Project FOMC29353 services include NGS sequencing of the V1V3 region of the 16S rRNA gene amplicons from the samples. First and foremost, please
download this report, as well as the sequence raw data from the download links provided below.
These links will expire after 60 days. We cannot guarantee the availability of your data after 60 days.
Full Bioinformatics analysis service was requested. We provide many analyses, starting from the raw sequence quality and noise filtering, pair reads merging, as well as chimera filtering for the sequences, using the
DADA2 denosing algorithm and pipeline.
We also provide many downstream analyses such as taxonomy assignment, alpha and beta diversity analyses, and differential abundance analysis.
For taxonomy assignment, most informative would be the taxonomy barplots. We provide an interactive barplots to show the relative abundance of microbes at different taxonomy levels (from Phylum to species) that you can choose.
If you specify which groups of samples you want to compare for differential abundance, we provide both ANCOM and LEfSe differential abundance analysis.
The samples were processed and analyzed with the ZymoBIOMICS® Service: Targeted
Metagenomic Sequencing (Zymo Research, Irvine, CA).
DNA Extraction: If DNA extraction was performed, the following DNA
extraction kit was used according to the manufacturer’s instructions:
☑
ZymoBIOMICS®-96 MagBead DNA Kit (Zymo Research, Irvine, CA)
☐
N/A (DNA Extraction Not Performed)
Elution Volume: 50µL
Additional Notes: NA
Targeted Library Preparation: The DNA samples were prepared for targeted
sequencing with the Quick-16S™ NGS Library Prep Kit (Zymo Research, Irvine, CA).
These primers were custom designed by Zymo Research to provide the best coverage
of the 16S gene while maintaining high sensitivity. The primer sets used in this project
are marked below:
☐
Quick-16S™ Primer Set V1-V2 (Zymo Research, Irvine, CA)
☑
Quick-16S™ Primer Set V1-V3 (Zymo Research, Irvine, CA)
☐
Quick-16S™ Primer Set V3-V4 (Zymo Research, Irvine, CA)
☐
Quick-16S™ Primer Set V4 (Zymo Research, Irvine, CA)
☐
Quick-16S™ Primer Set V6-V8 (Zymo Research, Irvine, CA)
Additional Notes: NA
The sequencing library was prepared using an innovative library preparation process in
which PCR reactions were performed in real-time PCR machines to control cycles and
therefore limit PCR chimera formation. The final PCR products were quantified with
qPCR fluorescence readings and pooled together based on equal molarity. The final
pooled library was cleaned up with the Select-a-Size DNA Clean & Concentrator™
(Zymo Research, Irvine, CA), then quantified with TapeStation® (Agilent Technologies,
Santa Clara, CA) and Qubit® (Thermo Fisher Scientific, Waltham, WA).
Control Samples: The ZymoBIOMICS® Microbial Community Standard (Zymo
Research, Irvine, CA) was used as a positive control for each DNA extraction, if
performed. The ZymoBIOMICS® Microbial Community DNA Standard (Zymo Research,
Irvine, CA) was used as a positive control for each targeted library preparation.
Negative controls (i.e. blank extraction control, blank library preparation control) were
included to assess the level of bioburden carried by the wet-lab process.
Sequencing: The final library was sequenced on Illumina® NextSeq 2000™ with a p1
(Illumina, Sand Diego, CA) reagent kit (600 cycles). The sequencing was performed
with 25% PhiX spike-in.
Absolute Abundance Quantification*: A quantitative real-time PCR was set up with a
standard curve. The standard curve was made with plasmid DNA containing one copy
of the 16S gene and one copy of the fungal ITS2 region prepared in 10-fold serial
dilutions. The primers used were the same as those used in Targeted Library
Preparation. The equation generated by the plasmid DNA standard curve was used to
calculate the number of gene copies in the reaction for each sample. The PCR input
volume (2 µl) was used to calculate the number of gene copies per microliter in each
DNA sample.
The number of genome copies per microliter DNA sample was calculated by dividing
the gene copy number by an assumed number of gene copies per genome. The value
used for 16S copies per genome is 4. The value used for ITS copies per genome is 200.
The amount of DNA per microliter DNA sample was calculated using an assumed
genome size of 4.64 x 106 bp, the genome size of Escherichia coli, for 16S samples, or
an assumed genome size of 1.20 x 107 bp, the genome size of Saccharomyces
cerevisiae, for ITS samples. This calculation is shown below:
Calculated Total DNA = Calculated Total Genome Copies × Assumed Genome Size (4.64 × 106 bp) ×
Average Molecular Weight of a DNA bp (660 g/mole/bp) ÷ Avogadro’s Number (6.022 x 1023/mole)
* Absolute Abundance Quantification is only available for 16S and ITS analyses.
The absolute abundance standard curve data can be viewed in Excel here:
The absolute abundance standard curve is shown below:
The complete report of your project, including all links in this report, can be downloaded by clicking the link provided below. The downloaded file is a compressed ZIP file and once unzipped, open the file “REPORT.html” (may only shown as "REPORT" in your computer) by double clicking it. Your default web browser will open it and you will see the exact content of this report.
Please download and save the file to your computer storage device. The download link will expire after 60 days upon your receiving of this report.
Complete report download link:
To view the report, please follow the following steps:
1.
Download the .zip file from the report link above.
2.
Extract all the contents of the downloaded .zip file to your desktop.
3.
Open the extracted folder and find the "REPORT.html" (may shown as only "REPORT").
4.
Open (double-clicking) the REPORT.html file. Your default browser will open the top age of the complete report. Within the
report, there are links to view all the analyses performed for the project.
The raw NGS sequence data is available for download with the link provided below. The data is a compressed ZIP file and can be unzipped to individual sequence files.
Since this is a Pac-Bio full-length (V1V9) 16S rRNA amplicon sequencing, raw sequences are available for download in a single compressed zip file in the download link below.
After unzipping, you will find individual sequence files for each of your samples with the file extension “*.fastq.gz”.
The files are in FASTQ format and are compressed. FASTQ format is a text-based data format for storing both a biological sequence
and its corresponding quality scores. Most sequence analysis software will be able to open them.
The Sample IDs associated with the fastq files are listed in the table below:
Sample ID
Original Sample ID
Read 1 File Name
Read 2 File Name
F29353.S100
original sample ID here
zr29353_100V1V3_R1.fastq.gz
zr29353_100V1V3_R2.fastq.gz
F29353.S101
original sample ID here
zr29353_101V1V3_R1.fastq.gz
zr29353_101V1V3_R2.fastq.gz
F29353.S102
original sample ID here
zr29353_102V1V3_R1.fastq.gz
zr29353_102V1V3_R2.fastq.gz
F29353.S103
original sample ID here
zr29353_103V1V3_R1.fastq.gz
zr29353_103V1V3_R2.fastq.gz
F29353.S104
original sample ID here
zr29353_104V1V3_R1.fastq.gz
zr29353_104V1V3_R2.fastq.gz
F29353.S105
original sample ID here
zr29353_105V1V3_R1.fastq.gz
zr29353_105V1V3_R2.fastq.gz
F29353.S106
original sample ID here
zr29353_106V1V3_R1.fastq.gz
zr29353_106V1V3_R2.fastq.gz
F29353.S107
original sample ID here
zr29353_107V1V3_R1.fastq.gz
zr29353_107V1V3_R2.fastq.gz
F29353.S108
original sample ID here
zr29353_108V1V3_R1.fastq.gz
zr29353_108V1V3_R2.fastq.gz
F29353.S109
original sample ID here
zr29353_109V1V3_R1.fastq.gz
zr29353_109V1V3_R2.fastq.gz
F29353.S010
original sample ID here
zr29353_10V1V3_R1.fastq.gz
zr29353_10V1V3_R2.fastq.gz
F29353.S110
original sample ID here
zr29353_110V1V3_R1.fastq.gz
zr29353_110V1V3_R2.fastq.gz
F29353.S111
original sample ID here
zr29353_111V1V3_R1.fastq.gz
zr29353_111V1V3_R2.fastq.gz
F29353.S112
original sample ID here
zr29353_112V1V3_R1.fastq.gz
zr29353_112V1V3_R2.fastq.gz
F29353.S113
original sample ID here
zr29353_113V1V3_R1.fastq.gz
zr29353_113V1V3_R2.fastq.gz
F29353.S114
original sample ID here
zr29353_114V1V3_R1.fastq.gz
zr29353_114V1V3_R2.fastq.gz
F29353.S115
original sample ID here
zr29353_115V1V3_R1.fastq.gz
zr29353_115V1V3_R2.fastq.gz
F29353.S116
original sample ID here
zr29353_116V1V3_R1.fastq.gz
zr29353_116V1V3_R2.fastq.gz
F29353.S117
original sample ID here
zr29353_117V1V3_R1.fastq.gz
zr29353_117V1V3_R2.fastq.gz
F29353.S118
original sample ID here
zr29353_118V1V3_R1.fastq.gz
zr29353_118V1V3_R2.fastq.gz
F29353.S119
original sample ID here
zr29353_119V1V3_R1.fastq.gz
zr29353_119V1V3_R2.fastq.gz
F29353.S011
original sample ID here
zr29353_11V1V3_R1.fastq.gz
zr29353_11V1V3_R2.fastq.gz
F29353.S120
original sample ID here
zr29353_120V1V3_R1.fastq.gz
zr29353_120V1V3_R2.fastq.gz
F29353.S121
original sample ID here
zr29353_121V1V3_R1.fastq.gz
zr29353_121V1V3_R2.fastq.gz
F29353.S122
original sample ID here
zr29353_122V1V3_R1.fastq.gz
zr29353_122V1V3_R2.fastq.gz
F29353.S123
original sample ID here
zr29353_123V1V3_R1.fastq.gz
zr29353_123V1V3_R2.fastq.gz
F29353.S124
original sample ID here
zr29353_124V1V3_R1.fastq.gz
zr29353_124V1V3_R2.fastq.gz
F29353.S125
original sample ID here
zr29353_125V1V3_R1.fastq.gz
zr29353_125V1V3_R2.fastq.gz
F29353.S126
original sample ID here
zr29353_126V1V3_R1.fastq.gz
zr29353_126V1V3_R2.fastq.gz
F29353.S127
original sample ID here
zr29353_127V1V3_R1.fastq.gz
zr29353_127V1V3_R2.fastq.gz
F29353.S128
original sample ID here
zr29353_128V1V3_R1.fastq.gz
zr29353_128V1V3_R2.fastq.gz
F29353.S129
original sample ID here
zr29353_129V1V3_R1.fastq.gz
zr29353_129V1V3_R2.fastq.gz
F29353.S012
original sample ID here
zr29353_12V1V3_R1.fastq.gz
zr29353_12V1V3_R2.fastq.gz
F29353.S130
original sample ID here
zr29353_130V1V3_R1.fastq.gz
zr29353_130V1V3_R2.fastq.gz
F29353.S131
original sample ID here
zr29353_131V1V3_R1.fastq.gz
zr29353_131V1V3_R2.fastq.gz
F29353.S132
original sample ID here
zr29353_132V1V3_R1.fastq.gz
zr29353_132V1V3_R2.fastq.gz
F29353.S133
original sample ID here
zr29353_133V1V3_R1.fastq.gz
zr29353_133V1V3_R2.fastq.gz
F29353.S134
original sample ID here
zr29353_134V1V3_R1.fastq.gz
zr29353_134V1V3_R2.fastq.gz
F29353.S135
original sample ID here
zr29353_135V1V3_R1.fastq.gz
zr29353_135V1V3_R2.fastq.gz
F29353.S136
original sample ID here
zr29353_136V1V3_R1.fastq.gz
zr29353_136V1V3_R2.fastq.gz
F29353.S137
original sample ID here
zr29353_137V1V3_R1.fastq.gz
zr29353_137V1V3_R2.fastq.gz
F29353.S138
original sample ID here
zr29353_138V1V3_R1.fastq.gz
zr29353_138V1V3_R2.fastq.gz
F29353.S139
original sample ID here
zr29353_139V1V3_R1.fastq.gz
zr29353_139V1V3_R2.fastq.gz
F29353.S013
original sample ID here
zr29353_13V1V3_R1.fastq.gz
zr29353_13V1V3_R2.fastq.gz
F29353.S140
original sample ID here
zr29353_140V1V3_R1.fastq.gz
zr29353_140V1V3_R2.fastq.gz
F29353.S141
original sample ID here
zr29353_141V1V3_R1.fastq.gz
zr29353_141V1V3_R2.fastq.gz
F29353.S142
original sample ID here
zr29353_142V1V3_R1.fastq.gz
zr29353_142V1V3_R2.fastq.gz
F29353.S143
original sample ID here
zr29353_143V1V3_R1.fastq.gz
zr29353_143V1V3_R2.fastq.gz
F29353.S144
original sample ID here
zr29353_144V1V3_R1.fastq.gz
zr29353_144V1V3_R2.fastq.gz
F29353.S145
original sample ID here
zr29353_145V1V3_R1.fastq.gz
zr29353_145V1V3_R2.fastq.gz
F29353.S146
original sample ID here
zr29353_146V1V3_R1.fastq.gz
zr29353_146V1V3_R2.fastq.gz
F29353.S147
original sample ID here
zr29353_147V1V3_R1.fastq.gz
zr29353_147V1V3_R2.fastq.gz
F29353.S148
original sample ID here
zr29353_148V1V3_R1.fastq.gz
zr29353_148V1V3_R2.fastq.gz
F29353.S149
original sample ID here
zr29353_149V1V3_R1.fastq.gz
zr29353_149V1V3_R2.fastq.gz
F29353.S014
original sample ID here
zr29353_14V1V3_R1.fastq.gz
zr29353_14V1V3_R2.fastq.gz
F29353.S150
original sample ID here
zr29353_150V1V3_R1.fastq.gz
zr29353_150V1V3_R2.fastq.gz
F29353.S151
original sample ID here
zr29353_151V1V3_R1.fastq.gz
zr29353_151V1V3_R2.fastq.gz
F29353.S152
original sample ID here
zr29353_152V1V3_R1.fastq.gz
zr29353_152V1V3_R2.fastq.gz
F29353.S153
original sample ID here
