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

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FOMC Service Report 1

16S rRNA Gene V1V3 Amplicon Sequencing

Version V1.11

The Forsyth Institute, Cambridge, MA, USA
October 30, 2021

Project ID: HOMD4401

I. Project Summary

Project HOMD4401 services include NGS sequencing of the V1V3 region of the 16S rRNA amplicons from the samples. First and foremost, please download this report, as well as the sequence raw data from the download links provided below. These links will expire after 60 days. We cannot guarantee the availability of your data after 60 days.

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

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

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

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

 

II. Workflow Checklist

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

III. NGS Sequencing

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

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

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

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

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

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

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

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

 

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


S01ligature15.NoSPMzr4401_10V1V3_R1.fastq.gzzr4401_10V1V3_R2.fastq.gz


S02ligature16.NoSPMzr4401_11V1V3_R1.fastq.gzzr4401_11V1V3_R2.fastq.gz


S03ligature18.NoSPMzr4401_12V1V3_R1.fastq.gzzr4401_12V1V3_R2.fastq.gz


S04ligature23.NoSPMzr4401_13V1V3_R1.fastq.gzzr4401_13V1V3_R2.fastq.gz


S05ligature24.NoSPMzr4401_14V1V3_R1.fastq.gzzr4401_14V1V3_R2.fastq.gz


S06ligature25.NoSPMzr4401_15V1V3_R1.fastq.gzzr4401_15V1V3_R2.fastq.gz


S07ligature26.NoSPMzr4401_16V1V3_R1.fastq.gzzr4401_16V1V3_R2.fastq.gz


S08ligature30.SPMzr4401_17V1V3_R1.fastq.gzzr4401_17V1V3_R2.fastq.gz


S09ligature31.SPMzr4401_18V1V3_R1.fastq.gzzr4401_18V1V3_R2.fastq.gz


S10ligature33.SPMzr4401_19V1V3_R1.fastq.gzzr4401_19V1V3_R2.fastq.gz


S11ligature34.SPMzr4401_1V1V3_R1.fastq.gzzr4401_1V1V3_R2.fastq.gz


S12ligature37.SPMzr4401_20V1V3_R1.fastq.gzzr4401_20V1V3_R2.fastq.gz


S13ligature38.SPMzr4401_21V1V3_R1.fastq.gzzr4401_21V1V3_R2.fastq.gz


S14ligature39.SPMzr4401_22V1V3_R1.fastq.gzzr4401_22V1V3_R2.fastq.gz


S15ligature40.SPMzr4401_23V1V3_R1.fastq.gzzr4401_23V1V3_R2.fastq.gz


S16Fecal2.BLzr4401_24V1V3_R1.fastq.gzzr4401_24V1V3_R2.fastq.gz


S17Fecal5.BLzr4401_25V1V3_R1.fastq.gzzr4401_25V1V3_R2.fastq.gz


S18Fecal10.BLzr4401_26V1V3_R1.fastq.gzzr4401_26V1V3_R2.fastq.gz


S19Fecal11.BLzr4401_27V1V3_R1.fastq.gzzr4401_27V1V3_R2.fastq.gz


S20Fecal15.ligNoSPMzr4401_28V1V3_R1.fastq.gzzr4401_28V1V3_R2.fastq.gz


S21Fecal18.ligNoSPMzr4401_29V1V3_R1.fastq.gzzr4401_29V1V3_R2.fastq.gz


S22Fecal19.ligNoSPMzr4401_2V1V3_R1.fastq.gzzr4401_2V1V3_R2.fastq.gz


S23Fecal23.ligNoSPMzr4401_30V1V3_R1.fastq.gzzr4401_30V1V3_R2.fastq.gz


S24Fecal24.ligNoSPMzr4401_31V1V3_R1.fastq.gzzr4401_31V1V3_R2.fastq.gz


S25Fecal25.ligNoSPMzr4401_32V1V3_R1.fastq.gzzr4401_32V1V3_R2.fastq.gz


S26Fecal33.ligSPMzr4401_33V1V3_R1.fastq.gzzr4401_33V1V3_R2.fastq.gz


S27Fecal37.ligSPMzr4401_34V1V3_R1.fastq.gzzr4401_34V1V3_R2.fastq.gz


S28Fecal39.ligSPMzr4401_35V1V3_R1.fastq.gzzr4401_35V1V3_R2.fastq.gz


S29Fecal40.ligSPMzr4401_36V1V3_R1.fastq.gzzr4401_36V1V3_R2.fastq.gz


S30Brain1.BLzr4401_37V1V3_R1.fastq.gzzr4401_37V1V3_R2.fastq.gz


S31Brain2.BLzr4401_38V1V3_R1.fastq.gzzr4401_38V1V3_R2.fastq.gz


S32Brain4.BLzr4401_39V1V3_R1.fastq.gzzr4401_39V1V3_R2.fastq.gz


S33Brain8.BLzr4401_3V1V3_R1.fastq.gzzr4401_3V1V3_R2.fastq.gz


S34Brain10.BLzr4401_40V1V3_R1.fastq.gzzr4401_40V1V3_R2.fastq.gz


S35Brain11.BLzr4401_41V1V3_R1.fastq.gzzr4401_41V1V3_R2.fastq.gz


S36Brain15.ligNoSPMzr4401_42V1V3_R1.fastq.gzzr4401_42V1V3_R2.fastq.gz


S37Brain16.ligNoSPMzr4401_43V1V3_R1.fastq.gzzr4401_43V1V3_R2.fastq.gz


S38Brain18.ligNoSPMzr4401_44V1V3_R1.fastq.gzzr4401_44V1V3_R2.fastq.gz


S39Brain23.ligNoSPMzr4401_45V1V3_R1.fastq.gzzr4401_45V1V3_R2.fastq.gz


S40Brain25.ligNoSPMzr4401_46V1V3_R1.fastq.gzzr4401_46V1V3_R2.fastq.gz


S41Brain26.ligNoSPMzr4401_47V1V3_R1.fastq.gzzr4401_47V1V3_R2.fastq.gz


S42Brain30.ligSPMzr4401_4V1V3_R1.fastq.gzzr4401_4V1V3_R2.fastq.gz


S43Brain31.ligSPMzr4401_5V1V3_R1.fastq.gzzr4401_5V1V3_R2.fastq.gz


S44Brain33.ligSPMzr4401_6V1V3_R1.fastq.gzzr4401_6V1V3_R2.fastq.gz


S45Brain37.ligSPMzr4401_7V1V3_R1.fastq.gzzr4401_7V1V3_R2.fastq.gz


S46Brain39.ligSPMzr4401_8V1V3_R1.fastq.gzzr4401_8V1V3_R2.fastq.gz


S47Brain40.ligSPMzr4401_9V1V3_R1.fastq.gzzr4401_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”.

