Project F20250501_Han services include NGS sequencing of the V1V3 region of the 16S rRNA gene amplicons from the samples. First and foremost, please
download this report, as well as the sequence raw data from the download links provided below.
These links will expire after 60 days. We cannot guarantee the availability of your data after 60 days.
Full Bioinformatics analysis service was requested. We provide many analyses, starting from the raw sequence quality and noise filtering, pair reads merging, as well as chimera filtering for the sequences, using the
DADA2 denosing algorithm and pipeline.
We also provide many downstream analyses such as taxonomy assignment, alpha and beta diversity analyses, and differential abundance analysis.
For taxonomy assignment, most informative would be the taxonomy barplots. We provide an interactive barplots to show the relative abundance of microbes at different taxonomy levels (from Phylum to species) that you can choose.
If you specify which groups of samples you want to compare for differential abundance, we provide both ANCOM and LEfSe differential abundance analysis.
The samples were processed and analyzed with the ZymoBIOMICS® Service: Targeted
Metagenomic Sequencing (Zymo Research, Irvine, CA).
DNA Extraction: If DNA extraction was performed, the following DNA
extraction kit was used according to the manufacturer’s instructions:
☑
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)
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® NextSeq 2000™ with a p1
(Illumina, Sand Diego, CA) reagent kit (600 cycles). The sequencing was performed
with 25% 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 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
SampleName
R1 Read Count
F20250501.S001
original sample ID here
c01
103883
F20250501.S002
original sample ID here
c05
95073
F20250501.S003
original sample ID here
i01
119529
F20250501.S004
original sample ID here
i05
86202
F20250501.S005
original sample ID here
C03
62176
F20250501.S006
original sample ID here
C04
59687
F20250501.S007
original sample ID here
C06
100587
F20250501.S008
original sample ID here
C08
94970
F20250501.S009
original sample ID here
C09
93103
F20250501.S010
original sample ID here
C10
45457
F20250501.S011
original sample ID here
C11
36749
F20250501.S012
original sample ID here
C12
41389
F20250501.S013
original sample ID here
C13
40296
F20250501.S014
original sample ID here
C14
44548
F20250501.S015
original sample ID here
C15
46701
F20250501.S016
original sample ID here
C16
88337
F20250501.S017
original sample ID here
C17
49810
F20250501.S018
original sample ID here
C18
96919
F20250501.S019
original sample ID here
C19
128942
F20250501.S020
original sample ID here
C20
43073
F20250501.S021
original sample ID here
C21
60579
F20250501.S022
original sample ID here
C22
44566
F20250501.S023
original sample ID here
C23
95550
F20250501.S024
original sample ID here
C24
98672
F20250501.S025
original sample ID here
C25
63822
F20250501.S026
original sample ID here
C26
72607
F20250501.S027
original sample ID here
C27
87733
F20250501.S028
original sample ID here
C28
61922
F20250501.S029
original sample ID here
C29
83881
F20250501.S030
original sample ID here
C30
88181
F20250501.S031
original sample ID here
C31
41082
F20250501.S032
original sample ID here
C32
68315
F20250501.S033
original sample ID here
C33
66116
F20250501.S034
original sample ID here
C34
34923
F20250501.S035
original sample ID here
C35
44632
F20250501.S036
original sample ID here
i03
101476
F20250501.S037
original sample ID here
i04
78609
F20250501.S038
original sample ID here
i06
68680
F20250501.S039
original sample ID here
i07
97200
F20250501.S040
original sample ID here
i08
63160
F20250501.S041
original sample ID here
i09
85669
F20250501.S042
original sample ID here
i10
95118
F20250501.S043
original sample ID here
i11
60835
F20250501.S044
original sample ID here
i12
96982
F20250501.S045
original sample ID here
i13
92257
F20250501.S046
original sample ID here
i14
92579
F20250501.S047
original sample ID here
