Project FOMC14848 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, one of three different DNA
extraction kits was used depending on the sample type and sample volume and were
used according to the manufacturer’s instructions, unless otherwise stated. The kit used
in this project is marked below:
☐
ZymoBIOMICS® DNA Miniprep Kit (Zymo Research, Irvine, CA)
☐
ZymoBIOMICS® DNA Microprep Kit (Zymo Research, Irvine, CA)
☐
ZymoBIOMICS®-96 MagBead DNA Kit (Zymo Research, Irvine, CA)
☑
N/A (DNA Extraction Not Performed)
Elution Volume: 50µL
Additional Notes: NA
Targeted Library Preparation: The DNA samples were prepared for targeted
sequencing with the Quick-16S™ NGS Library Prep Kit (Zymo Research, Irvine, CA).
These primers were custom designed by Zymo Research to provide the best coverage
of the 16S gene while maintaining high sensitivity. The primer sets used in this project
are marked below:
☐
Quick-16S™ Primer Set V1-V2 (Zymo Research, Irvine, CA)
☑
Quick-16S™ Primer Set V1-V3 (Zymo Research, Irvine, CA)
☐
Quick-16S™ Primer Set V3-V4 (Zymo Research, Irvine, CA)
☐
Quick-16S™ Primer Set V4 (Zymo Research, Irvine, CA)
☐
Quick-16S™ Primer Set V6-V8 (Zymo Research, Irvine, CA)
☐
Other: NA
Additional Notes: NA
The sequencing library was prepared using an innovative library preparation process in
which PCR reactions were performed in real-time PCR machines to control cycles and
therefore limit PCR chimera formation. The final PCR products were quantified with
qPCR fluorescence readings and pooled together based on equal molarity. The final
pooled library was cleaned up with the Select-a-Size DNA Clean & Concentrator™
(Zymo Research, Irvine, CA), then quantified with TapeStation® (Agilent Technologies,
Santa Clara, CA) and Qubit® (Thermo Fisher Scientific, Waltham, WA).
Control Samples: The ZymoBIOMICS® Microbial Community Standard (Zymo
Research, Irvine, CA) was used as a positive control for each DNA extraction, if
performed. The ZymoBIOMICS® Microbial Community DNA Standard (Zymo Research,
Irvine, CA) was used as a positive control for each targeted library preparation.
Negative controls (i.e. blank extraction control, blank library preparation control) were
included to assess the level of bioburden carried by the wet-lab process.
Sequencing: The final library was sequenced on Illumina® MiSeq™ with a V3 reagent kit
(600 cycles). The sequencing was performed with 10% PhiX spike-in.
Absolute Abundance Quantification*: A quantitative real-time PCR was set up with a
standard curve. The standard curve was made with plasmid DNA containing one copy
of the 16S gene and one copy of the fungal ITS2 region prepared in 10-fold serial
dilutions. The primers used were the same as those used in Targeted Library
Preparation. The equation generated by the plasmid DNA standard curve was used to
calculate the number of gene copies in the reaction for each sample. The PCR input
volume (2 µl) was used to calculate the number of gene copies per microliter in each
DNA sample.
The number of genome copies per microliter DNA sample was calculated by dividing
the gene copy number by an assumed number of gene copies per genome. The value
used for 16S copies per genome is 4. The value used for ITS copies per genome is 200.
The amount of DNA per microliter DNA sample was calculated using an assumed
genome size of 4.64 x 106 bp, the genome size of Escherichia coli, for 16S samples, or
an assumed genome size of 1.20 x 107 bp, the genome size of Saccharomyces
cerevisiae, for ITS samples. This calculation is shown below:
Calculated Total DNA = Calculated Total Genome Copies × Assumed Genome Size (4.64 × 106 bp) ×
Average Molecular Weight of a DNA bp (660 g/mole/bp) ÷ Avogadro’s Number (6.022 x 1023/mole)
* Absolute Abundance Quantification is only available for 16S and ITS analyses.
The absolute abundance standard curve data can be viewed in Excel here:
The absolute abundance standard curve is shown below:
The complete report of your project, including all links in this report, can be downloaded by clicking the link provided below. The downloaded file is a compressed ZIP file and once unzipped, open the file “REPORT.html” (may only shown as "REPORT" in your computer) by double clicking it. Your default web browser will open it and you will see the exact content of this report.
Please download and save the file to your computer storage device. The download link will expire after 60 days upon your receiving of this report.
Complete report download link:
To view the report, please follow the following steps:
1.
Download the .zip file from the report link above.
2.
Extract all the contents of the downloaded .zip file to your desktop.
3.
Open the extracted folder and find the "REPORT.html" (may shown as only "REPORT").
4.
Open (double-clicking) the REPORT.html file. Your default browser will open the top age of the complete report. Within the
report, there are links to view all the analyses performed for the project.
The raw NGS sequence data is available for download with the link provided below. The data is a compressed ZIP file and can be unzipped to individual sequence files.
Since this is a pair-end sequencing, each of your samples is represented by two sequence files, one for READ 1,
with the file extension “*_R1.fastq.gz”, another READ 2, with the file extension “*_R1.fastq.gz”.
The files are in FASTQ format and are compressed. FASTQ format is a text-based data format for storing both a biological sequence
and its corresponding quality scores. Most sequence analysis software will be able to open them.
The Sample IDs associated with the R1 and R2 fastq files are listed in the table below:
Sample ID
Original Sample ID
Read 1 File Name
Read 2 File Name
F14848.S100
original sample ID here
zr14848_100V1V3_R1.fastq.gz
zr14848_100V1V3_R2.fastq.gz
F14848.S101
original sample ID here
zr14848_101V1V3_R1.fastq.gz
zr14848_101V1V3_R2.fastq.gz
F14848.S102
original sample ID here
zr14848_102V1V3_R1.fastq.gz
zr14848_102V1V3_R2.fastq.gz
F14848.S103
original sample ID here
zr14848_103V1V3_R1.fastq.gz
zr14848_103V1V3_R2.fastq.gz
F14848.S104
original sample ID here
zr14848_104V1V3_R1.fastq.gz
zr14848_104V1V3_R2.fastq.gz
F14848.S105
original sample ID here
zr14848_105V1V3_R1.fastq.gz
zr14848_105V1V3_R2.fastq.gz
F14848.S106
original sample ID here
zr14848_106V1V3_R1.fastq.gz
zr14848_106V1V3_R2.fastq.gz
F14848.S107
original sample ID here
zr14848_107V1V3_R1.fastq.gz
zr14848_107V1V3_R2.fastq.gz
F14848.S108
original sample ID here
zr14848_108V1V3_R1.fastq.gz
zr14848_108V1V3_R2.fastq.gz
F14848.S109
original sample ID here
zr14848_109V1V3_R1.fastq.gz
zr14848_109V1V3_R2.fastq.gz
F14848.S010
original sample ID here
zr14848_10V1V3_R1.fastq.gz
zr14848_10V1V3_R2.fastq.gz
F14848.S110
original sample ID here
zr14848_110V1V3_R1.fastq.gz
zr14848_110V1V3_R2.fastq.gz
F14848.S111
original sample ID here
zr14848_111V1V3_R1.fastq.gz
zr14848_111V1V3_R2.fastq.gz
F14848.S112
original sample ID here
zr14848_112V1V3_R1.fastq.gz
zr14848_112V1V3_R2.fastq.gz
F14848.S113
original sample ID here
zr14848_113V1V3_R1.fastq.gz
zr14848_113V1V3_R2.fastq.gz
F14848.S114
original sample ID here
zr14848_114V1V3_R1.fastq.gz
zr14848_114V1V3_R2.fastq.gz
F14848.S115
original sample ID here
zr14848_115V1V3_R1.fastq.gz
zr14848_115V1V3_R2.fastq.gz
F14848.S116
original sample ID here
zr14848_116V1V3_R1.fastq.gz
zr14848_116V1V3_R2.fastq.gz
