Project Alzheimer_SRP3410871_oral services include NGS sequencing of the V3V4 region of the 16S rRNA gene amplicons from the samples. First and foremost, please
download this report, as well as the sequence raw data from the download links provided below.
These links will expire after 60 days. We cannot guarantee the availability of your data after 60 days.
Full Bioinformatics analysis service was requested. We provide many analyses, starting from the raw sequence quality and noise filtering, pair reads merging, as well as chimera filtering for the sequences, using the
DADA2 denosing algorithm and pipeline.
We also provide many downstream analyses such as taxonomy assignment, alpha and beta diversity analyses, and differential abundance analysis.
For taxonomy assignment, most informative would be the taxonomy barplots. We provide an interactive barplots to show the relative abundance of microbes at different taxonomy levels (from Phylum to species) that you can choose.
If you specify which groups of samples you want to compare for differential abundance, we provide both ANCOM and LEfSe differential abundance analysis.
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®-96 MagBead DNA Kit (Zymo Research, Irvine, CA)
☐
N/A (DNA Extraction Not Performed)
Elution Volume: 50µL
Additional Notes: NA
Targeted Library Preparation: The DNA samples were prepared for targeted
sequencing with the Quick-16S™ NGS Library Prep Kit (Zymo Research, Irvine, CA).
These primers were custom designed by Zymo Research to provide the best coverage
of the 16S gene while maintaining high sensitivity. The primer sets used in this project
are marked below:
☐
Quick-16S™ Primer Set V1-V2 (Zymo Research, Irvine, CA)
☐
Quick-16S™ Primer Set V1-V3 (Zymo Research, Irvine, CA)
☑
Quick-16S™ Primer Set V3-V4 (Zymo Research, Irvine, CA)
☐
Quick-16S™ Primer Set V4 (Zymo Research, Irvine, CA)
☐
Quick-16S™ Primer Set V6-V8 (Zymo Research, Irvine, CA)
☐
Other: NA
Additional Notes: NA
The sequencing library was prepared using an innovative library preparation process in
which PCR reactions were performed in real-time PCR machines to control cycles and
therefore limit PCR chimera formation. The final PCR products were quantified with
qPCR fluorescence readings and pooled together based on equal molarity. The final
pooled library was cleaned up with the Select-a-Size DNA Clean & Concentrator™
(Zymo Research, Irvine, CA), then quantified with TapeStation® (Agilent Technologies,
Santa Clara, CA) and Qubit® (Thermo Fisher Scientific, Waltham, WA).
Control Samples: The ZymoBIOMICS® Microbial Community Standard (Zymo
Research, Irvine, CA) was used as a positive control for each DNA extraction, if
performed. The ZymoBIOMICS® Microbial Community DNA Standard (Zymo Research,
Irvine, CA) was used as a positive control for each targeted library preparation.
Negative controls (i.e. blank extraction control, blank library preparation control) were
included to assess the level of bioburden carried by the wet-lab process.
Sequencing: The final library was sequenced on Illumina® MiSeq™ with a V3 reagent kit
(600 cycles). The sequencing was performed with 10% PhiX spike-in.
Absolute Abundance Quantification*: A quantitative real-time PCR was set up with a
standard curve. The standard curve was made with plasmid DNA containing one copy
of the 16S gene and one copy of the fungal ITS2 region prepared in 10-fold serial
dilutions. The primers used were the same as those used in Targeted Library
Preparation. The equation generated by the plasmid DNA standard curve was used to
calculate the number of gene copies in the reaction for each sample. The PCR input
volume (2 µl) was used to calculate the number of gene copies per microliter in each
DNA sample.
The number of genome copies per microliter DNA sample was calculated by dividing
the gene copy number by an assumed number of gene copies per genome. The value
used for 16S copies per genome is 4. The value used for ITS copies per genome is 200.