zr29353_153V1V3_R1.fastq.gz
zr29353_153V1V3_R2.fastq.gz
F29353.S154
original sample ID here
zr29353_154V1V3_R1.fastq.gz
zr29353_154V1V3_R2.fastq.gz
F29353.S155
original sample ID here
zr29353_155V1V3_R1.fastq.gz
zr29353_155V1V3_R2.fastq.gz
F29353.S156
original sample ID here
zr29353_156V1V3_R1.fastq.gz
zr29353_156V1V3_R2.fastq.gz
F29353.S157
original sample ID here
zr29353_157V1V3_R1.fastq.gz
zr29353_157V1V3_R2.fastq.gz
F29353.S158
original sample ID here
zr29353_158V1V3_R1.fastq.gz
zr29353_158V1V3_R2.fastq.gz
F29353.S159
original sample ID here
zr29353_159V1V3_R1.fastq.gz
zr29353_159V1V3_R2.fastq.gz
F29353.S015
original sample ID here
zr29353_15V1V3_R1.fastq.gz
zr29353_15V1V3_R2.fastq.gz
F29353.S160
original sample ID here
zr29353_160V1V3_R1.fastq.gz
zr29353_160V1V3_R2.fastq.gz
F29353.S161
original sample ID here
zr29353_161V1V3_R1.fastq.gz
zr29353_161V1V3_R2.fastq.gz
F29353.S162
original sample ID here
zr29353_162V1V3_R1.fastq.gz
zr29353_162V1V3_R2.fastq.gz
F29353.S163
original sample ID here
zr29353_163V1V3_R1.fastq.gz
zr29353_163V1V3_R2.fastq.gz
F29353.S164
original sample ID here
zr29353_164V1V3_R1.fastq.gz
zr29353_164V1V3_R2.fastq.gz
F29353.S165
original sample ID here
zr29353_165V1V3_R1.fastq.gz
zr29353_165V1V3_R2.fastq.gz
F29353.S166
original sample ID here
zr29353_166V1V3_R1.fastq.gz
zr29353_166V1V3_R2.fastq.gz
F29353.S167
original sample ID here
zr29353_167V1V3_R1.fastq.gz
zr29353_167V1V3_R2.fastq.gz
F29353.S168
original sample ID here
zr29353_168V1V3_R1.fastq.gz
zr29353_168V1V3_R2.fastq.gz
F29353.S169
original sample ID here
zr29353_169V1V3_R1.fastq.gz
zr29353_169V1V3_R2.fastq.gz
F29353.S016
original sample ID here
zr29353_16V1V3_R1.fastq.gz
zr29353_16V1V3_R2.fastq.gz
F29353.S170
original sample ID here
zr29353_170V1V3_R1.fastq.gz
zr29353_170V1V3_R2.fastq.gz
F29353.S171
original sample ID here
zr29353_171V1V3_R1.fastq.gz
zr29353_171V1V3_R2.fastq.gz
F29353.S172
original sample ID here
zr29353_172V1V3_R1.fastq.gz
zr29353_172V1V3_R2.fastq.gz
F29353.S173
original sample ID here
zr29353_173V1V3_R1.fastq.gz
zr29353_173V1V3_R2.fastq.gz
F29353.S174
original sample ID here
zr29353_174V1V3_R1.fastq.gz
zr29353_174V1V3_R2.fastq.gz
F29353.S175
original sample ID here
zr29353_175V1V3_R1.fastq.gz
zr29353_175V1V3_R2.fastq.gz
F29353.S176
original sample ID here
zr29353_176V1V3_R1.fastq.gz
zr29353_176V1V3_R2.fastq.gz
F29353.S177
original sample ID here
zr29353_177V1V3_R1.fastq.gz
zr29353_177V1V3_R2.fastq.gz
F29353.S178
original sample ID here
zr29353_178V1V3_R1.fastq.gz
zr29353_178V1V3_R2.fastq.gz
F29353.S179
original sample ID here
zr29353_179V1V3_R1.fastq.gz
zr29353_179V1V3_R2.fastq.gz
F29353.S017
original sample ID here
zr29353_17V1V3_R1.fastq.gz
zr29353_17V1V3_R2.fastq.gz
F29353.S180
original sample ID here
zr29353_180V1V3_R1.fastq.gz
zr29353_180V1V3_R2.fastq.gz
F29353.S181
original sample ID here
zr29353_181V1V3_R1.fastq.gz
zr29353_181V1V3_R2.fastq.gz
F29353.S182
original sample ID here
zr29353_182V1V3_R1.fastq.gz
zr29353_182V1V3_R2.fastq.gz
F29353.S183
original sample ID here
zr29353_183V1V3_R1.fastq.gz
zr29353_183V1V3_R2.fastq.gz
F29353.S184
original sample ID here
zr29353_184V1V3_R1.fastq.gz
zr29353_184V1V3_R2.fastq.gz
F29353.S185
original sample ID here
zr29353_185V1V3_R1.fastq.gz
zr29353_185V1V3_R2.fastq.gz
F29353.S186
original sample ID here
zr29353_186V1V3_R1.fastq.gz
zr29353_186V1V3_R2.fastq.gz
F29353.S187
original sample ID here
zr29353_187V1V3_R1.fastq.gz
zr29353_187V1V3_R2.fastq.gz
F29353.S188
original sample ID here
zr29353_188V1V3_R1.fastq.gz
zr29353_188V1V3_R2.fastq.gz
F29353.S189
original sample ID here
zr29353_189V1V3_R1.fastq.gz
zr29353_189V1V3_R2.fastq.gz
F29353.S018
original sample ID here
zr29353_18V1V3_R1.fastq.gz
zr29353_18V1V3_R2.fastq.gz
F29353.S190
original sample ID here
zr29353_190V1V3_R1.fastq.gz
zr29353_190V1V3_R2.fastq.gz
F29353.S191
original sample ID here
zr29353_191V1V3_R1.fastq.gz
zr29353_191V1V3_R2.fastq.gz
F29353.S192
original sample ID here
zr29353_192V1V3_R1.fastq.gz
zr29353_192V1V3_R2.fastq.gz
F29353.S193
original sample ID here
zr29353_193V1V3_R1.fastq.gz
zr29353_193V1V3_R2.fastq.gz
F29353.S194
original sample ID here
zr29353_194V1V3_R1.fastq.gz
zr29353_194V1V3_R2.fastq.gz
F29353.S195
original sample ID here
zr29353_195V1V3_R1.fastq.gz
zr29353_195V1V3_R2.fastq.gz
F29353.S196
original sample ID here
zr29353_196V1V3_R1.fastq.gz
zr29353_196V1V3_R2.fastq.gz
F29353.S197
original sample ID here
zr29353_197V1V3_R1.fastq.gz
zr29353_197V1V3_R2.fastq.gz
F29353.S198
original sample ID here
zr29353_198V1V3_R1.fastq.gz
zr29353_198V1V3_R2.fastq.gz
F29353.S199
original sample ID here
zr29353_199V1V3_R1.fastq.gz
zr29353_199V1V3_R2.fastq.gz
F29353.S019
original sample ID here
zr29353_19V1V3_R1.fastq.gz
zr29353_19V1V3_R2.fastq.gz
F29353.S001
original sample ID here
zr29353_1V1V3_R1.fastq.gz
zr29353_1V1V3_R2.fastq.gz
F29353.S200
original sample ID here
zr29353_200V1V3_R1.fastq.gz
zr29353_200V1V3_R2.fastq.gz
F29353.S201
original sample ID here
zr29353_201V1V3_R1.fastq.gz
zr29353_201V1V3_R2.fastq.gz
F29353.S202
original sample ID here
zr29353_202V1V3_R1.fastq.gz
zr29353_202V1V3_R2.fastq.gz
F29353.S203
original sample ID here
zr29353_203V1V3_R1.fastq.gz
zr29353_203V1V3_R2.fastq.gz
F29353.S204
original sample ID here
zr29353_204V1V3_R1.fastq.gz
zr29353_204V1V3_R2.fastq.gz
F29353.S205
original sample ID here
zr29353_205V1V3_R1.fastq.gz
zr29353_205V1V3_R2.fastq.gz
F29353.S206
original sample ID here
zr29353_206V1V3_R1.fastq.gz
zr29353_206V1V3_R2.fastq.gz
F29353.S207
original sample ID here
zr29353_207V1V3_R1.fastq.gz
zr29353_207V1V3_R2.fastq.gz
F29353.S208
original sample ID here
zr29353_208V1V3_R1.fastq.gz
zr29353_208V1V3_R2.fastq.gz
F29353.S209
original sample ID here
zr29353_209V1V3_R1.fastq.gz
zr29353_209V1V3_R2.fastq.gz
F29353.S020
original sample ID here
zr29353_20V1V3_R1.fastq.gz
zr29353_20V1V3_R2.fastq.gz
F29353.S210
original sample ID here
zr29353_210V1V3_R1.fastq.gz
zr29353_210V1V3_R2.fastq.gz
F29353.S211
original sample ID here
zr29353_211V1V3_R1.fastq.gz
zr29353_211V1V3_R2.fastq.gz
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F29353.S213
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F29353.S214
original sample ID here
zr29353_214V1V3_R1.fastq.gz
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F29353.S215
original sample ID here
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F29353.S216
original sample ID here
zr29353_216V1V3_R1.fastq.gz
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F29353.S217
original sample ID here
zr29353_217V1V3_R1.fastq.gz
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F29353.S218
original sample ID here
zr29353_218V1V3_R1.fastq.gz
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F29353.S219
original sample ID here
zr29353_219V1V3_R1.fastq.gz
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F29353.S021
original sample ID here
zr29353_21V1V3_R1.fastq.gz
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F29353.S220
original sample ID here
zr29353_220V1V3_R1.fastq.gz
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F29353.S221
original sample ID here
zr29353_221V1V3_R1.fastq.gz
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F29353.S222
original sample ID here
zr29353_222V1V3_R1.fastq.gz
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F29353.S223
original sample ID here
zr29353_223V1V3_R1.fastq.gz
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F29353.S224
original sample ID here
zr29353_224V1V3_R1.fastq.gz
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F29353.S225
original sample ID here
zr29353_225V1V3_R1.fastq.gz
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F29353.S226
original sample ID here
zr29353_226V1V3_R1.fastq.gz
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F29353.S227
original sample ID here
zr29353_227V1V3_R1.fastq.gz
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F29353.S228
original sample ID here
zr29353_228V1V3_R1.fastq.gz
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F29353.S229
original sample ID here
zr29353_229V1V3_R1.fastq.gz
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F29353.S022
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zr29353_22V1V3_R1.fastq.gz
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F29353.S230
original sample ID here
zr29353_230V1V3_R1.fastq.gz
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F29353.S231
original sample ID here
zr29353_231V1V3_R1.fastq.gz
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F29353.S232
original sample ID here
zr29353_232V1V3_R1.fastq.gz
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F29353.S233
original sample ID here
zr29353_233V1V3_R1.fastq.gz
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F29353.S234
original sample ID here
zr29353_234V1V3_R1.fastq.gz
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F29353.S235
original sample ID here
zr29353_235V1V3_R1.fastq.gz
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F29353.S236
original sample ID here
zr29353_236V1V3_R1.fastq.gz
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F29353.S237
original sample ID here
zr29353_237V1V3_R1.fastq.gz
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F29353.S238
original sample ID here
zr29353_238V1V3_R1.fastq.gz
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F29353.S239
original sample ID here
zr29353_239V1V3_R1.fastq.gz
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F29353.S023
original sample ID here
zr29353_23V1V3_R1.fastq.gz
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F29353.S240
original sample ID here
zr29353_240V1V3_R1.fastq.gz
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F29353.S241
original sample ID here
zr29353_241V1V3_R1.fastq.gz
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F29353.S242
original sample ID here
zr29353_242V1V3_R1.fastq.gz
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F29353.S243
original sample ID here
zr29353_243V1V3_R1.fastq.gz
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F29353.S244
original sample ID here
zr29353_244V1V3_R1.fastq.gz
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F29353.S245
original sample ID here
zr29353_245V1V3_R1.fastq.gz
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F29353.S246
original sample ID here
zr29353_246V1V3_R1.fastq.gz
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F29353.S247
original sample ID here
zr29353_247V1V3_R1.fastq.gz
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F29353.S248
original sample ID here
zr29353_248V1V3_R1.fastq.gz
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F29353.S249
original sample ID here
zr29353_249V1V3_R1.fastq.gz
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F29353.S024
original sample ID here
zr29353_24V1V3_R1.fastq.gz
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F29353.S250
original sample ID here
zr29353_250V1V3_R1.fastq.gz
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F29353.S251
original sample ID here
zr29353_251V1V3_R1.fastq.gz
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F29353.S252
original sample ID here
zr29353_252V1V3_R1.fastq.gz
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F29353.S253
original sample ID here
zr29353_253V1V3_R1.fastq.gz
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F29353.S254
original sample ID here
zr29353_254V1V3_R1.fastq.gz
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F29353.S255
original sample ID here
zr29353_255V1V3_R1.fastq.gz
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F29353.S256
original sample ID here
zr29353_256V1V3_R1.fastq.gz
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F29353.S257
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zr29353_257V1V3_R1.fastq.gz
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F29353.S258
original sample ID here
zr29353_258V1V3_R1.fastq.gz
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F29353.S259
original sample ID here
zr29353_259V1V3_R1.fastq.gz
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F29353.S025
original sample ID here
zr29353_25V1V3_R1.fastq.gz
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F29353.S260
original sample ID here