F14848.S117
original sample ID here
zr14848_117V1V3_R1.fastq.gz
zr14848_117V1V3_R2.fastq.gz
F14848.S118
original sample ID here
zr14848_118V1V3_R1.fastq.gz
zr14848_118V1V3_R2.fastq.gz
F14848.S119
original sample ID here
zr14848_119V1V3_R1.fastq.gz
zr14848_119V1V3_R2.fastq.gz
F14848.S011
original sample ID here
zr14848_11V1V3_R1.fastq.gz
zr14848_11V1V3_R2.fastq.gz
F14848.S120
original sample ID here
zr14848_120V1V3_R1.fastq.gz
zr14848_120V1V3_R2.fastq.gz
F14848.S121
original sample ID here
zr14848_121V1V3_R1.fastq.gz
zr14848_121V1V3_R2.fastq.gz
F14848.S122
original sample ID here
zr14848_122V1V3_R1.fastq.gz
zr14848_122V1V3_R2.fastq.gz
F14848.S123
original sample ID here
zr14848_123V1V3_R1.fastq.gz
zr14848_123V1V3_R2.fastq.gz
F14848.S124
original sample ID here
zr14848_124V1V3_R1.fastq.gz
zr14848_124V1V3_R2.fastq.gz
F14848.S125
original sample ID here
zr14848_125V1V3_R1.fastq.gz
zr14848_125V1V3_R2.fastq.gz
F14848.S126
original sample ID here
zr14848_126V1V3_R1.fastq.gz
zr14848_126V1V3_R2.fastq.gz
F14848.S127
original sample ID here
zr14848_127V1V3_R1.fastq.gz
zr14848_127V1V3_R2.fastq.gz
F14848.S128
original sample ID here
zr14848_128V1V3_R1.fastq.gz
zr14848_128V1V3_R2.fastq.gz
F14848.S129
original sample ID here
zr14848_129V1V3_R1.fastq.gz
zr14848_129V1V3_R2.fastq.gz
F14848.S012
original sample ID here
zr14848_12V1V3_R1.fastq.gz
zr14848_12V1V3_R2.fastq.gz
F14848.S130
original sample ID here
zr14848_130V1V3_R1.fastq.gz
zr14848_130V1V3_R2.fastq.gz
F14848.S131
original sample ID here
zr14848_131V1V3_R1.fastq.gz
zr14848_131V1V3_R2.fastq.gz
F14848.S132
original sample ID here
zr14848_132V1V3_R1.fastq.gz
zr14848_132V1V3_R2.fastq.gz
F14848.S133
original sample ID here
zr14848_133V1V3_R1.fastq.gz
zr14848_133V1V3_R2.fastq.gz
F14848.S134
original sample ID here
zr14848_134V1V3_R1.fastq.gz
zr14848_134V1V3_R2.fastq.gz
F14848.S135
original sample ID here
zr14848_135V1V3_R1.fastq.gz
zr14848_135V1V3_R2.fastq.gz
F14848.S136
original sample ID here
zr14848_136V1V3_R1.fastq.gz
zr14848_136V1V3_R2.fastq.gz
F14848.S137
original sample ID here
zr14848_137V1V3_R1.fastq.gz
zr14848_137V1V3_R2.fastq.gz
F14848.S138
original sample ID here
zr14848_138V1V3_R1.fastq.gz
zr14848_138V1V3_R2.fastq.gz
F14848.S139
original sample ID here
zr14848_139V1V3_R1.fastq.gz
zr14848_139V1V3_R2.fastq.gz
F14848.S013
original sample ID here
zr14848_13V1V3_R1.fastq.gz
zr14848_13V1V3_R2.fastq.gz
F14848.S140
original sample ID here
zr14848_140V1V3_R1.fastq.gz
zr14848_140V1V3_R2.fastq.gz
F14848.S141
original sample ID here
zr14848_141V1V3_R1.fastq.gz
zr14848_141V1V3_R2.fastq.gz
F14848.S142
original sample ID here
zr14848_142V1V3_R1.fastq.gz
zr14848_142V1V3_R2.fastq.gz
F14848.S143
original sample ID here
zr14848_143V1V3_R1.fastq.gz
zr14848_143V1V3_R2.fastq.gz
F14848.S144
original sample ID here
zr14848_144V1V3_R1.fastq.gz
zr14848_144V1V3_R2.fastq.gz
F14848.S145
original sample ID here
zr14848_145V1V3_R1.fastq.gz
zr14848_145V1V3_R2.fastq.gz
F14848.S146
original sample ID here
zr14848_146V1V3_R1.fastq.gz
zr14848_146V1V3_R2.fastq.gz
F14848.S147
original sample ID here
zr14848_147V1V3_R1.fastq.gz
zr14848_147V1V3_R2.fastq.gz
F14848.S148
original sample ID here
zr14848_148V1V3_R1.fastq.gz
zr14848_148V1V3_R2.fastq.gz
F14848.S149
original sample ID here
zr14848_149V1V3_R1.fastq.gz
zr14848_149V1V3_R2.fastq.gz
F14848.S014
original sample ID here
zr14848_14V1V3_R1.fastq.gz
zr14848_14V1V3_R2.fastq.gz
F14848.S150
original sample ID here
zr14848_150V1V3_R1.fastq.gz
zr14848_150V1V3_R2.fastq.gz
F14848.S151
original sample ID here
zr14848_151V1V3_R1.fastq.gz
zr14848_151V1V3_R2.fastq.gz
F14848.S152
original sample ID here
zr14848_152V1V3_R1.fastq.gz
zr14848_152V1V3_R2.fastq.gz
F14848.S153
original sample ID here
zr14848_153V1V3_R1.fastq.gz
zr14848_153V1V3_R2.fastq.gz
F14848.S154
original sample ID here
zr14848_154V1V3_R1.fastq.gz
zr14848_154V1V3_R2.fastq.gz
F14848.S155
original sample ID here
zr14848_155V1V3_R1.fastq.gz
zr14848_155V1V3_R2.fastq.gz
F14848.S156
original sample ID here
zr14848_156V1V3_R1.fastq.gz
zr14848_156V1V3_R2.fastq.gz
F14848.S157
original sample ID here
zr14848_157V1V3_R1.fastq.gz
zr14848_157V1V3_R2.fastq.gz
F14848.S158
original sample ID here
zr14848_158V1V3_R1.fastq.gz
zr14848_158V1V3_R2.fastq.gz
F14848.S159
original sample ID here
zr14848_159V1V3_R1.fastq.gz
zr14848_159V1V3_R2.fastq.gz
F14848.S015
original sample ID here
zr14848_15V1V3_R1.fastq.gz
zr14848_15V1V3_R2.fastq.gz
F14848.S160
original sample ID here
zr14848_160V1V3_R1.fastq.gz
zr14848_160V1V3_R2.fastq.gz
F14848.S161
original sample ID here
zr14848_161V1V3_R1.fastq.gz
zr14848_161V1V3_R2.fastq.gz
F14848.S162
original sample ID here
zr14848_162V1V3_R1.fastq.gz
zr14848_162V1V3_R2.fastq.gz
F14848.S163
original sample ID here
zr14848_163V1V3_R1.fastq.gz
zr14848_163V1V3_R2.fastq.gz
F14848.S164
original sample ID here
zr14848_164V1V3_R1.fastq.gz
zr14848_164V1V3_R2.fastq.gz
F14848.S165
original sample ID here
zr14848_165V1V3_R1.fastq.gz
zr14848_165V1V3_R2.fastq.gz
F14848.S166
original sample ID here
zr14848_166V1V3_R1.fastq.gz
zr14848_166V1V3_R2.fastq.gz
F14848.S167
original sample ID here
zr14848_167V1V3_R1.fastq.gz
zr14848_167V1V3_R2.fastq.gz
F14848.S168
original sample ID here
zr14848_168V1V3_R1.fastq.gz
zr14848_168V1V3_R2.fastq.gz
F14848.S169
original sample ID here
zr14848_169V1V3_R1.fastq.gz
zr14848_169V1V3_R2.fastq.gz
F14848.S016
original sample ID here
zr14848_16V1V3_R1.fastq.gz
zr14848_16V1V3_R2.fastq.gz
F14848.S170
original sample ID here
zr14848_170V1V3_R1.fastq.gz
zr14848_170V1V3_R2.fastq.gz
F14848.S171
original sample ID here
zr14848_171V1V3_R1.fastq.gz
zr14848_171V1V3_R2.fastq.gz
F14848.S172
original sample ID here
zr14848_172V1V3_R1.fastq.gz
zr14848_172V1V3_R2.fastq.gz
F14848.S173
original sample ID here
zr14848_173V1V3_R1.fastq.gz
zr14848_173V1V3_R2.fastq.gz
F14848.S174
original sample ID here
zr14848_174V1V3_R1.fastq.gz
zr14848_174V1V3_R2.fastq.gz
F14848.S175
original sample ID here
zr14848_175V1V3_R1.fastq.gz
zr14848_175V1V3_R2.fastq.gz
F14848.S176
original sample ID here
zr14848_176V1V3_R1.fastq.gz
zr14848_176V1V3_R2.fastq.gz
F14848.S177
original sample ID here
zr14848_177V1V3_R1.fastq.gz
zr14848_177V1V3_R2.fastq.gz
F14848.S178
original sample ID here
zr14848_178V1V3_R1.fastq.gz
zr14848_178V1V3_R2.fastq.gz
F14848.S179
original sample ID here
zr14848_179V1V3_R1.fastq.gz
zr14848_179V1V3_R2.fastq.gz
F14848.S017
original sample ID here
zr14848_17V1V3_R1.fastq.gz
zr14848_17V1V3_R2.fastq.gz
F14848.S180
original sample ID here
zr14848_180V1V3_R1.fastq.gz
zr14848_180V1V3_R2.fastq.gz
F14848.S181
original sample ID here
zr14848_181V1V3_R1.fastq.gz
zr14848_181V1V3_R2.fastq.gz
F14848.S182
original sample ID here
zr14848_182V1V3_R1.fastq.gz
zr14848_182V1V3_R2.fastq.gz
F14848.S183
original sample ID here
zr14848_183V1V3_R1.fastq.gz
zr14848_183V1V3_R2.fastq.gz
F14848.S184
original sample ID here
zr14848_184V1V3_R1.fastq.gz
zr14848_184V1V3_R2.fastq.gz
F14848.S185
original sample ID here
zr14848_185V1V3_R1.fastq.gz
zr14848_185V1V3_R2.fastq.gz
F14848.S186
original sample ID here
zr14848_186V1V3_R1.fastq.gz
zr14848_186V1V3_R2.fastq.gz
F14848.S187
original sample ID here
zr14848_187V1V3_R1.fastq.gz
zr14848_187V1V3_R2.fastq.gz
F14848.S188
original sample ID here
zr14848_188V1V3_R1.fastq.gz
zr14848_188V1V3_R2.fastq.gz
F14848.S189
original sample ID here
zr14848_189V1V3_R1.fastq.gz
zr14848_189V1V3_R2.fastq.gz
F14848.S018