The amount of DNA per microliter DNA sample was calculated using an assumed
genome size of 4.64 x 106 bp, the genome size of Escherichia coli, for 16S samples, or
an assumed genome size of 1.20 x 107 bp, the genome size of Saccharomyces
cerevisiae, for ITS samples. This calculation is shown below:
Calculated Total DNA = Calculated Total Genome Copies × Assumed Genome Size (4.64 × 106 bp) ×
Average Molecular Weight of a DNA bp (660 g/mole/bp) ÷ Avogadro’s Number (6.022 x 1023/mole)
* Absolute Abundance Quantification is only available for 16S and ITS analyses.
The absolute abundance standard curve data can be viewed in Excel here:
The absolute abundance standard curve is shown below:
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 Pac-Bio full-length (V1V9) 16S rRNA amplicon sequencing, raw sequences are available for download in a single compressed zip file in the download link below.
After unzipping, you will find individual sequence files for each of your samples with the file extension “*.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 fastq files are listed in the table below:
Sample ID
Original Sample ID
Read 1 File Name
Read 2 File Name
F3410871.S01
fastq_ori/SRR16303366_1.fastq
fastq_ori/SRR16303366_2.fastq
F3410871.S02
fastq_ori/SRR16303367_1.fastq
fastq_ori/SRR16303367_2.fastq
F3410871.S03
fastq_ori/SRR16303368_1.fastq
fastq_ori/SRR16303368_2.fastq
F3410871.S04
fastq_ori/SRR16303369_1.fastq
fastq_ori/SRR16303369_2.fastq
F3410871.S05
fastq_ori/SRR16303370_1.fastq
fastq_ori/SRR16303370_2.fastq
F3410871.S06
fastq_ori/SRR16303371_1.fastq
fastq_ori/SRR16303371_2.fastq
F3410871.S07
fastq_ori/SRR16303372_1.fastq
fastq_ori/SRR16303372_2.fastq
F3410871.S08
fastq_ori/SRR16303373_1.fastq
fastq_ori/SRR16303373_2.fastq
F3410871.S09
fastq_ori/SRR16303374_1.fastq
fastq_ori/SRR16303374_2.fastq
F3410871.S10
fastq_ori/SRR16303375_1.fastq
fastq_ori/SRR16303375_2.fastq
F3410871.S11
fastq_ori/SRR16303377_1.fastq
fastq_ori/SRR16303377_2.fastq
F3410871.S12
fastq_ori/SRR16303378_1.fastq
fastq_ori/SRR16303378_2.fastq
F3410871.S13
fastq_ori/SRR16303379_1.fastq
fastq_ori/SRR16303379_2.fastq
F3410871.S14
fastq_ori/SRR16303380_1.fastq
fastq_ori/SRR16303380_2.fastq
F3410871.S15
fastq_ori/SRR16303381_1.fastq
fastq_ori/SRR16303381_2.fastq
F3410871.S16
fastq_ori/SRR16303382_1.fastq
fastq_ori/SRR16303382_2.fastq
F3410871.S17
fastq_ori/SRR16303383_1.fastq
fastq_ori/SRR16303383_2.fastq
F3410871.S18
fastq_ori/SRR16303384_1.fastq
fastq_ori/SRR16303384_2.fastq
F3410871.S19
fastq_ori/SRR16303385_1.fastq
fastq_ori/SRR16303385_2.fastq
F3410871.S20
fastq_ori/SRR16303386_1.fastq
fastq_ori/SRR16303386_2.fastq
F3410871.S21
fastq_ori/SRR16303387_1.fastq
fastq_ori/SRR16303387_2.fastq
F3410871.S22
