Project FOMC6488 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.
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.
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.
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 ID
Original Sample ID
Read 1 File Name
Read 2 File Name
F6488.S10
zr6488_10V3V4_R1.fastq.gz
zr6488_10V3V4_R2.fastq.gz
F6488.S11
zr6488_11V3V4_R1.fastq.gz
zr6488_11V3V4_R2.fastq.gz
F6488.S12
zr6488_12V3V4_R1.fastq.gz
zr6488_12V3V4_R2.fastq.gz
F6488.S13
zr6488_13V3V4_R1.fastq.gz
zr6488_13V3V4_R2.fastq.gz
F6488.S14
zr6488_14V3V4_R1.fastq.gz
zr6488_14V3V4_R2.fastq.gz
F6488.S15
zr6488_15V3V4_R1.fastq.gz
zr6488_15V3V4_R2.fastq.gz
F6488.S16
zr6488_16V3V4_R1.fastq.gz
zr6488_16V3V4_R2.fastq.gz
F6488.S17
zr6488_17V3V4_R1.fastq.gz
zr6488_17V3V4_R2.fastq.gz
F6488.S18
zr6488_18V3V4_R1.fastq.gz
zr6488_18V3V4_R2.fastq.gz
F6488.S19
zr6488_19V3V4_R1.fastq.gz
zr6488_19V3V4_R2.fastq.gz
F6488.S01
zr6488_1V3V4_R1.fastq.gz
zr6488_1V3V4_R2.fastq.gz
F6488.S20
zr6488_20V3V4_R1.fastq.gz
zr6488_20V3V4_R2.fastq.gz
F6488.S21
zr6488_21V3V4_R1.fastq.gz
zr6488_21V3V4_R2.fastq.gz
F6488.S22
zr6488_22V3V4_R1.fastq.gz
zr6488_22V3V4_R2.fastq.gz
F6488.S23
zr6488_23V3V4_R1.fastq.gz
zr6488_23V3V4_R2.fastq.gz
F6488.S24
zr6488_24V3V4_R1.fastq.gz
zr6488_24V3V4_R2.fastq.gz
F6488.S25
zr6488_25V3V4_R1.fastq.gz
zr6488_25V3V4_R2.fastq.gz
F6488.S26
zr6488_26V3V4_R1.fastq.gz
zr6488_26V3V4_R2.fastq.gz
F6488.S27
zr6488_27V3V4_R1.fastq.gz
zr6488_27V3V4_R2.fastq.gz
F6488.S28
zr6488_28V3V4_R1.fastq.gz
zr6488_28V3V4_R2.fastq.gz
F6488.S29
zr6488_29V3V4_R1.fastq.gz
zr6488_29V3V4_R2.fastq.gz
F6488.S02
zr6488_2V3V4_R1.fastq.gz
zr6488_2V3V4_R2.fastq.gz
F6488.S30
zr6488_30V3V4_R1.fastq.gz
zr6488_30V3V4_R2.fastq.gz
F6488.S31
zr6488_31V3V4_R1.fastq.gz
zr6488_31V3V4_R2.fastq.gz
F6488.S32
zr6488_32V3V4_R1.fastq.gz
zr6488_32V3V4_R2.fastq.gz
F6488.S33
zr6488_33V3V4_R1.fastq.gz
zr6488_33V3V4_R2.fastq.gz
F6488.S34
zr6488_34V3V4_R1.fastq.gz
zr6488_34V3V4_R2.fastq.gz
F6488.S35
zr6488_35V3V4_R1.fastq.gz
zr6488_35V3V4_R2.fastq.gz
F6488.S36
zr6488_36V3V4_R1.fastq.gz
zr6488_36V3V4_R2.fastq.gz
F6488.S37
zr6488_37V3V4_R1.fastq.gz
zr6488_37V3V4_R2.fastq.gz
F6488.S38
zr6488_38V3V4_R1.fastq.gz
zr6488_38V3V4_R2.fastq.gz
F6488.S39
zr6488_39V3V4_R1.fastq.gz
zr6488_39V3V4_R2.fastq.gz
F6488.S03
zr6488_3V3V4_R1.fastq.gz
zr6488_3V3V4_R2.fastq.gz
F6488.S40
zr6488_40V3V4_R1.fastq.gz
zr6488_40V3V4_R2.fastq.gz
F6488.S41
zr6488_41V3V4_R1.fastq.gz
zr6488_41V3V4_R2.fastq.gz
F6488.S42
zr6488_42V3V4_R1.fastq.gz
zr6488_42V3V4_R2.fastq.gz
F6488.S43
zr6488_43V3V4_R1.fastq.gz
zr6488_43V3V4_R2.fastq.gz
F6488.S44
zr6488_44V3V4_R1.fastq.gz
zr6488_44V3V4_R2.fastq.gz
F6488.S45