original sample ID here
zr14848_18V1V3_R1.fastq.gz
zr14848_18V1V3_R2.fastq.gz
F14848.S190
original sample ID here
zr14848_190V1V3_R1.fastq.gz
zr14848_190V1V3_R2.fastq.gz
F14848.S191
original sample ID here
zr14848_191V1V3_R1.fastq.gz
zr14848_191V1V3_R2.fastq.gz
F14848.S192
original sample ID here
zr14848_192V1V3_R1.fastq.gz
zr14848_192V1V3_R2.fastq.gz
F14848.S193
original sample ID here
zr14848_193V1V3_R1.fastq.gz
zr14848_193V1V3_R2.fastq.gz
F14848.S194
original sample ID here
zr14848_194V1V3_R1.fastq.gz
zr14848_194V1V3_R2.fastq.gz
F14848.S195
original sample ID here
zr14848_195V1V3_R1.fastq.gz
zr14848_195V1V3_R2.fastq.gz
F14848.S196
original sample ID here
zr14848_196V1V3_R1.fastq.gz
zr14848_196V1V3_R2.fastq.gz
F14848.S197
original sample ID here
zr14848_197V1V3_R1.fastq.gz
zr14848_197V1V3_R2.fastq.gz
F14848.S198
original sample ID here
zr14848_198V1V3_R1.fastq.gz
zr14848_198V1V3_R2.fastq.gz
F14848.S199
original sample ID here
zr14848_199V1V3_R1.fastq.gz
zr14848_199V1V3_R2.fastq.gz
F14848.S019
original sample ID here
zr14848_19V1V3_R1.fastq.gz
zr14848_19V1V3_R2.fastq.gz
F14848.S001
original sample ID here
zr14848_1V1V3_R1.fastq.gz
zr14848_1V1V3_R2.fastq.gz
F14848.S200
original sample ID here
zr14848_200V1V3_R1.fastq.gz
zr14848_200V1V3_R2.fastq.gz
F14848.S201
original sample ID here
zr14848_201V1V3_R1.fastq.gz
zr14848_201V1V3_R2.fastq.gz
F14848.S202
original sample ID here
zr14848_202V1V3_R1.fastq.gz
zr14848_202V1V3_R2.fastq.gz
F14848.S203
original sample ID here
zr14848_203V1V3_R1.fastq.gz
zr14848_203V1V3_R2.fastq.gz
F14848.S204
original sample ID here
zr14848_204V1V3_R1.fastq.gz
zr14848_204V1V3_R2.fastq.gz
F14848.S205
original sample ID here
zr14848_205V1V3_R1.fastq.gz
zr14848_205V1V3_R2.fastq.gz
F14848.S206
original sample ID here
zr14848_206V1V3_R1.fastq.gz
zr14848_206V1V3_R2.fastq.gz
F14848.S207
original sample ID here
zr14848_207V1V3_R1.fastq.gz
zr14848_207V1V3_R2.fastq.gz
F14848.S208
original sample ID here
zr14848_208V1V3_R1.fastq.gz
zr14848_208V1V3_R2.fastq.gz
F14848.S209
original sample ID here
zr14848_209V1V3_R1.fastq.gz
zr14848_209V1V3_R2.fastq.gz
F14848.S020
original sample ID here
zr14848_20V1V3_R1.fastq.gz
zr14848_20V1V3_R2.fastq.gz
F14848.S210
original sample ID here
zr14848_210V1V3_R1.fastq.gz
zr14848_210V1V3_R2.fastq.gz
F14848.S211
original sample ID here
zr14848_211V1V3_R1.fastq.gz
zr14848_211V1V3_R2.fastq.gz
F14848.S021
original sample ID here
zr14848_21V1V3_R1.fastq.gz
zr14848_21V1V3_R2.fastq.gz
F14848.S022
original sample ID here
zr14848_22V1V3_R1.fastq.gz
zr14848_22V1V3_R2.fastq.gz
F14848.S023
original sample ID here
zr14848_23V1V3_R1.fastq.gz
zr14848_23V1V3_R2.fastq.gz
F14848.S024
original sample ID here
zr14848_24V1V3_R1.fastq.gz
zr14848_24V1V3_R2.fastq.gz
F14848.S025
original sample ID here
zr14848_25V1V3_R1.fastq.gz
zr14848_25V1V3_R2.fastq.gz
F14848.S026
original sample ID here
zr14848_26V1V3_R1.fastq.gz
zr14848_26V1V3_R2.fastq.gz
F14848.S027
original sample ID here
zr14848_27V1V3_R1.fastq.gz
zr14848_27V1V3_R2.fastq.gz
F14848.S028
original sample ID here
zr14848_28V1V3_R1.fastq.gz
zr14848_28V1V3_R2.fastq.gz
F14848.S029
original sample ID here
zr14848_29V1V3_R1.fastq.gz
zr14848_29V1V3_R2.fastq.gz
F14848.S002
original sample ID here
zr14848_2V1V3_R1.fastq.gz
zr14848_2V1V3_R2.fastq.gz
F14848.S030
original sample ID here
zr14848_30V1V3_R1.fastq.gz
zr14848_30V1V3_R2.fastq.gz
F14848.S031
original sample ID here
zr14848_31V1V3_R1.fastq.gz
zr14848_31V1V3_R2.fastq.gz
F14848.S032
original sample ID here
zr14848_32V1V3_R1.fastq.gz
zr14848_32V1V3_R2.fastq.gz
F14848.S033
original sample ID here
zr14848_33V1V3_R1.fastq.gz
zr14848_33V1V3_R2.fastq.gz
F14848.S034
original sample ID here
zr14848_34V1V3_R1.fastq.gz
zr14848_34V1V3_R2.fastq.gz
F14848.S035
original sample ID here
zr14848_35V1V3_R1.fastq.gz
zr14848_35V1V3_R2.fastq.gz
F14848.S036
original sample ID here
zr14848_36V1V3_R1.fastq.gz
zr14848_36V1V3_R2.fastq.gz
F14848.S037
original sample ID here
zr14848_37V1V3_R1.fastq.gz
zr14848_37V1V3_R2.fastq.gz
F14848.S038
original sample ID here
zr14848_38V1V3_R1.fastq.gz
zr14848_38V1V3_R2.fastq.gz
F14848.S039
original sample ID here
zr14848_39V1V3_R1.fastq.gz
zr14848_39V1V3_R2.fastq.gz
F14848.S003
original sample ID here
zr14848_3V1V3_R1.fastq.gz
zr14848_3V1V3_R2.fastq.gz
F14848.S040
original sample ID here
zr14848_40V1V3_R1.fastq.gz
zr14848_40V1V3_R2.fastq.gz
F14848.S041
original sample ID here
zr14848_41V1V3_R1.fastq.gz
zr14848_41V1V3_R2.fastq.gz
F14848.S042
original sample ID here
zr14848_42V1V3_R1.fastq.gz
zr14848_42V1V3_R2.fastq.gz
F14848.S043
original sample ID here
zr14848_43V1V3_R1.fastq.gz
zr14848_43V1V3_R2.fastq.gz
F14848.S044
original sample ID here
zr14848_44V1V3_R1.fastq.gz
zr14848_44V1V3_R2.fastq.gz
F14848.S045
original sample ID here
zr14848_45V1V3_R1.fastq.gz
zr14848_45V1V3_R2.fastq.gz
F14848.S046
original sample ID here
zr14848_46V1V3_R1.fastq.gz
zr14848_46V1V3_R2.fastq.gz
F14848.S047
original sample ID here
zr14848_47V1V3_R1.fastq.gz
zr14848_47V1V3_R2.fastq.gz
F14848.S048
original sample ID here
zr14848_48V1V3_R1.fastq.gz
zr14848_48V1V3_R2.fastq.gz
F14848.S049
original sample ID here
zr14848_49V1V3_R1.fastq.gz
zr14848_49V1V3_R2.fastq.gz
F14848.S004
original sample ID here
zr14848_4V1V3_R1.fastq.gz
zr14848_4V1V3_R2.fastq.gz
F14848.S050
original sample ID here
zr14848_50V1V3_R1.fastq.gz
zr14848_50V1V3_R2.fastq.gz
F14848.S051
original sample ID here
zr14848_51V1V3_R1.fastq.gz
zr14848_51V1V3_R2.fastq.gz
F14848.S052
original sample ID here
zr14848_52V1V3_R1.fastq.gz
zr14848_52V1V3_R2.fastq.gz
F14848.S053
original sample ID here
zr14848_53V1V3_R1.fastq.gz
zr14848_53V1V3_R2.fastq.gz
F14848.S054
original sample ID here
zr14848_54V1V3_R1.fastq.gz
zr14848_54V1V3_R2.fastq.gz
F14848.S055
original sample ID here
zr14848_55V1V3_R1.fastq.gz
zr14848_55V1V3_R2.fastq.gz
F14848.S056
original sample ID here
zr14848_56V1V3_R1.fastq.gz
zr14848_56V1V3_R2.fastq.gz
F14848.S057
original sample ID here
zr14848_57V1V3_R1.fastq.gz
zr14848_57V1V3_R2.fastq.gz
F14848.S058
original sample ID here
zr14848_58V1V3_R1.fastq.gz
zr14848_58V1V3_R2.fastq.gz
F14848.S059
original sample ID here
zr14848_59V1V3_R1.fastq.gz
zr14848_59V1V3_R2.fastq.gz
F14848.S005
original sample ID here
zr14848_5V1V3_R1.fastq.gz
zr14848_5V1V3_R2.fastq.gz
F14848.S060
original sample ID here
zr14848_60V1V3_R1.fastq.gz
zr14848_60V1V3_R2.fastq.gz
F14848.S061
original sample ID here
zr14848_61V1V3_R1.fastq.gz
zr14848_61V1V3_R2.fastq.gz
F14848.S062
original sample ID here
zr14848_62V1V3_R1.fastq.gz
zr14848_62V1V3_R2.fastq.gz
F14848.S063
original sample ID here
zr14848_63V1V3_R1.fastq.gz
zr14848_63V1V3_R2.fastq.gz
F14848.S064
original sample ID here
zr14848_64V1V3_R1.fastq.gz
zr14848_64V1V3_R2.fastq.gz
F14848.S065
original sample ID here
zr14848_65V1V3_R1.fastq.gz
zr14848_65V1V3_R2.fastq.gz
F14848.S066
original sample ID here
zr14848_66V1V3_R1.fastq.gz
zr14848_66V1V3_R2.fastq.gz
F14848.S067
original sample ID here
zr14848_67V1V3_R1.fastq.gz
zr14848_67V1V3_R2.fastq.gz
F14848.S068
original sample ID here
zr14848_68V1V3_R1.fastq.gz
zr14848_68V1V3_R2.fastq.gz
F14848.S069
original sample ID here
zr14848_69V1V3_R1.fastq.gz
zr14848_69V1V3_R2.fastq.gz
F14848.S006
original sample ID here
zr14848_6V1V3_R1.fastq.gz
zr14848_6V1V3_R2.fastq.gz
F14848.S070
original sample ID here
zr14848_70V1V3_R1.fastq.gz
zr14848_70V1V3_R2.fastq.gz
F14848.S071
original sample ID here
zr14848_71V1V3_R1.fastq.gz