Below is the link to a PDF file for viewing the quality plots for all samples:

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

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

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

R1/R2281271261251241231
32129.23%33.58%33.93%34.59%34.02%23.97%
31133.78%39.34%38.89%39.01%28.47%18.61%
30138.96%52.74%57.43%49.63%39.37%26.64%
29139.11%52.17%46.64%39.83%27.17%26.00%
28138.11%39.57%36.39%27.32%25.85%23.75%
27127.08%30.20%24.97%26.04%23.90%22.91%

Based on the above result, the trim length combination of R1 = 301 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 IDF4401.S01F4401.S02F4401.S03F4401.S04F4401.S05F4401.S06F4401.S07F4401.S08F4401.S09F4401.S10F4401.S11F4401.S12F4401.S13F4401.S14F4401.S15F4401.S16F4401.S17F4401.S18F4401.S19F4401.S20F4401.S21F4401.S22F4401.S23F4401.S24F4401.S25F4401.S26F4401.S27F4401.S28F4401.S29F4401.S30F4401.S31F4401.S32F4401.S33F4401.S34F4401.S35F4401.S36F4401.S37F4401.S38F4401.S39F4401.S40F4401.S41F4401.S42F4401.S43F4401.S44F4401.S45F4401.S46F4401.S47Row SumPercentage
input49,72847,88551,52350,83354,44161,75434,84939,70340,39834,02139,73740,88842,15244,06330,89646,79737,75243,01338,78641,69244,91745,15741,24243,69349,31540,96139,79140,88943,45942,37438,29744,71139,18545,77143,73844,13045,49943,23446,06244,61348,93945,52949,43147,97644,20639,96144,3702,058,361100.00%
filtered36,14834,05436,16135,14338,23444,75920,16623,09226,12921,61025,13726,36626,30828,0187,51531,70022,80429,16925,33826,76028,79532,34434,50236,28741,35732,25433,72734,27236,28435,33332,39636,97626,83138,77332,57637,41537,39736,06638,62737,25239,67832,29634,51731,44129,84227,95729,2911,489,09772.34%
denoisedF35,80433,73735,79534,75738,00644,49319,36122,35025,11420,71924,90325,11825,11126,4737,30830,45521,54128,41724,14726,01327,74232,06734,17835,84640,84931,85033,24133,82035,73134,78131,96536,63426,53938,37732,17137,03237,04935,60038,25236,79139,20731,96034,28630,93329,55327,64129,0281,462,74571.06%
denoisedR35,78733,75335,88334,95638,11944,42819,56322,36325,47420,65725,03725,46225,44826,7217,40130,37421,90128,35324,08326,10128,12332,18034,27536,10241,06331,99433,41634,02935,91134,94732,20136,68526,62038,50632,25837,06837,17435,77738,30236,99939,38132,09834,34930,69229,65527,79929,0891,468,55771.35%
merged35,06633,27535,00934,40937,61444,01615,33819,31320,73315,97324,64221,70221,13321,1343,31424,47517,99725,58219,53922,95425,03831,51933,33634,64040,14230,31132,52833,07634,31232,93831,43935,21526,25437,71329,93035,96035,79634,34837,02936,14438,20131,34133,77529,07929,19726,84428,3631,377,68666.93%
nonchim31,34627,18925,21724,52530,55136,5849,31212,05712,3859,70617,70912,92513,61113,3121,98514,20611,13113,36811,68714,39315,43423,85529,02031,35734,36427,25728,15828,48230,96829,71327,30831,01819,21631,77627,10732,14731,51729,94732,75831,27633,66722,84625,64119,03621,58020,56019,1571,078,36452.39%

This table can be downloaded as an Excel table below:

 

5. DADA2 Amplicon Sequence Variants (ASVs). A total of 4447 unique merged and chimera-free ASV sequences were identified, and their corresponding read counts for each sample are available in the "ASV Read Count Table" with rows for the ASV sequences and columns for sample. This read count table can be used for microbial profile comparison among different samples and the sequences provided in the table can be used to taxonomy assignment.

 

The table can be downloaded from this link:

 
 
 
 

VII. Analysis - Read Taxonomy Assignment

Read Taxonomy Assignment - Methods

 

The species-level, open-reference 16S rRNA NGS reads taxonomy assignment pipeline

Version 20210310
 

1. Raw sequences reads in FASTA format were BLASTN-searched against a combined set of 16S rRNA reference sequences. It consists of MOMD (version 0.1), the HOMD (version 15.2 http://www.homd.org/index.php?name=seqDownload&file&type=R ), HOMD 16S rRNA RefSeq Extended Version 1.1 (EXT), GreenGene Gold (GG) (http://greengenes.lbl.gov/Download/Sequence_Data/Fasta_data_files/gold_strains_gg16S_aligned.fasta.gz) , and the NCBI 16S rRNA reference sequence set (https://ftp.ncbi.nlm.nih.gov/blast/db/16S_ribosomal_RNA.tar.gz). These sequences were screened and combined to remove short sequences (<1000nt), chimera, duplicated and sub-sequences, as well as sequences with poor taxonomy annotation (e.g., without species information). This process resulted in 1,015 from HOMD V15.22, 495 from EXT, 3,940 from GG and 18,044 from NCBI, a total of 25,120 sequences. Altogether these sequence represent a total of 15,601 oral and non-oral microbial species.

The NCBI BLASTN version 2.7.1+ (Zhang et al, 2000) was used with the default parameters. Reads with ≥ 98% sequence identity to the matched reference and ≥ 90% alignment length (i.e., ≥ 90% of the read length that was aligned to the reference and was used to calculate the sequence percent identity) were classified based on the taxonomy of the reference sequence with highest sequence identity. If a read matched with reference sequences representing more than one species with equal percent identity and alignment length, it was subject to chimera checking with USEARCH program version v8.1.1861 (Edgar 2010). Non-chimeric reads with multi-species best hits were considered valid and were assigned with a unique species notation (e.g., spp) denoting unresolvable multiple species.

2. Unassigned reads (i.e., reads with < 98% identity or < 90% alignment length) were pooled together and reads < 200 bases were removed. The remaining reads were subject to the de novo operational taxonomy unit (OTU) calling and chimera checking using the USEARCH program version v8.1.1861 (Edgar 2010). The de novo OTU calling and chimera checking was done using 98% as the sequence identity cutoff, i.e., the species-level OTU. The output of this step produced species-level de novo clustered OTUs with 98% identity. Representative reads from each of the OTUs/species were then BLASTN-searched against the same reference sequence set again to determine the closest species for these potential novel species. These potential novel species were pooled together with the reads that were signed to specie-level in the previous step, for down-stream analyses.

Reference:
Edgar RC. Search and clustering orders of magnitude faster than BLAST. Bioinformatics. 2010 Oct 1;26(19):2460-1. doi: 10.1093/bioinformatics/btq461. Epub 2010 Aug 12. PubMed PMID: 20709691.

3. Designations used in the taxonomy:

	1) Taxonomy levels are indicated by these prefixes:
	
	   k__: domain/kingdom
	   p__: phylum
	   c__: class
	   o__: order
	   f__: family
	   g__: genus  
	   s__: species
	
	   Example: 
	
	   k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Lachnospiraceae;g__Blautia;s__faecis
		
	2) Unique level identified – known species:
	   
	   k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Lachnospiraceae;g__Roseburia;s__hominis
	
	   The above example shows some reads match to a single species (all levels are unique)
	
	3) Non-unique level identified – known species:

	   k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Lachnospiraceae;g__Roseburia;s__multispecies_spp123_3
	   
	   The above example “s__multispecies_spp123_3” indicates certain reads equally match to 3 species of the 
	   genus Roseburia; the “spp123” is a temporally assigned species ID.
	
	   k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Lachnospiraceae;g__multigenus;s__multispecies_spp234_5
	   
	   The above example indicates certain reads match equally to 5 different species, which belong to multiple genera.; 
	   the “spp234” is a temporally assigned species ID.
	
	4) Unique level identified – unknown species, potential novel species:
	   
	   k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Lachnospiraceae;g__Roseburia;s__ hominis_nov_97%
	   
	   The above example indicates that some reads have no match to any of the reference sequences with 
	   sequence identity ≥ 98% and percent coverage (alignment length)  ≥ 98% as well. However this groups 
	   of reads (actually the representative read from a de novo  OTU) has 96% percent identity to 
	   Roseburia hominis, thus this is a potential novel species, closest to Roseburia hominis. 
	   (But they are not the same species).
	
	5) Multiple level identified – unknown species, potential novel species:
	   k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Lachnospiraceae;g__Roseburia;s__ multispecies_sppn123_3_nov_96%
	
	   The above example indicates that some reads have no match to any of the reference sequences 
	   with sequence identity ≥ 98% and percent coverage (alignment length)  ≥ 98% as well. 
	   However this groups of reads (actually the representative read from a de novo  OTU) 
	   has 96% percent identity equally to 3 species in Roseburia. Thus this is no single 
	   closest species, instead this group of reads match equally to multiple species at 96%. 
	   Since they have passed chimera check so they represent a novel species. “sppn123” is a 
	   temporary ID for this potential novel species.