zr6488_45V3V4_R1.fastq.gz
zr6488_45V3V4_R2.fastq.gz
F6488.S46
zr6488_46V3V4_R1.fastq.gz
zr6488_46V3V4_R2.fastq.gz
F6488.S47
zr6488_47V3V4_R1.fastq.gz
zr6488_47V3V4_R2.fastq.gz
F6488.S48
zr6488_48V3V4_R1.fastq.gz
zr6488_48V3V4_R2.fastq.gz
F6488.S49
zr6488_49V3V4_R1.fastq.gz
zr6488_49V3V4_R2.fastq.gz
F6488.S04
zr6488_4V3V4_R1.fastq.gz
zr6488_4V3V4_R2.fastq.gz
F6488.S50
zr6488_50V3V4_R1.fastq.gz
zr6488_50V3V4_R2.fastq.gz
F6488.S51
zr6488_51V3V4_R1.fastq.gz
zr6488_51V3V4_R2.fastq.gz
F6488.S52
zr6488_52V3V4_R1.fastq.gz
zr6488_52V3V4_R2.fastq.gz
F6488.S53
zr6488_53V3V4_R1.fastq.gz
zr6488_53V3V4_R2.fastq.gz
F6488.S54
zr6488_54V3V4_R1.fastq.gz
zr6488_54V3V4_R2.fastq.gz
F6488.S55
zr6488_55V3V4_R1.fastq.gz
zr6488_55V3V4_R2.fastq.gz
F6488.S56
zr6488_56V3V4_R1.fastq.gz
zr6488_56V3V4_R2.fastq.gz
F6488.S57
zr6488_57V3V4_R1.fastq.gz
zr6488_57V3V4_R2.fastq.gz
F6488.S58
zr6488_58V3V4_R1.fastq.gz
zr6488_58V3V4_R2.fastq.gz
F6488.S59
zr6488_59V3V4_R1.fastq.gz
zr6488_59V3V4_R2.fastq.gz
F6488.S05
zr6488_5V3V4_R1.fastq.gz
zr6488_5V3V4_R2.fastq.gz
F6488.S60
zr6488_60V3V4_R1.fastq.gz
zr6488_60V3V4_R2.fastq.gz
F6488.S61
zr6488_61V3V4_R1.fastq.gz
zr6488_61V3V4_R2.fastq.gz
F6488.S62
zr6488_62V3V4_R1.fastq.gz
zr6488_62V3V4_R2.fastq.gz
F6488.S63
zr6488_63V3V4_R1.fastq.gz
zr6488_63V3V4_R2.fastq.gz
F6488.S64
zr6488_64V3V4_R1.fastq.gz
zr6488_64V3V4_R2.fastq.gz
F6488.S65
zr6488_65V3V4_R1.fastq.gz
zr6488_65V3V4_R2.fastq.gz
F6488.S66
zr6488_66V3V4_R1.fastq.gz
zr6488_66V3V4_R2.fastq.gz
F6488.S67
zr6488_67V3V4_R1.fastq.gz
zr6488_67V3V4_R2.fastq.gz
F6488.S68
zr6488_68V3V4_R1.fastq.gz
zr6488_68V3V4_R2.fastq.gz
F6488.S69
zr6488_69V3V4_R1.fastq.gz
zr6488_69V3V4_R2.fastq.gz
F6488.S06
zr6488_6V3V4_R1.fastq.gz
zr6488_6V3V4_R2.fastq.gz
F6488.S70
zr6488_70V3V4_R1.fastq.gz
zr6488_70V3V4_R2.fastq.gz
F6488.S71
zr6488_71V3V4_R1.fastq.gz
zr6488_71V3V4_R2.fastq.gz
F6488.S72
zr6488_72V3V4_R1.fastq.gz
zr6488_72V3V4_R2.fastq.gz
F6488.S73
zr6488_73V3V4_R1.fastq.gz
zr6488_73V3V4_R2.fastq.gz
F6488.S74
zr6488_74V3V4_R1.fastq.gz
zr6488_74V3V4_R2.fastq.gz
F6488.S75
zr6488_75V3V4_R1.fastq.gz
zr6488_75V3V4_R2.fastq.gz
F6488.S76
zr6488_76V3V4_R1.fastq.gz
zr6488_76V3V4_R2.fastq.gz
F6488.S77
zr6488_77V3V4_R1.fastq.gz
zr6488_77V3V4_R2.fastq.gz
F6488.S78
zr6488_78V3V4_R1.fastq.gz
zr6488_78V3V4_R2.fastq.gz
F6488.S79
zr6488_79V3V4_R1.fastq.gz
zr6488_79V3V4_R2.fastq.gz
F6488.S07
zr6488_7V3V4_R1.fastq.gz
zr6488_7V3V4_R2.fastq.gz
F6488.S08
zr6488_8V3V4_R1.fastq.gz
zr6488_8V3V4_R2.fastq.gz
F6488.S09
zr6488_9V3V4_R1.fastq.gz
zr6488_9V3V4_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.
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 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 -