zr14848_71V1V3_R2.fastq.gz
F14848.S072
original sample ID here
zr14848_72V1V3_R1.fastq.gz
zr14848_72V1V3_R2.fastq.gz
F14848.S073
original sample ID here
zr14848_73V1V3_R1.fastq.gz
zr14848_73V1V3_R2.fastq.gz
F14848.S074
original sample ID here
zr14848_74V1V3_R1.fastq.gz
zr14848_74V1V3_R2.fastq.gz
F14848.S075
original sample ID here
zr14848_75V1V3_R1.fastq.gz
zr14848_75V1V3_R2.fastq.gz
F14848.S076
original sample ID here
zr14848_76V1V3_R1.fastq.gz
zr14848_76V1V3_R2.fastq.gz
F14848.S077
original sample ID here
zr14848_77V1V3_R1.fastq.gz
zr14848_77V1V3_R2.fastq.gz
F14848.S078
original sample ID here
zr14848_78V1V3_R1.fastq.gz
zr14848_78V1V3_R2.fastq.gz
F14848.S079
original sample ID here
zr14848_79V1V3_R1.fastq.gz
zr14848_79V1V3_R2.fastq.gz
F14848.S007
original sample ID here
zr14848_7V1V3_R1.fastq.gz
zr14848_7V1V3_R2.fastq.gz
F14848.S080
original sample ID here
zr14848_80V1V3_R1.fastq.gz
zr14848_80V1V3_R2.fastq.gz
F14848.S081
original sample ID here
zr14848_81V1V3_R1.fastq.gz
zr14848_81V1V3_R2.fastq.gz
F14848.S082
original sample ID here
zr14848_82V1V3_R1.fastq.gz
zr14848_82V1V3_R2.fastq.gz
F14848.S083
original sample ID here
zr14848_83V1V3_R1.fastq.gz
zr14848_83V1V3_R2.fastq.gz
F14848.S084
original sample ID here
zr14848_84V1V3_R1.fastq.gz
zr14848_84V1V3_R2.fastq.gz
F14848.S085
original sample ID here
zr14848_85V1V3_R1.fastq.gz
zr14848_85V1V3_R2.fastq.gz
F14848.S086
original sample ID here
zr14848_86V1V3_R1.fastq.gz
zr14848_86V1V3_R2.fastq.gz
F14848.S087
original sample ID here
zr14848_87V1V3_R1.fastq.gz
zr14848_87V1V3_R2.fastq.gz
F14848.S088
original sample ID here
zr14848_88V1V3_R1.fastq.gz
zr14848_88V1V3_R2.fastq.gz
F14848.S089
original sample ID here
zr14848_89V1V3_R1.fastq.gz
zr14848_89V1V3_R2.fastq.gz
F14848.S008
original sample ID here
zr14848_8V1V3_R1.fastq.gz
zr14848_8V1V3_R2.fastq.gz
F14848.S090
original sample ID here
zr14848_90V1V3_R1.fastq.gz
zr14848_90V1V3_R2.fastq.gz
F14848.S091
original sample ID here
zr14848_91V1V3_R1.fastq.gz
zr14848_91V1V3_R2.fastq.gz
F14848.S092
original sample ID here
zr14848_92V1V3_R1.fastq.gz
zr14848_92V1V3_R2.fastq.gz
F14848.S093
original sample ID here
zr14848_93V1V3_R1.fastq.gz
zr14848_93V1V3_R2.fastq.gz
F14848.S094
original sample ID here
zr14848_94V1V3_R1.fastq.gz
zr14848_94V1V3_R2.fastq.gz
F14848.S095
original sample ID here
zr14848_95V1V3_R1.fastq.gz
zr14848_95V1V3_R2.fastq.gz
F14848.S096
original sample ID here
zr14848_96V1V3_R1.fastq.gz
zr14848_96V1V3_R2.fastq.gz
F14848.S097
original sample ID here
zr14848_97V1V3_R1.fastq.gz
zr14848_97V1V3_R2.fastq.gz
F14848.S098
original sample ID here
zr14848_98V1V3_R1.fastq.gz
zr14848_98V1V3_R2.fastq.gz
F14848.S099
original sample ID here
zr14848_99V1V3_R1.fastq.gz
zr14848_99V1V3_R2.fastq.gz
F14848.S009
original sample ID here
zr14848_9V1V3_R1.fastq.gz
zr14848_9V1V3_R2.fastq.gz
Please download and save the file to your computer storage device. The download link will expire after 60 days upon your receiving of this report.
DADA2 is a software package that models and corrects Illumina-sequenced amplicon errors.
DADA2 infers sample sequences exactly, without coarse-graining into OTUs,
and resolves differences of as little as one nucleotide. DADA2 identified more real variants
and output fewer spurious sequences than other methods.
DADA2’s advantage is that it uses more of the data. The DADA2 error model incorporates quality information,
which is ignored by all other methods after filtering. The DADA2 error model incorporates quantitative abundances,
whereas most other methods use abundance ranks if they use abundance at all.
The DADA2 error model identifies the differences between sequences, eg. A->C,
whereas other methods merely count the mismatches. DADA2 can parameterize its error model from the data itself,
rather than relying on previous datasets that may or may not reflect the PCR and sequencing protocols used in your study.
DADA2 pipeline includes several tools for read quality control, including quality filtering, trimming, denoising, pair merging and chimera filtering. Below are the major processing steps of DADA2:
Step 1. Read trimming based on sequence quality
The quality of NGS Illumina sequences often decreases toward the end of the reads.
DADA2 allows to trim off the poor quality read ends in order to improve the error
model building and pair mergicing performance.
Step 2. Learn the Error Rates
The DADA2 algorithm makes use of a parametric error model (err) and every
amplicon dataset has a different set of error rates. The learnErrors method
learns this error model from the data, by alternating estimation of the error
rates and inference of sample composition until they converge on a jointly
consistent solution. As in many machine-learning problems, the algorithm must
begin with an initial guess, for which the maximum possible error rates in
this data are used (the error rates if only the most abundant sequence is
correct and all the rest are errors).
Step 3. Infer amplicon sequence variants (ASVs) based on the error model built in previous step. This step is also called sequence "denoising".
The outcome of this step is a list of ASVs that are the equivalent of oligonucleotides.
Step 4. Merge paired reads. If the sequencing products are read pairs, DADA2 will merge the R1 and R2 ASVs into single sequences.
Merging is performed by aligning the denoised forward reads with the reverse-complement of the corresponding
denoised reverse reads, and then constructing the merged “contig” sequences.
By default, merged sequences are only output if the forward and reverse reads overlap by
at least 12 bases, and are identical to each other in the overlap region (but these conditions can be changed via function arguments).
Step 5. Remove chimera.
The core dada method corrects substitution and indel errors, but chimeras remain. Fortunately, the accuracy of sequence variants
after denoising makes identifying chimeric ASVs simpler than when dealing with fuzzy OTUs.
Chimeric sequences are identified if they can be exactly reconstructed by
combining a left-segment and a right-segment from two more abundant “parent” sequences. The frequency of chimeric sequences varies substantially
from dataset to dataset, and depends on on factors including experimental procedures and sample complexity.
Results
1. Read Quality Plots NGS sequence analaysis starts with visualizing the quality of the sequencing. Below are the quality plots of the first
sample for the R1 and R2 reads separately. In gray-scale is a heat map of the frequency of each quality score at each base position. The mean
quality score at each position is shown by the green line, and the quartiles of the quality score distribution by the orange lines.
The forward reads are usually of better quality. It is a common practice to trim the last few nucleotides to avoid less well-controlled errors
that can arise there. The trimming affects the downstream steps including error model building, merging and chimera calling. FOMC uses an empirical
approach to test many combinations of different trim length in order to achieve best final amplicon sequence variants (ASVs), see the next
section “Optimal trim length for ASVs”.
2. Optimal trim length for ASVs The final number of merged and chimera-filtered ASVs depends on the quality filtering (hence trimming) in the very beginning of the DADA2 pipeline.