 
4. The taxonomy assignment algorithm is illustrated in this flow char below:
 
 
 
 

Read Taxonomy Assignment - Result Summary

CodeCategoryRead Count (MC=1)*Read Count (MC=100)*
ATotal reads1,078,3641,078,364
BTotal assigned reads548,704548,704
CAssigned reads in species with read count < MC06,628
DAssigned reads in samples with read count < 5001,179981
ETotal samples4747
FSamples with reads >= 5004444
GSamples with reads < 50033
HTotal assigned reads used for analysis (B-C-D)547,525541,095
IReads assigned to single species315,582314,912
JReads assigned to multiple species14,02713,992
KReads assigned to novel species217,916212,191
LTotal number of species356163
MNumber of single species5837
NNumber of multi-species104
ONumber of novel species284122
PTotal unassigned reads529,660529,660
QChimeric reads351351
RReads without BLASTN hits490,921490,921
SOthers: short, low quality, singletons, etc.38,38838,388
A=B+P=C+D+H+Q+R+S
E=F+G
B=C+D+H
H=I+J+K
L=M+N+O
P=Q+R+S
* MC = Minimal Count per species, species with total read count < MC were removed.
* The assignment result from MC=1 was used in the downstream analyses.
 
 

Read Taxonomy Assignment - Sample Meta Information

#SampleIDSampleNamesubGroupcompGroupcolorGroupyyy
F4401.S01ligature15.NoSPMLigatureNoSPMligature.NoSPM
F4401.S02ligature16.NoSPMLigatureNoSPMligature.NoSPM
F4401.S03ligature18.NoSPMLigatureNoSPMligature.NoSPM
F4401.S04ligature23.NoSPMLigatureNoSPMligature.NoSPM
F4401.S05ligature24.NoSPMLigatureNoSPMligature.NoSPM
F4401.S06ligature25.NoSPMLigatureNoSPMligature.NoSPM
F4401.S07ligature26.NoSPMLigatureNoSPMligature.NoSPM
F4401.S08ligature30.SPMLigatureSPMligature.SPM
F4401.S09ligature31.SPMLigatureSPMligature.SPM
F4401.S10ligature33.SPMLigatureSPMligature.SPM
F4401.S11ligature34.SPMLigatureSPMligature.SPM
F4401.S12ligature37.SPMLigatureSPMligature.SPM
F4401.S13ligature38.SPMLigatureSPMligature.SPM
F4401.S14ligature39.SPMLigatureSPMligature.SPM
F4401.S15ligature40.SPMLigatureSPMligature.SPM
F4401.S16Fecal2.BLFecalBLFecal.BL
F4401.S17Fecal5.BLFecalBLFecal.BL
F4401.S18Fecal10.BLFecalBLFecal.BL
F4401.S19Fecal11.BLFecalBLFecal.BL
F4401.S20Fecal15.ligNoSPMFecalligNoSPMFecal.ligNoSPM
F4401.S21Fecal18.ligNoSPMFecalligNoSPMFecal.ligNoSPM
F4401.S22Fecal19.ligNoSPMFecalligNoSPMFecal.ligNoSPM
F4401.S23Fecal23.ligNoSPMFecalligNoSPMFecal.ligNoSPM
F4401.S24Fecal24.ligNoSPMFecalligNoSPMFecal.ligNoSPM
F4401.S25Fecal25.ligNoSPMFecalligNoSPMFecal.ligNoSPM
F4401.S26Fecal33.ligSPMFecalligSPMFecal.ligSPM
F4401.S27Fecal37.ligSPMFecalligSPMFecal.ligSPM
F4401.S28Fecal39.ligSPMFecalligSPMFecal.ligSPM
F4401.S29Fecal40.ligSPMFecalligSPMFecal.ligSPM
F4401.S30Brain1.BLBrainBLBrain.BL
F4401.S31Brain2.BLBrainBLBrain.BL
F4401.S32Brain4.BLBrainBLBrain.BL
F4401.S33Brain8.BLBrainBLBrain.BL
F4401.S34Brain10.BLBrainBLBrain.BL
F4401.S35Brain11.BLBrainBLBrain.BL
F4401.S36Brain15.ligNoSPMBrainligNoSPMBrain.ligNoSPM
F4401.S37Brain16.ligNoSPMBrainligNoSPMBrain.ligNoSPM
F4401.S38Brain18.ligNoSPMBrainligNoSPMBrain.ligNoSPM
F4401.S39Brain23.ligNoSPMBrainligNoSPMBrain.ligNoSPM
F4401.S40Brain25.ligNoSPMBrainligNoSPMBrain.ligNoSPM
F4401.S41Brain26.ligNoSPMBrainligNoSPMBrain.ligNoSPM
F4401.S42Brain30.ligSPMBrainligSPMBrain.ligSPM
F4401.S43Brain31.ligSPMBrainligSPMBrain.ligSPM
F4401.S44Brain33.ligSPMBrainligSPMBrain.ligSPM
F4401.S45Brain37.ligSPMBrainligSPMBrain.ligSPM
F4401.S46Brain39.ligSPMBrainligSPMBrain.ligSPM
F4401.S47Brain40.ligSPMBrainligSPMBrain.ligSPM
 
 

Read Taxonomy Assignment - ASV Read Counts by Samples

#Sample IDRead Count
F4401.S151985
F4401.S079312
F4401.S109706
F4401.S1711131
F4401.S1911687
F4401.S0812057
F4401.S0912385
F4401.S1212925
F4401.S1413312
F4401.S1813368
F4401.S1313611
F4401.S1614206
F4401.S2014393
F4401.S2115434
F4401.S1117709
F4401.S4419036
F4401.S4719157
F4401.S3319216
F4401.S4620560
F4401.S4521580
F4401.S4222846
F4401.S2223855
F4401.S0424525
F4401.S0325217
F4401.S4325641
F4401.S3527107
F4401.S0227189
F4401.S2627257
F4401.S3127308
F4401.S2728158
F4401.S2828482
F4401.S2329020
F4401.S3029713
F4401.S3829947
F4401.S0530551
F4401.S2930968
F4401.S3231018
F4401.S4031276
F4401.S0131346
F4401.S2431357
F4401.S3731517
F4401.S3431776
F4401.S3632147
F4401.S3932758
F4401.S4133667
F4401.S2534364
F4401.S0636584
 
 

