Project FOMC4468_ITS services include NGS sequencing of the ITS region of the 16S rRNA amplicons from the samples. First and foremost, please
download this report, as well as the sequence raw data from the download links provided below.
These links will expire after 60 days. We cannot guarantee the availability of your data after 60 days.
Full Bioinformatics analysis service was requested. We provide many analyses, starting from the raw sequence quality and noise filtering, pair reads merging, as well as chimera filtering for the sequences, using the
DADA2 denosing algorithm and pipeline.
We also provide many downstream analyses such as taxonomy assignment, alpha and beta diversity analyses, and differential abundance analysis.
For taxonomy assignment, most informative would be the taxonomy barplots. We provide an interactive barplots to show the relative abundance of microbes at different taxonomy levels (from Phylum to species) that you can choose.
If you specify which groups of samples you want to compare for differential abundance, we provide both ANCOM and LEfSe differential abundance analysis.
The samples were processed and analyzed with the ZymoBIOMICS® Service: Targeted
Metagenomic Sequencing (Zymo Research, Irvine, CA).
DNA Extraction: If DNA extraction was performed, one of three different DNA
extraction kits was used depending on the sample type and sample volume and were
used according to the manufacturer’s instructions, unless otherwise stated. The kit used
in this project is marked below:
☐
ZymoBIOMICS® DNA Miniprep Kit (Zymo Research, Irvine, CA)
☐
ZymoBIOMICS® DNA Microprep Kit (Zymo Research, Irvine, CA)
☐
ZymoBIOMICS®-96 MagBead DNA Kit (Zymo Research, Irvine, CA)
☑
N/A (DNA Extraction Not Performed)
Elution Volume: 50µL
Additional Notes: NA
Targeted Library Preparation: The DNA samples were prepared for targeted
sequencing with the Quick-16S™ NGS Library Prep Kit (Zymo Research, Irvine, CA).
These primers were custom designed by Zymo Research to provide the best coverage
of the 16S gene while maintaining high sensitivity. The primer sets used in this project
are marked below:
☐
Quick-16S™ Primer Set V1-V2 (Zymo Research, Irvine, CA)
☐
Quick-16S™ Primer Set V1-V3 (Zymo Research, Irvine, CA)
☐
Quick-16S™ Primer Set V3-V4 (Zymo Research, Irvine, CA)
☐
Quick-16S™ Primer Set V4 (Zymo Research, Irvine, CA)
☐
Quick-16S™ Primer Set V6-V8 (Zymo Research, Irvine, CA)
☑
ZymoBIOMICS® Services ITS2 Primer Set (Zymo Research, Irvine, CA)
☐
Other: NA
Additional Notes: NA
The sequencing library was prepared using an innovative library preparation process in
which PCR reactions were performed in real-time PCR machines to control cycles and
therefore limit PCR chimera formation. The final PCR products were quantified with
qPCR fluorescence readings and pooled together based on equal molarity. The final
pooled library was cleaned up with the Select-a-Size DNA Clean & Concentrator™
(Zymo Research, Irvine, CA), then quantified with TapeStation® (Agilent Technologies,
Santa Clara, CA) and Qubit® (Thermo Fisher Scientific, Waltham, WA).
Control Samples: The ZymoBIOMICS® Microbial Community Standard (Zymo
Research, Irvine, CA) was used as a positive control for each DNA extraction, if
performed. The ZymoBIOMICS® Microbial Community DNA Standard (Zymo Research,
Irvine, CA) was used as a positive control for each targeted library preparation.
Negative controls (i.e. blank extraction control, blank library preparation control) were
included to assess the level of bioburden carried by the wet-lab process.
Sequencing: The final library was sequenced on Illumina® MiSeq™ with a V3 reagent kit
(600 cycles). The sequencing was performed with 10% PhiX spike-in.
The complete report of your project, including all links in this report, can be downloaded by clicking the link provided below. The downloaded file is a compressed ZIP file and once unzipped, open the file “REPORT.html” (may only shown as "REPORT" in your computer) by double clicking it. Your default web browser will open it and you will see the exact content of this report.
Please download and save the file to your computer storage device. The download link will expire after 60 days upon your receiving of this report.
Complete report download link:
To view the report, please follow the following steps:
1.
Download the .zip file from the report link above.
2.
Extract all the contents of the downloaded .zip file to your desktop.
3.
Open the extracted folder and find the "REPORT.html" (may shown as only "REPORT").
4.
Open (double-clicking) the REPORT.html file. Your default browser will open the top age of the complete report. Within the
report, there are links to view all the analyses performed for the project.
The raw NGS sequence data is available for download with the link provided below. The data is a compressed ZIP file and can be unzipped to individual sequence files.
Since this is a pair-end sequencing, each of your samples is represented by two sequence files, one for READ 1,
with the file extension “*_R1.fastq.gz”, another READ 2, with the file extension “*_R1.fastq.gz”.
The files are in FASTQ format and are compressed. FASTQ format is a text-based data format for storing both a biological sequence
and its corresponding quality scores. Most sequence analysis software will be able to open them.
The Sample IDs associated with the R1 and R2 fastq files are listed in the table below:
Sample ID
Read 1 File Name
Read 2 File Name
S100
zr4468_100ITS2_R1.fastq.gz
zr4468_100ITS2_R2.fastq.gz
S101
zr4468_101ITS2_R1.fastq.gz
zr4468_101ITS2_R2.fastq.gz
S102
zr4468_102ITS2_R1.fastq.gz
zr4468_102ITS2_R2.fastq.gz
S103
zr4468_103ITS2_R1.fastq.gz
zr4468_103ITS2_R2.fastq.gz
S104
zr4468_104ITS2_R1.fastq.gz
zr4468_104ITS2_R2.fastq.gz
S105
zr4468_105ITS2_R1.fastq.gz
zr4468_105ITS2_R2.fastq.gz
S106
zr4468_106ITS2_R1.fastq.gz
zr4468_106ITS2_R2.fastq.gz
S107
zr4468_107ITS2_R1.fastq.gz
zr4468_107ITS2_R2.fastq.gz
S108
zr4468_108ITS2_R1.fastq.gz
zr4468_108ITS2_R2.fastq.gz
S109
zr4468_109ITS2_R1.fastq.gz
zr4468_109ITS2_R2.fastq.gz
S010
zr4468_10ITS2_R1.fastq.gz
zr4468_10ITS2_R2.fastq.gz
S110
zr4468_110ITS2_R1.fastq.gz
zr4468_110ITS2_R2.fastq.gz
S111
zr4468_111ITS2_R1.fastq.gz
zr4468_111ITS2_R2.fastq.gz
S112
zr4468_112ITS2_R1.fastq.gz
zr4468_112ITS2_R2.fastq.gz
S113
zr4468_113ITS2_R1.fastq.gz
zr4468_113ITS2_R2.fastq.gz
S114
zr4468_114ITS2_R1.fastq.gz
zr4468_114ITS2_R2.fastq.gz
S115
zr4468_115ITS2_R1.fastq.gz
zr4468_115ITS2_R2.fastq.gz
S116
zr4468_116ITS2_R1.fastq.gz
zr4468_116ITS2_R2.fastq.gz
S117
zr4468_117ITS2_R1.fastq.gz
zr4468_117ITS2_R2.fastq.gz
S118
zr4468_118ITS2_R1.fastq.gz
zr4468_118ITS2_R2.fastq.gz
S119
zr4468_119ITS2_R1.fastq.gz
zr4468_119ITS2_R2.fastq.gz
S011
zr4468_11ITS2_R1.fastq.gz
zr4468_11ITS2_R2.fastq.gz
S120
zr4468_120ITS2_R1.fastq.gz
zr4468_120ITS2_R2.fastq.gz
S121
zr4468_121ITS2_R1.fastq.gz
zr4468_121ITS2_R2.fastq.gz
S122
zr4468_122ITS2_R1.fastq.gz
zr4468_122ITS2_R2.fastq.gz
S123
zr4468_123ITS2_R1.fastq.gz
zr4468_123ITS2_R2.fastq.gz
S124
zr4468_124ITS2_R1.fastq.gz
zr4468_124ITS2_R2.fastq.gz
S125
zr4468_125ITS2_R1.fastq.gz
zr4468_125ITS2_R2.fastq.gz
S126
zr4468_126ITS2_R1.fastq.gz
zr4468_126ITS2_R2.fastq.gz
S127
zr4468_127ITS2_R1.fastq.gz
zr4468_127ITS2_R2.fastq.gz
S128
zr4468_128ITS2_R1.fastq.gz
zr4468_128ITS2_R2.fastq.gz
S129
zr4468_129ITS2_R1.fastq.gz
zr4468_129ITS2_R2.fastq.gz
S012
zr4468_12ITS2_R1.fastq.gz
zr4468_12ITS2_R2.fastq.gz
S130
zr4468_130ITS2_R1.fastq.gz
zr4468_130ITS2_R2.fastq.gz
S131
zr4468_131ITS2_R1.fastq.gz
zr4468_131ITS2_R2.fastq.gz
S132
zr4468_132ITS2_R1.fastq.gz
zr4468_132ITS2_R2.fastq.gz
S133
zr4468_133ITS2_R1.fastq.gz
zr4468_133ITS2_R2.fastq.gz
S134
zr4468_134ITS2_R1.fastq.gz
zr4468_134ITS2_R2.fastq.gz
S135
zr4468_135ITS2_R1.fastq.gz
zr4468_135ITS2_R2.fastq.gz