fastq_ori/SRR16303388_1.fastq
fastq_ori/SRR16303388_2.fastq
F3410871.S23
fastq_ori/SRR16303389_1.fastq
fastq_ori/SRR16303389_2.fastq
F3410871.S24
fastq_ori/SRR16303390_1.fastq
fastq_ori/SRR16303390_2.fastq
F3410871.S25
fastq_ori/SRR16303391_1.fastq
fastq_ori/SRR16303391_2.fastq
F3410871.S26
fastq_ori/SRR16303392_1.fastq
fastq_ori/SRR16303392_2.fastq
F3410871.S27
fastq_ori/SRR16303393_1.fastq
fastq_ori/SRR16303393_2.fastq
F3410871.S28
fastq_ori/SRR16303394_1.fastq
fastq_ori/SRR16303394_2.fastq
F3410871.S29
fastq_ori/SRR16303395_1.fastq
fastq_ori/SRR16303395_2.fastq
F3410871.S30
fastq_ori/SRR16303396_1.fastq
fastq_ori/SRR16303396_2.fastq
F3410871.S31
fastq_ori/SRR16303397_1.fastq
fastq_ori/SRR16303397_2.fastq
F3410871.S32
fastq_ori/SRR16303398_1.fastq
fastq_ori/SRR16303398_2.fastq
F3410871.S33
fastq_ori/SRR16303399_1.fastq
fastq_ori/SRR16303399_2.fastq
F3410871.S34
fastq_ori/SRR16303400_1.fastq
fastq_ori/SRR16303400_2.fastq
F3410871.S35
fastq_ori/SRR16303401_1.fastq
fastq_ori/SRR16303401_2.fastq
F3410871.S36
fastq_ori/SRR16303402_1.fastq
fastq_ori/SRR16303402_2.fastq
F3410871.S37
fastq_ori/SRR16303403_1.fastq
fastq_ori/SRR16303403_2.fastq
F3410871.S38
fastq_ori/SRR16303404_1.fastq
fastq_ori/SRR16303404_2.fastq
F3410871.S39
fastq_ori/SRR16303405_1.fastq
fastq_ori/SRR16303405_2.fastq
F3410871.S40
fastq_ori/SRR16303406_1.fastq
fastq_ori/SRR16303406_2.fastq
F3410871.S41
fastq_ori/SRR16303407_1.fastq
fastq_ori/SRR16303407_2.fastq
F3410871.S42
fastq_ori/SRR16303408_1.fastq
fastq_ori/SRR16303408_2.fastq
F3410871.S43
fastq_ori/SRR16303409_1.fastq
fastq_ori/SRR16303409_2.fastq
F3410871.S44
fastq_ori/SRR16303410_1.fastq
fastq_ori/SRR16303410_2.fastq
F3410871.S45
fastq_ori/SRR16303411_1.fastq
fastq_ori/SRR16303411_2.fastq
F3410871.S46
fastq_ori/SRR16303412_1.fastq
fastq_ori/SRR16303412_2.fastq
F3410871.S47
fastq_ori/SRR16303413_1.fastq
fastq_ori/SRR16303413_2.fastq
F3410871.S48
fastq_ori/SRR16303414_1.fastq
fastq_ori/SRR16303414_2.fastq
F3410871.S49
fastq_ori/SRR16303415_1.fastq
fastq_ori/SRR16303415_2.fastq
F3410871.S50
fastq_ori/SRR16303416_1.fastq
fastq_ori/SRR16303416_2.fastq
F3410871.S51
fastq_ori/SRR16303417_1.fastq
fastq_ori/SRR16303417_2.fastq
F3410871.S52
fastq_ori/SRR16303418_1.fastq
fastq_ori/SRR16303418_2.fastq
F3410871.S53
fastq_ori/SRR16303419_1.fastq
fastq_ori/SRR16303419_2.fastq
F3410871.S54
fastq_ori/SRR16303420_1.fastq
fastq_ori/SRR16303420_2.fastq
F3410871.S55
fastq_ori/SRR16303421_1.fastq
fastq_ori/SRR16303421_2.fastq
F3410871.S56
fastq_ori/SRR16303422_1.fastq
fastq_ori/SRR16303422_2.fastq
F3410871.S57
fastq_ori/SRR16303423_1.fastq
fastq_ori/SRR16303423_2.fastq
F3410871.S58
fastq_ori/SRR16303424_1.fastq
fastq_ori/SRR16303424_2.fastq
F3410871.S59
fastq_ori/SRR16303425_1.fastq