Read Taxonomy Assignment - ASV 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.
SPIDTaxonomyF4401.S01F4401.S02F4401.S03F4401.S04F4401.S05F4401.S06F4401.S07F4401.S08F4401.S09F4401.S10F4401.S11F4401.S12F4401.S13F4401.S14F4401.S15F4401.S16F4401.S17F4401.S18F4401.S19F4401.S20F4401.S21F4401.S22F4401.S23F4401.S24F4401.S25F4401.S26F4401.S27F4401.S28F4401.S29F4401.S30F4401.S31F4401.S32F4401.S33F4401.S34F4401.S35F4401.S36F4401.S37F4401.S38F4401.S39F4401.S40F4401.S41F4401.S42F4401.S43F4401.S44F4401.S45F4401.S46F4401.S47
SP1k__Bacteria;p__Bacteroidetes;c__Bacteroidia;o__Bacteroidales;f__Bacteroidaceae;g__Bacteroides;s__thetaiotaomicron327673087218327741366669127589096271371855091090112588374148609106917350000870690820000000000764233619763526341
SP10k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Ruminococcaceae;g__Acutalibacter;s__muris000000750065001011380280001510000000000000000000000000000
SP11k__Bacteria;p__Proteobacteria;c__Betaproteobacteria;o__Burkholderiales;f__Alcaligenaceae;g__Sutterella;s__sp._str._cont1.660000003194082713000232287138030227275113173211190000000000000000000000000
SP12k__Bacteria;p__Firmicutes;c__Erysipelotrichia;o__Erysipelotrichales;f__Erysipelotrichaceae;g__Erysipelatoclostridium;s__cocleatum0000000001200600450220373200000000000000000000000000
SP13k__Bacteria;p__Proteobacteria;c__Alphaproteobacteria;o__Rhizobiales;f__Bradyrhizobiaceae;g__Bradyrhizobium;s__japonicum0000000000000000000000001030970000001070472800081000000
SP14k__Bacteria;p__Actinobacteria;c__Actinobacteria;o__Actinomycetales;f__Propionibacteriaceae;g__Propionibacterium;s__acnes220001970000000000000000010127011311532513994125488307153008001512070035000
SP15k__Bacteria;p__Verrucomicrobia;c__Verrucomicrobiae;o__Verrucomicrobiales;f__Akkermansiaceae;g__Akkermansia;s__muciniphila16240000101815871661430774538468016837114156172312000000000000000000000030000
SP18k__Bacteria;p__Actinobacteria;c__Actinobacteria;o__Corynebacteriales;f__Corynebacteriaceae;g__Corynebacterium;s__mastitidis007934751167500008720000000000810000000000153000000006207856160210
SP2k__Bacteria;p__Firmicutes;c__Bacilli;o__Lactobacillales;f__Streptococcaceae;g__Streptococcus;s__danieliae64021153224017100005800000000003520000002200001100000000340282144139138251
SP21k__Bacteria;p__Firmicutes;c__Erysipelotrichia;o__Erysipelotrichales;f__Erysipelotrichaceae;g__Faecalibaculum;s__rodentium0016000312850130000000000000000000000000000000000000
SP22k__Bacteria;p__Firmicutes;c__Bacilli;o__Bacillales;f__Staphylococcaceae;g__Staphylococcus;s__saprophyticus00000000000000000000000000000000000011270000000000
SP23k__Bacteria;p__Proteobacteria;c__Alphaproteobacteria;o__Rhizobiales;f__Bradyrhizobiaceae;g__Bradyrhizobium;s__valentinum2400000000000000000000000122000000002805000000000000
SP26k__Bacteria;p__Firmicutes;c__Bacilli;o__Bacillales;f__Staphylococcaceae;g__Staphylococcus;s__cohnii2840000178000000000000000000000092101000005800330021512103200
SP29k__Bacteria;p__Actinobacteria;c__Actinobacteria;o__Micrococcales;f__Micrococcaceae;g__Kocuria;s__carniphila0000000000000000000000001460000000000000000000000
SP3k__Bacteria;p__Proteobacteria;c__Gammaproteobacteria;o__Pseudomonadales;f__Moraxellaceae;g__Acinetobacter;s__johnsonii0014150800000000000000001030053381015400000672741017600000090
SP31k__Bacteria;p__Firmicutes;c__Bacilli;o__Lactobacillales;f__Enterococcaceae;g__Enterococcus;s__faecalis62000000000000000000005000000000000000003700000180
SP32k__Bacteria;p__Proteobacteria;c__Gammaproteobacteria;o__Xanthomonadales;f__Xanthomonadaceae;g__Stenotrophomonas;s__maltophilia00000000000000000000005400000105000000000000000000
SP33k__Bacteria;p__Firmicutes;c__Bacilli;o__Bacillales;f__Staphylococcaceae;g__Staphylococcus;s__xylosus0000000000000000000000000000000000003080000000000
SP38k__Bacteria;p__Bacteroidetes;c__Bacteroidia;o__Bacteroidales;f__Tannerellaceae;g__Parabacteroides;s__goldsteinii000000000000204204000000000000000000000000000000000
SP39k__Bacteria;p__Proteobacteria;c__Betaproteobacteria;o__Burkholderiales;f__Burkholderiaceae;g__Ralstonia;s__sp._Oral_Taxon_40600000000000000000000001060068000000000000000000000
SP4k__Bacteria;p__Firmicutes;c__Bacilli;o__Lactobacillales;f__Lactobacillaceae;g__Lactobacillus;s__murinus15020101700004183389794711621581611621574435016352019800000030314700400024000516948961170221000
SP41k__Bacteria;p__Proteobacteria;c__Alphaproteobacteria;o__Rhizobiales;f__Bradyrhizobiaceae;g__Bradyrhizobium;s__lupini0000000000000000000000000671809411300022548600000000000
SP43k__Bacteria;p__Proteobacteria;c__Betaproteobacteria;o__Burkholderiales;f__Oxalobacteraceae;g__Oxalobacteraceae_[G];s__sp._Oral_Taxon_C910000000000000000000000027054000900000000060540000000
SP44k__Bacteria;p__Actinobacteria;c__Actinobacteria;o__Corynebacteriales;f__Corynebacteriaceae;g__Turicella;s__otitidis00000000000000000000000000950003600880000000000000
SP5k__Bacteria;p__Firmicutes;c__Bacilli;o__Lactobacillales;f__Streptococcaceae;g__Streptococcus;s__thoraltensis296042207312895101222205222409000059080000000000171360000019213300081050001939000013565162401555653833508773
SP52k__Bacteria;p__Actinobacteria;c__Actinobacteria;o__Corynebacteriales;f__Mycobacteriaceae;g__Mycobacterium;s__cookii0000000000000000000000000000000103000000000000000
SP53k__Bacteria;p__Proteobacteria;c__Gammaproteobacteria;o__Pseudomonadales;f__Moraxellaceae;g__Acinetobacter;s__pittii00000000000000000000000000007200000003409800000000
SP6k__Bacteria;p__Actinobacteria;c__Actinobacteria;o__Bifidobacteriales;f__Bifidobacteriaceae;g__Bifidobacterium;s__pseudolongum00880000100324802525600880024180000000054000000006700000000
SP61k__Bacteria;p__Firmicutes;c__Bacilli;o__Bacillales;f__Staphylococcaceae;g__Staphylococcus;s__auricularis0000000000000000000000000000000000105000000000000
SP65k__Bacteria;p__Proteobacteria;c__Alphaproteobacteria;o__Rhizobiales;f__Methylobacteriaceae;g__Methylobacterium;s__goesingense000000000000000000000000000148000000119000000000000
SP66k__Bacteria;p__Actinobacteria;c__Actinobacteria;o__Propionibacteriales;f__Propionibacteriaceae;g__Cutibacterium;s__namnetense0000000000000000000000000000000000000010200000000
SP68k__Bacteria;p__Proteobacteria;c__Betaproteobacteria;o__Burkholderiales;f__Sutterellaceae;g__Parasutterella;s__excrementihominis000000034715700000000055000000000000000000000000000
SP7k__Bacteria;p__Proteobacteria;c__Gammaproteobacteria;o__Enterobacteriales;f__Enterobacteriaceae;g__Escherichia;s__coli4523307387103700000000000000364074333072977021911409900152032000840650004900006795295
SP76k__Bacteria;p__Actinobacteria;c__Actinobacteria;o__Corynebacteriales;f__Corynebacteriaceae;g__Corynebacterium;s__pyruviciproducens0000000000000000000000000000000161000000000000000
SP77k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Clostridiaceae;g__Clostridium;s__saudiense00000000000000002600000011300000000000000000000000
SP8k__Bacteria;p__Bacteroidetes;c__Chitinophagia;o__Chitinophagales;f__Chitinophagaceae;g__Sediminibacterium;s__aquarii0000000000000000000000014400001678102304700000084000000
SP9k__Bacteria;p__Firmicutes;c__Bacilli;o__Lactobacillales;f__Enterococcaceae;g__Enterococcus;s__sp._str._F950000000000000000000001000000065000000000036000001110
SPN107k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Clostridiales Family XIII. Incertae Sedis;g__Ihubacter;s__massiliensis03100000000000000017000000000000000000000000000000
SPN11k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Ruminococcaceae;g__Ruminoclostridium;s__alkalicellum080000569483483397057339837003613372244064705630000000000000000055000032000