S136
zr4468_136ITS2_R1.fastq.gz
zr4468_136ITS2_R2.fastq.gz
S137
zr4468_137ITS2_R1.fastq.gz
zr4468_137ITS2_R2.fastq.gz
S138
zr4468_138ITS2_R1.fastq.gz
zr4468_138ITS2_R2.fastq.gz
S139
zr4468_139ITS2_R1.fastq.gz
zr4468_139ITS2_R2.fastq.gz
S013
zr4468_13ITS2_R1.fastq.gz
zr4468_13ITS2_R2.fastq.gz
S140
zr4468_140ITS2_R1.fastq.gz
zr4468_140ITS2_R2.fastq.gz
S141
zr4468_141ITS2_R1.fastq.gz
zr4468_141ITS2_R2.fastq.gz
S142
zr4468_142ITS2_R1.fastq.gz
zr4468_142ITS2_R2.fastq.gz
S143
zr4468_143ITS2_R1.fastq.gz
zr4468_143ITS2_R2.fastq.gz
S144
zr4468_144ITS2_R1.fastq.gz
zr4468_144ITS2_R2.fastq.gz
S145
zr4468_145ITS2_R1.fastq.gz
zr4468_145ITS2_R2.fastq.gz
S146
zr4468_146ITS2_R1.fastq.gz
zr4468_146ITS2_R2.fastq.gz
S147
zr4468_147ITS2_R1.fastq.gz
zr4468_147ITS2_R2.fastq.gz
S148
zr4468_148ITS2_R1.fastq.gz
zr4468_148ITS2_R2.fastq.gz
S149
zr4468_149ITS2_R1.fastq.gz
zr4468_149ITS2_R2.fastq.gz
S014
zr4468_14ITS2_R1.fastq.gz
zr4468_14ITS2_R2.fastq.gz
S150
zr4468_150ITS2_R1.fastq.gz
zr4468_150ITS2_R2.fastq.gz
S151
zr4468_151ITS2_R1.fastq.gz
zr4468_151ITS2_R2.fastq.gz
S152
zr4468_152ITS2_R1.fastq.gz
zr4468_152ITS2_R2.fastq.gz
S153
zr4468_153ITS2_R1.fastq.gz
zr4468_153ITS2_R2.fastq.gz
S154
zr4468_154ITS2_R1.fastq.gz
zr4468_154ITS2_R2.fastq.gz
S155
zr4468_155ITS2_R1.fastq.gz
zr4468_155ITS2_R2.fastq.gz
S156
zr4468_156ITS2_R1.fastq.gz
zr4468_156ITS2_R2.fastq.gz
S157
zr4468_157ITS2_R1.fastq.gz
zr4468_157ITS2_R2.fastq.gz
S158
zr4468_158ITS2_R1.fastq.gz
zr4468_158ITS2_R2.fastq.gz
S159
zr4468_159ITS2_R1.fastq.gz
zr4468_159ITS2_R2.fastq.gz
S015
zr4468_15ITS2_R1.fastq.gz
zr4468_15ITS2_R2.fastq.gz
S160
zr4468_160ITS2_R1.fastq.gz
zr4468_160ITS2_R2.fastq.gz
S161
zr4468_161ITS2_R1.fastq.gz
zr4468_161ITS2_R2.fastq.gz
S162
zr4468_162ITS2_R1.fastq.gz
zr4468_162ITS2_R2.fastq.gz
S163
zr4468_163ITS2_R1.fastq.gz
zr4468_163ITS2_R2.fastq.gz
S164
zr4468_164ITS2_R1.fastq.gz
zr4468_164ITS2_R2.fastq.gz
S165
zr4468_165ITS2_R1.fastq.gz
zr4468_165ITS2_R2.fastq.gz
S166
zr4468_166ITS2_R1.fastq.gz
zr4468_166ITS2_R2.fastq.gz
S167
zr4468_167ITS2_R1.fastq.gz
zr4468_167ITS2_R2.fastq.gz
S168
zr4468_168ITS2_R1.fastq.gz
zr4468_168ITS2_R2.fastq.gz
S169
zr4468_169ITS2_R1.fastq.gz
zr4468_169ITS2_R2.fastq.gz
S016
zr4468_16ITS2_R1.fastq.gz
zr4468_16ITS2_R2.fastq.gz
S170
zr4468_170ITS2_R1.fastq.gz
zr4468_170ITS2_R2.fastq.gz
S171
zr4468_171ITS2_R1.fastq.gz
zr4468_171ITS2_R2.fastq.gz
S172
zr4468_172ITS2_R1.fastq.gz
zr4468_172ITS2_R2.fastq.gz
S173
zr4468_173ITS2_R1.fastq.gz
zr4468_173ITS2_R2.fastq.gz
S174
zr4468_174ITS2_R1.fastq.gz
zr4468_174ITS2_R2.fastq.gz
S175
zr4468_175ITS2_R1.fastq.gz
zr4468_175ITS2_R2.fastq.gz
S176
zr4468_176ITS2_R1.fastq.gz
zr4468_176ITS2_R2.fastq.gz
S177
zr4468_177ITS2_R1.fastq.gz
zr4468_177ITS2_R2.fastq.gz
S178
zr4468_178ITS2_R1.fastq.gz
zr4468_178ITS2_R2.fastq.gz
S179
zr4468_179ITS2_R1.fastq.gz
zr4468_179ITS2_R2.fastq.gz
S017
zr4468_17ITS2_R1.fastq.gz
zr4468_17ITS2_R2.fastq.gz
S180
zr4468_180ITS2_R1.fastq.gz
zr4468_180ITS2_R2.fastq.gz
S181
zr4468_181ITS2_R1.fastq.gz
zr4468_181ITS2_R2.fastq.gz
S182
zr4468_182ITS2_R1.fastq.gz
zr4468_182ITS2_R2.fastq.gz
S183
zr4468_183ITS2_R1.fastq.gz
zr4468_183ITS2_R2.fastq.gz
S184
zr4468_184ITS2_R1.fastq.gz
zr4468_184ITS2_R2.fastq.gz
S185
zr4468_185ITS2_R1.fastq.gz
zr4468_185ITS2_R2.fastq.gz
S186
zr4468_186ITS2_R1.fastq.gz
zr4468_186ITS2_R2.fastq.gz
S187
zr4468_187ITS2_R1.fastq.gz
zr4468_187ITS2_R2.fastq.gz
S188
zr4468_188ITS2_R1.fastq.gz
zr4468_188ITS2_R2.fastq.gz
S189
zr4468_189ITS2_R1.fastq.gz
zr4468_189ITS2_R2.fastq.gz
S018
zr4468_18ITS2_R1.fastq.gz
zr4468_18ITS2_R2.fastq.gz
S190
zr4468_190ITS2_R1.fastq.gz
zr4468_190ITS2_R2.fastq.gz
S191
zr4468_191ITS2_R1.fastq.gz
zr4468_191ITS2_R2.fastq.gz
S192
zr4468_192ITS2_R1.fastq.gz
zr4468_192ITS2_R2.fastq.gz
S193
zr4468_193ITS2_R1.fastq.gz
zr4468_193ITS2_R2.fastq.gz
S194
zr4468_194ITS2_R1.fastq.gz
zr4468_194ITS2_R2.fastq.gz
S195
zr4468_195ITS2_R1.fastq.gz
zr4468_195ITS2_R2.fastq.gz
S196
zr4468_196ITS2_R1.fastq.gz
zr4468_196ITS2_R2.fastq.gz
S197
zr4468_197ITS2_R1.fastq.gz
zr4468_197ITS2_R2.fastq.gz
S198
zr4468_198ITS2_R1.fastq.gz
zr4468_198ITS2_R2.fastq.gz
S199
zr4468_199ITS2_R1.fastq.gz
zr4468_199ITS2_R2.fastq.gz
S019
zr4468_19ITS2_R1.fastq.gz
zr4468_19ITS2_R2.fastq.gz
S001
zr4468_1ITS2_R1.fastq.gz
zr4468_1ITS2_R2.fastq.gz
S200
zr4468_200ITS2_R1.fastq.gz
zr4468_200ITS2_R2.fastq.gz
S201
zr4468_201ITS2_R1.fastq.gz
zr4468_201ITS2_R2.fastq.gz
S202
zr4468_202ITS2_R1.fastq.gz
zr4468_202ITS2_R2.fastq.gz
S203
zr4468_203ITS2_R1.fastq.gz
zr4468_203ITS2_R2.fastq.gz
S204
zr4468_204ITS2_R1.fastq.gz
zr4468_204ITS2_R2.fastq.gz
S205
zr4468_205ITS2_R1.fastq.gz
zr4468_205ITS2_R2.fastq.gz
S206
zr4468_206ITS2_R1.fastq.gz
zr4468_206ITS2_R2.fastq.gz
S207
zr4468_207ITS2_R1.fastq.gz
zr4468_207ITS2_R2.fastq.gz
S208
zr4468_208ITS2_R1.fastq.gz
zr4468_208ITS2_R2.fastq.gz
S209
zr4468_209ITS2_R1.fastq.gz
zr4468_209ITS2_R2.fastq.gz
S020
zr4468_20ITS2_R1.fastq.gz
zr4468_20ITS2_R2.fastq.gz
S210
zr4468_210ITS2_R1.fastq.gz
zr4468_210ITS2_R2.fastq.gz
S211
zr4468_211ITS2_R1.fastq.gz
zr4468_211ITS2_R2.fastq.gz
S212
zr4468_212ITS2_R1.fastq.gz
zr4468_212ITS2_R2.fastq.gz
S213
zr4468_213ITS2_R1.fastq.gz
zr4468_213ITS2_R2.fastq.gz
S214
zr4468_214ITS2_R1.fastq.gz
zr4468_214ITS2_R2.fastq.gz
S215
zr4468_215ITS2_R1.fastq.gz
zr4468_215ITS2_R2.fastq.gz
S216
zr4468_216ITS2_R1.fastq.gz
zr4468_216ITS2_R2.fastq.gz
S217
zr4468_217ITS2_R1.fastq.gz
zr4468_217ITS2_R2.fastq.gz
S218
zr4468_218ITS2_R1.fastq.gz
zr4468_218ITS2_R2.fastq.gz
S219
zr4468_219ITS2_R1.fastq.gz
zr4468_219ITS2_R2.fastq.gz
S021
zr4468_21ITS2_R1.fastq.gz
zr4468_21ITS2_R2.fastq.gz
S220
zr4468_220ITS2_R1.fastq.gz
zr4468_220ITS2_R2.fastq.gz
S221
zr4468_221ITS2_R1.fastq.gz
zr4468_221ITS2_R2.fastq.gz
S222
zr4468_222ITS2_R1.fastq.gz
zr4468_222ITS2_R2.fastq.gz
S223
zr4468_223ITS2_R1.fastq.gz
zr4468_223ITS2_R2.fastq.gz
S224
zr4468_224ITS2_R1.fastq.gz
zr4468_224ITS2_R2.fastq.gz
S225
zr4468_225ITS2_R1.fastq.gz
zr4468_225ITS2_R2.fastq.gz
S226
zr4468_226ITS2_R1.fastq.gz
zr4468_226ITS2_R2.fastq.gz
S227
zr4468_227ITS2_R1.fastq.gz
zr4468_227ITS2_R2.fastq.gz
S228
zr4468_228ITS2_R1.fastq.gz
zr4468_228ITS2_R2.fastq.gz
S229
zr4468_229ITS2_R1.fastq.gz
zr4468_229ITS2_R2.fastq.gz
S022
zr4468_22ITS2_R1.fastq.gz
zr4468_22ITS2_R2.fastq.gz
S230
zr4468_230ITS2_R1.fastq.gz
zr4468_230ITS2_R2.fastq.gz
S231
zr4468_231ITS2_R1.fastq.gz
zr4468_231ITS2_R2.fastq.gz
S232
zr4468_232ITS2_R1.fastq.gz
zr4468_232ITS2_R2.fastq.gz
S233
zr4468_233ITS2_R1.fastq.gz
zr4468_233ITS2_R2.fastq.gz
S234
zr4468_234ITS2_R1.fastq.gz
zr4468_234ITS2_R2.fastq.gz
S235
zr4468_235ITS2_R1.fastq.gz
zr4468_235ITS2_R2.fastq.gz
S236
zr4468_236ITS2_R1.fastq.gz
zr4468_236ITS2_R2.fastq.gz
S237
zr4468_237ITS2_R1.fastq.gz
zr4468_237ITS2_R2.fastq.gz
S238
zr4468_238ITS2_R1.fastq.gz
zr4468_238ITS2_R2.fastq.gz
S239
zr4468_239ITS2_R1.fastq.gz
zr4468_239ITS2_R2.fastq.gz
S023
zr4468_23ITS2_R1.fastq.gz
zr4468_23ITS2_R2.fastq.gz
S240
zr4468_240ITS2_R1.fastq.gz
zr4468_240ITS2_R2.fastq.gz
S241
zr4468_241ITS2_R1.fastq.gz
zr4468_241ITS2_R2.fastq.gz
S242
zr4468_242ITS2_R1.fastq.gz
zr4468_242ITS2_R2.fastq.gz
S243
zr4468_243ITS2_R1.fastq.gz
zr4468_243ITS2_R2.fastq.gz
S244
zr4468_244ITS2_R1.fastq.gz
zr4468_244ITS2_R2.fastq.gz
S245
zr4468_245ITS2_R1.fastq.gz
zr4468_245ITS2_R2.fastq.gz
S246
zr4468_246ITS2_R1.fastq.gz
zr4468_246ITS2_R2.fastq.gz
S247
zr4468_247ITS2_R1.fastq.gz
zr4468_247ITS2_R2.fastq.gz
S248
zr4468_248ITS2_R1.fastq.gz
zr4468_248ITS2_R2.fastq.gz
S249
zr4468_249ITS2_R1.fastq.gz
zr4468_249ITS2_R2.fastq.gz
S024
zr4468_24ITS2_R1.fastq.gz
zr4468_24ITS2_R2.fastq.gz
S250
zr4468_250ITS2_R1.fastq.gz
zr4468_250ITS2_R2.fastq.gz
S251
zr4468_251ITS2_R1.fastq.gz
zr4468_251ITS2_R2.fastq.gz
S252
zr4468_252ITS2_R1.fastq.gz
zr4468_252ITS2_R2.fastq.gz
S253
zr4468_253ITS2_R1.fastq.gz
zr4468_253ITS2_R2.fastq.gz
S254
zr4468_254ITS2_R1.fastq.gz
zr4468_254ITS2_R2.fastq.gz
S255