In order to achieve highest number of ASVs, an empirical approach was used -
Create a random subset of each sample consisting of 5,000 R1 and 5,000 R2 (to reduce computation time)
Trim 10 bases at a time from the ends of both R1 and R2 up to 50 bases
For each combination of trimmed length (e.g., 300x300, 300x290, 290x290 etc), the trimmed reads are
subject to the entire DADA2 pipeline for chimera-filtered merged ASVs
The combination with highest percentage of the input reads becoming final ASVs is selected for the complete set of data
Below is the result of such operation, showing ASV percentages of total reads for all trimming combinations (1st Column = R1 lengths in bases; 1st Row = R2 lengths in bases):
R1/R2
281
271
261
251
241
231
321
75.86%
76.37%
76.63%
76.91%
73.00%
56.96%
311
75.93%
76.44%
76.70%
72.96%
56.96%
44.31%
301
76.04%
76.66%
72.88%
57.12%
44.61%
27.46%
291
76.15%
72.78%
56.96%
44.43%
27.32%
18.61%
281
72.21%
56.65%
44.22%
27.35%
18.50%
2.77%
271
56.26%
44.24%
27.19%
18.50%
2.75%
1.17%
Based on the above result, the trim length combination of R1 = 321 bases and R2 = 251 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
F14848.S001
F14848.S002
F14848.S003
F14848.S004
F14848.S005
F14848.S006
F14848.S007
F14848.S008
F14848.S009
F14848.S010
F14848.S011
F14848.S012
F14848.S013
F14848.S014
F14848.S015
F14848.S016
F14848.S017
F14848.S018
F14848.S019
F14848.S020
F14848.S021
F14848.S022
F14848.S023
F14848.S024
F14848.S025
F14848.S026
F14848.S027
F14848.S028
F14848.S029
F14848.S030
F14848.S031
F14848.S032
F14848.S033
F14848.S034
F14848.S035
F14848.S036
F14848.S037
F14848.S038
F14848.S039
F14848.S040
F14848.S041
F14848.S042
F14848.S043
F14848.S044
F14848.S045
F14848.S046
F14848.S047
F14848.S048
F14848.S049
F14848.S050
F14848.S051
F14848.S052
F14848.S053
F14848.S054
F14848.S055
F14848.S056
F14848.S057
F14848.S058
F14848.S059
F14848.S060
F14848.S061
F14848.S062
F14848.S063
F14848.S064
F14848.S065
F14848.S066
F14848.S067
F14848.S068
F14848.S069
F14848.S070
F14848.S071
F14848.S072
F14848.S073
F14848.S074
F14848.S075
F14848.S076
F14848.S077
F14848.S078
F14848.S079
F14848.S080
F14848.S081
F14848.S082
F14848.S083
F14848.S084
F14848.S085
F14848.S086
F14848.S087
F14848.S088
F14848.S089
F14848.S090
F14848.S091
F14848.S092
F14848.S093
F14848.S094
F14848.S095
F14848.S096
F14848.S097
F14848.S098
F14848.S099
F14848.S100
F14848.S101
F14848.S102
F14848.S103
F14848.S104
F14848.S105
F14848.S106
F14848.S107
F14848.S108
F14848.S109
F14848.S110
F14848.S111
F14848.S112
F14848.S113
F14848.S114
F14848.S115
F14848.S116
F14848.S117
F14848.S118
F14848.S119
F14848.S120
F14848.S121
F14848.S122
F14848.S123
F14848.S124
F14848.S125
F14848.S126
F14848.S127
F14848.S128
F14848.S129
F14848.S130
F14848.S131
F14848.S132
F14848.S133
F14848.S134
F14848.S135
F14848.S136
F14848.S137
F14848.S138
F14848.S139
F14848.S140
F14848.S141
F14848.S142
F14848.S143
F14848.S144
F14848.S145
F14848.S146
F14848.S147
F14848.S148
F14848.S149
F14848.S150
F14848.S151
F14848.S152
F14848.S153
F14848.S154
F14848.S155
F14848.S156
F14848.S157
F14848.S158
F14848.S159
F14848.S160
F14848.S161
F14848.S162
F14848.S163
F14848.S164
F14848.S165
F14848.S166
F14848.S167
F14848.S168
F14848.S169
F14848.S170
F14848.S171
F14848.S172
F14848.S173
F14848.S174
F14848.S175
F14848.S176
F14848.S177
F14848.S178
F14848.S179
F14848.S180
F14848.S181
F14848.S182
F14848.S183
F14848.S184
F14848.S185
F14848.S186
F14848.S187
F14848.S188
F14848.S189
F14848.S190
F14848.S191
F14848.S192
F14848.S193
F14848.S194
F14848.S195
F14848.S196
F14848.S197
F14848.S198
F14848.S199
F14848.S200
F14848.S201
F14848.S202
F14848.S203
F14848.S204
F14848.S205
F14848.S206
F14848.S207
F14848.S208
F14848.S209
F14848.S210
F14848.S211
Row Sum
Percentage
input
271,502
394,744
363,390
280,589
299,707
323,845
350,583
334,273
285,229
291,417
267,321
302,165
279,187
304,194
344,413
376,672
284,826
239,370
232,104
282,012
342,538
327,154
344,519
317,766
241,567
333,686
285,914
315,769
328,159
289,427
327,096
286,859
225,420
253,088
307,717
318,388
253,630
265,929
271,236
275,150
357,932
291,667
301,984
282,989
373,086
334,024
331,112
296,416
299,793
274,723
248,717
230,899
259,872
275,934
281,271
266,310
314,733
260,104
250,894
243,788
245,444
302,649
279,720
273,468
410,625
291,442
443,441
381,068
660,984
364,331
269,564
267,463
347,744
316,342
281,263
323,459
294,685
234,979
335,850
314,282
340,991
389,420
285,872
273,812
331,301
345,143
306,637
285,217
262,857
319,320
218,242
302,893
264,862
269,550
313,307
316,174
308,411
273,170
295,934
320,041
291,923
257,968
351,077
294,736
310,664
272,772
311,780
299,700
242,355
304,731
308,872
274,903
251,210
314,218
392,640
314,001
322,075
332,754
288,569
259,718
264,724
281,345
396,719
293,002
219,611
264,961
267,389
296,289
251,942
231,656
249,288
319,941
296,370
247,456
407,264
344,565
402,966
317,123
391,477
334,700
340,466
333,725
257,203
220,664
249,283
182,284
259,701
246,568
239,745
200,246
224,246
217,900
197,760
229,124
203,009
250,898
231,287
206,282
218,755
200,988
209,102
201,546
248,100
249,216
224,106
212,603
214,666
196,011
241,887
234,688
201,883
251,022
213,727
200,247
203,233
155,137
241,838
237,107
213,441
184,963
255,846
231,509
227,989
181,081
228,861
162,875
241,745
200,940
188,214
216,238
192,520
271,292
218,623
151,201
245,193
219,517
201,375
231,004
223,620
220,292
204,105
225,679
123,325
230,108
177,047
170,903
256,761
196,554
200,207
231,462
166,577
58,069,534
100.00%
filtered
246,634
359,018
330,327
254,703
272,305
294,213
318,844
303,712
258,908
264,588
242,892
274,183
253,789
276,067
312,585
342,443
259,068
217,817
211,086
255,878
310,960
297,035
312,939
288,982
219,588
303,097
259,954
286,859
297,840
262,688
296,897
260,424
204,705
229,956
279,424
289,241
230,467
241,604
246,436
250,309
324,965
264,871
274,581
257,173
338,925
303,840
301,123
269,397
272,710
249,626
225,964
209,703
235,631
251,217
255,846
241,842
286,532
236,195
228,120
221,368
223,138
275,163
253,871
248,728
372,867
265,389
403,069
346,353
601,296
331,322
245,459
242,878
315,970
287,481
255,594
293,840
267,618
213,551
304,544
285,725
309,770
354,199
259,772
248,454
301,191
314,174
278,946
258,709
238,909
290,276
198,105
274,923
240,450
244,980
284,856
287,046
280,119
248,135
269,217
290,996
265,738
234,639
319,144
267,680
282,328
248,089
283,339
272,416
220,032
277,065
280,753
249,635
228,419
285,457
356,485
285,643
292,535
302,141
262,121
235,868
240,398
255,631
360,424
266,338
199,515
240,773
242,602
269,081
229,123
210,832
226,713
290,432
269,883
225,164
370,183
312,761
365,909
288,151
355,490
303,928
309,726
303,576
233,537
199,860
226,434
165,570
235,904
224,325
217,661
181,825
203,726
198,075
179,642
207,818
184,482
227,630
210,366
187,173
198,613
182,276
189,972
183,008
225,205
226,505
203,599
192,928
194,737
177,869
219,598
213,196
183,079
227,763
194,313
181,978
184,619
140,968
219,929
215,367
194,036
168,052
232,461
210,386
207,229
164,542
208,046
147,780
219,612
182,440
170,925
196,477
174,962
246,460
198,804
137,280
223,210
199,284
182,719
210,012
202,971
200,013
185,602
205,089
111,983
209,164
161,152
155,029
233,306
178,605
181,781
210,341
151,423
52,762,063
90.86%
denoisedF
242,098
351,337
323,719
248,626
265,734
287,804
311,897
296,463
253,698
257,595
238,100
268,708
247,684
270,301
306,424
335,724
254,092
211,754
205,077
249,375
303,983
290,284
304,514
282,436
214,144
295,447
253,855
279,798
291,064
256,575
290,498
254,944
198,596
225,744
273,965
282,838
226,174
235,584
241,780
245,112
319,349
260,129
268,927
251,013
332,910
297,953
294,891
264,622
267,011
243,892
220,689
204,226
230,153
246,545
250,909
237,034
280,006
230,935
221,456
215,997
217,433
268,409
248,040
242,190
367,153
257,862
395,520
336,266
590,433
326,395
240,730
237,562
307,895
280,875
249,136
285,332
260,296
209,099
296,786
279,922
302,110
346,016
254,701
242,598
294,501
306,338
273,297
252,087
234,299
283,034
192,775
267,188
235,539
239,190
277,647
280,430
271,948
241,267
261,482
284,190
258,390
228,360
312,406
261,619
276,395
241,640
276,643
264,638
214,059
270,262
274,910
244,120
222,366
278,672
348,047
278,273
285,697
295,578
255,773
229,141
233,777
250,645
352,947
260,256
193,686
233,751
237,169
261,966
222,724
207,095
221,961
283,998
263,841
221,204
365,162
303,748
359,590
281,601
347,823
295,627
303,475
297,999
227,462
195,751
222,022
161,518
229,581
220,164
212,118
178,461
199,283
193,610
173,785
201,485
180,065
221,906
205,857
183,346
194,711
178,028
184,601
179,257
220,583
221,059
199,879
188,724
188,690
173,729
212,445
208,265
179,344
222,286
190,158
178,213
180,476
138,517
216,174
210,508
190,512
164,533