SPN112k__Bacteria;p__Bacteroidetes;c__Bacteroidia;o__Bacteroidales;f__Bacteroidaceae;g__Bacteroides;s__massiliensis000000000000242135001271327215017500000000000000000000000000
SPN117k__Bacteria;p__Firmicutes;c__Erysipelotrichia;o__Erysipelotrichales;f__Erysipelotrichaceae;g__Longibaculum;s__muris0000000203218004600000003200000000000000000000000000
SPN120k__Bacteria;p__Bacteroidetes;c__Bacteroidia;o__Bacteroidales;f__Bacteroidaceae;g__Bacteroides;s__uniformis0000008128135140195320023950090016000000082000000000000006000
SPN128k__Bacteria;p__Bacteroidetes;c__Bacteroidia;o__Bacteroidales;f__Prevotellaceae;g__Prevotella;s__fusca00000000000223340289020171200000000000000000000000000
SPN130k__Bacteria;p__Proteobacteria;c__Betaproteobacteria;o__Burkholderiales;f__Oxalobacteraceae;g__Massilia;s__yuzhufengensis000000000000000004980265135000000000000000000590030000
SPN137k__Bacteria;p__Firmicutes;c__Bacilli;o__Lactobacillales;f__Lactobacillaceae;g__Pediococcus;s__stilesii00000010010313411800919800003611814200000000000000000000000000
SPN138k__Bacteria;p__Firmicutes;c__Erysipelotrichia;o__Erysipelotrichales;f__Erysipelotrichaceae;g__Turicibacter;s__sanguinis02100000000000000012200000000000000000000000000000
SPN140k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Lachnospiraceae;g__Faecalicatena;s__orotica000000000006210669040976087050000000000000000000690000000
SPN141k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Eubacteriaceae_[XV];g__Eubacterium;s__hallii00000000000753317501700037500000000000000000000000036000
SPN142k__Bacteria;p__Firmicutes;c__Bacilli;o__Lactobacillales;f__Streptococcaceae;g__Streptococcus;s__danieliae00000000000066190025621618352000074000008700000001010000000
SPN143k__Bacteria;p__Actinobacteria;c__Coriobacteriia;o__Coriobacteriales;f__Coriobacteriaceae;g__Atopobium;s__parvulum00000000000981057105514556585101000000740000000000000000000
SPN144k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Lachnospiraceae;g__NA;s__rectale0000001091718660068156790490000000000000000000000000000000
SPN145k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Lachnospiraceae_[XIVa];g__Blautia;s__gnavus000000164000057147880119500470000000000000000000000000000
SPN146k__Bacteria;p__Firmicutes;c__Erysipelotrichia;o__Erysipelotrichales;f__Erysipelotrichaceae;g__Faecalicoccus;s__acidiformans00000033464700069720106674154702800000000000000000000000000
SPN147k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Ruminococcaceae;g__Anaerotruncus;s__rubiinfantis_nov_88.654%00000000000001506400640000000000000000000000000000
SPN148k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Eubacteriaceae;g__Eubacterium;s__eligens_nov_87.572%00000000911820001101620018198700000000000000000000000000
SPN149k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Ruminococcaceae;g__unclassified_Ruminococcaceae;s__sp._str._D16_nov_91.188%0000004201336002320400002140000000000000000000000000000
SPN150k__Bacteria;p__Proteobacteria;c__Deltaproteobacteria;o__Desulfovibrionales;f__Desulfovibrionaceae;g__Desulfovibrio;s__sp._HMT_040_nov_96.680%000000000004736173021656000000000000000000000000000000
SPN151k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Eubacteriaceae;g__Eubacterium;s__xylanophilum_nov_90.093%000000000000188131000006796000000000000000000000021000
SPN152k__Bacteria;p__Bacteroidetes;c__Bacteroidia;o__Bacteroidales;f__Rikenellaceae;g__Alistipes;s__finegoldii_nov_92.600%0000000000013078190017638009600000000000000000000000000
SPN153k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Lachnospiraceae;g__Faecalicatena;s__orotica_nov_92.773%00000080720000016900008006100000000000000000000000000
SPN154k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Ruminococcaceae;g__unclassified_Ruminococcaceae;s__sp._str._D16_nov_94.605%000000006535016321010105020690000000000000000000000000000
SPN155k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Lachnospiraceae;g__Lachnoclostridium;s__xylanolyticum_nov_94.632%0000000005107940250112001070000000000000000000000000000
SPN156k__Bacteria;p__Bacteroidetes;c__Bacteroidetes_[C-1];o__Bacteroidetes_[O-1];f__Bacteroidetes_[F-1];g__Bacteroidetes_[G-7];s__bacterium_HMT_911_nov_84.942%000000405565460030290031033055000170000000000000000000000
SPN157k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Clostridiaceae;g__Oxobacter;s__pfennigii_nov_82.669%0000006700001975730744100261900000000000000000000000000
SPN158k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__NA;g__Pseudoflavonifractor;s__phocaeensis_nov_90.979%00000070153000001080008000000000000000000000000000
SPN159k__Bacteria;p__Bacteroidetes;c__Bacteroidetes_[C-1];o__Bacteroidetes_[O-1];f__Bacteroidetes_[F-1];g__Bacteroidetes_[G-7];s__bacterium_HMT_911_nov_83.268%1600000223125414561356304310439526520195425604002047345528170010400009300007466035003300072000
SPN160k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Lachnospiraceae;g__Faecalicatena;s__orotica_nov_92.218%0000000043440004601103835770000000000000000000000000000
SPN161k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Ruminococcaceae;g__Ruminococcaceae_[G-2];s__bacterium_HMT_085_nov_91.411%000000560660008700044001012300000000000000000000000000
SPN162k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__NA;g__Intestinimonas;s__butyriciproducens_nov_94.790%0000004370230294181010800290000000000000000000000000000
SPN163k__Bacteria;p__Proteobacteria;c__Alphaproteobacteria;o__Alphaproteobacteria_[O];f__Alphaproteobacteria_[F];g__Alphaproteobacteria_[G];s__sp._Oral_Taxon_A28_nov_92.634%000000390000136190004156670000000000000000000000000000
SPN164k__Bacteria;p__Firmicutes;c__Clostridia;o__Thermoanaerobacterales;f__Thermodesulfobiaceae;g__Thermodesulfobium;s__narugense_nov_81.065%0000000000000000000000001420008600500000770000000000
SPN165k__Bacteria;p__Cyanobacteria;c__Gloeobacterophycideae;o__Gloeobacterales;f__Gloeobacteraceae;g__Gloeobacter;s__violaceus_nov_84.114%0000000000000000000000790000850029000750082000000000
SPN166k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Ruminococcaceae;g__Ruminococcaceae_[G-2];s__bacterium_HMT_085_nov_91.260%000000360550000290020001941500000000000000000000000000
SPN167k__Bacteria;p__Saccharibacteria_(TM7);c__Saccharibacteria_(TM7)_[C-1];o__Saccharibacteria_(TM7)_[O-1];f__Saccharibacteria_(TM7)_[F-1];g__Saccharibacteria_(TM7)_[G-3];s__bacterium_HMT_351_nov_94.004%0000000000043392205059049502700000000000000000000000000
SPN168k__Plantae;p__Angiosperms;c__Eudicots;o__Malpighiales;f__Euphorbiaceae;g__Manihot;s__esculenta_Oral_Taxon_C60_nov_96.903%0000000000000000000000003370000000000000000000000
SPN169k__Bacteria;p__Actinobacteria;c__Coriobacteriia;o__Coriobacteriales;f__Coriobacteriaceae;g__Atopobium;s__parvulum_nov_92.110%001900000031000000030711600000000000000000000000000
SPN170k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Lachnospiraceae_[XIVa];g__Lacnoclostridium;s__bolteae_nov_95.146%000000000004045320143005302100000000000000000000000000
SPN171k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Ruminococcaceae;g__Phocea;s__massiliensis_nov_87.945%00000012263082000310077013332000000000000000000000000000
SPN172k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Lachnospiraceae;g__Eisenbergiella;s__massiliensis_nov_89.734%0000004000790051690000000000000000000000000000023000
SPN173k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Ruminococcaceae;g__Ruminococcaceae_[G-2];s__bacterium_HMT_085_nov_89.837%00000000219240250000202500000000000000000000000000000