zr4468_255ITS2_R1.fastq.gz
zr4468_255ITS2_R2.fastq.gz
S256
zr4468_256ITS2_R1.fastq.gz
zr4468_256ITS2_R2.fastq.gz
S257
zr4468_257ITS2_R1.fastq.gz
zr4468_257ITS2_R2.fastq.gz
S258
zr4468_258ITS2_R1.fastq.gz
zr4468_258ITS2_R2.fastq.gz
S259
zr4468_259ITS2_R1.fastq.gz
zr4468_259ITS2_R2.fastq.gz
S025
zr4468_25ITS2_R1.fastq.gz
zr4468_25ITS2_R2.fastq.gz
S260
zr4468_260ITS2_R1.fastq.gz
zr4468_260ITS2_R2.fastq.gz
S261
zr4468_261ITS2_R1.fastq.gz
zr4468_261ITS2_R2.fastq.gz
S262
zr4468_262ITS2_R1.fastq.gz
zr4468_262ITS2_R2.fastq.gz
S263
zr4468_263ITS2_R1.fastq.gz
zr4468_263ITS2_R2.fastq.gz
S264
zr4468_264ITS2_R1.fastq.gz
zr4468_264ITS2_R2.fastq.gz
S265
zr4468_265ITS2_R1.fastq.gz
zr4468_265ITS2_R2.fastq.gz
S266
zr4468_266ITS2_R1.fastq.gz
zr4468_266ITS2_R2.fastq.gz
S267
zr4468_267ITS2_R1.fastq.gz
zr4468_267ITS2_R2.fastq.gz
S268
zr4468_268ITS2_R1.fastq.gz
zr4468_268ITS2_R2.fastq.gz
S269
zr4468_269ITS2_R1.fastq.gz
zr4468_269ITS2_R2.fastq.gz
S026
zr4468_26ITS2_R1.fastq.gz
zr4468_26ITS2_R2.fastq.gz
S270
zr4468_270ITS2_R1.fastq.gz
zr4468_270ITS2_R2.fastq.gz
S027
zr4468_27ITS2_R1.fastq.gz
zr4468_27ITS2_R2.fastq.gz
S028
zr4468_28ITS2_R1.fastq.gz
zr4468_28ITS2_R2.fastq.gz
S029
zr4468_29ITS2_R1.fastq.gz
zr4468_29ITS2_R2.fastq.gz
S002
zr4468_2ITS2_R1.fastq.gz
zr4468_2ITS2_R2.fastq.gz
S030
zr4468_30ITS2_R1.fastq.gz
zr4468_30ITS2_R2.fastq.gz
S031
zr4468_31ITS2_R1.fastq.gz
zr4468_31ITS2_R2.fastq.gz
S032
zr4468_32ITS2_R1.fastq.gz
zr4468_32ITS2_R2.fastq.gz
S033
zr4468_33ITS2_R1.fastq.gz
zr4468_33ITS2_R2.fastq.gz
S034
zr4468_34ITS2_R1.fastq.gz
zr4468_34ITS2_R2.fastq.gz
S035
zr4468_35ITS2_R1.fastq.gz
zr4468_35ITS2_R2.fastq.gz
S036
zr4468_36ITS2_R1.fastq.gz
zr4468_36ITS2_R2.fastq.gz
S037
zr4468_37ITS2_R1.fastq.gz
zr4468_37ITS2_R2.fastq.gz
S038
zr4468_38ITS2_R1.fastq.gz
zr4468_38ITS2_R2.fastq.gz
S039
zr4468_39ITS2_R1.fastq.gz
zr4468_39ITS2_R2.fastq.gz
S003
zr4468_3ITS2_R1.fastq.gz
zr4468_3ITS2_R2.fastq.gz
S040
zr4468_40ITS2_R1.fastq.gz
zr4468_40ITS2_R2.fastq.gz
S041
zr4468_41ITS2_R1.fastq.gz
zr4468_41ITS2_R2.fastq.gz
S042
zr4468_42ITS2_R1.fastq.gz
zr4468_42ITS2_R2.fastq.gz
S043
zr4468_43ITS2_R1.fastq.gz
zr4468_43ITS2_R2.fastq.gz
S044
zr4468_44ITS2_R1.fastq.gz
zr4468_44ITS2_R2.fastq.gz
S045
zr4468_45ITS2_R1.fastq.gz
zr4468_45ITS2_R2.fastq.gz
S046
zr4468_46ITS2_R1.fastq.gz
zr4468_46ITS2_R2.fastq.gz
S047
zr4468_47ITS2_R1.fastq.gz
zr4468_47ITS2_R2.fastq.gz
S048
zr4468_48ITS2_R1.fastq.gz
zr4468_48ITS2_R2.fastq.gz
S049
zr4468_49ITS2_R1.fastq.gz
zr4468_49ITS2_R2.fastq.gz
S004
zr4468_4ITS2_R1.fastq.gz
zr4468_4ITS2_R2.fastq.gz
S050
zr4468_50ITS2_R1.fastq.gz
zr4468_50ITS2_R2.fastq.gz
S051
zr4468_51ITS2_R1.fastq.gz
zr4468_51ITS2_R2.fastq.gz
S052
zr4468_52ITS2_R1.fastq.gz
zr4468_52ITS2_R2.fastq.gz
S053
zr4468_53ITS2_R1.fastq.gz
zr4468_53ITS2_R2.fastq.gz
S054
zr4468_54ITS2_R1.fastq.gz
zr4468_54ITS2_R2.fastq.gz
S055
zr4468_55ITS2_R1.fastq.gz
zr4468_55ITS2_R2.fastq.gz
S056
zr4468_56ITS2_R1.fastq.gz
zr4468_56ITS2_R2.fastq.gz
S057
zr4468_57ITS2_R1.fastq.gz
zr4468_57ITS2_R2.fastq.gz
S058
zr4468_58ITS2_R1.fastq.gz
zr4468_58ITS2_R2.fastq.gz
S059
zr4468_59ITS2_R1.fastq.gz
zr4468_59ITS2_R2.fastq.gz
S005
zr4468_5ITS2_R1.fastq.gz
zr4468_5ITS2_R2.fastq.gz
S060
zr4468_60ITS2_R1.fastq.gz
zr4468_60ITS2_R2.fastq.gz
S061
zr4468_61ITS2_R1.fastq.gz
zr4468_61ITS2_R2.fastq.gz
S062
zr4468_62ITS2_R1.fastq.gz
zr4468_62ITS2_R2.fastq.gz
S063
zr4468_63ITS2_R1.fastq.gz
zr4468_63ITS2_R2.fastq.gz
S064
zr4468_64ITS2_R1.fastq.gz
zr4468_64ITS2_R2.fastq.gz
S065
zr4468_65ITS2_R1.fastq.gz
zr4468_65ITS2_R2.fastq.gz
S066
zr4468_66ITS2_R1.fastq.gz
zr4468_66ITS2_R2.fastq.gz
S067
zr4468_67ITS2_R1.fastq.gz
zr4468_67ITS2_R2.fastq.gz
S068
zr4468_68ITS2_R1.fastq.gz
zr4468_68ITS2_R2.fastq.gz
S069
zr4468_69ITS2_R1.fastq.gz
zr4468_69ITS2_R2.fastq.gz
S006
zr4468_6ITS2_R1.fastq.gz
zr4468_6ITS2_R2.fastq.gz
S070
zr4468_70ITS2_R1.fastq.gz
zr4468_70ITS2_R2.fastq.gz
S071
zr4468_71ITS2_R1.fastq.gz
zr4468_71ITS2_R2.fastq.gz
S072
zr4468_72ITS2_R1.fastq.gz
zr4468_72ITS2_R2.fastq.gz
S073
zr4468_73ITS2_R1.fastq.gz
zr4468_73ITS2_R2.fastq.gz
S074
zr4468_74ITS2_R1.fastq.gz
zr4468_74ITS2_R2.fastq.gz
S075
zr4468_75ITS2_R1.fastq.gz
zr4468_75ITS2_R2.fastq.gz
S076
zr4468_76ITS2_R1.fastq.gz
zr4468_76ITS2_R2.fastq.gz
S077
zr4468_77ITS2_R1.fastq.gz
zr4468_77ITS2_R2.fastq.gz
S078
zr4468_78ITS2_R1.fastq.gz
zr4468_78ITS2_R2.fastq.gz
S079
zr4468_79ITS2_R1.fastq.gz
zr4468_79ITS2_R2.fastq.gz
S007
zr4468_7ITS2_R1.fastq.gz
zr4468_7ITS2_R2.fastq.gz
S080
zr4468_80ITS2_R1.fastq.gz
zr4468_80ITS2_R2.fastq.gz
S081
zr4468_81ITS2_R1.fastq.gz
zr4468_81ITS2_R2.fastq.gz
S082
zr4468_82ITS2_R1.fastq.gz
zr4468_82ITS2_R2.fastq.gz
S083
zr4468_83ITS2_R1.fastq.gz
zr4468_83ITS2_R2.fastq.gz
S084
zr4468_84ITS2_R1.fastq.gz
zr4468_84ITS2_R2.fastq.gz
S085
zr4468_85ITS2_R1.fastq.gz
zr4468_85ITS2_R2.fastq.gz
S086
zr4468_86ITS2_R1.fastq.gz
zr4468_86ITS2_R2.fastq.gz
S087
zr4468_87ITS2_R1.fastq.gz
zr4468_87ITS2_R2.fastq.gz
S088
zr4468_88ITS2_R1.fastq.gz
zr4468_88ITS2_R2.fastq.gz
S089
zr4468_89ITS2_R1.fastq.gz
zr4468_89ITS2_R2.fastq.gz
S008
zr4468_8ITS2_R1.fastq.gz
zr4468_8ITS2_R2.fastq.gz
S090
zr4468_90ITS2_R1.fastq.gz
zr4468_90ITS2_R2.fastq.gz
S091
zr4468_91ITS2_R1.fastq.gz
zr4468_91ITS2_R2.fastq.gz
S092
zr4468_92ITS2_R1.fastq.gz
zr4468_92ITS2_R2.fastq.gz
S093
zr4468_93ITS2_R1.fastq.gz
zr4468_93ITS2_R2.fastq.gz
S094
zr4468_94ITS2_R1.fastq.gz
zr4468_94ITS2_R2.fastq.gz
S095
zr4468_95ITS2_R1.fastq.gz
zr4468_95ITS2_R2.fastq.gz
S096
zr4468_96ITS2_R1.fastq.gz
zr4468_96ITS2_R2.fastq.gz
S097
zr4468_97ITS2_R1.fastq.gz
zr4468_97ITS2_R2.fastq.gz
S098
zr4468_98ITS2_R1.fastq.gz
zr4468_98ITS2_R2.fastq.gz
S099
zr4468_99ITS2_R1.fastq.gz
zr4468_99ITS2_R2.fastq.gz
S009
zr4468_9ITS2_R1.fastq.gz
zr4468_9ITS2_R2.fastq.gz
Please download and save the file to your computer storage device. The download link will expire after 60 days upon your receiving of this report.
DADA2 is a software package that models and corrects Illumina-sequenced amplicon errors.
DADA2 infers sample sequences exactly, without coarse-graining into OTUs,
and resolves differences of as little as one nucleotide. DADA2 identified more real variants
and output fewer spurious sequences than other methods.
DADA2’s advantage is that it uses more of the data. The DADA2 error model incorporates quality information,
which is ignored by all other methods after filtering. The DADA2 error model incorporates quantitative abundances,
whereas most other methods use abundance ranks if they use abundance at all.
The DADA2 error model identifies the differences between sequences, eg. A->C,
whereas other methods merely count the mismatches. DADA2 can parameterize its error model from the data itself,
rather than relying on previous datasets that may or may not reflect the PCR and sequencing protocols used in your study.
DADA2 pipeline includes several tools for read quality control, including quality filtering, trimming, denoising, pair merging and chimera filtering. Below are the major processing steps of DADA2:
Step 1. Read trimming based on sequence quality
The quality of NGS Illumina sequences often decreases toward the end of the reads.
DADA2 allows to trim off the poor quality read ends in order to improve the error
model building and pair mergicing performance.
Step 2. Learn the Error Rates
The DADA2 algorithm makes use of a parametric error model (err) and every
amplicon dataset has a different set of error rates. The learnErrors method
learns this error model from the data, by alternating estimation of the error
rates and inference of sample composition until they converge on a jointly
consistent solution. As in many machine-learning problems, the algorithm must
begin with an initial guess, for which the maximum possible error rates in
this data are used (the error rates if only the most abundant sequence is
correct and all the rest are errors).