227,526
205,756
203,131
160,210
202,555
144,705
214,435
178,024
166,078
191,550
171,082
241,979
194,571
134,262
218,497
194,511
178,275
205,583
199,174
195,469
182,325
200,798
109,940
203,680
156,628
150,554
225,621
173,174
176,425
203,759
147,350
51,551,930
88.78%
denoisedR
242,790
354,421
325,547
250,690
268,401
289,848
314,427
299,245
254,917
260,189
239,636
270,370
249,986
272,452
308,072
338,366
255,565
215,277
208,108
251,877
306,685
292,857
308,656
284,971
215,850
299,012
256,429
282,715
293,718
259,126
293,013
256,883
201,719
226,907
275,545
285,405
227,526
238,168
242,947
246,828
320,646
261,242
270,827
253,397
333,997
299,813
296,761
265,874
269,066
245,799
222,988
206,948
232,981
248,175
252,906
238,497
283,273
232,977
225,239
218,514
220,058
271,237
250,283
245,465
367,642
261,639
396,717
342,297
592,868
326,113
242,079
239,356
310,750
283,587
251,145
289,213
264,002
210,419
300,077
282,047
304,799
349,072
256,212
243,183
295,869
309,702
275,020
254,779
235,973
285,485
195,085
270,409
237,002
241,432
280,276
283,164
275,952
243,586
265,095
287,181
261,605
231,187
314,495
264,040
278,566
244,192
279,192
269,034
216,771
272,890
276,058
245,565
225,581
280,993
352,041
281,499
288,308
296,660
258,844
232,832
236,895
252,383
355,504
262,378
196,870
238,199
239,554
265,076
225,946
208,311
223,732
285,663
266,355
222,585
363,573
307,312
360,852
283,243
350,623
299,255
304,757
298,894
229,078
196,138
222,364
162,332
231,085
220,731
213,861
177,888
199,830
194,381
174,808
203,305
180,692
222,868
206,267
183,217
194,674
178,884
185,516
179,633
221,426
222,352
199,713
189,495
190,586
174,215
214,963
208,574
179,606
223,477
190,844
178,200
181,123
138,297
215,780
211,046
190,351
164,732
227,677
205,613
203,230
160,878
203,511
144,864
215,355
178,552
167,248
192,616
171,504
242,383
194,970
134,441
218,690
195,492
178,890
206,123
199,006
196,078
182,084
201,128
109,614
204,568
157,803
151,586
227,018
173,955
176,742
204,871
147,969
51,926,438
89.42%
merged
231,384
337,997
311,347
237,704
254,816
275,992
300,290
284,227
243,131
245,951
229,161
259,058
236,927
259,739
294,162
323,692
244,848
206,200
198,501
239,092
292,919
280,666
295,441
272,221
204,365
285,633
244,950
269,414
280,800
247,095
279,823
246,280
192,606
218,188
263,307
272,338
217,557
226,466
232,294
236,087
307,133
249,489
258,959
241,417
319,586
286,879
283,401
254,815
256,633
234,956
213,351
196,947
222,792
236,689
241,029
227,361
270,815
222,527
215,652
208,917
209,812
258,264
239,581
234,179
353,517
249,297
378,475
325,978
567,382
313,862
232,327
229,235
296,049
271,676
238,110
274,333
252,297
200,661
285,288
269,168
289,274
332,578
244,925
227,790
278,370
295,881
263,135
240,659
227,197
271,986
185,339
257,766
226,541
230,995
266,979
269,044
262,595
231,154
250,899
273,388
249,032
219,056
299,904
252,653
265,962
232,587
265,848
257,577
206,510
260,156
263,100
233,989
215,823
266,931
335,787
268,810
275,200
282,358
246,527
222,934
225,208
240,907
338,524
249,258
188,722
229,379
228,581
252,215
215,438
200,138
214,300
271,894
253,851
213,982
348,107
285,482
344,206
269,701
337,485
283,682
292,875
287,305
214,301
186,008
212,085
152,981
216,245
211,459
200,848
167,299
188,379
185,555
154,246
188,467
169,783
208,844
196,148
174,681
185,409
169,406
170,246
170,544
210,732
210,966
190,254
179,964
175,251
164,074
195,188
195,708
171,270
211,098
182,048
168,770
170,788
132,253
206,560
200,850
182,021
156,902
213,333
192,116
192,319
149,727
186,655
137,757
204,423
167,797
154,952
181,427
161,365
231,614
183,936
127,833
206,356
185,372
168,180
196,489
190,613
184,627
174,255
192,291
104,594
190,103
147,239
140,232
201,636
157,459
155,055
178,998
136,755
49,343,126
84.97%
nonchim
216,106
316,747
288,606
221,324
233,059
260,832
270,975
265,069
229,737
226,522
209,982
236,012
210,470
235,441
274,512
303,846
225,614
189,533
177,596
221,070
268,801
257,595
265,873
249,169
190,960
257,241
224,240
233,831
252,029
222,809
251,151
230,360
176,259
205,083
246,468
249,395
202,790
208,015
217,455
216,362
286,787
233,971
243,857
217,584
294,241
263,781
260,915
236,941
225,400
207,735
191,775
174,960
199,893
214,382
215,546
206,358
242,558
201,196
203,451
188,902
191,723
229,010
223,854
193,303
317,071
219,515
343,769
302,491
496,689
291,752
204,270
207,096
275,068
251,223
221,099
254,007
236,176
185,592
262,687
253,331
269,468
299,011
226,661
216,472
260,159
272,777
247,684
215,519
218,377
249,437
167,978
242,422
206,644
213,777
246,555
239,706
227,098
212,984
219,262
246,922
217,941
195,711
278,163
224,891
246,159
196,794
241,503
234,008
188,684
240,846
239,546
216,924
197,891
240,198
299,947
250,466
254,658
266,029
212,794
205,414
203,087
208,673
296,033
227,856
173,888
226,549
202,320
228,467
199,366
189,784
190,786
241,349
230,873
203,027
320,556
274,023
323,280
244,921
309,619
241,251
265,154
267,940
191,405
172,532
199,421
136,465
198,988
197,749
182,078
151,367
172,285
169,158
139,978
173,785
147,797
193,987
173,612
156,286
171,660
156,879
153,500
155,215
195,963
187,262
176,835
166,948
155,658
148,476
165,037
182,340
152,957
194,698
165,906
154,339
160,271
122,966
188,211
178,105
162,632
142,866
197,347
173,718
173,937
141,351
170,200
128,796
189,809
155,404
135,539
159,402
144,312
209,651
167,082
115,288
189,890
173,453
154,747
180,599
171,606
170,776
160,945
175,184
95,065
180,107
137,134
132,110
189,530
145,506
145,303
170,911
130,234
45,085,733
77.64%
This table can be downloaded as an Excel table below:
5. DADA2 Amplicon Sequence Variants (ASVs). A total of 14130 unique merged and chimera-free ASV sequences were identified, and their corresponding
read counts for each sample are available in the "ASV Read Count Table" with rows for the ASV sequences and columns for sample. This read count table can be used for
microbial profile comparison among different samples and the sequences provided in the table can be used to taxonomy assignment.
The species-level, open-reference 16S rRNA NGS reads taxonomy assignment pipeline
Version 20210310
1. Raw sequences reads in FASTA format were BLASTN-searched against a combined set of 16S rRNA reference sequences.
It consists of MOMD (version 0.1), the HOMD (version 15.2 http://www.homd.org/index.php?name=seqDownload&file&type=R ),
HOMD 16S rRNA RefSeq Extended Version 1.1 (EXT), GreenGene Gold (GG)
(http://greengenes.lbl.gov/Download/Sequence_Data/Fasta_data_files/gold_strains_gg16S_aligned.fasta.gz) ,
and the NCBI 16S rRNA reference sequence set (https://ftp.ncbi.nlm.nih.gov/blast/db/16S_ribosomal_RNA.tar.gz).
These sequences were screened and combined to remove short sequences (<1000nt), chimera, duplicated and sub-sequences,
as well as sequences with poor taxonomy annotation (e.g., without species information).
This process resulted in 1,015 from HOMD V15.22, 495 from EXT, 3,940 from GG and 18,044 from NCBI, a total of 25,120 sequences.
Altogether these sequence represent a total of 15,601 oral and non-oral microbial species.
The NCBI BLASTN version 2.7.1+ (Zhang et al, 2000) was used with the default parameters.
Reads with ≥ 98% sequence identity to the matched reference and ≥ 90% alignment length
(i.e., ≥ 90% of the read length that was aligned to the reference and was used to calculate
the sequence percent identity) were classified based on the taxonomy of the reference sequence
with highest sequence identity. If a read matched with reference sequences representing
more than one species with equal percent identity and alignment length, it was subject
to chimera checking with USEARCH program version v8.1.1861 (Edgar 2010). Non-chimeric reads with multi-species
best hits were considered valid and were assigned with a unique species
notation (e.g., spp) denoting unresolvable multiple species.
2. Unassigned reads (i.e., reads with < 98% identity or < 90% alignment length) were pooled together and reads < 200 bases were
removed. The remaining reads were subject to the de novo
operational taxonomy unit (OTU) calling and chimera checking using the USEARCH program version v8.1.1861 (Edgar 2010).
The de novo OTU calling and chimera checking was done using 98% as the sequence identity cutoff, i.e., the species-level OTU.
The output of this step produced species-level de novo clustered OTUs with 98% identity.
Representative reads from each of the OTUs/species were then BLASTN-searched
against the same reference sequence set again to determine the closest species for
these potential novel species. These potential novel species were pooled together with the reads that were signed to specie-level in
the previous step, for down-stream analyses.