zr29353_260V1V3_R1.fastq.gz
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F29353.S261
original sample ID here
zr29353_261V1V3_R1.fastq.gz
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F29353.S262
original sample ID here
zr29353_262V1V3_R1.fastq.gz
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F29353.S263
original sample ID here
zr29353_263V1V3_R1.fastq.gz
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F29353.S264
original sample ID here
zr29353_264V1V3_R1.fastq.gz
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F29353.S265
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zr29353_265V1V3_R1.fastq.gz
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F29353.S266
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zr29353_266V1V3_R1.fastq.gz
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F29353.S267
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zr29353_267V1V3_R1.fastq.gz
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F29353.S268
original sample ID here
zr29353_268V1V3_R1.fastq.gz
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F29353.S269
original sample ID here
zr29353_269V1V3_R1.fastq.gz
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F29353.S026
original sample ID here
zr29353_26V1V3_R1.fastq.gz
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F29353.S270
original sample ID here
zr29353_270V1V3_R1.fastq.gz
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F29353.S271
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zr29353_271V1V3_R1.fastq.gz
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F29353.S272
original sample ID here
zr29353_272V1V3_R1.fastq.gz
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F29353.S273
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zr29353_273V1V3_R1.fastq.gz
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F29353.S274
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zr29353_274V1V3_R1.fastq.gz
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F29353.S275
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zr29353_275V1V3_R1.fastq.gz
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F29353.S276
original sample ID here
zr29353_276V1V3_R1.fastq.gz
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F29353.S277
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zr29353_277V1V3_R1.fastq.gz
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F29353.S278
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zr29353_278V1V3_R1.fastq.gz
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F29353.S279
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zr29353_279V1V3_R1.fastq.gz
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F29353.S027
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zr29353_27V1V3_R1.fastq.gz
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F29353.S280
original sample ID here
zr29353_280V1V3_R1.fastq.gz
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F29353.S281
original sample ID here
zr29353_281V1V3_R1.fastq.gz
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F29353.S282
original sample ID here
zr29353_282V1V3_R1.fastq.gz
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F29353.S283
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zr29353_283V1V3_R1.fastq.gz
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F29353.S284
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zr29353_284V1V3_R1.fastq.gz
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F29353.S285
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F29353.S286
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F29353.S287
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F29353.S288
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zr29353_288V1V3_R1.fastq.gz
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F29353.S289
original sample ID here
zr29353_289V1V3_R1.fastq.gz
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F29353.S028
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zr29353_28V1V3_R1.fastq.gz
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F29353.S290
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zr29353_290V1V3_R1.fastq.gz
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F29353.S291
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F29353.S292
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F29353.S293
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F29353.S294
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F29353.S295
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F29353.S296
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F29353.S297
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F29353.S298
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F29353.S299
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F29353.S029
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F29353.S002
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zr29353_2V1V3_R1.fastq.gz
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F29353.S300
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F29353.S301
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F29353.S302
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F29353.S303
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F29353.S304
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F29353.S305
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F29353.S306
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F29353.S307
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F29353.S308
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F29353.S309
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F29353.S030
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F29353.S310
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F29353.S311
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F29353.S312
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F29353.S313
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F29353.S314
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F29353.S315
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F29353.S316
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F29353.S317
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F29353.S318
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F29353.S319
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F29353.S031
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F29353.S320
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F29353.S321
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F29353.S322
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F29353.S323
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F29353.S324
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F29353.S325
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F29353.S326
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F29353.S327
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F29353.S328
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F29353.S329
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F29353.S032
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F29353.S330
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F29353.S331
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F29353.S332
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F29353.S333
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F29353.S335
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F29353.S336
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F29353.S337
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F29353.S338
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F29353.S339
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F29353.S033
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F29353.S340
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F29353.S342
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F29353.S343
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F29353.S345
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F29353.S346
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F29353.S347
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F29353.S348
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F29353.S349
original sample ID here
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F29353.S034
original sample ID here
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F29353.S350
original sample ID here
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F29353.S351
original sample ID here
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F29353.S352
original sample ID here
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F29353.S353
original sample ID here
zr29353_353V1V3_R1.fastq.gz
zr29353_353V1V3_R2.fastq.gz
F29353.S354
original sample ID here
zr29353_354V1V3_R1.fastq.gz
zr29353_354V1V3_R2.fastq.gz
F29353.S355
original sample ID here
zr29353_355V1V3_R1.fastq.gz
zr29353_355V1V3_R2.fastq.gz
F29353.S356
original sample ID here
zr29353_356V1V3_R1.fastq.gz
zr29353_356V1V3_R2.fastq.gz
F29353.S357
original sample ID here
zr29353_357V1V3_R1.fastq.gz
zr29353_357V1V3_R2.fastq.gz
F29353.S358
original sample ID here
zr29353_358V1V3_R1.fastq.gz
zr29353_358V1V3_R2.fastq.gz
F29353.S359
original sample ID here
zr29353_359V1V3_R1.fastq.gz
zr29353_359V1V3_R2.fastq.gz
F29353.S035
original sample ID here
zr29353_35V1V3_R1.fastq.gz
zr29353_35V1V3_R2.fastq.gz
F29353.S360
original sample ID here
zr29353_360V1V3_R1.fastq.gz
zr29353_360V1V3_R2.fastq.gz
F29353.S361
original sample ID here
zr29353_361V1V3_R1.fastq.gz
zr29353_361V1V3_R2.fastq.gz
F29353.S362
original sample ID here
zr29353_362V1V3_R1.fastq.gz
zr29353_362V1V3_R2.fastq.gz
F29353.S363
original sample ID here
zr29353_363V1V3_R1.fastq.gz
zr29353_363V1V3_R2.fastq.gz
F29353.S364
original sample ID here
zr29353_364V1V3_R1.fastq.gz
zr29353_364V1V3_R2.fastq.gz
F29353.S365
original sample ID here
zr29353_365V1V3_R1.fastq.gz
zr29353_365V1V3_R2.fastq.gz
F29353.S366
original sample ID here
zr29353_366V1V3_R1.fastq.gz
zr29353_366V1V3_R2.fastq.gz
F29353.S367
original sample ID here
zr29353_367V1V3_R1.fastq.gz
zr29353_367V1V3_R2.fastq.gz
F29353.S368
original sample ID here
zr29353_368V1V3_R1.fastq.gz
zr29353_368V1V3_R2.fastq.gz
F29353.S369
original sample ID here
zr29353_369V1V3_R1.fastq.gz
zr29353_369V1V3_R2.fastq.gz
F29353.S036
original sample ID here
zr29353_36V1V3_R1.fastq.gz
zr29353_36V1V3_R2.fastq.gz
F29353.S370
original sample ID here
zr29353_370V1V3_R1.fastq.gz
zr29353_370V1V3_R2.fastq.gz
F29353.S371
original sample ID here
zr29353_371V1V3_R1.fastq.gz
zr29353_371V1V3_R2.fastq.gz
F29353.S372
original sample ID here
zr29353_372V1V3_R1.fastq.gz
zr29353_372V1V3_R2.fastq.gz
F29353.S373
original sample ID here
zr29353_373V1V3_R1.fastq.gz
zr29353_373V1V3_R2.fastq.gz
F29353.S374
original sample ID here
zr29353_374V1V3_R1.fastq.gz
zr29353_374V1V3_R2.fastq.gz
F29353.S375
original sample ID here
zr29353_375V1V3_R1.fastq.gz
zr29353_375V1V3_R2.fastq.gz
F29353.S376
original sample ID here
zr29353_376V1V3_R1.fastq.gz
zr29353_376V1V3_R2.fastq.gz
F29353.S377
original sample ID here
zr29353_377V1V3_R1.fastq.gz
zr29353_377V1V3_R2.fastq.gz
F29353.S378
original sample ID here
zr29353_378V1V3_R1.fastq.gz
zr29353_378V1V3_R2.fastq.gz
F29353.S379
original sample ID here
zr29353_379V1V3_R1.fastq.gz
zr29353_379V1V3_R2.fastq.gz
F29353.S037
original sample ID here
zr29353_37V1V3_R1.fastq.gz
zr29353_37V1V3_R2.fastq.gz
F29353.S380
original sample ID here
zr29353_380V1V3_R1.fastq.gz
zr29353_380V1V3_R2.fastq.gz
F29353.S381
original sample ID here
zr29353_381V1V3_R1.fastq.gz
zr29353_381V1V3_R2.fastq.gz
F29353.S382
original sample ID here
zr29353_382V1V3_R1.fastq.gz
zr29353_382V1V3_R2.fastq.gz
F29353.S383
original sample ID here
zr29353_383V1V3_R1.fastq.gz
zr29353_383V1V3_R2.fastq.gz
F29353.S384
original sample ID here
zr29353_384V1V3_R1.fastq.gz
zr29353_384V1V3_R2.fastq.gz
F29353.S385
original sample ID here
zr29353_385V1V3_R1.fastq.gz
zr29353_385V1V3_R2.fastq.gz
F29353.S386
original sample ID here
zr29353_386V1V3_R1.fastq.gz
zr29353_386V1V3_R2.fastq.gz
F29353.S387
original sample ID here