SPN174k__Bacteria;p__Proteobacteria;c__Betaproteobacteria;o__Burkholderiales;f__Alcaligenaceae;g__Sutterella;s__sp._str._cont1.66_nov_97.744%0000000373700004623792828270000000000000000000000000000
SPN175k__Bacteria;p__Bacteroidetes;c__Chitinophagia;o__Chitinophagales;f__Chitinophagaceae;g__Terrimonas;s__pekingensis_nov_90.822%0006000000000000000000010500001330000004300000000000
SPN176k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Lachnospiraceae;g__Lachnoclostridium;s__polysaccharolyticum_nov_89.555%00000048076800310000023002200000000000000000000000000
SPN177k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Lachnospiraceae_[XIVa];g__unclassified_Lachnospiraceae;s__sp._str._cont1.79_nov_92.039%0000000000000000002740000000000000000000000000000
SPN178k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Ruminococcaceae;g__Agathobaculum;s__desmolans_nov_91.845%00000000190013724300000000011600000000000000000000000
SPN179k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Ruminococcaceae;g__Phocea;s__massiliensis_nov_90.748%00000000040100240000000000099000000000000000000000
SPN180k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Ruminococcaceae;g__unclassified_Ruminococcaceae;s__sp._str._D16_nov_92.898%00000000233404300010200550000000000000000000000000000
SPN181k__Bacteria;p__Firmicutes;c__Bacilli;o__Lactobacillales;f__Lactobacillaceae;g__Lactobacillus;s__murinus_nov_94.528%0000000000000025700000000000000000000000000000000
SPN182k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Ruminococcaceae;g__unclassified_Ruminococcaceae;s__sp._str._D16_nov_90.249%000000000000000155260730000000000000000000000000000
SPN183k__Bacteria;p__Tenericutes;c__Mollicutes;o__Anaeroplasmatales;f__Anaeroplasmataceae;g__Anaeroplasma;s__abactoclasticum_nov_86.957%0000006030303400144801202400000000000000000000000000000
SPN184k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Lachnospiraceae_[XIVa];g__Marvinbryantia;s__formatexigens_nov_91.975%0000000020360080001090000000000000000000000000000000
SPN185k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Ruminococcaceae;g__Phocea;s__massiliensis_nov_89.286%00000008534210000003228093300000000000000000000000000
SPN186k__Bacteria;p__Bacteroidetes;c__Bacteroidetes_[C-1];o__Bacteroidetes_[O-1];f__Bacteroidetes_[F-1];g__Bacteroidetes_[G-7];s__bacterium_HMT_911_nov_85.352%000000000008149878751811061644441474126115800000000005600025000183900028000
SPN187k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Ruminococcaceae;g__Hydrogenoanaerobacterium;s__saccharovorans_nov_89.341%000000000001101450000860000000000000000000000000000
SPN188k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Lachnospiraceae_[XIVa];g__Lacnoclostridium;s__asparagiforme_nov_94.347%0000000000000002360000000000000000000000000000000
SPN189k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Lachnospiraceae;g__Anaerocolumna;s__cellulosilytica_nov_90.522%00000000000270740000310000000000000000000000000000
SPN190k__Bacteria;p__Bacteroidetes;c__Bacteroidia;o__Bacteroidales;f__Rikenellaceae;g__Alistipes;s__putredinis_nov_93.156%00000000000770000830720000000000000000000000000000
SPN191k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Ruminococcaceae;g__Eubacterium;s__siraeum_nov_91.849%000000026000850530000006600000000000000000000000000
SPN192k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Lachnospiraceae;g__Blautia;s__producta_nov_94.574%00000000642300010301200000000000000000000000000019000
SPN193k__Bacteria;p__Bacteroidetes;c__Bacteroidia;o__Bacteroidales;f__Rikenellaceae;g__Alistipes;s__ihumii_nov_93.905%00000000000184033003900473200000000000000000000000000
SPN194k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Ruminococcaceae;g__Eubacterium;s__siraeum_nov_91.984%0000002900450005206800100000000000000000000000000000
SPN195k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Lachnospiraceae;g__Anaerocolumna;s__cellulosilytica_nov_86.863%00000000000000000078000068000000000000560000000000
SPN196k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Lachnospiraceae_[XIVa];g__Anaerostipes;s__sp._str._3256FAA_nov_89.017%00000000000001020000260000000000000000000000000000
SPN197k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Lachnospiraceae;g__Eisenbergiella;s__massiliensis_nov_86.867%0000000000000000000000000000000000000080000047000
SPN198k__Bacteria;p__Proteobacteria;c__Gammaproteobacteria;o__Pseudomonadales;f__Moraxellaceae;g__Acinetobacter;s__johnsonii_nov_97.697%000000000000000000000005100007102400000000540000000
SPN199k__Bacteria;p__Actinobacteria;c__Coriobacteriia;o__Eggerthellales;f__Eggerthellaceae;g__Enterorhabdus;s__muris_nov_89.344%0000000000000000000000000000012600000000000000000
SPN2k__Bacteria;p__Bacteroidetes;c__Bacteroidetes_[C-1];o__Bacteroidetes_[O-1];f__Bacteroidetes_[F-1];g__Bacteroidetes_[G-7];s__bacterium_HMT_911000000204122246251024613515101492981052906322800000000000000000000000000
SPN200k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Ruminococcaceae;g__unclassified_Ruminococcaceae;s__sp._str._D16_nov_94.423%0000000000000790000460000000000000000000000000000
SPN201k__Bacteria;p__Tenericutes;c__Mollicutes;o__Anaeroplasmatales;f__Anaeroplasmataceae;g__Anaeroplasma;s__abactoclasticum_nov_87.059%0000000000000000125000000000000000000000000000000
SPN202k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Ruminococcaceae;g__Agathobaculum;s__desmolans_nov_91.794%000000000000256601700000000000000000000000000015000
SPN203k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Oscillospiraceae;g__Oscillibacter;s__valericigenes_nov_93.798%00000000263000064000000000000000000000000000000000
SPN204k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Lachnospiraceae_[XIVa];g__Dorea;s__longicatena_nov_94.563%0000000000022000000980000000000000000000000000000
SPN205k__Bacteria;p__Bacteroidetes;c__Bacteroidia;o__Bacteroidales;f__Porphyromonadaceae;g__Gabonia;s__massiliensis_nov_86.704%00000064140749243504033782040570281228301313330005600000000000000000000000
SPN206k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Lachnospiraceae;g__Blautia;s__producta_nov_94.778%0000000000000117000000000000000000000000000000000
SPN207k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Ruminococcaceae;g__Ruminiclostridium;s__cellulolyticum_nov_82.505%0000002401600180350000220000000000000000000000000000
SPN208k__Bacteria;p__Firmicutes;c__Erysipelotrichia;o__Erysipelotrichales;f__Erysipelotrichaceae;g__Faecalicoccus;s__acidiformans_nov_90.075%0000001309001700042001291200000000000000000000000000
SPN209k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Lachnospiraceae;g__Muricomes;s__intestini_nov_88.998%000000350000370750420900000000000000000000000000000
SPN210k__Bacteria;p__Bacteroidetes;c__Bacteroidetes_[C-1];o__Bacteroidetes_[O-1];f__Bacteroidetes_[F-1];g__Bacteroidetes_[G-7];s__bacterium_HMT_911_nov_85.659%0000000000000000011400000000000000000000000000000
SPN211k__Bacteria;p__Bacteroidetes;c__Bacteroidia;o__Bacteroidales;f__Rikenellaceae;g__Alistipes;s__senegalensis_nov_96.905%0000000000000006251000000000000000000000000000000
SPN212k__Bacteria;p__Firmicutes;c__Erysipelotrichia;o__Erysipelotrichales;f__Erysipelotrichaceae;g__Longibaculum;s__muris_nov_93.517%0000007391520015160000000000000000000000000000000000
SPN213k__Bacteria;p__Bacteroidetes;c__Bacteroidia;o__Bacteroidales;f__Barnesiellaceae;g__Barnesiella;s__viscericola_nov_83.673%0000000000000000010900000000000000000000000000000