Reference:
Edgar RC. Search and clustering orders of magnitude faster than BLAST.
Bioinformatics. 2010 Oct 1;26(19):2460-1. doi: 10.1093/bioinformatics/btq461. Epub 2010 Aug 12. PubMed PMID: 20709691.
3. Designations used in the taxonomy:
1) Taxonomy levels are indicated by these prefixes:
k__: domain/kingdom
p__: phylum
c__: class
o__: order
f__: family
g__: genus
s__: species
Example:
k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Lachnospiraceae;g__Blautia;s__faecis
2) Unique level identified – known species:
k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Lachnospiraceae;g__Roseburia;s__hominis
The above example shows some reads match to a single species (all levels are unique)
3) Non-unique level identified – known species:
k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Lachnospiraceae;g__Roseburia;s__multispecies_spp123_3
The above example “s__multispecies_spp123_3” indicates certain reads equally match to 3 species of the
genus Roseburia; the “spp123” is a temporally assigned species ID.
k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Lachnospiraceae;g__multigenus;s__multispecies_spp234_5
The above example indicates certain reads match equally to 5 different species, which belong to multiple genera.;
the “spp234” is a temporally assigned species ID.
4) Unique level identified – unknown species, potential novel species:
k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Lachnospiraceae;g__Roseburia;s__ hominis_nov_97%
The above example indicates that some reads have no match to any of the reference sequences with
sequence identity ≥ 98% and percent coverage (alignment length) ≥ 98% as well. However this groups
of reads (actually the representative read from a de novo OTU) has 96% percent identity to
Roseburia hominis, thus this is a potential novel species, closest to Roseburia hominis.
(But they are not the same species).
5) Multiple level identified – unknown species, potential novel species:
k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Lachnospiraceae;g__Roseburia;s__ multispecies_sppn123_3_nov_96%
The above example indicates that some reads have no match to any of the reference sequences
with sequence identity ≥ 98% and percent coverage (alignment length) ≥ 98% as well.
However this groups of reads (actually the representative read from a de novo OTU)
has 96% percent identity equally to 3 species in Roseburia. Thus this is no single
closest species, instead this group of reads match equally to multiple species at 96%.
Since they have passed chimera check so they represent a novel species. “sppn123” is a
temporary ID for this potential novel species.
4. The taxonomy assignment algorithm is illustrated in this flow char below:
Read Taxonomy Assignment - Result Summary *
Code
Category
MPC=0% (>=1 read)
MPC=0.01%(>=4498 reads)
A
Total reads
45,085,733
45,085,733
B
Total assigned reads
44,981,605
44,981,605
C
Assigned reads in species with read count < MPC
0
885,897
D
Assigned reads in samples with read count < 500
0
0
E
Total samples
211
211
F
Samples with reads >= 500
211
211
G
Samples with reads < 500
0
0
H
Total assigned reads used for analysis (B-C-D)
44,981,605
44,095,708
I
Reads assigned to single species
27,749,802
27,567,747
J
Reads assigned to multiple species
937,584
923,424
K
Reads assigned to novel species
16,294,219
15,604,537
L
Total number of species
2,446
217
M
Number of single species
417
95
N
Number of multi-species
39
9
O
Number of novel species
1,990
113
P
Total unassigned reads
104,128
104,128
Q
Chimeric reads
7,998
7,998
R
Reads without BLASTN hits
22,537
22,537
S
Others: short, low quality, singletons, etc.
73,593
73,593
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.
Saliva Group 1 vs Saliva Group 2 vs Saliva Group 3 vs Saliva Group 4 vs Saliva Group 5 vs Saliva Group 6 vs Saliva Group 7 vs Saliva Group 8 vs Saliva Group 9
Plaque Group 1 vs Plaque Group 2 vs Plaque Group 3 vs Plaque Group 4 vs Plaque Group 5 vs Plaque Group 6 vs Plaque Group 7 vs Plaque Group 8 vs Plaque Group 9
In ecology, alpha diversity (α-diversity) is the mean species diversity in sites or habitats at a local scale.
The term was introduced by R. H. Whittaker[1][2] together with the terms beta diversity (β-diversity)
and gamma diversity (γ-diversity). Whittaker's idea was that the total species diversity in a landscape
(gamma diversity) is determined by two different things, the mean species diversity in sites or habitats
at a more local scale (alpha diversity) and the differentiation among those habitats (beta diversity).
Diversity measures are affected by the sampling depth. Rarefaction is a technique to assess species richness from the results of sampling. Rarefaction allows
the calculation of species richness for a given number of individual samples, based on the construction
of so-called rarefaction curves. This curve is a plot of the number of species as a function of the
number of samples. Rarefaction curves generally grow rapidly at first, as the most common species are found,
but the curves plateau as only the rarest species remain to be sampled.
The two main factors taken into account when measuring diversity are richness and evenness.
Richness is a measure of the number of different kinds of organisms present in a particular area.
Evenness compares the similarity of the population size of each of the species present. There are
many different ways to measure the richness and evenness. These measurements are called "estimators" or "indices".
Below is a diversity of 3 commonly used indices showing the values for all the samples (dots) and in groups (boxes).
Alpha Diversity Box Plots for All Groups
Alpha Diversity Box Plots for Individual Comparisons
Comparison 1
Saliva Group 1 vs Saliva Group 2 vs Saliva Group 3 vs Saliva Group 4 vs Saliva Group 5 vs Saliva Group 6 vs Saliva Group 7 vs Saliva Group 8 vs Saliva Group 9
Plaque Group 1 vs Plaque Group 2 vs Plaque Group 3 vs Plaque Group 4 vs Plaque Group 5 vs Plaque Group 6 vs Plaque Group 7 vs Plaque Group 8 vs Plaque Group 9
To test whether the alpha diversity among different comparison groups are different statistically, we use the Kruskal Wallis H test
provided the "alpha-group-significance" fucntion in the QIIME 2 "diversity" package. Kruskal Wallis H test is the non-parametric alternative
to the One Way ANOVA. Non-parametric means that the test doesn’t assume your data comes from a particular distribution. The H test is used
when the assumptions for ANOVA aren’t met (like the assumption of normality). It is sometimes called the one-way ANOVA on ranks,
as the ranks of the data values are used in the test rather than the actual data points. The H test determines whether the medians of two
or more groups are different.
Below are the Kruskal Wallis H test results for each comparison based on three different alpha diversity measures: 1) Observed species (features),
2) Shannon index, and 3) Simpson index.
Comparison 1.
Saliva Group 1 vs Saliva Group 2 vs Saliva Group 3 vs Saliva Group 4 vs Saliva Group 5 vs Saliva Group 6 vs Saliva Group 7 vs Saliva Group 8 vs Saliva Group 9
Plaque Group 1 vs Plaque Group 2 vs Plaque Group 3 vs Plaque Group 4 vs Plaque Group 5 vs Plaque Group 6 vs Plaque Group 7 vs Plaque Group 8 vs Plaque Group 9
Beta diversity compares the similarity (or dissimilarity) of microbial profiles between different
groups of samples. There are many different similarity/dissimilarity metrics.
In general, they can be quantitative (using sequence abundance, e.g., Bray-Curtis or weighted UniFrac)
or binary (considering only presence-absence of sequences, e.g., binary Jaccard or unweighted UniFrac).
They can be even based on phylogeny (e.g., UniFrac metrics) or not (non-UniFrac metrics, such as Bray-Curtis, etc.).
For microbiome studies, species profiles of samples can be compared with the Bray-Curtis dissimilarity,
which is based on the count data type. The pair-wise Bray-Curtis dissimilarity matrix of all samples can then be
subject to either multi-dimensional scaling (MDS, also known as PCoA) or non-metric MDS (NMDS).
MDS/PCoA is a
scaling or ordination method that starts with a matrix of similarities or dissimilarities
between a set of samples and aims to produce a low-dimensional graphical plot of the data
in such a way that distances between points in the plot are close to original dissimilarities.
NMDS is similar to MDS, however it does not use the dissimilarities data, instead it converts them into
the ranks and use these ranks in the calculation.
In our beta diversity analysis, Bray-Curtis dissimilarity matrix was first calculated and then plotted by the PCoA and
NMDS separately. Below are beta diveristy results for all groups together:
NMDS and PCoA Plots for All Groups
The above PCoA and NMDS plots are based on count data. The count data can also be transformed into centered log ratio (CLR)
for each species. The CLR data is no longer count data and cannot be used in Bray-Curtis dissimilarity calculation. Instead
CLR can be compared with Euclidean distances. When CLR data are compared by Euclidean distance, the distance is also called
Aitchison distance.
Below are the NMDS and PCoA plots of the Aitchison distances of the samples:
NMDS and PCoA Plots for Individual Comparisons
Comparison No.
Comparison Name
NMDA
PCoA
Bray-Curtis
CLR Euclidean
Bray-Curtis
CLR Euclidean
Comparison 1
Saliva Group 1 vs Saliva Group 2 vs Saliva Group 3 vs Saliva Group 4 vs Saliva Group 5 vs Saliva Group 6 vs Saliva Group 7 vs Saliva Group 8 vs Saliva Group 9
Plaque Group 1 vs Plaque Group 2 vs Plaque Group 3 vs Plaque Group 4 vs Plaque Group 5 vs Plaque Group 6 vs Plaque Group 7 vs Plaque Group 8 vs Plaque Group 9
Interactive 3D PCoA Plots - Bray-Curtis Dissimilarity
Interactive 3D PCoA Plots - Euclidean Distance
Interactive 3D PCoA Plots - Correlation Coefficients
Group Significance of Beta-diversity Indices
To test whether the between-group dissimilarities are significantly greater than the within-group dissimilarities,
the "beta-group-significance" function provided in the QIIME 2 "diversity" package was used with PERMANOVA
(permutational multivariate analysis of variance) as the group significant testing method.