zr29353_387V1V3_R1.fastq.gz
zr29353_387V1V3_R2.fastq.gz
F29353.S388
original sample ID here
zr29353_388V1V3_R1.fastq.gz
zr29353_388V1V3_R2.fastq.gz
F29353.S389
original sample ID here
zr29353_389V1V3_R1.fastq.gz
zr29353_389V1V3_R2.fastq.gz
F29353.S038
original sample ID here
zr29353_38V1V3_R1.fastq.gz
zr29353_38V1V3_R2.fastq.gz
F29353.S390
original sample ID here
zr29353_390V1V3_R1.fastq.gz
zr29353_390V1V3_R2.fastq.gz
F29353.S391
original sample ID here
zr29353_391V1V3_R1.fastq.gz
zr29353_391V1V3_R2.fastq.gz
F29353.S392
original sample ID here
zr29353_392V1V3_R1.fastq.gz
zr29353_392V1V3_R2.fastq.gz
F29353.S393
original sample ID here
zr29353_393V1V3_R1.fastq.gz
zr29353_393V1V3_R2.fastq.gz
F29353.S394
original sample ID here
zr29353_394V1V3_R1.fastq.gz
zr29353_394V1V3_R2.fastq.gz
F29353.S395
original sample ID here
zr29353_395V1V3_R1.fastq.gz
zr29353_395V1V3_R2.fastq.gz
F29353.S396
original sample ID here
zr29353_396V1V3_R1.fastq.gz
zr29353_396V1V3_R2.fastq.gz
F29353.S397
original sample ID here
zr29353_397V1V3_R1.fastq.gz
zr29353_397V1V3_R2.fastq.gz
F29353.S398
original sample ID here
zr29353_398V1V3_R1.fastq.gz
zr29353_398V1V3_R2.fastq.gz
F29353.S399
original sample ID here
zr29353_399V1V3_R1.fastq.gz
zr29353_399V1V3_R2.fastq.gz
F29353.S039
original sample ID here
zr29353_39V1V3_R1.fastq.gz
zr29353_39V1V3_R2.fastq.gz
F29353.S003
original sample ID here
zr29353_3V1V3_R1.fastq.gz
zr29353_3V1V3_R2.fastq.gz
F29353.S400
original sample ID here
zr29353_400V1V3_R1.fastq.gz
zr29353_400V1V3_R2.fastq.gz
F29353.S401
original sample ID here
zr29353_401V1V3_R1.fastq.gz
zr29353_401V1V3_R2.fastq.gz
F29353.S402
original sample ID here
zr29353_402V1V3_R1.fastq.gz
zr29353_402V1V3_R2.fastq.gz
F29353.S403
original sample ID here
zr29353_403V1V3_R1.fastq.gz
zr29353_403V1V3_R2.fastq.gz
F29353.S040
original sample ID here
zr29353_40V1V3_R1.fastq.gz
zr29353_40V1V3_R2.fastq.gz
F29353.S041
original sample ID here
zr29353_41V1V3_R1.fastq.gz
zr29353_41V1V3_R2.fastq.gz
F29353.S042
original sample ID here
zr29353_42V1V3_R1.fastq.gz
zr29353_42V1V3_R2.fastq.gz
F29353.S043
original sample ID here
zr29353_43V1V3_R1.fastq.gz
zr29353_43V1V3_R2.fastq.gz
F29353.S044
original sample ID here
zr29353_44V1V3_R1.fastq.gz
zr29353_44V1V3_R2.fastq.gz
F29353.S045
original sample ID here
zr29353_45V1V3_R1.fastq.gz
zr29353_45V1V3_R2.fastq.gz
F29353.S046
original sample ID here
zr29353_46V1V3_R1.fastq.gz
zr29353_46V1V3_R2.fastq.gz
F29353.S047
original sample ID here
zr29353_47V1V3_R1.fastq.gz
zr29353_47V1V3_R2.fastq.gz
F29353.S048
original sample ID here
zr29353_48V1V3_R1.fastq.gz
zr29353_48V1V3_R2.fastq.gz
F29353.S049
original sample ID here
zr29353_49V1V3_R1.fastq.gz
zr29353_49V1V3_R2.fastq.gz
F29353.S004
original sample ID here
zr29353_4V1V3_R1.fastq.gz
zr29353_4V1V3_R2.fastq.gz
F29353.S050
original sample ID here
zr29353_50V1V3_R1.fastq.gz
zr29353_50V1V3_R2.fastq.gz
F29353.S051
original sample ID here
zr29353_51V1V3_R1.fastq.gz
zr29353_51V1V3_R2.fastq.gz
F29353.S052
original sample ID here
zr29353_52V1V3_R1.fastq.gz
zr29353_52V1V3_R2.fastq.gz
F29353.S053
original sample ID here
zr29353_53V1V3_R1.fastq.gz
zr29353_53V1V3_R2.fastq.gz
F29353.S054
original sample ID here
zr29353_54V1V3_R1.fastq.gz
zr29353_54V1V3_R2.fastq.gz
F29353.S055
original sample ID here
zr29353_55V1V3_R1.fastq.gz
zr29353_55V1V3_R2.fastq.gz
F29353.S056
original sample ID here
zr29353_56V1V3_R1.fastq.gz
zr29353_56V1V3_R2.fastq.gz
F29353.S057
original sample ID here
zr29353_57V1V3_R1.fastq.gz
zr29353_57V1V3_R2.fastq.gz
F29353.S058
original sample ID here
zr29353_58V1V3_R1.fastq.gz
zr29353_58V1V3_R2.fastq.gz
F29353.S059
original sample ID here
zr29353_59V1V3_R1.fastq.gz
zr29353_59V1V3_R2.fastq.gz
F29353.S005
original sample ID here
zr29353_5V1V3_R1.fastq.gz
zr29353_5V1V3_R2.fastq.gz
F29353.S060
original sample ID here
zr29353_60V1V3_R1.fastq.gz
zr29353_60V1V3_R2.fastq.gz
F29353.S061
original sample ID here
zr29353_61V1V3_R1.fastq.gz
zr29353_61V1V3_R2.fastq.gz
F29353.S062
original sample ID here
zr29353_62V1V3_R1.fastq.gz
zr29353_62V1V3_R2.fastq.gz
F29353.S063
original sample ID here
zr29353_63V1V3_R1.fastq.gz
zr29353_63V1V3_R2.fastq.gz
F29353.S064
original sample ID here
zr29353_64V1V3_R1.fastq.gz
zr29353_64V1V3_R2.fastq.gz
F29353.S065
original sample ID here
zr29353_65V1V3_R1.fastq.gz
zr29353_65V1V3_R2.fastq.gz
F29353.S066
original sample ID here
zr29353_66V1V3_R1.fastq.gz
zr29353_66V1V3_R2.fastq.gz
F29353.S067
original sample ID here
zr29353_67V1V3_R1.fastq.gz
zr29353_67V1V3_R2.fastq.gz
F29353.S068
original sample ID here
zr29353_68V1V3_R1.fastq.gz
zr29353_68V1V3_R2.fastq.gz
F29353.S069
original sample ID here
zr29353_69V1V3_R1.fastq.gz
zr29353_69V1V3_R2.fastq.gz
F29353.S006
original sample ID here
zr29353_6V1V3_R1.fastq.gz
zr29353_6V1V3_R2.fastq.gz
F29353.S070
original sample ID here
zr29353_70V1V3_R1.fastq.gz
zr29353_70V1V3_R2.fastq.gz
F29353.S071
original sample ID here
zr29353_71V1V3_R1.fastq.gz
zr29353_71V1V3_R2.fastq.gz
F29353.S072
original sample ID here
zr29353_72V1V3_R1.fastq.gz
zr29353_72V1V3_R2.fastq.gz
F29353.S073
original sample ID here
zr29353_73V1V3_R1.fastq.gz
zr29353_73V1V3_R2.fastq.gz
F29353.S074
original sample ID here
zr29353_74V1V3_R1.fastq.gz
zr29353_74V1V3_R2.fastq.gz
F29353.S075
original sample ID here
zr29353_75V1V3_R1.fastq.gz
zr29353_75V1V3_R2.fastq.gz
F29353.S076
original sample ID here
zr29353_76V1V3_R1.fastq.gz
zr29353_76V1V3_R2.fastq.gz
F29353.S077
original sample ID here
zr29353_77V1V3_R1.fastq.gz
zr29353_77V1V3_R2.fastq.gz
F29353.S078
original sample ID here
zr29353_78V1V3_R1.fastq.gz
zr29353_78V1V3_R2.fastq.gz
F29353.S079
original sample ID here
zr29353_79V1V3_R1.fastq.gz
zr29353_79V1V3_R2.fastq.gz
F29353.S007
original sample ID here
zr29353_7V1V3_R1.fastq.gz
zr29353_7V1V3_R2.fastq.gz
F29353.S080
original sample ID here
zr29353_80V1V3_R1.fastq.gz
zr29353_80V1V3_R2.fastq.gz
F29353.S081
original sample ID here
zr29353_81V1V3_R1.fastq.gz
zr29353_81V1V3_R2.fastq.gz
F29353.S082
original sample ID here
zr29353_82V1V3_R1.fastq.gz
zr29353_82V1V3_R2.fastq.gz
F29353.S083
original sample ID here
zr29353_83V1V3_R1.fastq.gz
zr29353_83V1V3_R2.fastq.gz
F29353.S084
original sample ID here
zr29353_84V1V3_R1.fastq.gz
zr29353_84V1V3_R2.fastq.gz
F29353.S085
original sample ID here
zr29353_85V1V3_R1.fastq.gz
zr29353_85V1V3_R2.fastq.gz
F29353.S086
original sample ID here
zr29353_86V1V3_R1.fastq.gz
zr29353_86V1V3_R2.fastq.gz
F29353.S087
original sample ID here
zr29353_87V1V3_R1.fastq.gz
zr29353_87V1V3_R2.fastq.gz
F29353.S088
original sample ID here
zr29353_88V1V3_R1.fastq.gz
zr29353_88V1V3_R2.fastq.gz
F29353.S089
original sample ID here
zr29353_89V1V3_R1.fastq.gz
zr29353_89V1V3_R2.fastq.gz
F29353.S008
original sample ID here
zr29353_8V1V3_R1.fastq.gz
zr29353_8V1V3_R2.fastq.gz
F29353.S090
original sample ID here
zr29353_90V1V3_R1.fastq.gz
zr29353_90V1V3_R2.fastq.gz
F29353.S091
original sample ID here
zr29353_91V1V3_R1.fastq.gz
zr29353_91V1V3_R2.fastq.gz
F29353.S092
original sample ID here
zr29353_92V1V3_R1.fastq.gz
zr29353_92V1V3_R2.fastq.gz
F29353.S093
original sample ID here
zr29353_93V1V3_R1.fastq.gz
zr29353_93V1V3_R2.fastq.gz
F29353.S094
original sample ID here
zr29353_94V1V3_R1.fastq.gz
zr29353_94V1V3_R2.fastq.gz
F29353.S095
original sample ID here
zr29353_95V1V3_R1.fastq.gz
zr29353_95V1V3_R2.fastq.gz
F29353.S096
original sample ID here
zr29353_96V1V3_R1.fastq.gz
zr29353_96V1V3_R2.fastq.gz
F29353.S097
original sample ID here
zr29353_97V1V3_R1.fastq.gz
zr29353_97V1V3_R2.fastq.gz
F29353.S098
original sample ID here
zr29353_98V1V3_R1.fastq.gz
zr29353_98V1V3_R2.fastq.gz
F29353.S099
original sample ID here
zr29353_99V1V3_R1.fastq.gz
zr29353_99V1V3_R2.fastq.gz
F29353.S009
original sample ID here
zr29353_9V1V3_R1.fastq.gz
zr29353_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 [1].
DADA2 infers sample sequences exactly, without coarse-graining into OTUs,
and resolves differences of as little as one nucleotide. DADA2 identified more real variants
and output fewer spurious sequences than other methods.
DADA2’s advantage is that it uses more of the data. The DADA2 error model incorporates quality information,
which is ignored by all other methods after filtering. The DADA2 error model incorporates quantitative abundances,
whereas most other methods use abundance ranks if they use abundance at all.
The DADA2 error model identifies the differences between sequences, eg. A->C,
whereas other methods merely count the mismatches. DADA2 can parameterize its error model from the data itself,
rather than relying on previous datasets that may or may not reflect the PCR and sequencing protocols used in your study.
Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJ, Holmes SP. DADA2: High-resolution sample inference from Illumina amplicon data. Nat Methods. 2016 Jul;13(7):581-3. doi: 10.1038/nmeth.3869. Epub 2016 May 23. PMID: 27214047; PMCID: PMC4927377.
Analysis Procedures:
DADA2 pipeline includes several tools for read quality control, including quality filtering, trimming, denoising, pair merging and chimera filtering. Below are the major processing steps of DADA2:
Step 1. Read trimming based on sequence quality
The quality of NGS Illumina sequences often decreases toward the end of the reads.
DADA2 allows to trim off the poor quality read ends in order to improve the error
model building and pair mergicing performance.
Step 2. Learn the Error Rates
The DADA2 algorithm makes use of a parametric error model (err) and every
amplicon dataset has a different set of error rates. The learnErrors method
learns this error model from the data, by alternating estimation of the error
rates and inference of sample composition until they converge on a jointly
consistent solution. As in many machine-learning problems, the algorithm must
begin with an initial guess, for which the maximum possible error rates in
this data are used (the error rates if only the most abundant sequence is
correct and all the rest are errors).
Step 3. Infer amplicon sequence variants (ASVs) based on the error model built in previous step. This step is also called sequence "denoising".
The outcome of this step is a list of ASVs that are the equivalent of oligonucleotides.
Step 4. Merge paired reads. If the sequencing products are read pairs, DADA2 will merge the R1 and R2 ASVs into single sequences.
Merging is performed by aligning the denoised forward reads with the reverse-complement of the corresponding
denoised reverse reads, and then constructing the merged “contig” sequences.
By default, merged sequences are only output if the forward and reverse reads overlap by
at least 12 bases, and are identical to each other in the overlap region (but these conditions can be changed via function arguments).
Step 5. Remove chimera.
The core dada method corrects substitution and indel errors, but chimeras remain. Fortunately, the accuracy of sequence variants
after denoising makes identifying chimeric ASVs simpler than when dealing with fuzzy OTUs.
Chimeric sequences are identified if they can be exactly reconstructed by
combining a left-segment and a right-segment from two more abundant “parent” sequences. The frequency of chimeric sequences varies substantially
from dataset to dataset, and depends on on factors including experimental procedures and sample complexity.
Results
1. Read Quality Plots NGS sequence analaysis starts with visualizing the quality of the sequencing. Below are the quality plots of the first
sample for the R1 and R2 reads separately. In gray-scale is a heat map of the frequency of each quality score at each base position. The mean
quality score at each position is shown by the green line, and the quartiles of the quality score distribution by the orange lines.
The forward reads are usually of better quality. It is a common practice to trim the last few nucleotides to avoid less well-controlled errors
that can arise there. The trimming affects the downstream steps including error model building, merging and chimera calling. FOMC uses an empirical
approach to test many combinations of different trim length in order to achieve best final amplicon sequence variants (ASVs), see the next
section “Optimal trim length for ASVs”.