Create a random subset of each sample consisting of 5,000 R1 and 5,000 R2 (to reduce computation time)
Trim 10 bases at a time from the ends of both R1 and R2 up to 50 bases
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
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/R2
281
271
261
251
241
231
321
33.91%
42.59%
42.45%
42.44%
42.61%
42.86%
311
37.80%
47.60%
47.67%
47.86%
48.48%
48.70%
301
37.76%
47.50%
47.80%
47.85%
48.58%
48.58%
291
37.64%
47.34%
47.31%
48.28%
48.49%
48.68%
281
38.06%
47.66%
47.89%
48.70%
48.89%
49.03%
271
38.00%
47.41%
47.54%
48.52%
49.04%
49.11%
Based on the above result, the trim length combination of R1 = 271 bases and R2 = 231 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 ID
F6488.S01
F6488.S02
F6488.S03
F6488.S04
F6488.S05
F6488.S06
F6488.S07
F6488.S08
F6488.S09
F6488.S10
F6488.S11
F6488.S12
F6488.S13
F6488.S14
F6488.S15
F6488.S16
F6488.S17
F6488.S18
F6488.S19
F6488.S20
F6488.S21
F6488.S22
F6488.S23
F6488.S24
F6488.S25
F6488.S26
F6488.S27
F6488.S28
F6488.S29
F6488.S30
F6488.S31
F6488.S32
F6488.S33
F6488.S34
F6488.S35
F6488.S36
F6488.S37
F6488.S38
F6488.S39
F6488.S40
F6488.S41
F6488.S42
F6488.S43
F6488.S44
F6488.S45
F6488.S46
F6488.S47
F6488.S48
F6488.S49
F6488.S50
F6488.S51
F6488.S52
F6488.S53
F6488.S54
F6488.S55
F6488.S56
F6488.S57
F6488.S58
F6488.S59
F6488.S60
F6488.S61
F6488.S62
F6488.S63
F6488.S64
F6488.S65
F6488.S66
F6488.S67
F6488.S68
F6488.S69
F6488.S70
F6488.S71
F6488.S72
F6488.S73
F6488.S74
F6488.S75
F6488.S76
F6488.S77
F6488.S78
F6488.S79
Row Sum
Percentage
input
40,752
45,860
37,229
37,320
34,792
41,593
47,536
45,711
35,421
45,557
36,489
49,219
34,673
43,818
44,178
44,267
40,296
47,568
40,406
46,884
32,728
50,376
48,452
43,541
40,464
49,988
39,012
46,839
32,344
46,804
49,003
51,404
36,390
43,032
38,865
46,169
40,599
46,272
54,561
46,189
48,805
59,781
48,081
55,405
47,257
65,105
56,467
61,237
36,278
45,070
33,348
45,060
35,472
42,475
46,171
40,540
54,552
59,379
47,721
60,893
57,674
55,276
57,898
47,557
41,489
45,711
45,566
44,912
38,437
52,556
53,811
49,574
43,930
46,569
35,503
45,076
97
62,883
50,585
3,586,802
100.00%
filtered
40,413
45,444
36,919
37,037
34,509
41,242
47,155
45,324
35,129
45,181
36,168
48,801
34,388
43,459
43,798
43,890
39,968
47,164
40,084
46,520
32,482
49,961
48,054
43,173
40,119
49,583
38,673
46,417
32,070
46,426
48,579
50,964
36,082
42,675
38,536
45,727
40,253
45,885
54,060
45,781
48,403
59,294
47,671
54,960
46,836
64,616
55,993
60,696
35,981
44,691
33,023
44,692
35,155
42,066
45,809
40,190
54,121
58,862
47,327
60,359
57,245
54,811
57,381
47,188
41,126
45,341
45,168
44,547
38,151
52,101
53,317
49,171
43,515