Three beta diversity matrics were used: 1) Bray–Curtis dissimilarity 2) Correlation coefficient matrix , and 3) Aitchison distance
(Euclidean distance between clr-transformed compositions).
Comparison 1.
Saliva Group 1 vs Saliva Group 2 vs Saliva Group 3 vs Saliva Group 4 vs Saliva Group 5 vs Saliva Group 6 vs Saliva Group 7 vs Saliva Group 8 vs Saliva Group 9
Plaque Group 1 vs Plaque Group 2 vs Plaque Group 3 vs Plaque Group 4 vs Plaque Group 5 vs Plaque Group 6 vs Plaque Group 7 vs Plaque Group 8 vs Plaque Group 9
16S rRNA next generation sequencing (NGS) generates a fixed number of reads that reflect the proportion of different
species in a sample, i.e., the relative abundance of species, instead of the absolute abundance.
In Mathematics, measurements involving probabilities, proportions, percentages, and ppm can all
be thought of as compositional data. This makes the microbiome read count data “compositional”
(Gloor et al, 2017). In general, compositional data represent parts of a whole which only
carry relative information (http://www.compositionaldata.com/).
The problem of microbiome data being compositional arises when comparing two groups of samples for
identifying “differentially abundant” species. A species with the same absolute abundance between two
conditions, its relative abundances in the two conditions (e.g., percent abundance) can become different
if the relative abundance of other species change greatly. This problem can lead to incorrect conclusion
in terms of differential abundance for microbial species in the samples.
When studying differential abundance (DA), the current better approach is to transform the read count
data into log ratio data. The ratios are calculated between read counts of all species in a sample to
a “reference” count (e.g., mean read count of the sample). The log ratio data allow the detection of DA
species without being affected by percentage bias mentioned above
In this report, a compositional DA analysis tool “ANCOM” (analysis of composition of microbiomes)
was used. ANCOM transforms the count data into log-ratios and thus is more suitable for comparing
the composition of microbiomes in two or more populations. "ANCOM" generates a table of features with
W-statistics and whether the null hypothesis is rejected. The “W” is the W-statistic, or number of
features that a single feature is tested to be significantly different against. Hence the higher the "W"
the more statistical sifgnificant that a feature/species is differentially abundant.
References:
Gloor GB, Macklaim JM, Pawlowsky-Glahn V, Egozcue JJ. Microbiome Datasets Are Compositional: And This Is Not Optional. Front Microbiol.
2017 Nov 15;8:2224. doi: 10.3389/fmicb.2017.02224. PMID: 29187837; PMCID: PMC5695134.
Mandal S, Van Treuren W, White RA, Eggesbø M, Knight R, Peddada SD. Analysis of composition of
microbiomes: a novel method for studying microbial composition. Microb Ecol Health Dis.
2015 May 29;26:27663. doi: 10.3402/mehd.v26.27663. PMID: 26028277; PMCID: PMC4450248.
Lin H, Peddada SD. Analysis of compositions of microbiomes with bias correction.
Nat Commun. 2020 Jul 14;11(1):3514. doi: 10.1038/s41467-020-17041-7.
PMID: 32665548; PMCID: PMC7360769.
Starting with version V1.2, we include the results of ANCOM-BC (Analysis of Compositions of
Microbiomes with Bias Correction) (Lin and Peddada 2020). ANCOM-BC is an updated version of "ANCOM" that:
(a) provides statistically valid test with appropriate p-values,
(b) provides confidence intervals for differential abundance of each taxon,
(c) controls the False Discovery Rate (FDR),
(d) maintains adequate power, and
(e) is computationally simple to implement.
The bias correction (BC) addresses a challenging problem of the bias introduced by differences in
the sampling fractions across samples. This bias has been a major hurdle in performing DA analysis of microbiome data.
ANCOM-BC estimates the unknown sampling fractions and corrects the bias induced by their differences among samples.
The absolute abundance data are modeled using a linear regression framework.
Starting with version V1.43, ANCOM-BC2 is used instead of ANCOM-BC, So that multiple pairwise directional test can be performed (if there are more than two gorups in a comparison).
When 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; Grandhi, Guo, and Peddada 2016). For more detail
explanation and additional features of ANCOM-BC2 please see author's documentation.
References:
Lin H, Peddada SD. Analysis of compositions of microbiomes with bias correction.
Nat Commun. 2020 Jul 14;11(1):3514. doi: 10.1038/s41467-020-17041-7.
PMID: 32665548; PMCID: PMC7360769.
Guo W, Sarkar SK, Peddada SD. Controlling false discoveries in multidimensional directional decisions, with applications to gene expression data on ordered categories. Biometrics. 2010 Jun;66(2):485-92. doi: 10.1111/j.1541-0420.2009.01292.x. Epub 2009 Jul 23. PMID: 19645703; PMCID: PMC2895927.
Grandhi A, Guo W, Peddada SD. A multiple testing procedure for multi-dimensional pairwise comparisons with application to gene expression studies. BMC Bioinformatics. 2016 Feb 25;17:104. doi: 10.1186/s12859-016-0937-5. PMID: 26917217; PMCID: PMC4768411.
LEfSe (Linear Discriminant Analysis Effect Size) is an alternative method to find "organisms, genes, or
pathways that consistently explain the differences between two or more microbial communities" (Segata et al., 2011).
Specifically, LEfSe uses rank-based Kruskal-Wallis (KW) sum-rank test to detect features with significant
differential (relative) abundance with respect to the class of interest. Since it is rank-based, instead of proportional based,
the differential species identified among the comparison groups is less biased (than percent abundance based).
Reference:
Segata N, Izard J, Waldron L, Gevers D, Miropolsky L, Garrett WS, Huttenhower C. Metagenomic biomarker discovery and explanation. Genome Biol. 2011 Jun 24;12(6):R60. doi: 10.1186/gb-2011-12-6-r60. PMID: 21702898; PMCID: PMC3218848.
Saliva Group 1 vs Saliva Group 2 vs Saliva Group 3 vs Saliva Group 4 vs Saliva Group 5 vs Saliva Group 6 vs Saliva Group 7 vs Saliva Group 8 vs Saliva Group 9
Species vs Sample Abundance Heatmap for All Samples
Heatmaps for Individual Comparisons
A) Two-way clustering - clustered on both columns (Samples) and rows (organism)
Comparison No.
Comparison Name
Family Level
Genus Level
Species Level
Comparison 1
Saliva Group 1 vs Saliva Group 2 vs Saliva Group 3 vs Saliva Group 4 vs Saliva Group 5 vs Saliva Group 6 vs Saliva Group 7 vs Saliva Group 8 vs Saliva Group 9
Plaque Group 1 vs Plaque Group 2 vs Plaque Group 3 vs Plaque Group 4 vs Plaque Group 5 vs Plaque Group 6 vs Plaque Group 7 vs Plaque Group 8 vs Plaque Group 9
B) One-way clustering - clustered on rows (organism) only
Comparison No.
Comparison Name
Family Level
Genus Level
Species Level
Comparison 1
Saliva Group 1 vs Saliva Group 2 vs Saliva Group 3 vs Saliva Group 4 vs Saliva Group 5 vs Saliva Group 6 vs Saliva Group 7 vs Saliva Group 8 vs Saliva Group 9
Plaque Group 1 vs Plaque Group 2 vs Plaque Group 3 vs Plaque Group 4 vs Plaque Group 5 vs Plaque Group 6 vs Plaque Group 7 vs Plaque Group 8 vs Plaque Group 9
Saliva Group 1 vs Saliva Group 2 vs Saliva Group 3 vs Saliva Group 4 vs Saliva Group 5 vs Saliva Group 6 vs Saliva Group 7 vs Saliva Group 8 vs Saliva Group 9
Plaque Group 1 vs Plaque Group 2 vs Plaque Group 3 vs Plaque Group 4 vs Plaque Group 5 vs Plaque Group 6 vs Plaque Group 7 vs Plaque Group 8 vs Plaque Group 9
To analyze the co-occurrence or co-exclusion between microbial species among different samples, network correlation
analysis tools are usually used for this purpose. However, microbiome count data are compositional. If count data are normalized to the total number of counts in the
sample, the data become not independent and traditional statistical metrics (e.g., correlation) for the detection
of specie-species relationships can lead to spurious results. In addition, sequencing-based studies typically
measure hundreds of OTUs (species) on few samples; thus, inference of OTU-OTU association networks is severely
under-powered. Here we use SPIEC-EASI (SParse InversECovariance Estimation
for Ecological Association Inference), a statistical method for the inference of microbial
ecological networks from amplicon sequencing datasets that addresses both of these issues (Kurtz et al., 2015).
SPIEC-EASI combines data transformations developed for compositional data analysis with a graphical model
inference framework that assumes the underlying ecological association network is sparse. SPIEC-EASI provides
two algorithms for network inferencing – 1) Meinshausen-Bühlmann's neighborhood selection (MB method) and inverse covariance selection
(GLASSO method, i.e., graphical least absolute shrinkage and selection operator). This is fundamentally distinct from SparCC, which essentially estimate pairwise correlations. In addition
to these two methods, we provide the results of a third method - SparCC (Sparse Correlations for Compositional Data)(Friedman & Alm 2012), which
is also a method for inferring correlations from compositional data. SparCC estimates the linear Pearson correlations between
the log-transformed components.
References:
Kurtz ZD, Müller CL, Miraldi ER, Littman DR, Blaser MJ, Bonneau RA. Sparse and compositionally robust inference of microbial ecological networks. PLoS Comput Biol. 2015 May 7;11(5):e1004226. doi: 10.1371/journal.pcbi.1004226. PMID: 25950956; PMCID: PMC4423992.
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.