2. Optimal trim length for ASVs The final number of merged and chimera-filtered ASVs depends on the quality filtering (hence trimming) in the very beginning of the DADA2 pipeline.
In order to achieve highest number of ASVs, an empirical approach was used -
Create a random subset of each sample consisting of 5,000 R1 and 5,000 R2 (to reduce computation time)
Trim 10 bases at a time from the ends of both R1 and R2 up to 50 bases
For each combination of trimmed length (e.g., 300x300, 300x290, 290x290 etc), the trimmed reads are
subject to the entire DADA2 pipeline for chimera-filtered merged ASVs
The combination with highest percentage of the input reads becoming final ASVs is selected for the complete set of data
Below is the result of such operation, showing ASV percentages of total reads for all trimming combinations (1st Column = R1 lengths in bases; 1st Row = R2 lengths in bases):
R1/R2
301
291
281
271
261
251
301
80.37%
83.06%
83.09%
83.28%
83.26%
77.10%
291
80.40%
83.13%
83.16%
83.13%
76.77%
59.56%
281
80.50%
83.19%
83.04%
76.70%
59.51%
28.30%
271
80.83%
83.33%
76.89%
59.74%
28.41%
19.84%
261
80.82%
77.06%
59.80%
28.52%
19.90%
11.15%
251
74.96%
60.34%
28.89%
20.25%
11.30%
5.88%
Based on the above result, the trim length combination of R1 = 271 bases and R2 = 291 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
F29353.S001
F29353.S002
F29353.S003
F29353.S004
F29353.S005
F29353.S006
F29353.S007
F29353.S008
F29353.S009
F29353.S010
F29353.S011
F29353.S012
F29353.S013
F29353.S014
F29353.S015
F29353.S016
F29353.S017
F29353.S018
F29353.S019
F29353.S020
F29353.S021
F29353.S022
F29353.S023
F29353.S024
F29353.S025
F29353.S026
F29353.S027
F29353.S028
F29353.S029
F29353.S030
F29353.S031
F29353.S032
F29353.S033
F29353.S034
F29353.S035
F29353.S036
F29353.S037
F29353.S038
F29353.S039
F29353.S040
F29353.S041
F29353.S042
F29353.S043
F29353.S044
F29353.S045
F29353.S046
F29353.S047
F29353.S048
F29353.S049
F29353.S050
F29353.S051
F29353.S052
F29353.S053
F29353.S054
F29353.S055
F29353.S056
F29353.S057
F29353.S058
F29353.S059
F29353.S060
F29353.S061
F29353.S062
F29353.S063
F29353.S064
F29353.S065
F29353.S066
F29353.S067
F29353.S068
F29353.S069
F29353.S070
F29353.S071
F29353.S072
F29353.S073
F29353.S074
F29353.S075
F29353.S076
F29353.S077
F29353.S078
F29353.S079
F29353.S080
F29353.S081
F29353.S082
F29353.S083
F29353.S084
F29353.S085
F29353.S086
F29353.S087
F29353.S088
F29353.S089
F29353.S090
F29353.S091
F29353.S092
F29353.S093
F29353.S094
F29353.S095
F29353.S096
F29353.S097
F29353.S098
F29353.S099
F29353.S100
F29353.S101
F29353.S102
F29353.S103
F29353.S104
F29353.S105
F29353.S106
F29353.S107
F29353.S108
F29353.S109
F29353.S110
F29353.S111
F29353.S112
F29353.S113
F29353.S114
F29353.S115
F29353.S116
F29353.S117
F29353.S118
F29353.S119
F29353.S120
F29353.S121
F29353.S122
F29353.S123
F29353.S124
F29353.S125
F29353.S126
F29353.S127
F29353.S128
F29353.S129
F29353.S130
F29353.S131
F29353.S132
F29353.S133
F29353.S134
F29353.S135
F29353.S136
F29353.S137
F29353.S138
F29353.S139
F29353.S140
F29353.S141
F29353.S142
F29353.S143
F29353.S144
F29353.S145
F29353.S146
F29353.S147
F29353.S148
F29353.S149
F29353.S150
F29353.S151
F29353.S152
F29353.S153
F29353.S154
F29353.S155
F29353.S156
F29353.S157
F29353.S158
F29353.S159
F29353.S160
F29353.S161
F29353.S162
F29353.S163
F29353.S164
F29353.S165
F29353.S166
F29353.S167
F29353.S168
F29353.S169
F29353.S170
F29353.S171
F29353.S172
F29353.S173
F29353.S174
F29353.S175
F29353.S176
F29353.S177
F29353.S178
F29353.S179
F29353.S180
F29353.S181
F29353.S182
F29353.S183
F29353.S184
F29353.S185
F29353.S186
F29353.S187
F29353.S188
F29353.S189
F29353.S190
F29353.S191
F29353.S192
F29353.S193
F29353.S194
F29353.S195
F29353.S196
F29353.S197
F29353.S198
F29353.S199
F29353.S200
F29353.S201
F29353.S202
F29353.S203
F29353.S204
F29353.S205
F29353.S206
F29353.S207
F29353.S208
F29353.S209
F29353.S210
F29353.S211
F29353.S212
F29353.S213
F29353.S214
F29353.S215
F29353.S216
F29353.S217
F29353.S218
F29353.S219
F29353.S220
F29353.S221
F29353.S222
F29353.S223
F29353.S224
F29353.S225
F29353.S226
F29353.S227
F29353.S228
F29353.S229
F29353.S230
F29353.S231
F29353.S232
F29353.S233
F29353.S234
F29353.S235
F29353.S236
F29353.S237
F29353.S238
F29353.S239
F29353.S240
F29353.S241
F29353.S242
F29353.S243
F29353.S244
F29353.S245
F29353.S246
F29353.S247
F29353.S248
F29353.S249
F29353.S250
F29353.S251
F29353.S252
F29353.S253
F29353.S254
F29353.S255
F29353.S256
F29353.S257
F29353.S258
F29353.S259
F29353.S260
F29353.S261
F29353.S262
F29353.S263
F29353.S264
F29353.S265
F29353.S266
F29353.S267
F29353.S268
F29353.S269
F29353.S270
F29353.S271
F29353.S272
F29353.S273
F29353.S274
F29353.S275
F29353.S276
F29353.S277
F29353.S278
F29353.S279
F29353.S280
F29353.S281
F29353.S282
F29353.S283
F29353.S284
F29353.S285
F29353.S286
F29353.S287
F29353.S288
F29353.S289
F29353.S290
F29353.S291
F29353.S292
F29353.S293
F29353.S294
F29353.S295
F29353.S296
F29353.S297
F29353.S298
F29353.S299
F29353.S300
F29353.S301
F29353.S302
F29353.S303
F29353.S304
F29353.S305
F29353.S306
F29353.S307
F29353.S308
F29353.S309
F29353.S310
F29353.S311
F29353.S312
F29353.S313
F29353.S314
F29353.S315
F29353.S316
F29353.S317
F29353.S318
F29353.S319
F29353.S320
F29353.S321
F29353.S322
F29353.S323
F29353.S324
F29353.S325
F29353.S326
F29353.S327
F29353.S328
F29353.S329
F29353.S330
F29353.S331
F29353.S332
F29353.S333
F29353.S334
F29353.S335
F29353.S336
F29353.S337
F29353.S338
F29353.S339
F29353.S340
F29353.S341
F29353.S342
F29353.S343
F29353.S344
F29353.S345
F29353.S346
F29353.S347
F29353.S348
F29353.S349
F29353.S350
F29353.S351
F29353.S352
F29353.S353
F29353.S354
F29353.S355
F29353.S356
F29353.S357
F29353.S358
F29353.S359
F29353.S360
F29353.S361
F29353.S362
F29353.S363
F29353.S364
F29353.S365
F29353.S366
F29353.S367
F29353.S368
F29353.S369
F29353.S370
F29353.S371
F29353.S372
F29353.S373
F29353.S374
F29353.S375
F29353.S376
F29353.S377
F29353.S378
F29353.S379
F29353.S380
F29353.S381
F29353.S382
F29353.S383
F29353.S384
F29353.S385
F29353.S386
F29353.S387
F29353.S388
F29353.S389
F29353.S390
F29353.S391
F29353.S392
F29353.S393
F29353.S394
F29353.S395
F29353.S396
F29353.S397
F29353.S398
F29353.S399
F29353.S400
F29353.S401
F29353.S402
F29353.S403
Row Sum
Percentage
input
71,599
90,374
89,525
55,209
66,821
78,153
64,227
73,772
68,710
78,465
72,620
62,273
71,922
54,358
74,573
82,426
81,762
61,633
70,927
72,389
61,138
68,643
69,090
77,112
45,864
94,142
66,807
71,732
55,574
64,915
60,867
74,221
73,495
69,975
71,248
63,451
67,393
59,485
58,503
73,464
80,169
70,856
66,649
76,716
78,329
83,909
65,486
53,205
102,310
103,989
85,065
66,493
81,061
94,781
80,070
76,784
60,917
86,467
64,759
60,851
78,891
101,321
66,762
72,104
100,775
74,368
103,685
97,659
145,245
70,188
51,133
66,589
74,899
67,103
82,163
67,070
66,195
66,081
76,475
57,228
80,478
100,182
71,775
75,996
70,343
77,217
60,330
86,268
74,710
61,504
62,395
89,019
75,180
66,029
61,697
76,276
53,918
67,669
74,698
88,729
69,826
74,272
88,940
78,646
73,086
83,996
77,017
83,415
74,782
61,187
80,918
79,053
51,679
69,000
50,644
87,417
79,834
86,865
75,443
79,345
62,839
66,377
61,636
55,288
66,637
65,101
75,887
60,478
62,495
60,555
57,210
77,833
77,639
63,737
87,460
70,911
73,917
73,603
65,562
74,969
70,749
73,882
67,199
66,894
72,009
63,708
77,132
73,749
75,681
72,493
63,773
73,075
65,889
72,658
68,024
71,044
83,904
70,460
73,616
66,687
70,619
62,494
77,572
83,838
82,134
60,559
83,352
62,131
57,387
72,716
57,035
79,934
75,958
50,967
62,850
53,357
73,432
65,200
70,066
72,553
75,974
68,839
75,706
66,744
57,862
58,309
70,466
50,992
55,013
51,840
54,359
81,287
60,041
63,112
71,762
57,613
54,321
59,303
58,255
51,431
65,761
92,585
32,741
63,340
50,143
38,832
72,420
67,529
57,507
66,092
65,050
58,583
60,245
47,765
54,264
79,479
75,140
59,118
55,328
71,867
52,807
52,653
62,579
63,860
77,767
80,598
60,882
70,770
75,898
58,804
79,591
66,655
58,682
63,998
63,388
66,677
56,312
63,079
61,412
56,239
40,684
36,379
55,696
41,422
31,558
41,303
31,455
41,557
51,244
33,744
39,866
40,305
45,798
35,294
47,126
36,500
33,483
38,378
47,730
59,539
49,691
58,550
69,676
54,410
51,565
59,118
43,939
39,623
51,383
39,090
81,727
61,341
50,357
59,064
51,044
61,071
74,307
67,901
56,249
69,493
55,273
51,238
74,588
75,015
67,813
90,058
85,717
70,987
63,912
70,361
63,065
86,507
74,390
61,638
59,473
65,886
69,292
74,863
52,055
71,224
80,422
86,915
67,717
75,831
113,464
80,553
78,792
60,741
74,831
54,469
79,668
76,181
76,731
76,526
77,149
91,999
90,821
95,349
73,794
75,395
70,311
89,484
100,052
59,090
93,338
67,422
72,165
73,663
82,130
84,311
66,379
76,711
92,380
55,384
85,934
78,598
64,580
74,662
73,221
71,672
77,215
76,472
75,541
85,940
70,695
67,398
72,648
92,112
70,047
76,034
84,126
66,754
71,366
89,359
87,147
78,364
85,401
74,222
100,790
102,658
124,545
113,060
78,259
101,797
116,583
62,067
117,034
106,707
122,771
77,856
95,864
103,048
73,474
126,499
93,601
76,807
112,535
126,122
101,278
126,039
103,523
104,941
66,209
116,071
90,532
116,345
145,873
118,516
101,200
74,376
78,679
90,304
74,612
93,353
108,023
53,611
162,820
96,590
87,862
108,734
142,153
90,236
119,481
29,305,147
100.00%
filtered
71,599
90,373
89,525
55,209
66,821
78,153
64,226
73,772
68,710
78,464
72,620
62,273
71,922
54,357
74,573
82,426
81,762
61,633
70,927
72,388
61,138
68,643
69,089
77,112
45,863
94,142
66,807
71,732
55,573
64,915
60,867
74,220
73,495
69,974
71,248
63,450
67,392
59,484
58,503
73,463
80,167
70,856
66,649
76,716
78,328
83,908
65,486
53,205
102,310
103,989
85,064
66,493
81,061
94,781
80,070
76,784
60,917
86,467
64,759
60,851
78,891
101,321
66,762
72,104
100,775
74,367
103,685
97,659
145,245
70,188
51,133
66,589
74,899
67,103
82,163
67,070
66,195
66,081
76,475
57,228
80,478
100,182
71,775
75,996
70,343
77,217
60,330
86,268
74,710
61,503
62,395
89,019
75,178
66,029
61,696
76,276
53,918
67,669
74,698
88,729
69,825
74,272
88,940
78,646
73,086
83,996
77,017
83,414
74,782
61,187
80,918
79,053
51,679
68,999
50,644
87,417
79,834
86,865
75,442
79,345
62,839
66,376
61,636
55,288
66,637
65,101
75,887
60,477
62,495
60,555
57,209
77,832
77,639
63,737
87,460
70,911
73,915
73,603
65,560
74,969
70,749
73,882
67,199
66,894
72,009
63,708
77,132
73,749
75,680
72,493
63,773
73,075
65,889
72,658
68,024
71,044
83,903
70,460
73,615
66,687
70,619
62,494
77,572
83,838
82,134
60,559
83,352
62,131
57,387
72,716
57,035
79,934