Step 3. Infer amplicon sequence variants (ASVs) based on the error model built in previous step. This step is also called sequence "denoising".
The outcome of this step is a list of ASVs that are the equivalent of oligonucleotides.
Step 4. Merge paired reads. If the sequencing products are read pairs, DADA2 will merge the R1 and R2 ASVs into single sequences.
Merging is performed by aligning the denoised forward reads with the reverse-complement of the corresponding
denoised reverse reads, and then constructing the merged “contig” sequences.
By default, merged sequences are only output if the forward and reverse reads overlap by
at least 12 bases, and are identical to each other in the overlap region (but these conditions can be changed via function arguments).
Step 5. Remove chimera.
The core dada method corrects substitution and indel errors, but chimeras remain. Fortunately, the accuracy of sequence variants
after denoising makes identifying chimeric ASVs simpler than when dealing with fuzzy OTUs.
Chimeric sequences are identified if they can be exactly reconstructed by
combining a left-segment and a right-segment from two more abundant “parent” sequences. The frequency of chimeric sequences varies substantially
from dataset to dataset, and depends on on factors including experimental procedures and sample complexity.
Results
1. Read Quality Plots NGS sequence analaysis starts with visualizing the quality of the sequencing. Below are the quality plots of the first
sample for the R1 and R2 reads separately. In gray-scale is a heat map of the frequency of each quality score at each base position. The mean
quality score at each position is shown by the green line, and the quartiles of the quality score distribution by the orange lines.
The forward reads are usually of better quality. It is a common practice to trim the last few nucleotides to avoid less well-controlled errors
that can arise there. The trimming affects the downstream steps including error model building, merging and chimera calling. FOMC uses an empirical
approach to test many combinations of different trim length in order to achieve best final amplicon sequence variants (ASVs), see the next
section “Optimal trim length for ASVs”.
Below is the link to a PDF file for viewing the quality plots for all samples:
2. Optimal trim length for ASVs The final number of merged and chimera-filtered ASVs depends on the quality filtering (hence trimming) in the very beginning of the DADA2 pipeline.
In order to achieve highest number of ASVs, an empirical approach was used -
Create a random subset of each sample consisting of 5,000 R1 and 5,000 R2 (to reduce computation time)
Trim 10 bases at a time from the ends of both R1 and R2 up to 50 bases
For each combination of trimmed length (e.g., 300x300, 300x290, 290x290 etc), the trimmed reads are
subject to the entire DADA2 pipeline for chimera-filtered merged ASVs
The combination with highest percentage of the input reads becoming final ASVs is selected for the complete set of data
Below is the result of such operation, showing ASV percentages of total reads for all trimming combinations (1st Column = R1 lengths in bases; 1st Row = R2 lengths in bases):
R1/R2
281
271
261
251
241
231
321
52.85%
68.59%
72.12%
74.57%
77.37%
78.63%
311
55.33%
72.99%
77.09%
79.61%
82.38%
83.57%
301
55.37%
73.11%
77.20%
79.77%
82.55%
83.76%
291
55.56%
73.28%
77.38%
79.98%
82.90%
83.95%
281
55.64%
73.48%
77.58%
80.22%
83.10%
84.19%
271
55.69%
73.53%
77.63%
80.26%
83.22%
84.35%
Based on the above result, the trim length combination of R1 = 271 bases and R2 = 231 bases (highlighted red above), was chosen for generating final ASVs for all sequences.
This combination generated highest number of merged non-chimeric ASVs and was used for downstream analyses, if requested.
3. Error plots from learning the error rates
After DADA2 building the error model for the set of data, it is always worthwhile, as a sanity check if nothing else, to visualize the estimated error rates.
The error rates for each possible transition (A→C, A→G, …) are shown below. Points are the observed error rates for each consensus quality score.
The black line shows the estimated error rates after convergence of the machine-learning algorithm.
The red line shows the error rates expected under the nominal definition of the Q-score.
The ideal result would be the estimated error rates (black line) are a good fit to the observed rates (points), and the error rates drop
with increased quality as expected.
Forward Read R1 Error Plot
Reverse Read R2 Error Plot
The PDF version of these plots are available here:
4. DADA2 Result Summary The table below shows the summary of the DADA2 analysis,
tracking paired read counts of each samples for all the steps during DADA2 denoising process -
including end-trimming (filtered), denoising (denoisedF, denoisedF), pair merging (merged) and chimera removal (nonchim).
Sample ID
F4468.S001
F4468.S002
F4468.S003
F4468.S004
F4468.S005
F4468.S006
F4468.S007
F4468.S008
F4468.S009
F4468.S010
F4468.S011
F4468.S012
F4468.S013
F4468.S014
F4468.S015
F4468.S016
F4468.S017
F4468.S018
F4468.S019
F4468.S020
F4468.S021
F4468.S022
F4468.S023
F4468.S024
F4468.S025
F4468.S026
F4468.S027
F4468.S028
F4468.S029
F4468.S030
F4468.S031
F4468.S032
F4468.S033
F4468.S034
F4468.S035
F4468.S036
F4468.S037
F4468.S038
F4468.S039
F4468.S040
F4468.S041
F4468.S042
F4468.S043
F4468.S044
F4468.S045
F4468.S046
F4468.S047
F4468.S048
F4468.S049
F4468.S050
F4468.S051
F4468.S052
F4468.S053
F4468.S054
F4468.S055
F4468.S056
F4468.S057
F4468.S058
F4468.S059
F4468.S060
F4468.S061
F4468.S062
F4468.S063
F4468.S064
F4468.S065
F4468.S066
F4468.S067
F4468.S068
F4468.S069
F4468.S070
F4468.S071
F4468.S072
F4468.S073
F4468.S074
F4468.S075
F4468.S076
F4468.S077
F4468.S078
F4468.S079
F4468.S080
F4468.S081
F4468.S082
F4468.S083
F4468.S084
F4468.S085
F4468.S086
F4468.S087
F4468.S088
F4468.S089
F4468.S090
F4468.S091
F4468.S092
F4468.S093
F4468.S094
F4468.S095
F4468.S096
F4468.S097
F4468.S098
F4468.S099
F4468.S100
F4468.S101
F4468.S102
F4468.S103
F4468.S104
F4468.S105
F4468.S106
F4468.S107
F4468.S108
F4468.S109
F4468.S110
F4468.S111
F4468.S112
F4468.S113
F4468.S114
F4468.S115
F4468.S116
F4468.S117
F4468.S118
F4468.S119
F4468.S120
F4468.S121
F4468.S122
F4468.S123
F4468.S124
F4468.S125
F4468.S126
F4468.S127
F4468.S128
F4468.S129
F4468.S130
F4468.S131
F4468.S132
F4468.S133
F4468.S134
F4468.S135
F4468.S136
F4468.S137
F4468.S138
F4468.S139
F4468.S140
F4468.S141
F4468.S142
F4468.S143
F4468.S144
F4468.S145
F4468.S146
F4468.S147
F4468.S148
F4468.S149
F4468.S150
F4468.S151
F4468.S152
F4468.S153
F4468.S154
F4468.S155
F4468.S156
F4468.S157
F4468.S158
F4468.S159
F4468.S160
F4468.S161
F4468.S162
F4468.S163
F4468.S164
F4468.S165
F4468.S166
F4468.S167
F4468.S168
F4468.S169
F4468.S170
F4468.S171
F4468.S172
F4468.S173
F4468.S174
F4468.S175
F4468.S176
F4468.S177
F4468.S178
F4468.S179
F4468.S180
F4468.S181
F4468.S182
F4468.S183
F4468.S184
F4468.S185
F4468.S186
F4468.S187
F4468.S188
F4468.S189
F4468.S190
F4468.S191
F4468.S192
F4468.S193
F4468.S194
F4468.S195
F4468.S196
F4468.S197
F4468.S198
F4468.S199
F4468.S200
F4468.S201
F4468.S202
F4468.S203
F4468.S204
F4468.S205
F4468.S206
F4468.S207
F4468.S208
F4468.S209
F4468.S210
F4468.S211
F4468.S212
F4468.S213
F4468.S214
F4468.S215
F4468.S216
F4468.S217
F4468.S218
F4468.S219
F4468.S220
F4468.S221
F4468.S222
F4468.S223
F4468.S224
F4468.S225
F4468.S226
F4468.S227
F4468.S228
F4468.S229
F4468.S230
F4468.S231
F4468.S232
F4468.S233
F4468.S234
F4468.S235
F4468.S236
F4468.S237
F4468.S238
F4468.S239
F4468.S240
F4468.S241
F4468.S242
F4468.S243
F4468.S244
F4468.S245
F4468.S246
F4468.S247
F4468.S248
F4468.S249
F4468.S250
F4468.S251
F4468.S252
F4468.S253
F4468.S254
F4468.S255
F4468.S256
F4468.S257
F4468.S258
F4468.S259
F4468.S260
F4468.S261
F4468.S262
F4468.S263
F4468.S264
F4468.S265
F4468.S266
F4468.S267
F4468.S268
F4468.S269
F4468.S270
Row Sum
Percentage
input
72,618
61,674
38,154
77,817
60,839
71,668
81,222
70,832
627
232
63,617
57,420
51,964
62,246
58,273
51,652
55,958
54,525
752
485
50,580
54,022
49,276
42,913
50,432
56,994
60,655
54,722
643
531
64,343
44,604
69,883
72,909
82,010
78,545
87,850
84,458
36,232
290
64,766
11,353
72,290
72,988
52,186
68,710
65,787
74,941
3,042
158
68,854
25,567
58,822
78,967
66,673
66,157
65,543
75,917
21,818
76,039
82,804
81,031
77,953
71,369
62,309
66,433
88,220
71,194
438
409
80,875
65,245
67,457
62,394
78,770
53,921
65,282
51,546
370
289
89,176
53,675
89,923
80,290
61,441
58,380
95,971
22,835
1,066
193
50,859
51,416
51,077
57,189
43,390
90,201
110,004
66,695
69,581
34,263
77,593
83,024
54,012
59,656
74,499
49,615
58,068
46,271
201
186
71,818
83,077
85,745
66,210
73,610
65,576
69,198
76,664
307
348
90,236
85,119
76,402
73,578
73,605
74,814
97,646
88,602
362
220
19,652
574
73,847
89,471
65,271
70,407
65,271
61,917
215
140
70,880
79,269
59,871
71,252
55,741
57,794
60,135
65,854
156
171
30,503
34,107
60,895
60,114
21,602
67,587
32,718
73,564
817
270
74,363
59,261
74,556
57,362
67,210
80,113
54,172
64,706
51,881
194
77,980
60,243
44,930
95,202
51,415
96,591
103,064
77,617
262
148
97,814
103,774
67,016
70,769
87,103
72,605
74,468
69,219
657
1,290
62,967
43,564
45,495
59,290
62,683
57,795
49,173
54,503
503
593
53,389
59,834
51,119
50,200
54,797
58,772
65,590
54,703
3,955
30,987
68,094
63,815
58,030
63,714
59,772
48,839