fastq_ori/SRR16303425_2.fastq
F3410871.S60
fastq_ori/SRR16303426_1.fastq
fastq_ori/SRR16303426_2.fastq
F3410871.S61
fastq_ori/SRR16303427_1.fastq
fastq_ori/SRR16303427_2.fastq
F3410871.S62
fastq_ori/SRR16303428_1.fastq
fastq_ori/SRR16303428_2.fastq
F3410871.S63
fastq_ori/SRR16303429_1.fastq
fastq_ori/SRR16303429_2.fastq
F3410871.S64
fastq_ori/SRR16303430_1.fastq
fastq_ori/SRR16303430_2.fastq
F3410871.S65
fastq_ori/SRR16303431_1.fastq
fastq_ori/SRR16303431_2.fastq
F3410871.S66
fastq_ori/SRR16303432_1.fastq
fastq_ori/SRR16303432_2.fastq
F3410871.S67
fastq_ori/SRR16303434_1.fastq
fastq_ori/SRR16303434_2.fastq
F3410871.S68
fastq_ori/SRR16303435_1.fastq
fastq_ori/SRR16303435_2.fastq
F3410871.S69
fastq_ori/SRR16303436_1.fastq
fastq_ori/SRR16303436_2.fastq
F3410871.S70
fastq_ori/SRR16303437_1.fastq
fastq_ori/SRR16303437_2.fastq
F3410871.S71
fastq_ori/SRR16303438_1.fastq
fastq_ori/SRR16303438_2.fastq
F3410871.S72
fastq_ori/SRR16303439_1.fastq
fastq_ori/SRR16303439_2.fastq
F3410871.S73
fastq_ori/SRR16303440_1.fastq
fastq_ori/SRR16303440_2.fastq
F3410871.S74
fastq_ori/SRR16303441_1.fastq
fastq_ori/SRR16303441_2.fastq
F3410871.S75
fastq_ori/SRR16303442_1.fastq
fastq_ori/SRR16303442_2.fastq
F3410871.S76
fastq_ori/SRR16303443_1.fastq
fastq_ori/SRR16303443_2.fastq
F3410871.S77
fastq_ori/SRR16303444_1.fastq
fastq_ori/SRR16303444_2.fastq
F3410871.S78
fastq_ori/SRR16303445_1.fastq
fastq_ori/SRR16303445_2.fastq
F3410871.S79
fastq_ori/SRR16303446_1.fastq
fastq_ori/SRR16303446_2.fastq
F3410871.S80
fastq_ori/SRR16303447_1.fastq
fastq_ori/SRR16303447_2.fastq
F3410871.S81
fastq_ori/SRR16303448_1.fastq
fastq_ori/SRR16303448_2.fastq
F3410871.S82
fastq_ori/SRR16303449_1.fastq
fastq_ori/SRR16303449_2.fastq
F3410871.S83
fastq_ori/SRR16303450_1.fastq
fastq_ori/SRR16303450_2.fastq
F3410871.S84
fastq_ori/SRR16303451_1.fastq
fastq_ori/SRR16303451_2.fastq
F3410871.S85
fastq_ori/SRR16303452_1.fastq
fastq_ori/SRR16303452_2.fastq
F3410871.S86
fastq_ori/SRR16303461_1.fastq
fastq_ori/SRR16303461_2.fastq
F3410871.S87
fastq_ori/SRR16303472_1.fastq
fastq_ori/SRR16303472_2.fastq
F3410871.S88
fastq_ori/SRR16303483_1.fastq
fastq_ori/SRR16303483_2.fastq
F3410871.S89
fastq_ori/SRR16303494_1.fastq
fastq_ori/SRR16303494_2.fastq
F3410871.S90
fastq_ori/SRR16303516_1.fastq
fastq_ori/SRR16303516_2.fastq
F3410871.S91
fastq_ori/SRR16303527_1.fastq
fastq_ori/SRR16303527_2.fastq
F3410871.S92
fastq_ori/SRR16303538_1.fastq
fastq_ori/SRR16303538_2.fastq
F3410871.S93
fastq_ori/SRR16303549_1.fastq
fastq_ori/SRR16303549_2.fastq
F3410871.S94
fastq_ori/SRR16303561_1.fastq
fastq_ori/SRR16303561_2.fastq
F3410871.S95
fastq_ori/SRR16303562_1.fastq
fastq_ori/SRR16303562_2.fastq
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 [1].