75,957
50,967
62,850
53,356
73,432
65,200
70,065
72,553
75,974
68,839
75,704
66,744
57,861
58,309
70,466
50,992
55,013
51,840
54,359
81,287
60,041
63,112
71,762
57,613
54,321
59,302
58,255
51,430
65,761
92,584
32,741
63,340
50,143
38,832
72,418
67,529
57,507
66,092
65,050
58,583
60,244
47,765
54,264
79,479
75,140
59,118
55,328
71,866
52,807
52,652
62,579
63,860
77,767
80,597
60,882
70,770
75,897
58,804
79,590
66,655
58,682
63,998
63,388
66,677
56,312
63,079
61,411
56,239
40,684
36,379
55,695
41,422
31,558
41,303
31,455
41,557
51,243
33,744
39,866
40,305
45,798
35,294
47,126
36,500
33,483
38,378
47,730
59,539
49,690
58,550
69,676
54,409
51,565
59,118
43,939
39,623
51,383
39,090
81,726
61,341
50,357
59,064
51,043
61,071
74,307
67,901
56,249
69,493
55,273
51,238
74,588
75,014
67,813
90,058
85,717
70,987
63,912
70,361
63,065
86,507
74,390
61,638
59,473
65,885
69,292
74,861
52,055
71,224
80,422
86,914
67,717
75,830
113,464
80,553
78,792
60,741
74,830
54,469
79,668
76,181
76,731
76,526
77,148
91,999
90,821
95,348
73,794
75,395
70,311
89,484
100,052
59,089
93,337
67,422
72,165
73,663
82,130
84,311
66,379
76,711
92,379
55,384
85,934
78,598
64,580
74,662
73,221
71,672
77,213
76,472
75,541
85,940
70,695
67,397
72,648
92,112
70,046
76,034
84,126
66,754
71,366
89,359
87,147
78,364
85,401
74,221
100,790
102,658
124,545
113,060
78,259
101,797
116,583
62,067
117,034
106,707
122,771
77,856
95,862
103,048
73,473
126,499
93,601
76,807
112,535
126,122
101,278
126,039
103,523
104,940
66,209
116,071
90,532
116,344
145,873
118,516
101,200
74,376
78,679
90,304
74,612
93,353
108,023
53,611
162,820
96,590
87,862
108,734
142,152
90,236
119,481
29,305,061
100.00%
denoisedF
71,051
89,946
88,650
54,825
66,233
77,697
63,686
73,270
68,152
77,961
72,019
61,816
71,447
53,936
74,097
81,865
81,314
61,051
70,393
72,092
60,688
68,058
68,426
76,668
45,395
93,474
66,417
71,299
55,199
64,610
60,053
73,589
72,683
69,404
70,685
62,863
66,865
59,042
58,166
72,936
79,595
70,332
66,148
75,933
77,855
83,354
65,017
52,752
101,517
103,232
84,398
65,953
80,719
94,155
79,387
76,184
60,513
85,922
63,956
60,409
78,261
100,881
66,065
71,668
100,121
73,758
103,190
96,734
144,602
69,672
50,707
65,846
74,328
66,597
81,839
66,665
65,680
65,518
76,101
56,968
79,939
99,717
71,292
75,402
69,834
76,782
60,030
85,933
74,085
61,055
61,733
88,771
74,685
65,401
61,238
75,881
53,461
67,037
74,033
88,046
69,204
73,675
88,294
78,094
72,673
83,155
76,333
82,635
74,235
60,818
80,380
78,409
51,312
68,321
50,148
86,128
79,467
86,222
74,852
78,850
62,593
66,120
61,005
54,928
65,876
64,604
75,360
60,067
61,979
60,040
56,838
76,877
77,067
63,116
87,031
70,407
73,511
73,255
65,046
74,464
70,258
73,472
66,527
66,250
71,310
63,070
76,302
73,169
75,011
72,075
63,278
72,696
65,479
72,160
67,577
70,585
83,274
70,047
73,058
66,230
69,955
61,908
76,937
83,429
81,645
59,792
82,810
61,644
56,953
71,992
56,583
79,141
75,394
50,625
62,349
52,848
72,879
64,718
69,521
72,209
75,407
68,309
75,283
66,436
57,398
57,999
69,672
50,402
54,542
51,495
53,912
80,604
59,683
62,729
71,137
57,049
53,617
58,813
57,783
51,067
64,869
91,996
32,457
62,903
49,744
38,523
71,740
67,045
56,903
65,642
64,550
58,231
59,799
47,396
53,799
78,913
74,450
58,665
54,808
71,199
52,298
52,399
62,031
62,994
77,364
80,178
59,987
70,373
75,450
58,353
79,100
66,079
58,182
63,553
62,860
66,164
55,866
62,570
60,980
55,785
40,312
36,061
55,225
41,079
31,219
40,879
31,203
41,358
50,995
33,457
39,381
39,991
45,464
34,941
46,692
36,132
33,126
37,918
47,243
58,945
49,370
58,132
69,290
53,806
51,191
58,579
43,532
39,209
50,933
38,755
81,121
60,852
49,883
58,628
50,585
60,531
73,728
67,471
55,838
68,674
54,986
50,832
73,999
74,440
67,480
89,205
85,061
70,524
63,337
69,922
62,447
85,947
73,911
61,254
59,036
65,395
68,665
74,489
51,637
70,497
79,922
86,415
67,264
75,259
112,977
79,831
78,245
60,181
74,201
54,139
78,940
75,569
76,223
75,860
76,685
91,415
90,182
94,691
73,331
74,920
69,726
88,706
99,461
58,855
91,734
66,861
71,506
73,249
81,800
84,008
65,926
76,047
91,553
54,748
85,363
77,992
64,285
73,696
72,615
71,191
76,677
75,844
75,076
85,397
70,310
66,909
72,109
91,389
69,389
75,387
83,463
66,384
70,886
88,866
86,563
77,906
84,766
73,634
100,342
101,984
123,983
112,615
77,816
101,263
115,896
61,421
116,565
106,227
122,047
77,340
95,452
102,552
73,091
125,772
93,099
76,249
112,049
125,365
100,548
125,709
103,048
104,458
65,824
115,487
89,967
115,944
145,279
117,799
100,787
73,961
78,030
89,801
74,237
92,807
107,372
53,257
162,258
96,055
87,454
108,593
141,848
89,846
118,586
29,094,395
99.28%
denoisedR
70,714
89,357
87,073
54,070
65,672
77,094
62,992
72,589
67,199
77,862
70,824
61,002
70,798
53,341
72,970
80,476
80,590
60,338
70,036
71,950
59,796
67,072
68,006
76,255
44,636
92,034
65,348
70,861
54,758
64,390
59,623
72,323
72,147
68,538
69,836
61,853
66,098
58,145
57,210
72,858
79,088
69,863
65,345
74,579
77,162
83,041
64,382
51,551
100,706
102,252
83,355
65,680
80,308
93,135
78,830
75,623
59,598
85,333
62,747
60,103
78,132
100,618
65,347
71,244
99,445
73,253
102,304
95,997
143,040
68,995
50,056
64,974
74,059
66,083
81,593
65,293
65,258
65,118
75,440
56,790
79,099
99,471
70,803
74,734
68,980
76,062
59,892
85,545
73,505
59,784
61,470
88,547
73,725
64,787
60,560
75,467
52,171
66,428
73,092
87,368
69,014
73,046
87,829
77,122
72,432
82,532
75,449
81,611
73,499
59,991
79,766
77,849
50,332
67,696
49,659
85,766
79,044
84,963
74,163
78,463
62,449
65,776
59,884
54,242
64,808
64,087
74,731
60,104
61,397
59,749
56,231
75,199
76,116
62,836
86,012
69,910
72,659
72,670
64,160
73,822
69,488
73,119
65,570
65,471
70,759
62,733
75,525
72,096
74,067
71,162
63,021
72,212
65,388
71,517
67,224
70,235
82,301
69,885
72,112
65,630
69,935
60,867
76,005
83,175
81,268
59,295
81,847
61,385
56,223
70,994
56,305
78,022
74,815
50,391
61,979
51,825
72,509
63,341
69,172
72,139
75,006
67,678
75,013
66,177
57,116
57,889
68,787
49,532
53,737
51,252
53,312
80,012
58,941
62,310
70,680
56,519
52,770
58,354
56,795
50,307
64,779
92,048
31,746
62,240
48,562
37,901
70,749
66,270
56,709
65,167
63,953
57,519
59,533
46,944
53,161
77,935
73,881
58,052
54,204
70,192
50,918
52,313
60,910
62,319
76,785
79,788
59,912
69,443
74,893
57,516
78,210
65,193
57,522
62,991
61,181
65,608
55,496
62,009
60,627
55,279
40,225
35,248
55,124
40,708
30,784
39,895
30,304
41,004
50,692
33,225
38,512
39,828
44,969
34,392
46,166
35,708
32,644
37,411
46,535
57,847
49,206
57,328
68,985
53,627
50,552
57,854
43,173
38,577
50,617
37,966
80,631
60,517
48,952
57,139
49,607
60,190
72,993
67,124
55,297
67,792
54,558
50,335
72,888
73,681
66,920
88,120
84,444
70,088
62,571
69,564
61,663
85,140
73,395
60,694
58,290
64,639
67,499
73,941
51,002
69,947
78,917
85,452
66,244
74,077
112,557
79,437
76,878
59,130
73,158
53,349
77,962
74,911
75,605
74,786
75,565
90,654
88,867
93,993
72,546
74,098
68,920
87,874
98,740
58,613
91,173
65,809
71,020
72,895
81,220
83,929
65,355
75,875
90,290
53,754
84,761
77,538
63,746
73,663
71,762
70,670
75,664
74,376
74,775
84,639
69,432
66,637
71,620
89,903
67,976
74,855
82,635
65,683
70,555
88,604
85,778
77,187
83,622
73,153
100,105
100,799
123,412
111,773
77,019
100,924
114,759
60,045
115,996
105,736
120,925
76,178
94,790
101,774
72,040
125,017
92,798
74,821
111,477
124,156
99,757
125,384
102,568
103,669
65,419
114,798
89,180
115,331
145,063
116,967
100,074
73,013
77,449
88,963
73,617
92,244
105,952
52,901
161,656
95,190
86,521
108,388
141,491
89,083
117,913
28,825,413
98.36%
merged
68,064
86,465
83,075
51,945
63,030
71,854
60,124
69,838
63,522
76,274
67,071
58,067
69,061
51,490
68,834
77,533
78,443
57,645
67,845
70,769
56,781
63,821
65,378
73,138
42,430
88,227
62,691
69,324
52,945
62,870
56,767
69,044
69,465
65,406
67,174
58,716
63,700
55,788
55,144
71,122
76,613
67,467
62,352
71,349
74,081
80,314
61,692
48,769
96,749
98,263
79,951
63,426
78,366
90,153
75,581
70,536
57,018
82,388
58,983
58,415
76,414
98,629
62,972
69,354
95,834
70,813
99,013
90,714
138,613
66,520
48,089
61,285
71,308
63,176
79,643
62,850
63,232
62,747
71,956
55,831
76,550
97,722
68,342
72,040
65,881
73,566
58,556
83,920
70,764
56,554
59,507
87,315
69,884
61,661
58,190
72,752
49,298
63,625
69,967
84,542
67,104
70,824
84,270
73,275
70,767
78,907
72,147
76,505
70,400
58,014
75,921
75,361
48,004
65,121
47,554
83,163
77,226
81,605
70,829
76,172
61,460
64,192
55,692
51,922
61,425
62,258
71,861
58,774
59,486
57,956
54,192
69,996
72,479
61,119
82,192
67,732
70,002
71,071
61,240
71,325
65,460
71,482
62,262
62,195
67,730
60,132
72,237
69,564
70,883
68,450
58,661
70,538
64,094
69,007
65,043
68,084
77,630
67,865
69,239
63,653
67,438
58,282
72,586
81,246
78,859
56,637
78,106
59,062
53,847
67,815
54,539
74,228
72,144
48,661
60,035
49,125
70,196
60,096
67,346
70,188
72,353
64,643
72,837
63,897
55,248
56,864
64,649
46,898
51,555
49,505
51,529
77,209
56,979
60,263
67,528
53,941
48,790
56,158
53,901
47,863
63,333
90,149
30,518
59,857
45,768
36,026
67,895
63,367
54,887
62,428
60,757
55,342
57,039
45,017
50,636
74,396
71,401
56,003
52,143
67,450
46,999
51,605
58,078
57,694
73,901
77,895
58,465
66,899
72,541
55,174
75,549
62,516
55,416
60,842
57,553
63,630
53,492
59,696
58,087
53,047
38,912
33,256
53,516
39,322
29,150
37,432
28,868
39,616
49,669
32,179
36,078
38,413
43,332
32,940
44,015
34,470
31,421
35,810
44,074
55,017
47,680
54,654
67,525
52,101
48,945
55,265
41,710
36,644
48,740
36,028
72,302
58,347
46,337
53,869
47,131
57,932
70,459
64,587
52,723
64,452
51,911
48,643
69,918
71,312
65,175
85,000
81,890
68,304
60,165
67,640
58,939
82,264
71,127
58,118
55,760
61,891
64,468
72,038
49,145
66,805
76,112
82,803
63,894
70,761
110,714
76,662
74,104
56,120
69,646
50,907
74,640
72,459
72,920
71,610
72,499
86,987
85,000
91,315
70,226
71,500
66,317
84,858
96,326
57,138
87,198
62,721
68,307
71,292
79,342
82,884
63,169
73,679
85,876
50,450
81,933
75,252
61,610
70,880
68,911
68,679
72,416
71,477
72,434
81,634
67,216
64,958
68,808
86,358
64,577
72,501
79,132
61,980
68,581
86,160
82,952
75,026
80,571
69,854
96,814
96,939
120,631
107,583
74,074
97,843
109,875
56,183
112,851
102,859
116,494
72,676
92,088
98,570
69,423
120,038
90,216
71,401
108,466
118,453
95,788
123,257
99,089
100,349
63,119
110,814
84,964
112,572
141,798
112,015
97,815
69,981
74,795
85,906
70,584
88,879
101,757
49,517
157,879
91,921
82,881
107,584
138,600
85,927
113,397
27,747,699
94.69%
nonchim