SPN214k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Ruminococcaceae;g__unclassified_Ruminococcaceae;s__sp._str._D16_nov_95.192%0000000002300122202707017000000000000000000000000000
SPN215k__Bacteria;p__Firmicutes;c__Bacilli;o__Lactobacillales;f__Streptococcaceae;g__Streptococcus;s__troglodytidis_nov_90.257%0000000000000000000000000000000000000000000001050
SPN216k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Clostridiales_[F-1];g__Clostridiales_[F-1][G-1];s__bacterium_HMT_093_nov_92.067%0000000131000028340160000000000000000000000000000000
SPN217k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Ruminococcaceae;g__Bacteroides;s__capillosus_nov_90.421%00000000000000000015304100000000000000000000000000
SPN22k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Lachnospiraceae;g__Eisenbergiella;s__massiliensis00000035005036351500351100000000000000000000000000000
SPN223k__Bacteria;p__Bacteroidetes;c__Bacteroidetes_[C-1];o__Bacteroidetes_[O-1];f__Bacteroidetes_[F-1];g__Bacteroidetes_[G-7];s__bacterium_HMT_911_nov_86.127%000000364332371367018732825003053811363623341830005300000000000000062000000
SPN228k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Ruminococcaceae;g__unclassified_Ruminococcaceae;s__sp._str._D16_nov_92.692%0000000000018200000000000000000000000000000000000
SPN234k__Bacteria;p__Bacteroidetes;c__Bacteroidia;o__Bacteroidales;f__Rikenellaceae;g__Alistipes;s__senegalensis_nov_93.846%000000000003295076801590245753147118900000000000000000000009000
SPN239k__Bacteria;p__Bacteroidetes;c__Bacteroidia;o__Bacteroidales;f__Barnesiellaceae;g__Barnesiella;s__viscericola_nov_82.991%00000000000030990050000000000000000000000000000000
SPN244k__Bacteria;p__Bacteroidetes;c__Bacteroidetes_[C-1];o__Bacteroidetes_[O-1];f__Bacteroidetes_[F-1];g__Bacteroidetes_[G-7];s__bacterium_HMT_911_nov_85.127%000000284472400314010315946076179010721321300930000000000000000108000000
SPN252k__Bacteria;p__Bacteroidetes;c__Bacteroidetes_[C-1];o__Bacteroidetes_[O-1];f__Bacteroidetes_[F-1];g__Bacteroidetes_[G-7];s__bacterium_HMT_911_nov_85.853%0000002684264904030113725806722108422519800000000000000000000006000
SPN257k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__NA;g__Pseudoflavonifractor;s__phocaeensis_nov_92.486%000000000000296206124000000000000000000000000000000
SPN260k__Bacteria;p__Bacteroidetes;c__Bacteroidetes_[C-1];o__Bacteroidetes_[O-1];f__Bacteroidetes_[F-1];g__Bacteroidetes_[G-7];s__bacterium_HMT_911_nov_86.940%0000005685134894720013314003159009382000000000000046000000000000
SPN268k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Christensenellaceae;g__Christensenella;s__massiliensis_nov_82.150%000000004819015204000180001600000000000000000000000000
SPN31k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Ruminococcaceae;g__Anaerotruncus;s__rubiinfantis000000390470960428653300302168413332900000000000000000000007000
SPN33k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Lachnospiraceae_[XIVa];g__Lacnoclostridium;s__clostridioforme0000000000003680000212301100000000000000000000000000
SPN40k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Lachnospiraceae_[XIVa];g__Lacnoclostridium;s__hathewayi10000000000019422323703291552162018017300000000000000000000000000
SPN44k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Ruminococcaceae;g__Phocea;s__massiliensis00000001160000490000000000000000000000000000000000
SPN48k__Bacteria;p__Tenericutes;c__Mollicutes;o__Anaeroplasmatales;f__Anaeroplasmataceae;g__Anaeroplasma;s__abactoclasticum000000197331177370204198870411020013410700000000000000000000000000
SPN54k__Bacteria;p__Actinobacteria;c__Coriobacteriia;o__Eggerthellales;f__Eggerthellaceae;g__Enterorhabdus;s__muris00000000070004803500710000000000000000000000000000
SPN61k__Bacteria;p__Firmicutes;c__Bacilli;o__Lactobacillales;f__Streptococcaceae;g__Streptococcus;s__troglodytidis000000000000000001313000000007500000000000000000000
SPN64k__Bacteria;p__Proteobacteria;c__Alphaproteobacteria;o__Rhodobacterales;f__Hyphomonadaceae;g__Algimonas;s__arctica0000000000000000001600000000000000000000000000000
SPN69k__Bacteria;p__Firmicutes;c__Clostridia;o__Thermoanaerobacterales;f__Thermodesulfobiaceae;g__Thermodesulfobium;s__narugense0000001471520009313812301431140207130112001800000000000000000000000
SPN75k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Eubacteriaceae;g__Eubacterium;s__coprostanoligenes000000002200850000000331600000000000000000000000000
SPN77k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Peptostreptococcaceae_[XI];g__Peptoclostridium;s__sticklandii017249411076000000106270000000000423107200002320001088200042000073556554688513859157652170
SPN83k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Ruminococcaceae;g__Ruminiclostridium;s__methylpentosum000000143010821501182242970940039010700000000000000000000000000
SPN86k__Bacteria;p__Actinobacteria;c__Actinobacteria;o__Corynebacteriales;f__Corynebacteriaceae;g__Turicella;s__otitidis00000000530032000006900000000000000000000000000000
SPN90k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Lachnospiraceae;g__Fusicatenibacter;s__saccharivorans00000009913757013613316601026613014107900000000000000000000000000
SPN94k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Clostridiaceae;g__Clostridium;s__tepidiprofundi9400000000000000020172900000000000000000000016100016000
SPN96k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Clostridiaceae;g__Lactonifactor;s__longoviformis00000000000054000000000000000010000000000000000000
SPP1k__Bacteria;p__Actinobacteria;c__Actinobacteria;o__Propionibacteriales;f__Propionibacteriaceae;g__Cutibacterium;s__acnes0000000000000000000000700100891138523140065013700038000000000
SPP4k__Bacteria;p__Proteobacteria;c__Gammaproteobacteria;o__Enterobacteriales;f__Enterobacteriaceae;g__multigenus;s__multispecies_spp4_319113781718016250000000000001565360136112720144004210377000440000000226000003022315
SPP5k__Bacteria;p__Firmicutes;c__Bacilli;o__Bacillales;f__Bacillaceae;g__Bacillus;s__multispecies_spp5_20000000000000000000000000000930000000000580000000
SPP7k__Bacteria;p__Actinobacteria;c__Actinobacteria;o__multiorder;f__multifamily;g__multigenus;s__multispecies_spp7_200000000000000000000001070000050062000000000139000000
SPPN10k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__multifamily;g__Lachnoclostridium;s__scindens000000000000040013700670000000000000000000000000000
SPPN11k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Lachnospiraceae_[XIVa];g__Dorea;s__multispecies_sppn11_2_nov_95.146%0000000000000720350000000000000000000000000000000
SPPN12k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Ruminococcaceae;g__multigenus;s__multispecies_sppn12_2_nov_83.366%0000000000002482000000000000000000000000000000000
SPPN13k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__multifamily;g__Lacnoclostridium;s__bolteae0000000000000001000000000000000000000000000000000
SPPN16k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__multifamily;g__Acetoanaerobium;s__sticklandii00000000630000000570058000000000000000000000000000
SPPN4k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__multifamily;g__Lachnoclostridium;s__bolteae0000000000000001490000000000000000000000000000000
SPPN8k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__multifamily;g__Clostridium;s__glycyrrhizinilyticum00000043000037088052400760000000000000000000000000000
SPPN9k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__multifamily;g__multigenus;s__multispecies_sppn9_2_nov_92.621%0000000000000112014400230000000000000000000000000000
 