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.
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.
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”.
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
249
239
229
219
209
199
250
57.73%
59.54%
60.28%
60.67%
61.05%
61.41%
240
85.91%
87.98%
88.90%
89.40%
89.94%
90.31%
230
85.96%
88.02%
88.95%
89.46%
90.01%
90.38%
220
86.13%
88.17%
89.08%
89.55%
90.09%
90.60%
210
86.18%
88.24%
89.16%
89.61%
90.17%
90.67%
200
86.31%
88.36%
89.28%
89.73%
90.26%
90.75%
Based on the above result, the trim length combination of R1 = 200 bases and R2 = 199 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
F3410871.S01
F3410871.S02
F3410871.S03
F3410871.S04
F3410871.S05
F3410871.S06
F3410871.S07
F3410871.S08
F3410871.S09
F3410871.S10
F3410871.S11
F3410871.S12
F3410871.S13
F3410871.S14
F3410871.S15
F3410871.S16
F3410871.S17
F3410871.S18
F3410871.S19
F3410871.S20
F3410871.S21
F3410871.S22
F3410871.S23
F3410871.S24
F3410871.S25
F3410871.S26
F3410871.S27
F3410871.S28
F3410871.S29
F3410871.S30
F3410871.S31
F3410871.S32
F3410871.S33
F3410871.S34
F3410871.S35
F3410871.S36
F3410871.S37
F3410871.S38
F3410871.S39
F3410871.S40
F3410871.S41
F3410871.S42
F3410871.S43
F3410871.S44
F3410871.S45
F3410871.S46
F3410871.S47
F3410871.S48
F3410871.S49
F3410871.S50
F3410871.S51
F3410871.S52
F3410871.S53
F3410871.S54
F3410871.S55
F3410871.S56
F3410871.S57
F3410871.S58
F3410871.S59
F3410871.S60
F3410871.S61
F3410871.S62
F3410871.S63
F3410871.S64
F3410871.S65
F3410871.S66
F3410871.S67
F3410871.S68
F3410871.S69
F3410871.S70
F3410871.S71
F3410871.S72
F3410871.S73
F3410871.S74
F3410871.S75
F3410871.S76
F3410871.S77
F3410871.S78
F3410871.S79
F3410871.S80
F3410871.S81
F3410871.S82
F3410871.S83
F3410871.S84
F3410871.S85
F3410871.S86
F3410871.S87
F3410871.S88
F3410871.S89
F3410871.S90
F3410871.S91
F3410871.S92
F3410871.S93
F3410871.S94
F3410871.S95
Row Sum
Percentage
input
55,209
37,003
39,944
48,973
30,022
7,959
17,699
35,384
37,834
1,945
94,540
52,805
45,983
42,345
48,385
21,798
61,378
23,743
46,730
21,855
41,503
16,088
53,787
48,767
56,376
62,926
49,029
37,035
45,205
62,335
5,628
65,048
41,145
55,492
84,676
30,364
78,665
43,429
58,375
51,585
24,396
11,294
48,683
6,212
47,930
51,186
61,744
44,022
89,299
61,292
45,883
47,681
6,899
38,118
71,144
55,408
64,665
73,056
55,402
44,657
63,241
65,330
121,072
20,994
64,314
78,260
71,534
83,666
79,922
70,708
70,184
61,409
81,031
11,849
68,251
94,413
70,494
66,317
63,995
40,727
53,668
107,937
53,639
69,497
14,096
85,584
15,308
13,334
4,338
25,521
10,825
21,302
8,456
13,542
18,110
4,570,831
100.00%
filtered
54,824
36,763
39,644
48,676
29,845
7,888
17,567
35,105
37,527
1,942
94,527
52,797
45,979
42,337
48,383
21,795
61,366
23,735
46,728
21,851
41,493
16,084
53,778
48,758
56,371
62,912
49,023
37,030
45,192
62,332
5,624
65,040
41,134
55,491
84,667
30,360
78,658
43,422
58,370
51,574
24,389
11,286
48,672
6,207
47,927
51,178
61,736
44,019
89,292
61,282
45,878
47,675
6,889
38,105
71,134
55,400
64,660
73,050
55,395
44,647
63,227
65,318
121,059
20,994
64,294
78,256
71,523
83,648
79,904
70,693
70,174
61,404
81,019
11,829
68,247
94,406
70,474
66,304
63,981
40,714
53,649