60,593
72,797
75,533
45,689
55,485
60,058
52,002
62,371
58,049
69,219
61,740
52,530
61,499
46,790
62,453
71,339
72,742
54,818
59,468
58,587
51,643
57,684
61,228
62,814
38,466
81,432
57,829
62,333
46,973
53,093
52,636
64,159
63,682
57,404
62,042
53,868
59,529
47,421
49,666
62,908
67,764
61,152
56,088
66,191
67,464
68,730
51,590
44,636
84,679
89,178
69,054
53,827
68,040
80,848
68,660
62,594
51,767
73,925
50,012
48,357
59,245
84,643
56,969
60,921
86,677
59,454
89,908
76,934
124,669
59,840
43,557
56,912
59,923
58,105
68,749
58,526
58,174
55,196
63,564
49,650
67,608
88,252
59,594
64,231
60,733
64,494
51,772
72,273
64,368
51,720
51,341
75,499
63,516
55,311
47,663
63,397
45,663
54,075
62,329
75,541
55,888
64,223
70,790
69,056
60,348
71,830
65,181
69,143
63,584
50,602
66,960
62,853
44,160
60,021
42,808
76,032
72,302
76,709
61,623
67,470
56,377
53,413
50,699
46,603
57,575
56,418
64,856
45,847
53,840
47,647
49,637
64,863
65,553
54,828
72,726
60,310
64,667
63,585
56,138
62,240
60,472
60,994
55,498
58,324
60,510
53,108
65,381
62,904
64,183
61,204
50,369
63,449
51,604
64,016
53,205
60,332
69,310
56,916
61,455
57,484
54,677
54,074
63,495
69,464
71,327
51,037
67,821
49,029
49,860
62,926
49,220
67,916
61,930
41,900
53,277
45,569
61,247
56,623
63,115
62,673
62,771
57,191
62,959
48,638
47,255
50,655
55,226
43,391
45,097
40,235
45,851
69,811
50,586
49,747
58,359
47,039
44,225
50,373
48,736
41,355
57,423
83,643
27,485
55,121
41,818
32,701
62,748
58,077
45,235
53,467
51,472
49,570
52,097
41,324
46,530
67,771
63,591
49,911
45,020
62,512
43,643
47,774
52,653
53,251
65,393
70,689
50,354
61,036
67,292
50,339
68,561
57,818
49,977
55,558
54,145
56,953
48,479
55,412
50,279
49,166
32,418
30,099
47,111
36,199
25,996
34,725
26,945
35,813
43,645
28,613
32,706
33,862
38,866
30,131
39,438
30,949
28,214
31,983
42,227
49,979
42,703
46,973
57,413
46,002
42,740
49,705
37,256
33,606
44,252
32,969
62,872
53,054
41,817
51,498
42,943
51,087
64,000
56,887
47,871
58,709
45,556
42,641
65,394
62,091
59,200
79,861
74,168
64,004
56,030
59,714
54,585
69,006
64,622
50,526
49,659
57,032
61,281
65,819
45,267
57,184
69,809
77,598
59,653
63,571
100,339
68,244
66,341
50,777
65,917
46,060
68,265
67,968
65,250
65,603
65,507
77,637
77,636
81,585
64,472
65,403
60,163
78,475
88,567
52,680
83,642
57,512
63,842
62,283
71,988
71,287
56,302
61,291
79,723
47,911
73,794
66,250
55,372
65,695
61,378
61,189
67,456
67,062
59,706
72,527
61,676
58,364
60,438
79,186
61,270
65,697
73,550
55,096
63,076
79,342
76,228
68,542
74,242
59,789
78,134
88,680
111,030
98,809
69,218
87,580
97,608
51,933
104,957
92,636
109,135
66,202
82,126
88,761
64,003
109,457
81,328
65,951
94,130
109,066
88,641
110,337
83,219
86,222
53,900
98,385
79,092
97,180
126,252
97,682
88,246
64,279
70,043
80,182
64,036
81,459
95,464
44,542
143,919
87,992
76,747
96,354
130,372
80,385
100,305
24,873,336
84.88%
This table can be downloaded as an Excel table below:
5. DADA2 Amplicon Sequence Variants (ASVs). A total of 28836 unique merged and chimera-free ASV sequences were identified, and their corresponding
read counts for each sample are available in the "ASV Read Count Table" with rows for the ASV sequences and columns for sample. This read count table can be used for
microbial profile comparison among different samples and the sequences provided in the table can be used to taxonomy assignment.
The species-level, open-reference 16S rRNA NGS reads taxonomy assignment pipeline
Version 20210310a
The close-reference taxonomy assignment of the ASV sequences using BLASTN is based on the algorithm published by Al-Hebshi et. al. (2015)[2].
1. Raw sequences reads in FASTA format were BLASTN-searched against a combined set of 16S rRNA reference sequences - the FOMC 16S rRNA Reference Sequences version 20221029 (https://microbiome.forsyth.org/ftp/refseq/).
This set consists of the HOMD (version 15.22 http://www.homd.org/index.php?name=seqDownload&file&type=R ), Mouse Oral Microbiome Database (MOMD version 5.1 https://momd.org/ftp/16S_rRNA_refseq/MOMD_16S_rRNA_RefSeq/V5.1/),
and the NCBI 16S rRNA reference sequence set (https://ftp.ncbi.nlm.nih.gov/blast/db/16S_ribosomal_RNA.tar.gz).
These sequences were screened and combined to remove short sequences (<1000nt), chimera, duplicated and sub-sequences,
as well as sequences with poor taxonomy annotation (e.g., without species information).
This process resulted in 1,015 full-length 16S rRNA sequences from HOMD V15.22, 356 from MOMD V5.1, and 22,126 from NCBI, a total of 23,497 sequences.
Altogether these sequence represent a total of 17,035 oral and non-oral microbial species.
The NCBI BLASTN version 2.7.1+ (Zhang et al, 2000) [3] was used with the default parameters.
Reads with ≥ 98% sequence identity to the matched reference and ≥ 90% alignment length
(i.e., ≥ 90% of the read length that was aligned to the reference and was used to calculate
the sequence percent identity) were classified based on the taxonomy of the reference sequence
with highest sequence identity. If a read matched with reference sequences representing
more than one species with equal percent identity and alignment length, it was subject
to chimera checking with USEARCH program version v8.1.1861 (Edgar 2010). Non-chimeric reads with multi-species
best hits were considered valid and were assigned with a unique species
notation (e.g., spp) denoting unresolvable multiple species.
2. Unassigned reads (i.e., reads with < 98% identity or < 90% alignment length) were pooled together and reads < 200 bases were
removed. The remaining reads were subject to the de novo
operational taxonomy unit (OTU) calling and chimera checking using the USEARCH program version v8.1.1861 (Edgar 2010)[4].
The de novo OTU calling and chimera checking was done using 98% as the sequence identity cutoff, i.e., the species-level OTU.
The output of this step produced species-level de novo clustered OTUs with 98% identity.
Representative reads from each of the OTUs/species were then BLASTN-searched
against the same reference sequence set again to determine the closest species for
these potential novel species. These potential novel species were pooled together with the reads that were signed to specie-level in
the previous step, for down-stream analyses.
Reference:
Al-Hebshi NN, Nasher AT, Idris AM, Chen T. Robust species taxonomy assignment algorithm for 16S rRNA NGS reads: application
to oral carcinoma samples. J Oral Microbiol. 2015 Sep 29;7:28934. doi: 10.3402/jom.v7.28934. PMID: 26426306; PMCID: PMC4590409.
Zhang Z, Schwartz S, Wagner L, Miller W. A greedy algorithm for aligning DNA sequences. J Comput Biol. 2000 Feb-Apr;7(1-2):203-14. doi: 10.1089/10665270050081478. PMID: 10890397.
Edgar RC. Search and clustering orders of magnitude faster than BLAST.
Bioinformatics. 2010 Oct 1;26(19):2460-1. doi: 10.1093/bioinformatics/btq461. Epub 2010 Aug 12. PubMed PMID: 20709691.
3. Designations used in the taxonomy:
1) Taxonomy levels are indicated by these prefixes:
k__: domain/kingdom
p__: phylum
c__: class
o__: order
f__: family
g__: genus
s__: species
Example:
k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Lachnospiraceae;g__Blautia;s__faecis
2) Unique level identified – known species:
k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Lachnospiraceae;g__Roseburia;s__hominis
The above example shows some reads match to a single species (all levels are unique)
3) Non-unique level identified – known species:
k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Lachnospiraceae;g__Roseburia;s__multispecies_spp123_3
The above example “s__multispecies_spp123_3” indicates certain reads equally match to 3 species of the
genus Roseburia; the “spp123” is a temporally assigned species ID.
k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Lachnospiraceae;g__multigenus;s__multispecies_spp234_5
The above example indicates certain reads match equally to 5 different species, which belong to multiple genera.;
the “spp234” is a temporally assigned species ID.
4) Unique level identified – unknown species, potential novel species:
k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Lachnospiraceae;g__Roseburia;s__ hominis_nov_97%
The above example indicates that some reads have no match to any of the reference sequences with
sequence identity ≥ 98% and percent coverage (alignment length) ≥ 98% as well. However this groups
of reads (actually the representative read from a de novo OTU) has 96% percent identity to
Roseburia hominis, thus this is a potential novel species, closest to Roseburia hominis.
(But they are not the same species).
5) Multiple level identified – unknown species, potential novel species:
k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Lachnospiraceae;g__Roseburia;s__ multispecies_sppn123_3_nov_96%
The above example indicates that some reads have no match to any of the reference sequences
with sequence identity ≥ 98% and percent coverage (alignment length) ≥ 98% as well.
However this groups of reads (actually the representative read from a de novo OTU)
has 96% percent identity equally to 3 species in Roseburia. Thus this is no single
closest species, instead this group of reads match equally to multiple species at 96%.
Since they have passed chimera check so they represent a novel species. “sppn123” is a
temporary ID for this potential novel species.
4. The taxonomy assignment algorithm is illustrated in this flow char below:
Read Taxonomy Assignment - Result Summary *
Code
Category
MPC=0% (>=1 read)
MPC=0.01%(>=2477 reads)
A
Total reads
24,873,336
24,873,336
B
Total assigned reads
24,770,040
24,770,040
C
Assigned reads in species with read count < MPC
0
221,871
D
Assigned reads in samples with read count < 500
0
0
E
Total samples
403
403
F
Samples with reads >= 500
403
403
G
Samples with reads < 500
0
0
H
Total assigned reads used for analysis (B-C-D)
24,770,040
24,548,169
I
Reads assigned to single species
23,000,219
22,876,798
J
Reads assigned to multiple species
978,337
958,878
K
Reads assigned to novel species
791,484
712,493
L
Total number of species
1,392
320
M
Number of single species
526
265
N
Number of multi-species
71
13
O
Number of novel species
795
42
P
Total unassigned reads
103,296
103,296
Q
Chimeric reads
5,917
5,917
R
Reads without BLASTN hits
7,321
7,321
S
Others: short, low quality, singletons, etc.