 
Download Read Count Tables at Different Taxonomy Levels
domain
phylum
class
order
family
genus
species
;
 
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.
 
 
 
 

Sample Taxonomy Bar Plots

 

VIII. Analysis - Alpha Diversity

 

In ecology, alpha diversity (α-diversity) is the mean species diversity in sites or habitats at a local scale. The term was introduced by R. H. Whittaker[1][2] together with the terms beta diversity (β-diversity) and gamma diversity (γ-diversity). Whittaker's idea was that the total species diversity in a landscape (gamma diversity) is determined by two different things, the mean species diversity in sites or habitats at a more local scale (alpha diversity) and the differentiation among those habitats (beta diversity).

References:
Whittaker, R. H. (1960) Vegetation of the Siskiyou Mountains, Oregon and California. Ecological Monographs, 30, 279–338. doi:10.2307/1943563
Whittaker, R. H. (1972). Evolution and Measurement of Species Diversity. Taxon, 21, 213-251. doi:10.2307/1218190

 

Boxplot of Alpha-diversity indices

The two main factors taken into account when measuring diversity are richness and evenness. Richness is a measure of the number of different kinds of organisms present in a particular area. Evenness compares the similarity of the population size of each of the species present. There are many different ways to measure the richness and evenness. These measurements are called "estimators" or "indices". Below is a diversity of 3 commonly used indices showing the values for all the samples (dots) and in groups (boxes).

 
Alpha diversity box plots for all groups
 
 
 
 
 

Alpha Diversity Analysis by Rarefaction

Diversity measures are affected by the sampling depth. Rarefaction is a technique to assess species richness from the results of sampling. Rarefaction allows the calculation of species richness for a given number of individual samples, based on the construction of so-called rarefaction curves. This curve is a plot of the number of species as a function of the number of samples. Rarefaction curves generally grow rapidly at first, as the most common species are found, but the curves plateau as only the rarest species remain to be sampled.

References:
Willis AD. Rarefaction, Alpha Diversity, and Statistics. Front Microbiol. 2019 Oct 23;10:2407. doi: 10.3389/fmicb.2019.02407. PMID: 31708888; PMCID: PMC6819366.

 
 

IX. Analysis - Beta Diversity

 

NMDS and PCoA Plots

Beta diversity compares the similarity (or dissimilarity) of microbial profiles between different groups of samples. There are many different similarity/dissimilarity metrics. In general, they can be quantitative (using sequence abundance, e.g., Bray-Curtis or weighted UniFrac) or binary (considering only presence-absence of sequences, e.g., binary Jaccard or unweighted UniFrac). They can be even based on phylogeny (e.g., UniFrac metrics) or not (non-UniFrac metrics, such as Bray-Curtis, etc.).

For microbiome studies, species profiles of samples can be compared with the Bray-Curtis dissimilarity, which is based on the count data type. The pair-wise Bray-Curtis dissimilarity matrix of all samples can then be subject to either multi-dimensional scaling (MDS, also known as PCoA) or non-metric MDS (NMDS).

MDS/PCoA is a scaling or ordination method that starts with a matrix of similarities or dissimilarities between a set of samples and aims to produce a low-dimensional graphical plot of the data in such a way that distances between points in the plot are close to original dissimilarities.

NMDS is similar to MDS, however it does not use the dissimilarities data, instead it converts them into the ranks and use these ranks in the calculation.

In our beta diversity analysis, Bray-Curtis dissimilarity matrix was first calculated and then plotted by the PCoA and NMDS separately. The results are shown below:

 
 
 
 
 

The above PCoA and NMDS plots are based on count data. The count data can also be transformed into centered log ratio (CLR) for each species. The CLR data is no longer count data and cannot be used in Bray-Curtis dissimilarity calculation. Instead CLR can be compared with Euclidean distances. When CLR data are compared by Euclidean distance, the distance is also called Aitchison distance.

Below are the NMDS and PCoA plots of the Aitchison distances of the samples:

 
 
 
 
 

Interactive 3D PCoA Plots - Bray-Curtis Dissimilarity

 
 
 

Interactive 3D PCoA Plots - Euclidean Distance

 
 
 

Interactive 3D PCoA Plots - Correlation Coefficients

 
 
 

X. Analysis - Differential Abundance

16S rRNA next generation sequencing (NGS) generates a fixed number of reads that reflect the proportion of different species in a sample, i.e., the relative abundance of species, instead of the absolute abundance. In Mathematics, measurements involving probabilities, proportions, percentages, and ppm can all be thought of as compositional data. This makes the microbiome read count data “compositional” (Gloor et al, 2017). In general, compositional data represent parts of a whole which only carry relative information (http://www.compositionaldata.com/).

The problem of microbiome data being compositional arises when comparing two groups of samples for identifying “differentially abundant” species. A species with the same absolute abundance between two conditions, its relative abundances in the two conditions (e.g., percent abundance) can become different if the relative abundance of other species change greatly. This problem can lead to incorrect conclusion in terms of differential abundance for microbial species in the samples.

When studying differential abundance (DA), the current better approach is to transform the read count data into log ratio data. The ratios are calculated between read counts of all species in a sample to a “reference” count (e.g., mean read count of the sample). The log ratio data allow the detection of DA species without being affected by percentage bias mentioned above

In this report, a compositional DA analysis tool “ANCOM” (analysis of composition of microbiomes) was used. ANCOM transforms the count data into log-ratios and thus is more suitable for comparing the composition of microbiomes in two or more populations

References:

Gloor GB, Macklaim JM, Pawlowsky-Glahn V, Egozcue JJ. Microbiome Datasets Are Compositional: And This Is Not Optional. Front Microbiol. 2017 Nov 15;8:2224. doi: 10.3389/fmicb.2017.02224. PMID: 29187837; PMCID: PMC5695134.

Mandal S, Van Treuren W, White RA, Eggesbø M, Knight R, Peddada SD. Analysis of composition of microbiomes: a novel method for studying microbial composition. Microb Ecol Health Dis. 2015 May 29;26:27663. doi: 10.3402/mehd.v26.27663. PMID: 26028277; PMCID: PMC4450248.

 

ANCOM Differential Abundance Analysis

 
View ANCOM results
Comparison No.Comparison Name
Comparison 1.Ligature: SPM vs NoSPM
Comparison 2.Fecal: SPM vs BL
Comparison 3.Fecal: NoSPM vs BL
Comparison 4.Fecal: SPM vs NoSPM
Comparison 5.Brain: SPM vs BL
Comparison 6.Brain: NoSPM vs BL
Comparison 7.Brain: SPM vs NoSPM
 
 
 

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.

 
Ligature: SPM vs NoSPM
 
 
 
 
 
 
 
All LEfSe Comparisons
Comparison No.Comparison Name
Comparison 1.Ligature: SPM vs NoSPM
Comparison 2.Fecal: SPM vs BL
Comparison 3.Fecal: NoSPM vs BL
Comparison 4.Fecal: SPM vs NoSPM
Comparison 5.Brain: SPM vs BL
Comparison 6.Brain: NoSPM vs BL
Comparison 7.Brain: SPM vs NoSPM
 
 

XI. Analysis - Heatmap Profile

 

Species vs Sample Abundance Heatmap for All Samples

 
 
 

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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