i16
73031
F20250501.S048
original sample ID here
i17
77947
F20250501.S049
original sample ID here
i18
68083
F20250501.S050
original sample ID here
i19
103865
F20250501.S051
original sample ID here
i20
31114
F20250501.S052
original sample ID here
i21
43648
F20250501.S053
original sample ID here
i22
64569
F20250501.S054
original sample ID here
i24
50207
F20250501.S055
original sample ID here
i25
75772
F20250501.S056
original sample ID here
i27
56204
F20250501.S057
original sample ID here
i28
97819
F20250501.S058
original sample ID here
C36
82607
F20250501.S059
original sample ID here
C37
91816
F20250501.S060
original sample ID here
C38
82610
F20250501.S061
original sample ID here
C39
95757
F20250501.S062
original sample ID here
C40
83103
F20250501.S063
original sample ID here
C41
83056
F20250501.S064
original sample ID here
C42
84977
F20250501.S065
original sample ID here
C43
92517
F20250501.S066
original sample ID here
C44
91564
F20250501.S067
original sample ID here
C45
94642
F20250501.S068
original sample ID here
C46
79666
F20250501.S069
original sample ID here
C47
80948
F20250501.S070
original sample ID here
C48
99693
F20250501.S071
original sample ID here
C49
91790
F20250501.S072
original sample ID here
C50
116863
F20250501.S073
original sample ID here
C51
89988
F20250501.S074
original sample ID here
C52
76145
F20250501.S075
original sample ID here
C53
48834
F20250501.S076
original sample ID here
C54
38974
F20250501.S077
original sample ID here
C55
68309
F20250501.S078
original sample ID here
C56
68887
F20250501.S079
original sample ID here
C57
86128
F20250501.S080
original sample ID here
C58
111027
F20250501.S081
original sample ID here
C59
104414
F20250501.S082
original sample ID here
C68
95327
F20250501.S083
original sample ID here
C69
86847
F20250501.S084
original sample ID here
C70
115606
F20250501.S085
original sample ID here
C71
58334
F20250501.S086
original sample ID here
C72
93645
F20250501.S087
original sample ID here
C60
107448
F20250501.S088
original sample ID here
C61
153430
F20250501.S089
original sample ID here
C62
99116
F20250501.S090
original sample ID here
C63
99047
F20250501.S091
original sample ID here
C64
135324
F20250501.S092
original sample ID here
C65
99394
F20250501.S093
original sample ID here
C66
76162
F20250501.S094
original sample ID here
C67
127940
F20250501.S095
original sample ID here
i54
171562
F20250501.S096
original sample ID here
v09
30148
F20250501.S097
original sample ID here
i30
86579
F20250501.S098
original sample ID here
i31
98001
F20250501.S099
original sample ID here
i32
114923
F20250501.S100
original sample ID here
i33
108826
F20250501.S101
original sample ID here
i34
95408
F20250501.S102
original sample ID here
i35
85006
F20250501.S103
original sample ID here
i36
108182
F20250501.S104
original sample ID here
i37
85416
F20250501.S105
original sample ID here
i38
87352
F20250501.S106
original sample ID here
i39
73442
F20250501.S107
original sample ID here
i40
101771
F20250501.S108
original sample ID here
i41
100386
F20250501.S109
original sample ID here
i42
106817
F20250501.S110
original sample ID here
i43
57879
F20250501.S111
original sample ID here
i44
83993
F20250501.S112
original sample ID here
i45
93783
F20250501.S113
original sample ID here
i46
73010
F20250501.S114
original sample ID here
i47
81543
F20250501.S115
original sample ID here
i48
87777
F20250501.S116
original sample ID here
i49
32205
F20250501.S117
original sample ID here
i50
46445
F20250501.S118
original sample ID here
i51
46203
F20250501.S119
original sample ID here
i52
29728
F20250501.S120
original sample ID here
i53
36519
F20250501.S121
original sample ID here
i55
40135
F20250501.S122
original sample ID here
v01
529
F20250501.S123
original sample ID here
v02
4262
F20250501.S124
original sample ID here
v03
54125
F20250501.S125
original sample ID here
v04
57155
F20250501.S126
original sample ID here
v05
72003
F20250501.S127
original sample ID here
v06
70760
F20250501.S128
original sample ID here
v07
53518
F20250501.S129
original sample ID here
v08
61880