90,058
90,058
A=B+P=C+D+H+Q+R+S
E=F+G
B=C+D+H
H=I+J+K
L=M+N+O
P=Q+R+S
* MPC = Minimal percent (of all assigned reads) read count per species, species with read count < MPC were removed.
* Samples with reads < 500 were removed from downstream analyses.
* The assignment result from MPC=0.1% was used in the downstream analyses.
Read Taxonomy Assignment - ASV Species-Level Read Counts Table
This table shows the read counts for each sample (columns) and each species identified based on the ASV sequences.
The downstream analyses were based on this table.
The species listed in the table has full taxonomy and a dynamically assigned species ID specific to this report.
When some reads match with the reference sequences of more than one species equally (i.e., same percent identiy and alignmnet coverage),
they can't be assigned to a particular species. Instead, they are assigned to multiple species with the species notaton
"s__multispecies_spp2_2". In this notation, spp2 is the dynamic ID assigned to these reads that hit multiple sequences and the "_2"
at the end of the notation means there are two species in the spp2.
You can look up which species are included in the multi-species assignment, in this table below:
Another type of notation is "s__multispecies_sppn2_2", in which the "n" in the sppn2 means it's a potential novel species because all the reads in this species
have < 98% idenity to any of the reference sequences. They were grouped together based on de novo OTU clustering at 98% identity cutoff. And then
a representative sequence was chosed to BLASTN search against the reference database to find the closest match (but will still be < 98%). This representative
sequence also matched equally to more than one species, hence the "spp" was given in the label.
In ecology, alpha diversity (α-diversity) is the mean species diversity in sites or habitats at a local scale.
The term was introduced by R. H. Whittaker[5][6] together with the terms beta diversity (β-diversity)
and gamma diversity (γ-diversity). Whittaker's idea was that the total species diversity in a landscape
(gamma diversity) is determined by two different things, the mean species diversity in sites or habitats
at a more local scale (alpha diversity) and the differentiation among those habitats (beta diversity).
Diversity measures are affected by the sampling depth. Rarefaction is a technique to assess species richness from the results of sampling. Rarefaction allows
the calculation of species richness for a given number of individual samples, based on the construction
of so-called rarefaction curves. This curve is a plot of the number of species as a function of the
number of samples. Rarefaction curves generally grow rapidly at first, as the most common species are found,
but the curves plateau as only the rarest species remain to be sampled [7].
The two main factors taken into account when measuring diversity are richness and evenness.
Richness is a measure of the number of different kinds of organisms present in a particular area.
Evenness compares the similarity of the population size of each of the species present. There are
many different ways to measure the richness and evenness. These measurements are called "estimators" or "indices".
Below is a diversity of 3 commonly used indices showing the values for all the samples (dots) and in groups (boxes) at the species level.
Printed on each graph is the statistical significance p values of the difference between the groups.
The significance is calculated using either Kruskal-Wallis test or the Wilcoxon rank sum test, both are non-parametric methods (since
microbiome read count data are considered non-normally distributed) for testing
whether samples originate from the same distribution (i.e., no difference between groups). The Kruskal-Wallis test is used to compare three or more
independent groups to determine if there are statistically significant differences between their medians. The Wilcoxon Rank Sum test, also known as
the Mann-Whitney U test, is used to compare two independent groups to determine if there is a significant difference between their distributions.
The p-value is shown on the top of each graph. A p-value < 0.05 is considered statistically significant between/among the test groups.
Alpha Diversity Box Plots for All Groups - Species Level
Alpha Diversity Box Plots for Individual Comparisons at Species level
Beta diversity compares the similarity (or dissimilarity) of microbial profiles between different
groups of samples. There are many different similarity/dissimilarity metrics [8].
In general, they can be quantitative (using sequence abundance, e.g., Bray-Curtis or weighted UniFrac)
or binary (considering only presence-absence of sequences, e.g., binary Jaccard or unweighted UniFrac).
They can be even based on phylogeny (e.g., UniFrac metrics) or not (non-UniFrac metrics, such as Bray-Curtis, etc.).
For microbiome studies, species profiles of samples can be compared with the Bray-Curtis dissimilarity,
which is based on the count data type. The pair-wise Bray-Curtis dissimilarity matrix of all samples can then be
subject to either multi-dimensional scaling (MDS, also known as PCoA) or non-metric MDS (NMDS).
MDS/PCoA is a
scaling or ordination method that starts with a matrix of similarities or dissimilarities
between a set of samples and aims to produce a low-dimensional graphical plot of the data
in such a way that distances between points in the plot are close to original dissimilarities.
NMDS is similar to MDS, however it does not use the dissimilarities data, instead it converts them into
the ranks and use these ranks in the calculation.
In our beta diversity analysis, Bray-Curtis dissimilarity matrix was first calculated and then plotted by the PCoA and
NMDS separately. Below are beta diveristy results for all groups together, at the Species level:
NMDS and PCoA Plots for All Groups - Species Level
The above PCoA and NMDS plots are based on count data. The count data can also be transformed into centered log ratio (CLR)
for each species. The CLR data is no longer count data and cannot be used in Bray-Curtis dissimilarity calculation. Instead
CLR can be compared with Euclidean distances. When CLR data are compared by Euclidean distance, the distance is also called
Aitchison distance.
Below are the NMDS and PCoA plots of the Aitchison distances of the samples at the Species level:
NMDS and PCoA Plots for Individual Comparisons at Species level
16S rRNA next generation sequencing (NGS) generates a fixed number of reads that reflect the proportion of different
species in a sample, i.e., the relative abundance of species, instead of the absolute abundance.
In Mathematics, measurements involving probabilities, proportions, percentages, and ppm can all
be thought of as compositional data. This makes the microbiome read count data “compositional”
(Gloor et al, 2017). In general, compositional data represent parts of a whole which only
carry relative information [9].
The problem of microbiome data being compositional arises when comparing two groups of samples for
identifying “differentially abundant” species. A species with the same absolute abundance between two
conditions, its relative abundances in the two conditions (e.g., percent abundance) can become different
if the relative abundance of other species change greatly. This problem can lead to incorrect conclusion
in terms of differential abundance for microbial species in the samples.
When studying differential abundance (DA), the current better approach is to transform the read count
data into log ratio data. The ratios are calculated between read counts of all species in a sample to
a “reference” count (e.g., mean read count of the sample). The log ratio data allow the detection of DA
species without being affected by percentage bias mentioned above
In this report, a compositional DA analysis tool “ANCOM” (analysis of composition of microbiomes)
was used [10]. ANCOM transforms the count data into log-ratios and thus is more suitable for comparing
the composition of microbiomes in two or more populations. "ANCOM" generates a table of features with
W-statistics and whether the null hypothesis is rejected. The “W” is the W-statistic, or number of
features that a single feature is tested to be significantly different against. Hence the higher the "W"
the more statistical sifgnificant that a feature/species is differentially abundant.
Starting with version V1.2, we include the results of ANCOM-BC (Analysis of Compositions of
Microbiomes with Bias Correction) (Lin and Peddada 2020) [9]. 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 [10]; Grandhi, Guo, and Peddada 2016 [11]). For more detail
explanation and additional features of ANCOM-BC2 please see author's documentation.
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.
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.
"ALDEx2 is a compositional data analysis tool designed to enhance the statistical analysis of high-throughput sequencing datasets,
including RNA-seq, ChIP-seq, 16S rRNA gene sequencing, metagenomic analysis, and selective growth experiments.
Despite the fundamental similarities in data structure across these various experimental designs—namely,
counts of sequencing reads mapped to numerous features—traditional data analysis methods
have remained disparate and non-transferable between experiment types.
ALDEx2 addresses this challenge by employing compositional data analysis methods from the physical and geological sciences,
which convert raw data into relative abundances. This transformation leads to analyses that are more robust and reproducible.
Utilizing Bayesian methods to infer technical and statistical errors, ALDEx2 has demonstrated its applicability and effectiveness
across diverse datasets. It accurately identifies differential abundance and the direction of changes in selective growth experiments,
aligns closely with leading tools in identifying differentially expressed genes in RNA-seq datasets,
and successfully distinguishes differential taxa in the Human Microbiome Project 16S rRNA gene abundance dataset."
In this paired-sample differential abundance test, ALDEx2 was used with the Wilcoxon rank-sum test to identify features at different taxonomy ranks (from Phylum to Species)
that are significantly differentially abundant between two conditions. p-values were adjusted using "Holm" or "Benjamini-Hochberg" (BH) method to control the false discovery rate (FDR).
The simplest but strict p-value adjustment method is the Bonferroni method in which the p-values are multiplied by the number of comparisons.
Both Holm (1979) and Benjamini & Hochberg (1995) ("BH" or its alias "fdr") provide less conservative corrections.
In the below ALDEx2 result folder, comparisons were done with these two adjustment methods. Also, analyses were done with and without "paired sample" options for comparison.
References:
Fernandes AD, Macklaim JM, Linn TG, Reid G, Gloor GB. ANOVA-like differential expression (ALDEx) analysis for mixed population RNA-Seq. PLoS One. 2013 Jul 2;8(7):e67019. doi: 10.1371/journal.pone.0067019. PMID: 23843979; PMCID: PMC3699591.
Fernandes AD, Reid JN, Macklaim JM, McMurrough TA, Edgell DR, Gloor GB. Unifying the analysis of high-throughput sequencing datasets: characterizing RNA-seq, 16S rRNA gene sequencing and selective growth experiments by compositional data analysis. Microbiome. 2014 May 5;2:15. doi: 10.1186/2049-2618-2-15. PMID: 24910773; PMCID: PMC4030730.
Bonferroni, C. E., Teoria statistica delle classi e calcolo delle probabilità, Pubblicazioni del R Istituto Superiore di Scienze Economiche e Commerciali di Firenze 1936
Holm, S. (1979). A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics, 6, 65--70. http://www.jstor.org/stable/4615733.
Benjamini, Y., and Hochberg, Y. (1995). Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society Series B, 57, 289--300. http://www.jstor.org/stable/2346101.
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) [17].
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.
1) Paired Differences - Paired difference testing and boxplots: This section uses QIIME2's "qiime longitudinal pairwise-differences" package to perform paired difference testing between samples from each subject. Sample
pairs may represent a typical intervention study (e.g., samples collected
pre- and post-treatment), paired samples from two different timepoints
(e.g., in a longitudinal study design), or identical samples receiving
different treatments. This action tests whether the change in a numeric
metadata value "metric" differs from zero and differs between groups (e.g.,
groups of subjects receiving different treatments), and produces boxplots of
paired difference distributions for each group. Note that "metric" can be
derived from a feature table or metadata.
2) Pairwise-distances - Paired pairwise distance testing and boxplots: This section uses QIIME2's "qiime longitudinal pairwise-distances" package to performs pairwise distance testing between sample pairs from each subject.
Sample pairs may represent a typical intervention study, e.g., samples
collected pre- and post-treatment; paired samples from two different
timepoints (e.g., in a longitudinal study design), or identical samples
receiving different two different treatments. This action tests whether the
pairwise distance between each subject pair differs between groups (e.g.,
groups of subjects receiving different treatments) and produces boxplots of
paired distance distributions for each group.
3) Volatility - Interactive control chart of longitudinal volatility: This section uses QIIME2's "qiime longitudinal volatility" package to generate an interactive control chart depicting the longitudinal volatility
of sample metadata and/or feature frequencies across time (as set using the
"state_column" parameter). Any numeric metadata column (and metadata-
transformable artifacts, e.g., alpha diversity results) can be plotted on
the y-axis, and are selectable using the "metric_column" selector. Metric
values are averaged to compare across any categorical metadata column using
the "group_column" selector. Longitudinal volatility for individual subjects
sampled over time is co-plotted as "spaghetti" plots if the
"individual_id_column" parameter is used. state_column will typically be a
measure of time, but any numeric metadata column can be used.
To analyze the co-occurrence or co-exclusion between microbial species among different samples, network correlation
analysis tools are usually used for this purpose. However, microbiome count data are compositional. If count data are normalized to the total number of counts in the
sample, the data become not independent and traditional statistical metrics (e.g., correlation) for the detection
of specie-species relationships can lead to spurious results. In addition, sequencing-based studies typically
measure hundreds of OTUs (species) on few samples; thus, inference of OTU-OTU association networks is severely
under-powered. We provide the network association result with SparCC (Sparse Correlations for Compositional data)(Friedman & Alm 2012), which
is a method for inferring correlations from compositional data. SparCC estimates the linear Pearson correlations between
the log-transformed components.
The results of this analysis are for research purpose only. They are not intended to diagnose, treat, cure, or prevent any disease. Forsyth and FOMC
are not responsible for use of information provided in this report outside the research area.