34,833
60,442
61,510
128
51,931
69,148
62,642
68,426
58,150
62,960
65,032
58,355
68,560
117
68,980
67,577
55,813
57,774
82,378
43,625
49,598
47,951
436
239
75,178
74,357
83,228
89,773
93,238
77,400
91,638
107,904
230
246
66,679
83,626
20,601
66,458
61,841
62,994
24,527
70,444
1,084
137
82,613
72,903
70,410
79,778
67,375
82,553
88,904
73,073
22
4
14,621,290
100.00%
filtered
47,792
40,769
24,018
42,226
42,791
46,838
54,827
48,163
29
21
35,197
30,679
28,325
37,294
34,483
27,767
33,921
29,795
53
31
33,217
33,760
30,716
26,542
32,114
35,499
38,725
33,159
56
57
35,538
24,556
39,013
39,882
46,886
42,954
66,045
46,368
13,102
30
42,444
7,502
48,706
45,399
36,389
44,891
40,527
56,640
18
10
44,001
8,431
36,984
51,597
43,781
42,599
43,498
42,491
828
44,146
54,905
49,829
46,562
46,431
40,144
41,143
56,150
40,736
25
18
43,323
34,250
42,754
34,206
54,868
32,559
42,723
22,564
23
22
64,402
33,115
58,792
55,509
42,601
37,158
68,283
12,516
37
20
34,467
34,007
35,877
36,685
30,056
64,658
78,954
43,525
43,943
24,831
49,463
43,994
35,905
41,145
46,739
34,489
39,117
30,521
25
29
55,830
65,384
67,309
52,400
55,045
45,249
53,213
60,293
75
79
69,947
66,467
58,731
57,143
55,658
57,303
77,649
70,778
39
39
14,122
32
38,412
53,455
32,369
45,105
37,485
34,157
50
32
46,155
52,476
37,838
45,468
29,171
36,868
36,842
41,147
11
21
18,488
21,047
29,805
37,365
12,310
47,070
18,321
45,343
20
26
53,029
43,056
53,268
41,529
48,150
49,921
37,719
46,193
37,055
35
56,178
40,466
30,770
67,419
35,141
70,172
76,237
51,759
52
33
75,926
82,837
52,302
55,627
68,574
57,129
58,783
51,876
42
184
38,972
20,049
25,816
37,252
37,907
31,113
29,866
28,465
15
24
29,319
31,889
23,661
27,047
28,702
31,623
31,852
23,501
1,574
14,319
45,186
45,201
43,589
46,332
40,826
31,656
23,907
44,883
44,783
19
31,606
39,962
36,483
37,196
34,713
35,387
36,869
34,157
43,963
21
45,758
43,936
36,032
36,426
59,249
27,438
30,127
29,293
21
25
59,984
58,892
65,907
70,509
70,197
58,524
72,233
83,684
16
28
47,222
56,706
1,508
44,953
37,671
39,407
18,545
47,187
12
2
62,512
56,199
54,571
61,293
52,166
64,555
68,489
56,709
1
1
9,637,015
65.91%
denoisedF
47,641
40,628
23,912
42,072
42,584
46,582
54,610
47,976
16
2
35,110
30,466
28,193
37,119
34,320
27,711
33,794
29,578
36
15
33,080
33,659
30,634
26,359
31,992
35,371
38,408
32,904
46
48
35,346
24,422
38,915
39,739
46,711
42,840
65,903
46,208
13,015
15
42,250
7,481
48,585
45,316
36,272
44,693
40,276
56,424
8
3
43,835
8,400
36,912
51,474
43,674
42,381
43,376
42,343
796
44,006
54,750
49,711
46,473
46,249
40,048
41,060
55,993
40,594
2
3
43,247
34,182
42,652
34,115
54,768
32,502
42,643
22,507
11
15
64,297
32,919
58,617
55,314
42,549
37,083
68,133
12,471
18
4
34,396
33,953
35,775
36,623
29,984
64,459
78,796
43,431
43,778
24,733
49,342
43,909
35,805
41,042
46,646
34,414
39,042
30,447
12
4
55,777
65,290
67,230
52,340
54,945
45,110
53,141
60,197
70
62
69,824
66,388
58,632
56,941
55,556
56,962
77,549
70,696
26
26
14,084
13
38,308
53,291
32,178
45,004
37,414
34,042
21
6
45,961
52,325
37,636
45,304
28,971
36,762
36,752
40,861
1
2
18,396
20,997
29,755
37,274
12,276
46,970
18,267
45,170
13
14
52,931
42,974
53,180
41,439
48,063
49,813
37,571
46,107
36,930
28
56,074
40,398
30,685
67,297
35,047
70,061
76,127
51,613
34
17
75,832
82,742
52,220
55,535
68,503
57,058
58,730
51,764
25
168
38,831
20,000
25,714
37,113
37,756
31,054
29,775
28,352
2
8
29,200
31,769
23,565
26,984
28,562
31,494
31,763
23,411
1,542
14,282
45,081
45,080
43,465
46,230
40,755
31,531
23,828
44,797
44,679
5
31,373
39,892
36,433
37,149
34,463
35,303
36,825
34,093
43,867
7
45,624
43,773
35,883
36,363
59,145
27,267
30,066
29,197
14
16
59,901
58,676
65,703
70,419
70,017
58,391
72,169
83,614
10
24
47,070
56,621
1,503
44,858
37,545
39,365
18,516
47,031
7
2
62,426
56,067
54,523
61,217
52,056
64,464
68,418
56,645
1
1
9,610,443
65.73%
denoisedR
47,720
40,677
23,979
42,180
42,734
46,793
54,779
48,067
3
1
35,153
30,610
28,262
37,182
34,437
27,750
33,895
29,630
38
11
33,206
33,690
30,684
26,508
32,045
35,454
38,587
33,052
44
48
35,487
24,539
38,930
39,781
46,779
42,843
66,016
46,306
13,057
10
42,409
7,478
48,684
45,366
36,332
44,859
40,497
56,601
2
1
43,976
8,419
36,959
51,572
43,738
42,566
43,475
42,452
798
44,125
54,854
49,728
46,526
46,412
40,125
41,079
55,951
40,601
1
2
42,707
34,196
42,734
34,170
54,838
32,533
42,703
22,540
9
8
64,378
33,087
58,753
55,474
42,580
37,141
68,250
12,492
19
3
34,453
33,995
35,828
36,647
30,043
64,608
78,928
43,507
43,900
24,803
49,444
43,967
35,871
41,126
46,720
34,467
39,025
30,499
10
9
55,817
65,371
67,295
52,378
55,021
45,224
53,202
59,165
67
62
69,928
66,451
58,699
57,112
55,647
57,264
77,614
70,750
26
23
14,093
9
38,362
53,410
32,343
45,079
37,470
34,133
13
9
46,065
52,400
37,691
45,325
29,093
36,740
36,724
41,047
3
1
18,463
21,022
29,772
37,296
12,294
47,050
18,303
45,314
5
7
53,011
43,019
53,177
41,464
48,104
49,893
37,625
46,145
36,996
20
56,157
40,452
30,758
67,396
35,059
70,139
76,214
51,656
31
17
75,902
82,806
52,279
55,597
68,534
57,111
58,765
51,838
27
162
38,898
20,006
25,686
37,212
37,882
31,032
29,851
28,435
4
2
29,291
31,801
23,624
27,017
28,663
31,594
31,778
23,467
1,535
14,270
45,139
45,162
43,562
46,299
40,807
31,634
23,845
44,855
44,757
3
31,590
39,938
36,452
37,171
34,676
35,367
36,848
34,124
43,829
4
45,697
43,901
35,975
36,376
58,478
27,345
30,066
29,248
16
16
59,961
58,601
65,876
69,199
70,149
58,495
72,221
83,667
8
24
47,211
56,680
1,504
44,937
37,663
39,391
18,537
47,181
5
2
62,458
56,197
54,562
61,281
52,160
64,523
68,480
56,699
1
1
9,622,928
65.81%
merged
47,047
40,359
23,739
41,822
42,162
45,972
54,173
47,664
3
0
34,072
29,884
27,595
36,235
33,859
26,299
33,209
28,432
2
0
32,628
33,127
30,465
26,016
31,523
34,771
37,301
31,911
44
37
34,155
23,552
36,998
38,090
44,904
41,582
64,783
44,277
12,126
3
41,947
7,376
48,230
44,793
36,174
44,536
40,057
55,706
0
0
43,701
8,345
36,895
51,440
43,293
41,103
39,111
42,145
793
43,882
54,628
49,628
46,223
46,094
39,389
40,486
55,542
40,387
0
0
42,651
33,900
42,620
32,797
54,745
32,493
42,540
22,482
0
8
64,240
32,893
58,173
55,283
42,511
37,074
67,910
12,363
18
0
34,319
33,695
35,249
35,905
29,903
64,208
78,480
42,992
42,913
24,594
49,234
43,742
35,282
40,615
46,329
34,371
33,785
30,280
6
2
55,201
63,779
65,649
52,321
53,825
44,358
51,856
59,053
67
62
68,205
64,824
58,601
55,578
55,505
56,934
75,707
68,940
26
23
14,072
9
38,058
53,020
30,316
44,882
37,379
33,969
13
6
44,676
51,138
36,397
44,113
27,751
35,999
35,733
39,875
0
0
18,357
20,980
29,368
36,546
12,254
46,880
18,264
44,037
5
7
52,119
42,517
52,610
40,883
47,609
49,748
37,024
45,607
36,576
20
54,702
39,876
30,577
66,425
34,797
68,640
75,987
50,442
25
17
74,246
80,800
51,069
54,332
66,899
55,506
58,661
51,678
25
162
37,337
17,048
25,421
36,406
37,284
30,551
28,172
27,238
0
2
28,923
31,608
23,445
26,073
28,376
31,401
30,517
23,295
1,528
13,684
44,839
44,573
43,190
46,157
40,278
31,268
23,343
44,761
44,618
3
31,120
39,858
36,408
37,101
34,251
34,574
36,534
34,053
42,993
4
45,374
43,319
35,762
35,946
58,195
26,818
29,777
29,084
14
16
58,492
56,841
64,109
69,086
69,081
57,835
70,463
81,445
8
24
46,926
56,477
1,503
44,846
36,569
39,350
18,515
46,982
5
2
61,006
55,887
53,131
59,559
51,755
63,336
66,600
55,289
0
1
9,465,190
64.74%
nonchim
43,102
37,789
23,281
39,720
38,528
41,268
50,604
44,700
3
0
32,965
29,163
26,990
34,815
33,288
25,349
32,364
26,622
2
0
31,817
31,880
29,246
25,265
30,310
33,268
35,766
30,999
44
37
32,586
22,155
33,455
33,785
41,146
38,263
63,683
40,949
10,362
3
40,846
7,376
45,918
43,167
35,044
42,458
37,971
54,447
0
0
42,483
8,345
36,023
50,634
41,048
39,707
38,412
41,335
793
42,696
53,870
48,268
45,370
44,134
38,144
37,809
52,886
38,273
0
0
42,377
33,667
41,807
32,462
53,435
32,134
42,140
21,756
0
8
62,722
32,062
57,241
53,704
42,456
36,770
64,645
12,310
18
0
34,111
32,917
34,801
35,152
29,414
61,493
77,232
42,586
41,636
24,594
48,698
42,505
33,863
39,078
45,145
33,488
33,296
29,766
6
2
55,150
63,603
65,649
52,321
53,094
44,003
51,803
59,027
67
62
67,974
64,670
58,559
55,494
55,351
56,766
75,707
68,935
26
23
14,072
9
37,530
51,382
30,316
44,035
36,166
33,720
13
6
41,556
48,426
31,841
41,271
21,299
31,962
33,097
36,717
0
0
17,932
20,241
23,107
35,952
11,656
46,245
17,908
42,437
5
7
51,130
41,469
52,218
40,413
47,358
48,741
36,512
45,377
36,398
20
54,359
39,628
30,556
65,365
34,703
67,958
75,485
49,512
25
17
74,088
80,761
51,012
54,234
66,878
55,465
58,661
51,333
25
162
36,718
16,970
25,283
36,223
37,037
28,165
27,337
26,742
0
2
28,792
31,454
22,683
25,937
26,616
31,084
30,341
23,026
1,528
13,619
43,950
43,893
43,071
45,759
39,140
30,994
23,123
44,681
44,138
3
30,985
39,858
36,366
37,101
33,964
34,519
36,455
34,053
39,524
4
44,896
42,748
35,550
35,749
57,795
26,605
29,487
28,910
14
16
58,463
56,779
63,650
69,086
64,805
53,707
70,463
81,445
8
24
46,622
55,278
1,503
43,890
35,538
39,350
18,515
46,441
5
2
58,638
53,291
51,738
59,171
48,404
61,613
66,600
53,988
0
1
9,233,507
63.15%
This table can be downloaded as an Excel table below:
5. DADA2 Amplicon Sequence Variants (ASVs). A total of 2938 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 A set of 23,423 fungal ITS sequences representing
all named species (16,595 species) in UNITE’s database v7.1 (https://unite.ut.ee/repository.php; 22 August 2016 dynamic release; untrimmed sequences)
(Kõljalg 2013)
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.