F20250501.S130
original sample ID here
v10
50301
F20250501.S131
original sample ID here
v11
61630
F20250501.S132
original sample ID here
v12
77125
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
250
240
230
220
210
200
250
79.04%
79.17%
79.21%
7.03%
1.01%
0.14%
240
79.12%
79.21%
6.97%
1.00%
0.14%
0.15%
230
79.33%
6.97%
1.01%
0.15%
0.15%
0.16%
220
6.90%
1.00%
0.14%
0.15%
0.16%
0.16%
210
0.99%
0.14%
0.15%
0.15%
0.16%
0.17%
200
0.14%
0.14%
0.15%
0.16%
0.16%
0.17%
Based on the above result, the trim length combination of R1 = 230 bases and R2 = 250 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
F20250501.S001
F20250501.S002
F20250501.S003
F20250501.S004
F20250501.S005
F20250501.S006
F20250501.S007
F20250501.S008
F20250501.S009
F20250501.S010
F20250501.S011
F20250501.S012
F20250501.S013
F20250501.S014
F20250501.S015
F20250501.S016
F20250501.S017
F20250501.S018
F20250501.S019
F20250501.S020
F20250501.S021
F20250501.S022
F20250501.S023
F20250501.S024
F20250501.S025
F20250501.S026
F20250501.S027
F20250501.S028
F20250501.S029
F20250501.S030
F20250501.S031
F20250501.S032
F20250501.S033
F20250501.S034
F20250501.S035
F20250501.S036
F20250501.S037
F20250501.S038
F20250501.S039
F20250501.S040
F20250501.S041
F20250501.S042
F20250501.S043
F20250501.S044
F20250501.S045
F20250501.S046
F20250501.S047
F20250501.S048
F20250501.S049
F20250501.S050
F20250501.S051
F20250501.S052
F20250501.S053
F20250501.S054
F20250501.S055
F20250501.S056
F20250501.S057
F20250501.S058
F20250501.S059
F20250501.S060
F20250501.S061
F20250501.S062
F20250501.S063
F20250501.S064
F20250501.S065
F20250501.S066
F20250501.S067
F20250501.S068
F20250501.S069
F20250501.S070
F20250501.S071
F20250501.S072
F20250501.S073
F20250501.S074
F20250501.S075
F20250501.S076
F20250501.S077
F20250501.S078
F20250501.S079
F20250501.S080
F20250501.S081
F20250501.S082
F20250501.S083
F20250501.S084
F20250501.S085
F20250501.S086
F20250501.S087
F20250501.S088
F20250501.S089
F20250501.S090
F20250501.S091
F20250501.S092
F20250501.S093
F20250501.S094
F20250501.S095
F20250501.S096
F20250501.S097
F20250501.S098
F20250501.S099
F20250501.S100
F20250501.S101
F20250501.S102
F20250501.S103
F20250501.S104
F20250501.S105
F20250501.S106
F20250501.S107
F20250501.S108
F20250501.S109
F20250501.S110
F20250501.S111
F20250501.S112
F20250501.S113
F20250501.S114
F20250501.S115
F20250501.S116
F20250501.S117
F20250501.S118
F20250501.S119
F20250501.S120
F20250501.S121
F20250501.S122
F20250501.S123
F20250501.S124
F20250501.S125
F20250501.S126
F20250501.S127
F20250501.S128
F20250501.S129
F20250501.S130
F20250501.S131
F20250501.S132
Row Sum
Percentage
input
103,883
95,073
119,529
86,202
62,176
59,687
100,587
94,970
93,103
45,457
36,749
41,389
40,296
44,548
46,701
88,337
49,810
96,919
128,942
43,073
60,579
44,566
95,550
98,672
63,822
72,607
87,733
61,922
83,881
88,181
41,082
68,315
66,116
34,923
44,632
101,476
78,609
68,680
97,200
63,160
85,669
95,118
60,835
96,982
92,257
92,579
73,031
77,947
68,083
103,865
31,114
43,648
64,569
50,207
75,772
56,204
97,819
82,607
91,816
82,610
95,757
83,103
83,056
84,977
92,517
91,564
94,642
79,666
80,948
99,693
91,790
116,863
89,988
76,145
48,834
38,974
68,309
68,887
86,128
111,027
104,414
95,327
86,847
115,606
58,334
93,645
107,448
153,430
99,116
99,047
135,324
99,394
76,162
127,940
171,562
30,148
86,579
98,001
114,923
108,826
95,408
85,006
108,182
85,416
87,352
73,442
101,771
100,386
106,817
57,879
83,993
93,783
73,010
81,543
87,777
32,205
46,445
46,203
29,728
36,519
40,135
529
4,262
54,125
57,155
72,003
70,760
53,518
61,880
50,301
61,630
77,125
10,283,098
100.00%
filtered
103,701
94,908
119,336
86,040
62,117
59,632
100,515
94,903
93,029
45,426
36,715
41,347
40,261
44,514
46,661
88,290
49,765
96,847
128,852
43,040
60,528
44,532
95,475
98,600
63,786
72,565
87,666
61,872
83,814
88,117
41,046
68,261
66,064
34,895
44,596
101,402
78,537
68,620
97,140
63,110
85,611
95,042
60,789
96,905
92,187
92,512
72,979
77,874
68,046
103,788
31,084
43,611
64,520
50,168
75,724
56,164
97,738
82,218
91,416
82,204
95,329
82,757
82,661
84,627
92,097