46,121
35,184
44,736
96
62,335
50,147
3,556,519
99.16%
denoisedF
39,002
43,995
35,815
35,728
33,351
38,840
45,357
44,455
33,836
43,674
34,602
47,196
32,943
42,253
42,503
42,948
39,108
45,636
38,790
45,212
31,438
48,806
47,576
41,705
38,244
47,148
37,684
45,176
30,683
45,294
47,354
49,490
34,953
40,710
36,032
44,413
38,404
45,197
51,183
44,487
46,749
56,428
46,498
52,887
45,198
61,587
53,939
58,919
34,803
43,722
32,199
41,368
32,772
40,276
41,783
38,132
51,362
56,416
44,671
57,141
54,271
51,510
53,385
42,877
38,476
43,216
43,358
40,900
34,916
48,593
49,118
46,017
37,864
43,262
31,357
42,002
2
57,380
47,921
3,394,496
94.64%
denoisedR
39,310
44,306
36,168
35,886
33,695
39,198
45,723
44,301
33,979
43,843
35,173
47,326
33,438
42,323
42,601
42,824
39,158
46,045
39,010
45,358
31,825
48,774
47,394
41,886
38,766
47,300
37,945
45,076
31,174
45,177
47,317
49,384
35,028
40,981
36,917
44,589
38,918
44,980
51,535
44,668
46,663
56,627
46,787
53,478
45,664
61,912
54,124
59,032
34,944
43,512
32,156
42,283
33,241
40,269
42,879
38,422
51,735
56,467
45,099
57,522
55,198
52,109
54,044
44,093
38,921
43,047
43,524
41,972
35,906
49,704
50,282
46,756
40,745
44,052
33,019
42,136
1
59,450
48,511
3,425,585
95.51%
merged
37,301
42,127
34,674
33,612
31,973
33,182
41,886
42,713
31,548
39,961
31,910
44,444
31,369
39,433
39,684
40,846
37,370
43,469
37,302
43,475
30,593
46,849
46,944
39,823
34,922
42,102
36,868
43,200
28,638
43,551
45,412
46,340
33,642
36,102
32,463
42,585
35,739
44,077
45,461
42,751
42,754
50,577
45,135
48,605
42,573
54,842
47,947
55,826
32,438
41,869
31,171
35,614
28,627
35,786
30,696
33,357
45,084
51,184
39,670
50,239
49,356
45,741
45,941
37,479
34,502
39,445
39,203
35,805
29,906
42,458
41,417
39,925
29,973
38,269
26,515
36,672
0
50,588
43,687
3,107,217
86.63%
nonchim
6,704
11,040
7,281
8,239
5,098
11,173
10,562
9,089
6,528
10,438
10,412
10,649
6,155
7,894
8,853
7,046
5,448
7,253
7,154
7,668
4,432
9,821
8,152
9,839
10,277
12,501
6,543
9,925
6,679
10,595
11,431
10,586
6,035
11,812
10,559
10,261
8,448
7,803
15,999
9,602
8,521
14,195
9,636
12,733
8,873
15,467
12,187
9,628
6,302
7,760
4,904
17,089
13,373
13,085
12,492
10,523
15,993
12,287
13,099
14,213
15,239
16,248
22,719
14,134
12,198
9,362
10,131
16,819
11,326
13,937
17,530
16,545
16,497
14,452
12,647
13,394
0
26,817
12,655
860,994
24.00%
This table can be downloaded as an Excel table below:
5. DADA2 Amplicon Sequence Variants (ASVs). A total of 16197 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 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 *
Code
Category
MPC=0% (>=1 read)
MPC=0.1%(>=842 reads)
A
Total reads
860,994
860,994
B
Total assigned reads
842,077