107,924
53,625
69,474
14,094
85,571
15,304
13,327
4,335
25,520
10,825
21,302
8,450
13,532
18,109
4,567,922
99.94%
denoisedF
54,236
36,093
39,030
48,016
29,345
6,469
16,904
33,913
36,505
1,916
92,701
52,175
45,141
41,153
47,752
21,392
59,910
22,497
46,068
21,792
40,553
15,530
52,338
47,936
55,239
61,799
48,297
35,954
43,932
61,771
5,594
64,243
39,896
54,881
83,735
29,506
77,966
42,858
57,463
50,898
23,626
11,227
47,349
5,956
47,092
50,272
60,804
43,008
88,436
60,246
44,985
46,687
6,861
37,159
70,016
54,369
63,800
72,268
54,447
43,802
62,127
63,211
118,559
20,942
62,268
76,624
69,391
81,061
77,714
68,708
67,752
59,742
79,363
11,763
66,851
92,666
68,130
64,194
62,275
39,006
51,503
105,813
52,302
67,283
14,003
84,116
15,225
13,278
4,288
25,443
10,781
21,210
8,424
13,470
18,053
4,473,346
97.87%
denoisedR
53,360
35,544
38,382
47,149
28,944
6,201
16,461
33,037
35,699
1,895
89,466
50,701
43,699
39,728
46,476
20,571
58,422
21,614
44,553
21,715
39,427
15,009
50,479
45,976
53,909
60,481
46,795
34,552
42,526
60,403
5,564
62,549
38,865
53,313
81,902
28,507
76,206
41,533
56,414
49,790
22,690
11,173
46,230
5,742
45,788
48,832
59,123
41,743
86,639
58,904
43,715
45,146
6,838
36,352
68,319
52,809
62,444
70,640
52,775
42,773
61,049
61,132
115,286
20,910
60,221
74,678
67,408
78,963
75,585
66,672
65,770
57,796
77,361
11,734
65,323
89,724
66,369
62,262
60,269
37,800
50,190
103,156
50,572
64,993
13,957
82,010
15,226
13,225
4,261
25,352
10,773
21,144
8,407
13,417
17,972
4,357,459
95.33%
merged
52,123
35,162
37,676
45,931
28,678
5,825
15,530
32,077
34,835
1,858
86,440
49,362
42,261
37,878
45,070
19,927
56,842
21,173
41,936
21,428
38,243
14,769
47,912
43,070
52,672
59,903
45,299
33,247
40,838
59,759
5,490
60,359
38,084
51,572
80,488
27,413
72,978
39,893
55,551
49,178
22,083
11,066
45,151
5,595
44,714
47,537
56,397
40,368
84,448
57,363
42,686
43,276
6,817
35,994
66,682
51,030
60,232
68,334
50,074
42,206
60,284
59,556
113,123
20,824
57,741
73,466
65,487
76,712
72,785
64,475
63,753
56,304
74,409
11,665
63,887
86,685
64,646
60,695
58,293
36,643
49,367
101,412
49,284
62,867
13,791
79,405
15,062
13,091
4,099
24,599
10,567
20,728
8,361
13,252
17,823
4,235,924
92.67%
nonchim
50,372
35,032
36,779
44,573
28,596
5,733
15,351
31,557
34,310
1,858
85,253
48,170
41,501
37,437
44,345
19,676
55,713
21,019
40,456
21,374
37,657
14,769
47,304
41,912
51,053
59,339
44,253
32,659
39,656
58,188
5,364
59,144
37,946
50,403
78,394
27,268
70,122
39,156
54,590
48,590
21,857
10,943
44,879
5,580
44,178
47,064
53,932
39,981
83,311
56,551
41,649
42,553
6,817
35,986
64,636
49,236
57,613
66,275
49,254
41,968
59,947
58,966
109,944
20,666
56,409
72,270
64,036
76,088
72,421
62,913
62,597
55,489
73,561
11,665
62,841
84,381
64,300
59,800
57,373
36,567
49,347
98,302
48,806
62,667
13,791
77,240
15,041
12,962
4,099
24,597
10,564
20,726
8,361
13,249
17,823
4,160,944
91.03%
This table can be downloaded as an Excel table below:
5. DADA2 Amplicon Sequence Variants (ASVs). A total of 730 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 20210310a
The close-reference taxonomy assignment of the ASV sequences using BLASTN is based on the algorithm published by Al-Hebshi et. al. (2015)[2].