91,202
94,206
79,312
80,566
99,236
91,397
116,288
89,582
75,751
48,610
38,819
68,007
68,590
85,783
110,559
103,964
94,946
86,471
115,103
58,141
93,257
107,333
153,222
98,991
98,963
135,179
99,273
76,070
127,795
171,339
30,113
86,230
97,576
114,400
108,378
95,004
84,643
107,711
85,002
86,956
73,121
101,331
99,934
106,344
57,642
83,658
93,362
72,694
81,201
87,388
32,042
46,258
46,013
29,600
36,330
39,933
528
4,245
53,891
56,923
71,691
70,435
53,285
61,633
50,070
61,377
76,819
10,256,292
99.74%
denoisedF
103,486
94,734
119,156
85,772
61,970
59,508
100,396
94,806
92,904
45,275
36,653
41,209
40,198
44,361
46,600
87,656
49,697
96,754
128,569
42,951
60,401
44,470
95,381
98,495
63,710
72,518
87,533
61,826
83,743
88,047
40,968
68,137
65,951
34,856
44,406
101,226
78,450
68,536
97,035
63,037
85,515
94,913
60,687
96,753
92,065
92,337
72,798
77,812
67,935
103,712
31,047
43,530
64,438
50,120
75,631
56,115
97,651
81,847
91,115
81,857
94,904
82,478
82,344
84,184
91,779
90,868
93,976
79,005
80,384
99,105
91,022
116,079
89,477
75,397
48,403
38,546
67,682
68,002
85,581
110,360
103,766
94,658
86,238
114,835
57,962
92,916
107,102
152,617
98,791
98,815
134,773
99,080
75,774
127,497
170,927
30,061
85,948
97,249
114,109
108,018
94,604
84,322
107,484
84,473
86,682
72,097
101,119
99,654
105,926
57,259
83,456
92,752
72,472
81,034
87,059
31,793
46,163
45,775
29,213
36,022
39,595
507
4,195
53,773
56,796
71,540
70,246
53,076
61,494
49,805
60,999
76,599
10,227,955
99.46%
denoisedR
102,905
93,939
118,401
85,301
61,650
59,105
99,479
94,142
92,238
44,957
36,434
40,956
39,985
44,071
46,326
87,063
49,326
96,116
127,975
42,669
60,204
44,182
94,787
97,870
63,072
71,928
87,042
61,408
83,260
87,485
40,662
67,725
65,562
34,583
44,181
100,697
77,953
68,123
96,415
62,583
84,946
94,125
60,246
96,252
91,481
91,713
72,390
77,305
67,444
103,045
30,843
43,258
64,055
49,778
75,207
55,772
96,953
81,381
90,421
81,354
94,473
81,910
81,717
83,763
91,246
90,306
93,427
78,711
80,047
98,523
90,649
115,375
89,130
74,977
48,106
38,347
67,390
67,422
85,080
109,844
103,253
94,138
85,716
114,356
57,699
92,312
106,289
151,377
97,783
97,868
133,370
98,144
75,101
126,395
169,602
29,739
85,530
96,686
113,551
107,526
94,107
83,764
107,029
83,970
86,328
72,137
100,652
99,149
105,534
56,553
83,117
92,204
71,962
80,554
86,573
31,481
45,906
45,447
29,059
35,558
39,488
500
4,166
53,503
56,560
71,279
69,904
52,737
61,238
49,618
60,711
76,196
10,164,591
98.85%
merged
101,779
92,095
115,932
83,670
61,374
58,144
99,260
93,732
91,727
43,898
36,225
40,142
39,753
42,581
46,101
86,187
49,130
95,925
127,582
42,454
60,016
43,983
94,512
97,631
62,407
71,853
86,827
61,194
82,876
87,238
40,458
67,308
65,129
34,152
43,597
99,296
77,374
67,865
96,247
62,435
84,604
92,960
59,916
95,792
90,970
91,544
72,031
77,077
66,432
102,848
30,677
43,025
63,882
49,700
75,024
55,704
96,828
79,870
88,929
79,687
93,007
81,143
80,170
81,948
89,769
89,251
92,966
77,030
79,234
97,109
88,598
114,970
88,301
74,094
47,404
37,958
66,727
66,579
84,419
109,565
102,945
92,741
84,266
113,715
56,692
91,028
105,705
149,283
97,005
97,022
131,541
97,114
74,875
125,169
167,794
29,546
85,031
95,885
112,946
106,869
92,360
82,216
106,515
81,560
85,571
69,799
100,241
98,787
105,005
53,775
82,782
90,015
71,670
79,822
84,992
30,835
45,635
44,701
28,035
34,376
39,050
447
4,089
53,055
55,895
70,583
69,009
50,712
60,822
48,133
59,304
74,824
10,059,618
97.83%
nonchim
91,909
51,980
109,864
49,531
58,065
34,963
82,262
74,020
76,618
33,511
28,975
22,368
30,499
21,246
37,956
70,253
43,506
95,401
124,031
36,178
57,480
37,947
81,891
89,946
50,248
71,391
80,397
51,640
72,690
78,844
32,866
61,477
53,408
26,241
35,061
61,037
59,421
54,477
85,391
51,691
81,153
79,346
48,159
75,655
68,463
85,857
49,298
64,422
53,574
97,757
27,348
37,906
60,964
45,494
74,492
52,268
96,815
67,078
68,969
55,690
84,392
43,616
51,710
57,031
74,084
65,994
57,171
48,250
55,427
77,495
69,089
114,120
73,000
70,674
28,027
18,376
52,924
47,907
63,745
102,481
89,130
70,399
62,267
111,706
39,495
48,165
85,117
139,023
85,265
86,799
93,531
73,896