Kõljalg U, Nilsson RH, Abarenkov K, Tedersoo L, Taylor AF, Bahram M, Bates ST, Bruns TD, Bengtsson-Palme J, Callaghan TM,
Douglas B, Drenkhan T, Eberhardt U, Dueñas M, Grebenc T, Griffith GW, Hartmann M, Kirk PM, Kohout P, Larsson E, Lindahl BD,
Lücking R, Martín MP, Matheny PB, Nguyen NH, Niskanen T, Oja J, Peay KG, Peintner U, Peterson M, Põldmaa K, Saag L, Saar I,
Schüßler A, Scott JA, Senés C, Smith ME, Suija A, Taylor DL, Telleria MT, Weiss M, Larsson KH. Towards a unified paradigm
for sequence-based identification of fungi. Mol Ecol. 2013 Nov;22(21):5271-7. doi: 10.1111/mec.12481. Epub 2013 Sep 24. PMID: 24112409.
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
Read Count (MC=1)*
Read Count (MC=100)*
A
Total reads
9,233,507
9,233,507
B
Total assigned reads
6,322,516
6,322,516
C
Assigned reads in species with read count < MC
0
4,647
D
Assigned reads in samples with read count < 500
3,903
3,630
E
Total samples
251
251
F
Samples with reads >= 500
211
211
G
Samples with reads < 500
40
40
H
Total assigned reads used for analysis (B-C-D)
6,318,613
6,314,239
I
Reads assigned to single species
5,653,481
5,651,837
J
Reads assigned to multiple species
159,074
159,074
K
Reads assigned to novel species
506,058
503,328
L
Total number of species
539
390
M
Number of single species
246
198
N
Number of multi-species
7
7
O
Number of novel species
265
183
P
Total unassigned reads
2,910,991
2,910,991
Q
Chimeric reads
28
28
R
Reads without BLASTN hits
2,894,535
2,894,535
S
Others: short, low quality, singletons, etc.
16,428
16,428
A=B+P=C+D+H+Q+R+S
E=F+G
B=C+D+H
H=I+J+K
L=M+N+O
P=Q+R+S
* MC = Minimal Count per species, species with total read count < MC were removed.
* The assignment result from MC=100 was used in the downstream analyses.
Read Taxonomy Assignment - Sample Meta Information
#SampleID
Sample_Name
Group1
Group2
Group3
Group4
F4468.S001
105.BV0.RBX
105
Baseline Visit
Right Buccal Mucosa
BV0-RBX
F4468.S002
105.BV0.LBX
105
Baseline Visit
Left Buccal Mucosa
BV0-LBX
F4468.S003
105.BV0.RLT
105
Baseline Visit
Right Tongue
BV0-RLT
F4468.S004
105.BV0.LLT
105
Baseline Visit
Left Tongue
BV0-LLT
F4468.S005
105.BV0.ULP
105
Baseline Visit
Upper Lip
BV0-ULP
F4468.S006
105.BV0.LLP
105
Baseline Visit
Lower Lip
BV0-LLP
F4468.S007
105.BV0.FOM
105
Baseline Visit
Floor of Mouth
BV0-FOM
F4468.S008
105.BV0.THR
105
Baseline Visit
Throat
BV0-THR
F4468.S009
105.BV0.SLP
105
Baseline Visit
Saline Rinse Pellet
BV0-SLP
F4468.S010
105.BV0.SSP
105
Baseline Visit
Stimulated Saliva Pellet
BV0-SSP
F4468.S011
121.BV0.RBX
121
Baseline Visit
Right Buccal Mucosa
BV0-RBX
F4468.S012
121.BV0.LBX
121
Baseline Visit
Left Buccal Mucosa
BV0-LBX
F4468.S013
121.BV0.RLT
121
Baseline Visit
Right Tongue
BV0-RLT
F4468.S014
121.BV0.LLT
121
Baseline Visit
Left Tongue
BV0-LLT
F4468.S015
121.BV0.ULP
121
Baseline Visit
Upper Lip
BV0-ULP
F4468.S016
121.BV0.LLP
121
Baseline Visit
Lower Lip
BV0-LLP
F4468.S017
121.BV0.FOM
121
Baseline Visit
Floor of Mouth
BV0-FOM
F4468.S018
121.BV0.THR
121
Baseline Visit
Throat
BV0-THR
F4468.S019
121.BV0.SLP
121
Baseline Visit
Saline Rinse Pellet
BV0-SLP
F4468.S020
121.BV0.SSP
121
Baseline Visit
Stimulated Saliva Pellet
BV0-SSP
F4468.S021
108.BV0.RBX
108
Baseline Visit
Right Buccal Mucosa
BV0-RBX
F4468.S022
108.BV0.LBX
108
Baseline Visit
Left Buccal Mucosa
BV0-LBX
F4468.S023
108.BV0.RLT
108
Baseline Visit
Right Tongue
BV0-RLT
F4468.S024
108.BV0.LLT
108
Baseline Visit
Left Tongue
BV0-LLT
F4468.S025
108.BV0.ULP
108
Baseline Visit
Upper Lip
BV0-ULP
F4468.S026
108.BV0.LLP
108
Baseline Visit
Lower Lip
BV0-LLP
F4468.S027
108.BV0.FOM
108
Baseline Visit
Floor of Mouth
BV0-FOM
F4468.S028
108.BV0.THR
108
Baseline Visit
Throat
BV0-THR
F4468.S029
108.BV0.SLP
108
Baseline Visit
Saline Rinse Pellet
BV0-SLP
F4468.S030
108.BV0.SSP
108
Baseline Visit
Stimulated Saliva Pellet
BV0-SSP
F4468.S031
108.FIV.RBX
108
Final Intervention Visit
Right Buccal Mucosa
FIV-RBX
F4468.S032
108.FIV.LBX
108
Final Intervention Visit
Left Buccal Mucosa
FIV-LBX
F4468.S033
108.FIV.RLT
108
Final Intervention Visit
Right Tongue
FIV-RLT
F4468.S034
108.FIV.LLT
108
Final Intervention Visit
Left Tongue
FIV-LLT
F4468.S035
108.FIV.ULP
108
Final Intervention Visit
Upper Lip
FIV-ULP
F4468.S036
108.FIV.LLP
108
Final Intervention Visit
Lower Lip
FIV-LLP
F4468.S037
108.FIV.FOM
108
Final Intervention Visit
Floor of Mouth
FIV-FOM
F4468.S038
108.FIV.THR
108
Final Intervention Visit
Throat
FIV-THR
F4468.S039
108.FIV.SLP
108
Final Intervention Visit
Saline Rinse Pellet
FIV-SLP
F4468.S040
108.FIV.SSP
108
Final Intervention Visit
Stimulated Saliva Pellet
FIV-SSP
F4468.S041
108.PIV.RBX
108
Post Intervention Visit
Right Buccal Mucosa
PIV-RBX
F4468.S042
108.PIV.LBX
108
Post Intervention Visit
Left Buccal Mucosa
PIV-LBX
F4468.S043
108.PIV.RLT
108
Post Intervention Visit
Right Tongue
PIV-RLT
F4468.S044
108.PIV.LLT
108
Post Intervention Visit
Left Tongue
PIV-LLT
F4468.S045
108.PIV.ULP
108
Post Intervention Visit
Upper Lip
PIV-ULP
F4468.S046
108.PIV.LLP
108
Post Intervention Visit
Lower Lip
PIV-LLP
F4468.S047
108.PIV.FOM
108
Post Intervention Visit
Floor of Mouth
PIV-FOM
F4468.S048
108.PIV.THR
108
Post Intervention Visit
Throat
PIV-THR
F4468.S049
108.PIV.SLP
108
Post Intervention Visit
Saline Rinse Pellet
PIV-SLP
F4468.S050
108.PIV.SSP
108
Post Intervention Visit
Stimulated Saliva Pellet
PIV-SSP
F4468.S051
105.FIV.RBX
105
Final Intervention Visit
Right Buccal Mucosa
FIV-RBX
F4468.S052
105.FIV.LBX
105
Final Intervention Visit
Left Buccal Mucosa
FIV-LBX
F4468.S053
105.FIV.RLT
105
Final Intervention Visit
Right Tongue
FIV-RLT
F4468.S054
105.FIV.LLT
105
Final Intervention Visit
Left Tongue
FIV-LLT
F4468.S055
105.FIV.ULP
105
Final Intervention Visit
Upper Lip
FIV-ULP
F4468.S056
105.FIV.LLP
105
Final Intervention Visit
Lower Lip
FIV-LLP
F4468.S057
105.FIV.FOM
105
Final Intervention Visit
Floor of Mouth
FIV-FOM
F4468.S058
105.FIV.THR
105
Final Intervention Visit
Throat
FIV-THR
F4468.S059
105.FIV.SLP
105
Final Intervention Visit
Saline Rinse Pellet
FIV-SLP
F4468.S060
105.FIV.SSP
105
Final Intervention Visit
Stimulated Saliva Pellet
FIV-SSP
F4468.S061
105.PIV.RBX
105
Post Intervention Visit
Right Buccal Mucosa
PIV-RBX
F4468.S062
105.PIV.LBX
105
Post Intervention Visit
Left Buccal Mucosa
PIV-LBX
F4468.S063
105.PIV.RLT
105
Post Intervention Visit
Right Tongue
PIV-RLT
F4468.S064
105.PIV.LLT
105
Post Intervention Visit
Left Tongue
PIV-LLT
F4468.S065
105.PIV.ULP
105
Post Intervention Visit
Upper Lip
PIV-ULP
F4468.S066
105.PIV.LLP
105
Post Intervention Visit
Lower Lip
PIV-LLP
F4468.S067
105.PIV.FOM
105
Post Intervention Visit
Floor of Mouth
PIV-FOM
F4468.S068
105.PIV.THR
105
Post Intervention Visit
Throat
PIV-THR
F4468.S069
105.PIV.SLP
105
Post Intervention Visit
Saline Rinse Pellet
PIV-SLP
F4468.S070
105.PIV.SSP
105
Post Intervention Visit
Stimulated Saliva Pellet
PIV-SSP
F4468.S071
110.BV0.RBX
110
Baseline Visit
Right Buccal Mucosa
BV0-RBX
F4468.S072
110.BV0.LBX
110
Baseline Visit
Left Buccal Mucosa
BV0-LBX
F4468.S073
110.BV0.RLT
110
Baseline Visit
Right Tongue
BV0-RLT
F4468.S074
110.BV0.LLT
110
Baseline Visit
Left Tongue
BV0-LLT
F4468.S075
110.BV0.ULP
110
Baseline Visit
Upper Lip
BV0-ULP
F4468.S076
110.BV0.LLP
110
Baseline Visit
Lower Lip
BV0-LLP
F4468.S077
110.BV0.FOM
110
Baseline Visit
Floor of Mouth
BV0-FOM
F4468.S078
110.BV0.THR
110
Baseline Visit
Throat
BV0-THR
F4468.S079
110.BV0.SLP
110
Baseline Visit
Saline Rinse Pellet
BV0-SLP
F4468.S080
110.BV0.SSP
110
Baseline Visit
Stimulated Saliva Pellet
BV0-SSP
F4468.S081
110.FIV.RBX
110
Final Intervention Visit
Right Buccal Mucosa
FIV-RBX
F4468.S082
110.FIV.LBX
110
Final Intervention Visit
Left Buccal Mucosa
FIV-LBX
F4468.S083
110.FIV.RLT
110
Final Intervention Visit
Right Tongue
FIV-RLT
F4468.S084
110.FIV.LLT
110
Final Intervention Visit
Left Tongue
FIV-LLT
F4468.S085
110.FIV.ULP
110
Final Intervention Visit
Upper Lip
FIV-ULP
F4468.S086
110.FIV.LLP
110
Final Intervention Visit
Lower Lip
FIV-LLP
F4468.S087
110.FIV.FOM
110
Final Intervention Visit
Floor of Mouth
FIV-FOM
F4468.S088
110.FIV.THR
110
Final Intervention Visit
Throat
FIV-THR
F4468.S089
110.FIV.SLP
110
Final Intervention Visit
Saline Rinse Pellet
FIV-SLP
F4468.S090
110.FIV.SSP
110
Final Intervention Visit
Stimulated Saliva Pellet
FIV-SSP
F4468.S091
110.PIV.RBX
110
Post Intervention Visit
Right Buccal Mucosa
PIV-RBX
F4468.S092
110.PIV.LBX
110
Post Intervention Visit
Left Buccal Mucosa
PIV-LBX
F4468.S093
110.PIV.RLT
110
Post Intervention Visit
Right Tongue
PIV-RLT
F4468.S094
110.PIV.LLT
110
Post Intervention Visit
Left Tongue
PIV-LLT
F4468.S095
110.PIV.ULP
110
Post Intervention Visit
Upper Lip
PIV-ULP
F4468.S096
110.PIV.LLP
110
Post Intervention Visit
Lower Lip
PIV-LLP
F4468.S097
110.PIV.FOM
110
Post Intervention Visit
Floor of Mouth
PIV-FOM
F4468.S098
110.PIV.THR