1. Raw sequences reads in FASTA format were BLASTN-searched against a combined set of 16S rRNA reference sequences - the FOMC 16S rRNA Reference Sequences version 20221029 (https://microbiome.forsyth.org/ftp/refseq/).
This set consists of the HOMD (version 15.22 http://www.homd.org/index.php?name=seqDownload&file&type=R ), Mouse Oral Microbiome Database (MOMD version 5.1 https://momd.org/ftp/16S_rRNA_refseq/MOMD_16S_rRNA_RefSeq/V5.1/),
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 full-length 16S rRNA sequences from HOMD V15.22, 356 from MOMD V5.1, and 22,126 from NCBI, a total of 23,497 sequences.
Altogether these sequence represent a total of 17,035 oral and non-oral microbial species.
The NCBI BLASTN version 2.7.1+ (Zhang et al, 2000) [3] 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)[4].
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:
Al-Hebshi NN, Nasher AT, Idris AM, Chen T. Robust species taxonomy assignment algorithm for 16S rRNA NGS reads: application
to oral carcinoma samples. J Oral Microbiol. 2015 Sep 29;7:28934. doi: 10.3402/jom.v7.28934. PMID: 26426306; PMCID: PMC4590409.
Zhang Z, Schwartz S, Wagner L, Miller W. A greedy algorithm for aligning DNA sequences. J Comput Biol. 2000 Feb-Apr;7(1-2):203-14. doi: 10.1089/10665270050081478. PMID: 10890397.
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=0.01%(>=414 reads)
A
Total reads
4,160,944
4,160,944
B
Total assigned reads
4,142,463
4,142,463
C
Assigned reads in species with read count < MPC
0
18,961
D
Assigned reads in samples with read count < 500
0
0
E
Total samples
95
95
F
Samples with reads >= 500
95
95
G
Samples with reads < 500
0
0
H
Total assigned reads used for analysis (B-C-D)
4,142,463
4,123,502
I
Reads assigned to single species
1,048,454
1,035,416
J
Reads assigned to multiple species
3,081,871
3,076,828
K
Reads assigned to novel species
12,138
11,258
L
Total number of species
411
154
M
Number of single species
244
93
N
Number of multi-species
128
54
O
Number of novel species
39
7
P
Total unassigned reads
18,481
18,481
Q
Chimeric reads
0
0
R
Reads without BLASTN hits
2,509
2,509
S
Others: short, low quality, singletons, etc.
15,972
15,972
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[5][6] 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 [7].
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) at the species level.
Printed on each graph is the statistical significance p values of the difference between the groups.
The significance is calculated using either Kruskal-Wallis test or the Wilcoxon rank sum test, both are non-parametric methods (since
microbiome read count data are considered non-normally distributed) for testing
whether samples originate from the same distribution (i.e., no difference between groups). The Kruskal-Wallis test is used to compare three or more
independent groups to determine if there are statistically significant differences between their medians. The Wilcoxon Rank Sum test, also known as
the Mann-Whitney U test, is used to compare two independent groups to determine if there is a significant difference between their distributions.
The p-value is shown on the top of each graph. A p-value < 0.05 is considered statistically significant between/among the test groups.
Alpha Diversity Box Plots for All Groups - Species Level
Alpha Diversity Box Plots for Individual Comparisons at Species level
Beta diversity compares the similarity (or dissimilarity) of microbial profiles between different
groups of samples. There are many different similarity/dissimilarity metrics [8].