44,390
114,922
143,160
22,685
43,894
80,442
109,155
98,749
66,430
53,785
99,055
68,207
77,233
63,437
86,899
96,487
103,783
20,112
79,222
74,039
36,039
67,034
66,072
20,324
42,805
39,314
24,838
32,567
34,791
404
3,929
37,667
44,404
62,858
62,262
32,195
44,614
32,343
22,602
32,210
8,183,052
79.58%
This table can be downloaded as an Excel table below:
5. DADA2 Amplicon Sequence Variants (ASVs). A total of 3223 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%(>=811 reads)
A
Total reads
8,183,052
8,183,052
B
Total assigned reads
8,114,520
8,114,520
C
Assigned reads in species with read count < MPC
0
33,487
D
Assigned reads in samples with read count < 500
391
391
E
Total samples
132
132
F
Samples with reads >= 500
131
131
G
Samples with reads < 500
1
1
H
Total assigned reads used for analysis (B-C-D)
8,114,129
8,080,642
I
Reads assigned to single species
4,303,342
4,290,464
J
Reads assigned to multiple species
3,676,565
3,670,629
K
Reads assigned to novel species
134,222
119,549
L
Total number of species
949
50
M
Number of single species
273
22
N
Number of multi-species
149
17
O
Number of novel species
527
11
P
Total unassigned reads
68,532
68,532
Q
Chimeric reads
181
181
R
Reads without BLASTN hits
6,814
6,814
S
Others: short, low quality, singletons, etc.
61,537
61,537
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).
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
Alpha Diversity Box Plots for Individual Comparisons at Species level
The above comparisons are at the species-level. Comparisons of other taxonomy levels, from phylum to genus, are also available:
Group Significance Evaluation of Alpha-diversity Indices with QIIME2
The above comparisons and significance tests were done under the R environment. For compasison (also because this was included in the pipeline
early on) we also use the Kruskal Wallis H test
provided the "alpha-group-significance" fucntion in the QIIME 2 "diversity" package. As mentioned above, Kruskal Wallis 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 (assumption of normality). It is sometimes called the one-way ANOVA on ranks,
as the ranks of the data values are used in the test rather than the actual data points. The H test determines whether the medians of two
or more groups are different.
Below are the Kruskal Wallis H test results for each comparison based on three different alpha diversity measures: 1) Observed species (features),
2) Shannon index, and 3) Simpson index.
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:
The above PCoA and NMDS plots are based on count data. The count data can also be transformed into centered log ratio (CLR)
for each species. The CLR data is no longer count data and cannot be used in Bray-Curtis dissimilarity calculation. Instead
CLR can be compared with Euclidean distances. When CLR data are compared by Euclidean distance, the distance is also called
Aitchison distance.
Below are the NMDS and PCoA plots of the Aitchison distances of the samples:
NMDS and PCoA Plots for Individual Comparisons at Species level
Interactive 3D PCoA Plots - Bray-Curtis Dissimilarity
Interactive 3D PCoA Plots - Euclidean Distance
Interactive 3D PCoA Plots - Correlation Coefficients
Group Significance of Beta-diversity Indices
To test whether the between-group dissimilarities are significantly greater than the within-group dissimilarities,
the "beta-group-significance" function provided in the QIIME 2 "diversity" package was used with PERMANOVA
(permutational multivariate analysis of variance) as the group significant testing method.
Three beta diversity matrics were used: 1) Bray–Curtis dissimilarity 2) Correlation coefficient matrix , and 3) Aitchison distance
(Euclidean distance between clr-transformed compositions).
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. 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) [15].
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)[16], 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.
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.
