
Comparison 2.WT Normal vs WT HFD
Comparison 3.SAA KO Normal vs SAA KO HFD
Comparison 4.SAA KO Normal vs SAA KO BL HFD
Comparison 5.SAA KO Normal vs SAA KO BL HFD
 
 

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 1WT Normal vs WT HFD vs SAA KO Normal vs SAA KO HFD vs SAA KO BL HFDPDFSVGPDFSVGPDFSVG
Comparison 2WT Normal vs WT HFDPDFSVGPDFSVGPDFSVG
Comparison 3SAA KO Normal vs SAA KO HFDPDFSVGPDFSVGPDFSVG
Comparison 4SAA KO Normal vs SAA KO BL HFDPDFSVGPDFSVGPDFSVG
Comparison 5SAA KO Normal vs SAA KO BL HFDPDFSVGPDFSVGPDFSVG
 
 
B) One-way clustering - clustered on rows (organism) only
Comparison No.Comparison NameFamily LevelGenus LevelSpecies Level
Comparison 1WT Normal vs WT HFD vs SAA KO Normal vs SAA KO HFD vs SAA KO BL HFDPDFSVGPDFSVGPDFSVG
Comparison 2WT Normal vs WT HFDPDFSVGPDFSVGPDFSVG
Comparison 3SAA KO Normal vs SAA KO HFDPDFSVGPDFSVGPDFSVG
Comparison 4SAA KO Normal vs SAA KO BL HFDPDFSVGPDFSVGPDFSVG
Comparison 5SAA KO Normal vs SAA KO BL HFDPDFSVGPDFSVGPDFSVG
 
 
C) No clustering
Comparison No.Comparison NameFamily LevelGenus LevelSpecies Level
Comparison 1WT Normal vs WT HFD vs SAA KO Normal vs SAA KO HFD vs SAA KO BL HFDPDFSVGPDFSVGPDFSVG
Comparison 2WT Normal vs WT HFDPDFSVGPDFSVGPDFSVG
Comparison 3SAA KO Normal vs SAA KO HFDPDFSVGPDFSVGPDFSVG
Comparison 4SAA KO Normal vs SAA KO BL HFDPDFSVGPDFSVGPDFSVG
Comparison 5SAA KO Normal vs SAA KO BL HFDPDFSVGPDFSVGPDFSVG
 
 

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

 
 

 
 

XIII. Disclaimer

The results of this analysis are for research purpose only. They are not intended to diagnose, treat, cure, or prevent any disease. Forsyth and FOMC are not responsible for use of information provided in this report outside the research area.

 
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