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, at the Species level:
NMDS and PCoA Plots for All Groups - Species Level
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 at the Species level:
NMDS and PCoA Plots for Individual Comparisons at Species level
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 [9].
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 [10]. ANCOM transforms the count data into log-ratios and thus is more suitable for comparing
the composition of microbiomes in two or more populations. "ANCOM" generates a table of features with
W-statistics and whether the null hypothesis is rejected. The “W” is the W-statistic, or number of
features that a single feature is tested to be significantly different against. Hence the higher the "W"
the more statistical sifgnificant that a feature/species is differentially abundant.
References:
Gloor GB, Macklaim JM, Pawlowsky-Glahn V, Egozcue JJ. Microbiome Datasets Are Compositional: And This Is Not Optional. Front Microbiol.
2017 Nov 15;8:2224. doi: 10.3389/fmicb.2017.02224. PMID: 29187837; PMCID: PMC5695134.
Mandal S, Van Treuren W, White RA, Eggesbø M, Knight R, Peddada SD. Analysis of composition of
microbiomes: a novel method for studying microbial composition. Microb Ecol Health Dis.
2015 May 29;26:27663. doi: 10.3402/mehd.v26.27663. PMID: 26028277; PMCID: PMC4450248.
Starting with version V1.2, we include the results of ANCOM-BC (Analysis of Compositions of
Microbiomes with Bias Correction) (Lin and Peddada 2020) [11]. ANCOM-BC is an updated version of "ANCOM" that:
(a) provides statistically valid test with appropriate p-values,
(b) provides confidence intervals for differential abundance of each taxon,
(c) controls the False Discovery Rate (FDR),
(d) maintains adequate power, and
(e) is computationally simple to implement.
The bias correction (BC) addresses a challenging problem of the bias introduced by differences in
the sampling fractions across samples. This bias has been a major hurdle in performing DA analysis of microbiome data.
ANCOM-BC estimates the unknown sampling fractions and corrects the bias induced by their differences among samples.
The absolute abundance data are modeled using a linear regression framework.
Starting with version V1.43, ANCOM-BC2 is used instead of ANCOM-BC, So that multiple pairwise directional test can be performed (if there are more than two gorups in a comparison).
When performing pairwise directional test, the mixed directional false discover rate (mdFDR) is taken into account. The mdFDR
is the combination of false discovery rate due to multiple testing, multiple pairwise comparisons, and directional tests within
each pairwise comparison. The mdFDR is adopted from (Guo, Sarkar, and Peddada 2010 [12]; Grandhi, Guo, and Peddada 2016 [13]). For more detail
explanation and additional features of ANCOM-BC2 please see author's documentation.
References:
Lin H, Peddada SD. Analysis of compositions of microbiomes with bias correction.
Nat Commun. 2020 Jul 14;11(1):3514. doi: 10.1038/s41467-020-17041-7.
PMID: 32665548; PMCID: PMC7360769.
Guo W, Sarkar SK, Peddada SD. Controlling false discoveries in multidimensional directional decisions, with applications to gene expression data on ordered categories. Biometrics. 2010 Jun;66(2):485-92. doi: 10.1111/j.1541-0420.2009.01292.x. Epub 2009 Jul 23. PMID: 19645703; PMCID: PMC2895927.
Grandhi A, Guo W, Peddada SD. A multiple testing procedure for multi-dimensional pairwise comparisons with application to gene expression studies. BMC Bioinformatics. 2016 Feb 25;17:104. doi: 10.1186/s12859-016-0937-5. PMID: 26917217; PMCID: PMC4768411.
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) [14].
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.
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. We provide the network association result with SparCC (Sparse Correlations for Compositional data)(Friedman & Alm 2012), which
is a method for inferring correlations from compositional data. SparCC estimates the linear Pearson correlations between
the log-transformed components.
The results of this analysis are for research purpose only. They are not intended to diagnose, treat, cure, or prevent any disease. Forsyth and FOMC
are not responsible for use of information provided in this report outside the research area.