110
Post Intervention Visit
Throat
PIV-THR
F4468.S099
110.PIV.SLP
110
Post Intervention Visit
Saline Rinse Pellet
PIV-SLP
F4468.S100
110.PIV.SSP
110
Post Intervention Visit
Stimulated Saliva Pellet
PIV-SSP
F4468.S101
118.BV0.RBX
118
Baseline Visit
Right Buccal Mucosa
BV0-RBX
F4468.S102
118.BV0.LBX
118
Baseline Visit
Left Buccal Mucosa
BV0-LBX
F4468.S103
118.BV0.RLT
118
Baseline Visit
Right Tongue
BV0-RLT
F4468.S104
118.BV0.LLT
118
Baseline Visit
Left Tongue
BV0-LLT
F4468.S105
118.BV0.ULP
118
Baseline Visit
Upper Lip
BV0-ULP
F4468.S106
118.BV0.LLP
118
Baseline Visit
Lower Lip
BV0-LLP
F4468.S107
118.BV0.FOM
118
Baseline Visit
Floor of Mouth
BV0-FOM
F4468.S108
118.BV0.THR
118
Baseline Visit
Throat
BV0-THR
F4468.S109
118.BV0.SLP
118
Baseline Visit
Saline Rinse Pellet
BV0-SLP
F4468.S110
118.BV0.SSP
118
Baseline Visit
Stimulated Saliva Pellet
BV0-SSP
F4468.S111
118.FIV.RBX
118
Final Intervention Visit
Right Buccal Mucosa
FIV-RBX
F4468.S112
118.FIV.LBX
118
Final Intervention Visit
Left Buccal Mucosa
FIV-LBX
F4468.S113
118.FIV.RLT
118
Final Intervention Visit
Right Tongue
FIV-RLT
F4468.S114
118.FIV.LLT
118
Final Intervention Visit
Left Tongue
FIV-LLT
F4468.S115
118.FIV.ULP
118
Final Intervention Visit
Upper Lip
FIV-ULP
F4468.S116
118.FIV.LLP
118
Final Intervention Visit
Lower Lip
FIV-LLP
F4468.S117
118.FIV.FOM
118
Final Intervention Visit
Floor of Mouth
FIV-FOM
F4468.S118
118.FIV.THR
118
Final Intervention Visit
Throat
FIV-THR
F4468.S119
118.FIV.SLP
118
Final Intervention Visit
Saline Rinse Pellet
FIV-SLP
F4468.S120
118.FIV.SSP
118
Final Intervention Visit
Stimulated Saliva Pellet
FIV-SSP
F4468.S121
118.PIV.RBX
118
Post Intervention Visit
Right Buccal Mucosa
PIV-RBX
F4468.S122
118.PIV.LBX
118
Post Intervention Visit
Left Buccal Mucosa
PIV-LBX
F4468.S123
118.PIV.RLT
118
Post Intervention Visit
Right Tongue
PIV-RLT
F4468.S124
118.PIV.LLT
118
Post Intervention Visit
Left Tongue
PIV-LLT
F4468.S125
118.PIV.ULP
118
Post Intervention Visit
Upper Lip
PIV-ULP
F4468.S126
118.PIV.LLP
118
Post Intervention Visit
Lower Lip
PIV-LLP
F4468.S127
118.PIV.FOM
118
Post Intervention Visit
Floor of Mouth
PIV-FOM
F4468.S128
118.PIV.THR
118
Post Intervention Visit
Throat
PIV-THR
F4468.S129
118.PIV.SLP
118
Post Intervention Visit
Saline Rinse Pellet
PIV-SLP
F4468.S130
118.PIV.SSP
118
Post Intervention Visit
Stimulated Saliva Pellet
PIV-SSP
F4468.S131
114.BV0.RBX
114
Baseline Visit
Right Buccal Mucosa
BV0-RBX
F4468.S132
114.BV0.LBX
114
Baseline Visit
Left Buccal Mucosa
BV0-LBX
F4468.S133
114.BV0.RLT
114
Baseline Visit
Right Tongue
BV0-RLT
F4468.S134
114.BV0.LLT
114
Baseline Visit
Left Tongue
BV0-LLT
F4468.S135
114.BV0.ULP
114
Baseline Visit
Upper Lip
BV0-ULP
F4468.S136
114.BV0.LLP
114
Baseline Visit
Lower Lip
BV0-LLP
F4468.S137
114.BV0.FOM
114
Baseline Visit
Floor of Mouth
BV0-FOM
F4468.S138
114.BV0.THR
114
Baseline Visit
Throat
BV0-THR
F4468.S139
114.BV0.SLP
114
Baseline Visit
Saline Rinse Pellet
BV0-SLP
F4468.S140
114.BV0.SSP
114
Baseline Visit
Stimulated Saliva Pellet
BV0-SSP
F4468.S141
114.FIV.RBX
114
Final Intervention Visit
Right Buccal Mucosa
FIV-RBX
F4468.S142
114.FIV.LBX
114
Final Intervention Visit
Left Buccal Mucosa
FIV-LBX
F4468.S143
114.FIV.RLT
114
Final Intervention Visit
Right Tongue
FIV-RLT
F4468.S144
114.FIV.LLT
114
Final Intervention Visit
Left Tongue
FIV-LLT
F4468.S145
114.FIV.ULP
114
Final Intervention Visit
Upper Lip
FIV-ULP
F4468.S146
114.FIV.LLP
114
Final Intervention Visit
Lower Lip
FIV-LLP
F4468.S147
114.FIV.FOM
114
Final Intervention Visit
Floor of Mouth
FIV-FOM
F4468.S148
114.FIV.THR
114
Final Intervention Visit
Throat
FIV-THR
F4468.S149
114.FIV.SLP
114
Final Intervention Visit
Saline Rinse Pellet
FIV-SLP
F4468.S150
114.FIV.SSP
114
Final Intervention Visit
Stimulated Saliva Pellet
FIV-SSP
F4468.S151
114.PIV.RBX
114
Post Intervention Visit
Right Buccal Mucosa
PIV-RBX
F4468.S152
114.PIV.LBX
114
Post Intervention Visit
Left Buccal Mucosa
PIV-LBX
F4468.S153
114.PIV.RLT
114
Post Intervention Visit
Right Tongue
PIV-RLT
F4468.S154
114.PIV.LLT
114
Post Intervention Visit
Left Tongue
PIV-LLT
F4468.S155
114.PIV.ULP
114
Post Intervention Visit
Upper Lip
PIV-ULP
F4468.S156
114.PIV.LLP
114
Post Intervention Visit
Lower Lip
PIV-LLP
F4468.S157
114.PIV.FOM
114
Post Intervention Visit
Floor of Mouth
PIV-FOM
F4468.S158
114.PIV.THR
114
Post Intervention Visit
Throat
PIV-THR
F4468.S159
114.PIV.SLP
114
Post Intervention Visit
Saline Rinse Pellet
PIV-SLP
F4468.S160
114.PIV.SSP
114
Post Intervention Visit
Stimulated Saliva Pellet
PIV-SSP
F4468.S161
120.BV0.RBX
120
Baseline Visit
Right Buccal Mucosa
BV0-RBX
F4468.S162
120.BV0.LBX
120
Baseline Visit
Left Buccal Mucosa
BV0-LBX
F4468.S163
120.BV0.RLT
120
Baseline Visit
Right Tongue
BV0-RLT
F4468.S164
120.BV0.LLT
120
Baseline Visit
Left Tongue
BV0-LLT
F4468.S165
120.BV0.ULP
120
Baseline Visit
Upper Lip
BV0-ULP
F4468.S166
120.BV0.LLP
120
Baseline Visit
Lower Lip
BV0-LLP
F4468.S167
120.BV0.FOM
120
Baseline Visit
Floor of Mouth
BV0-FOM
F4468.S168
120.BV0.THR
120
Baseline Visit
Throat
BV0-THR
F4468.S169
120.BV0.SLP
120
Baseline Visit
Saline Rinse Pellet
BV0-SLP
F4468.S170
120.BV0.SSP
120
Baseline Visit
Stimulated Saliva Pellet
BV0-SSP
F4468.S171
120.FIV.RBX
120
Final Intervention Visit
Right Buccal Mucosa
FIV-RBX
F4468.S172
120.FIV.LBX
120
Final Intervention Visit
Left Buccal Mucosa
FIV-LBX
F4468.S173
120.FIV.RLT
120
Final Intervention Visit
Right Tongue
FIV-RLT
F4468.S174
120.FIV.LLT
120
Final Intervention Visit
Left Tongue
FIV-LLT
F4468.S175
120.FIV.ULP
120
Final Intervention Visit
Upper Lip
FIV-ULP
F4468.S176
120.FIV.LLP
120
Final Intervention Visit
Lower Lip
FIV-LLP
F4468.S177
120.FIV.FOM
120
Final Intervention Visit
Floor of Mouth
FIV-FOM
F4468.S178
120.FIV.THR
120
Final Intervention Visit
Throat
FIV-THR
F4468.S179
120.FIV.SLP
120
Final Intervention Visit
Saline Rinse Pellet
FIV-SLP
F4468.S180
120.FIV.SSP
120
Final Intervention Visit
Stimulated Saliva Pellet
FIV-SSP
F4468.S181
120.PIV.RBX
120
Post Intervention Visit
Right Buccal Mucosa
PIV-RBX
F4468.S182
120.PIV.LBX
120
Post Intervention Visit
Left Buccal Mucosa
PIV-LBX
F4468.S183
120.PIV.RLT
120
Post Intervention Visit
Right Tongue
PIV-RLT
F4468.S184
120.PIV.LLT
120
Post Intervention Visit
Left Tongue
PIV-LLT
F4468.S185
120.PIV.ULP
120
Post Intervention Visit
Upper Lip
PIV-ULP
F4468.S186
120.PIV.LLP
120
Post Intervention Visit
Lower Lip
PIV-LLP
F4468.S187
120.PIV.FOM
120
Post Intervention Visit
Floor of Mouth
PIV-FOM
F4468.S188
120.PIV.THR
120
Post Intervention Visit
Throat
PIV-THR
F4468.S189
120.PIV.SLP
120
Post Intervention Visit
Saline Rinse Pellet
PIV-SLP
F4468.S190
120.PIV.SSP
120
Post Intervention Visit
Stimulated Saliva Pellet
PIV-SSP
F4468.S191
121.FIV.RBX
121
Final Intervention Visit
Right Buccal Mucosa
FIV-RBX
F4468.S192
121.FIV.LBX
121
Final Intervention Visit
Left Buccal Mucosa
FIV-LBX
F4468.S193
121.FIV.RLT
121
Final Intervention Visit
Right Tongue
FIV-RLT
F4468.S194
121.FIV.LLT
121
Final Intervention Visit
Left Tongue
FIV-LLT
F4468.S195
121.FIV.ULP
121
Final Intervention Visit
Upper Lip
FIV-ULP
F4468.S196
121.FIV.LLP
121
Final Intervention Visit
Lower Lip
FIV-LLP
F4468.S197
121.FIV.FOM
121
Final Intervention Visit
Floor of Mouth
FIV-FOM
F4468.S198
121.FIV.THR
121
Final Intervention Visit
Throat
FIV-THR
F4468.S199
121.FIV.SLP
121
Final Intervention Visit
Saline Rinse Pellet
FIV-SLP
F4468.S200
121.FIV.SSP
121
Final Intervention Visit
Stimulated Saliva Pellet
FIV-SSP
F4468.S201
121.PIV.RBX
121
Post Intervention Visit
Right Buccal Mucosa
PIV-RBX
F4468.S202
121.PIV.LBX
121
Post Intervention Visit
Left Buccal Mucosa
PIV-LBX
F4468.S203
121.PIV.RLT
121
Post Intervention Visit
Right Tongue
PIV-RLT
F4468.S204
121.PIV.LLT
121
Post Intervention Visit
Left Tongue
PIV-LLT
F4468.S205
121.PIV.ULP
121
Post Intervention Visit
Upper Lip
PIV-ULP
F4468.S206
121.PIV.LLP
121
Post Intervention Visit
Lower Lip
PIV-LLP
F4468.S207
121.PIV.FOM
121
Post Intervention Visit
Floor of Mouth
PIV-FOM
F4468.S208
121.PIV.THR
121
Post Intervention Visit
Throat
PIV-THR
F4468.S209
121.PIV.SLP
121
Post Intervention Visit
Saline Rinse Pellet
PIV-SLP
F4468.S210
121.PIV.SSP
121
Post Intervention Visit
Stimulated Saliva Pellet
PIV-SSP
F4468.S211
122.BV0.RBX
122
Baseline Visit
Right Buccal Mucosa
BV0-RBX
F4468.S212
122.BV0.LBX
122
Baseline Visit
Left Buccal Mucosa
BV0-LBX
F4468.S213
122.BV0.RLT
122
Baseline Visit
Right Tongue
BV0-RLT
F4468.S214
122.BV0.LLT
122
Baseline Visit
Left Tongue
BV0-LLT
F4468.S215
122.BV0.ULP
122
Baseline Visit
Upper Lip
BV0-ULP
F4468.S216
122.BV0.LLP
122
Baseline Visit
Lower Lip
BV0-LLP
F4468.S217
122.BV0.FOM
122
Baseline Visit
Floor of Mouth
BV0-FOM
F4468.S218
122.BV0.THR
122
Baseline Visit
Throat
BV0-THR
F4468.S219
122.BV0.SLP
122
Baseline Visit
Saline Rinse Pellet
BV0-SLP
F4468.S220
122.BV0.SSP
122