842,077
C
Assigned reads in species with read count < MPC
0
163,157
D
Assigned reads in samples with read count < 500
0
0
E
Total samples
78
78
F
Samples with reads >= 500
78
78
G
Samples with reads < 500
0
0
H
Total assigned reads used for analysis (B-C-D)
842,077
678,920
I
Reads assigned to single species
381,405
311,211
J
Reads assigned to multiple species
273,533
253,229
K
Reads assigned to novel species
187,139
114,480
L
Total number of species
1,329
206
M
Number of single species
429
107
N
Number of multi-species
138
36
O
Number of novel species
762
63
P
Total unassigned reads
18,917
18,917
Q
Chimeric reads
2,177
2,177
R
Reads without BLASTN hits
114
114
S
Others: short, low quality, singletons, etc.
16,626
16,626
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.
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.
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).
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.
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 0
Saliva Healthy Control vs Saliva Periodontitis vs Saliva Parkinson + Periodontitis
To test whether the alpha diversity among different comparison groups are different statisticall, we use the Kruskal Wallis H test
provided the "alpha-group-significance" fucntion in the QIIME 2 diversity package. Kruskal Wallis H test is the non parametric alternative
to the One Way ANOVA. Non parametric means that the test doesn’t assume your data comes from a particular distribution. The H test is used
when the assumptions for ANOVA aren’t met (like the assumption of normality). It is sometimes called the one-way ANOVA on ranks,
as the ranks of the data values are used in the test rather than the actual data points. The test determines whether the medians of two
or more groups are different.
Below are the Kruskal Wallis H test results for each comparison based on three different alpha diversity measures: 1) Observed species (features),
2) Shannon index, and 3) Simpson index.
Comparison 0.
Saliva Healthy Control vs Saliva Periodontitis vs Saliva Parkinson + Periodontitis
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:
Interactive 3D PCoA Plots - Bray-Curtis Dissimilarity
Interactive 3D PCoA Plots - Euclidean Distance
Interactive 3D PCoA Plots - Correlation Coefficients
Group Significance of Beta-diversity Indices
To test whether the between-group dissimilarities are significantly greater than the within-group dissimilarities,
the "beta-group-significance" function provided in the QIIME 2 "diversity" package was used with PERMANOVA
(permutational multivariate analysis of variance) chosen s the group significan testing method.