Baseline Visit
Stimulated Saliva Pellet
BV0-SSP
F4468.S221
122.FIV.RBX
122
Final Intervention Visit
Right Buccal Mucosa
FIV-RBX
F4468.S222
122.FIV.LBX
122
Final Intervention Visit
Left Buccal Mucosa
FIV-LBX
F4468.S223
122.FIV.RLT
122
Final Intervention Visit
Right Tongue
FIV-RLT
F4468.S224
122.FIV.LLT
122
Final Intervention Visit
Left Tongue
FIV-LLT
F4468.S225
122.FIV.ULP
122
Final Intervention Visit
Upper Lip
FIV-ULP
F4468.S226
122.FIV.LLP
122
Final Intervention Visit
Lower Lip
FIV-LLP
F4468.S227
122.FIV.FOM
122
Final Intervention Visit
Floor of Mouth
FIV-FOM
F4468.S228
122.FIV.THR
122
Final Intervention Visit
Throat
FIV-THR
F4468.S229
122.FIV.SLP
122
Final Intervention Visit
Saline Rinse Pellet
FIV-SLP
F4468.S230
122.FIV.SSP
122
Final Intervention Visit
Stimulated Saliva Pellet
FIV-SSP
F4468.S231
122.PIV.RBX
122
Post Intervention Visit
Right Buccal Mucosa
PIV-RBX
F4468.S232
122.PIV.LBX
122
Post Intervention Visit
Left Buccal Mucosa
PIV-LBX
F4468.S233
122.PIV.RLT
122
Post Intervention Visit
Right Tongue
PIV-RLT
F4468.S234
122.PIV.LLT
122
Post Intervention Visit
Left Tongue
PIV-LLT
F4468.S235
122.PIV.ULP
122
Post Intervention Visit
Upper Lip
PIV-ULP
F4468.S236
122.PIV.LLP
122
Post Intervention Visit
Lower Lip
PIV-LLP
F4468.S237
122.PIV.FOM
122
Post Intervention Visit
Floor of Mouth
PIV-FOM
F4468.S238
122.PIV.THR
122
Post Intervention Visit
Throat
PIV-THR
F4468.S239
122.PIV.SLP
122
Post Intervention Visit
Saline Rinse Pellet
PIV-SLP
F4468.S240
122.PIV.SSP
122
Post Intervention Visit
Stimulated Saliva Pellet
PIV-SSP
F4468.S241
126.BV0.RBX
126
Baseline Visit
Right Buccal Mucosa
BV0-RBX
F4468.S242
126.BV0.LBX
126
Baseline Visit
Left Buccal Mucosa
BV0-LBX
F4468.S243
126.BV0.RLT
126
Baseline Visit
Right Tongue
BV0-RLT
F4468.S244
126.BV0.LLT
126
Baseline Visit
Left Tongue
BV0-LLT
F4468.S245
126.BV0.ULP
126
Baseline Visit
Upper Lip
BV0-ULP
F4468.S246
126.BV0.LLP
126
Baseline Visit
Lower Lip
BV0-LLP
F4468.S247
126.BV0.FOM
126
Baseline Visit
Floor of Mouth
BV0-FOM
F4468.S248
126.BV0.THR
126
Baseline Visit
Throat
BV0-THR
F4468.S249
126.BV0.SLP
126
Baseline Visit
Saline Rinse Pellet
BV0-SLP
F4468.S250
126.BV0.SSP
126
Baseline Visit
Stimulated Saliva Pellet
BV0-SSP
F4468.S251
126.FIV.RBX
126
Final Intervention Visit
Right Buccal Mucosa
FIV-RBX
F4468.S252
126.FIV.LBX
126
Final Intervention Visit
Left Buccal Mucosa
FIV-LBX
F4468.S253
126.FIV.RLT
126
Final Intervention Visit
Right Tongue
FIV-RLT
F4468.S254
126.FIV.LLT
126
Final Intervention Visit
Left Tongue
FIV-LLT
F4468.S255
126.FIV.ULP
126
Final Intervention Visit
Upper Lip
FIV-ULP
F4468.S256
126.FIV.LLP
126
Final Intervention Visit
Lower Lip
FIV-LLP
F4468.S257
126.FIV.FOM
126
Final Intervention Visit
Floor of Mouth
FIV-FOM
F4468.S258
126.FIV.THR
126
Final Intervention Visit
Throat
FIV-THR
F4468.S259
126.FIV.SLP
126
Final Intervention Visit
Saline Rinse Pellet
FIV-SLP
F4468.S260
126.FIV.SSP
126
Final Intervention Visit
Stimulated Saliva Pellet
FIV-SSP
F4468.S261
126.PIV.RBX
126
Post Intervention Visit
Right Buccal Mucosa
PIV-RBX
F4468.S262
126.PIV.LBX
126
Post Intervention Visit
Left Buccal Mucosa
PIV-LBX
F4468.S263
126.PIV.RLT
126
Post Intervention Visit
Right Tongue
PIV-RLT
F4468.S264
126.PIV.LLT
126
Post Intervention Visit
Left Tongue
PIV-LLT
F4468.S265
126.PIV.ULP
126
Post Intervention Visit
Upper Lip
PIV-ULP
F4468.S266
126.PIV.LLP
126
Post Intervention Visit
Lower Lip
PIV-LLP
F4468.S267
126.PIV.FOM
126
Post Intervention Visit
Floor of Mouth
PIV-FOM
F4468.S268
126.PIV.THR
126
Post Intervention Visit
Throat
PIV-THR
F4468.S269
126.PIV.SLP
126
Post Intervention Visit
Saline Rinse Pellet
PIV-SLP
F4468.S270
126.PIV.SSP
126
Post Intervention Visit
Stimulated Saliva Pellet
PIV-SSP
Read Taxonomy Assignment - ASV Read Counts by Samples
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).
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 analysis by rarefaction
Diversity measures are affected by the sampling depth. Rarefaction is a technique to assess species richness from the results of sampling. Rarefaction allows
the calculation of species richness for a given number of individual samples, based on the construction
of so-called rarefaction curves. This curve is a plot of the number of species as a function of the
number of samples. Rarefaction curves generally grow rapidly at first, as the most common species are found,
but the curves plateau as only the rarest species remain to be sampled.
Beta diversity compares the similarity (or dissimilarity) of microbial profiles between different
groups of samples. There are many different similarity/dissimilarity metrics.
In general, they can be quantitative (using sequence abundance, e.g., Bray-Curtis or weighted UniFrac)
or binary (considering only presence-absence of sequences, e.g., binary Jaccard or unweighted UniFrac).
They can be even based on phylogeny (e.g., UniFrac metrics) or not (non-UniFrac metrics, such as Bray-Curtis, etc.).
For microbiome studies, species profiles of samples can be compared with the Bray-Curtis dissimilarity,
which is based on the count data type. The pair-wise Bray-Curtis dissimilarity matrix of all samples can then be
subject to either multi-dimensional scaling (MDS, also known as PCoA) or non-metric MDS (NMDS).
MDS/PCoA is a
scaling or ordination method that starts with a matrix of similarities or dissimilarities
between a set of samples and aims to produce a low-dimensional graphical plot of the data
in such a way that distances between points in the plot are close to original dissimilarities.
NMDS is similar to MDS, however it does not use the dissimilarities data, instead it converts them into
the ranks and use these ranks in the calculation.
In our beta diversity analysis, Bray-Curtis dissimilarity matrix was first calculated and then plotted by the PCoA and
NMDS separately. The results are shown below:
The above PCoA and NMDS plots are based on count data. The count data can also be transformed into centered log ratio (CLR)
for each species. The CLR data is no longer count data and cannot be used in Bray-Curtis dissimilarity calculation. Instead
CLR can be compared with Euclidean distances. When CLR data are compared by Euclidean distance, the distance is also called
Aitchison distance.
Below are the NMDS and PCoA plots of the Aitchison distances of the samples:
Interactive 3D PCoA Plots - Bray-Curtis Dissimilarity
Interactive 3D PCoA Plots - Euclidean Distance
Interactive 3D PCoA Plots - Correlation Coefficients
16S rRNA next generation sequencing (NGS) generates a fixed number of reads that reflect the proportion of different species in a sample, i.e., the relative abundance of species, instead of the absolute abundance. In Mathematics, measurements involving probabilities, proportions, percentages, and ppm can all be thought of as compositional data. This makes the microbiome read count data “compositional” (Gloor et al, 2017). In general, compositional data represent parts of a whole which only carry relative information (http://www.compositionaldata.com/).
The problem of microbiome data being compositional arises when comparing two groups of samples for identifying “differentially abundant” species. A species with the same absolute abundance between two conditions, its relative abundances in the two conditions (e.g., percent abundance) can become different if the relative abundance of other species change greatly. This problem can lead to incorrect conclusion in terms of differential abundance for microbial species in the samples.
When studying differential abundance (DA), the current better approach is to transform the read count data into log ratio data. The ratios are calculated between read counts of all species in a sample to a “reference” count (e.g., mean read count of the sample). The log ratio data allow the detection of DA species without being affected by percentage bias mentioned above
In this report, a compositional DA analysis tool “ANCOM” (analysis of composition of microbiomes) was used. ANCOM transforms the count data into log-ratios and thus is more suitable for comparing the composition of microbiomes in two or more populations
References:
Gloor GB, Macklaim JM, Pawlowsky-Glahn V, Egozcue JJ. Microbiome Datasets Are Compositional: And This Is Not Optional. Front Microbiol.
2017 Nov 15;8:2224. doi: 10.3389/fmicb.2017.02224. PMID: 29187837; PMCID: PMC5695134.
Mandal S, Van Treuren W, White RA, Eggesbø M, Knight R, Peddada SD. Analysis of composition of
microbiomes: a novel method for studying microbial composition. Microb Ecol Health Dis.
2015 May 29;26:27663. doi: 10.3402/mehd.v26.27663. PMID: 26028277; PMCID: PMC4450248.
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.
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.