Three beta diversity matrics were used: 1) Bray–Curtis dissimilarity 2) Correlation coefficient matrix , and 3) Aitchison distance
(Euclidean distance between clr-transformed compositions).
Comparison 0.
Saliva Healthy Control vs Saliva Periodontitis vs Saliva Parkinson + Periodontitis
16S rRNA next generation sequencing (NGS) generates a fixed number of reads that reflect the proportion of different
species in a sample, i.e., the relative abundance of species, instead of the absolute abundance.
In Mathematics, measurements involving probabilities, proportions, percentages, and ppm can all
be thought of as compositional data. This makes the microbiome read count data “compositional”
(Gloor et al, 2017). In general, compositional data represent parts of a whole which only
carry relative information (http://www.compositionaldata.com/).
The problem of microbiome data being compositional arises when comparing two groups of samples for
identifying “differentially abundant” species. A species with the same absolute abundance between two
conditions, its relative abundances in the two conditions (e.g., percent abundance) can become different
if the relative abundance of other species change greatly. This problem can lead to incorrect conclusion
in terms of differential abundance for microbial species in the samples.
When studying differential abundance (DA), the current better approach is to transform the read count
data into log ratio data. The ratios are calculated between read counts of all species in a sample to
a “reference” count (e.g., mean read count of the sample). The log ratio data allow the detection of DA
species without being affected by percentage bias mentioned above
In this report, a compositional DA analysis tool “ANCOM” (analysis of composition of microbiomes)
was used. ANCOM transforms the count data into log-ratios and thus is more suitable for comparing
the composition of microbiomes in two or more populations. "ANCOM" generates a table of features with
W-statistics and whether the null hypothesis is rejected. The “W” is the W-statistic, or number of
features that a single feature is tested to be significantly different against. Hence the higher the "W"
the more statistical sifgnificane that a feature/species is differentially abundant.
References:
Gloor GB, Macklaim JM, Pawlowsky-Glahn V, Egozcue JJ. Microbiome Datasets Are Compositional: And This Is Not Optional. Front Microbiol.
2017 Nov 15;8:2224. doi: 10.3389/fmicb.2017.02224. PMID: 29187837; PMCID: PMC5695134.
Mandal S, Van Treuren W, White RA, Eggesbø M, Knight R, Peddada SD. Analysis of composition of
microbiomes: a novel method for studying microbial composition. Microb Ecol Health Dis.
2015 May 29;26:27663. doi: 10.3402/mehd.v26.27663. PMID: 26028277; PMCID: PMC4450248.
Lin H, Peddada SD. Analysis of compositions of microbiomes with bias correction.
Nat Commun. 2020 Jul 14;11(1):3514. doi: 10.1038/s41467-020-17041-7.
PMID: 32665548; PMCID: PMC7360769.
Starting with version V1.2, we also include the results of ANCOM-BC (Analysis of Compositions of
Microbiomes with Bias Correction) (Lin and Peddada 2020). ANCOM-BC is an updated version of "ANCOM" that:
(a) provides statistically valid test with appropriate p-values,
(b) provides confidence intervals for differential abundance of each taxon,
(c) controls the False Discovery Rate (FDR),
(d) maintains adequate power, and
(e) is computationally simple to implement.
The bias correction (BC) addresses a challenging problem of the bias introduced by differences in
the sampling fractions across samples. This bias has been a major hurdle in performing DA analysis of microbiome data.
ANCOM-BC estimates the unknown sampling fractions and corrects the bias induced by their differences among samples.
The absolute abundance data are modeled using a linear regression framework.
References:
Lin H, Peddada SD. Analysis of compositions of microbiomes with bias correction.
Nat Commun. 2020 Jul 14;11(1):3514. doi: 10.1038/s41467-020-17041-7.
PMID: 32665548; PMCID: PMC7360769.
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.
Saliva Healthy Control vs Saliva Periodontitis vs Saliva Parkinson + Periodontitis
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 InversECovariance 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.
