Project FOMC16861_4 services include NGS sequencing of the V1V3 region of the 16S rRNA gene amplicons from the samples. First and foremost, please
download this report, as well as the sequence raw data from the download links provided below.
These links will expire after 60 days. We cannot guarantee the availability of your data after 60 days.
Full Bioinformatics analysis service was requested. We provide many analyses, starting from the raw sequence quality and noise filtering, pair reads merging, as well as chimera filtering for the sequences, using the
DADA2 denosing algorithm and pipeline.
We also provide many downstream analyses such as taxonomy assignment, alpha and beta diversity analyses, and differential abundance analysis.
For taxonomy assignment, most informative would be the taxonomy barplots. We provide an interactive barplots to show the relative abundance of microbes at different taxonomy levels (from Phylum to species) that you can choose.
If you specify which groups of samples you want to compare for differential abundance, we provide both ANCOM and LEfSe differential abundance analysis.
The samples were processed and analyzed with the ZymoBIOMICS® Service: Targeted
Metagenomic Sequencing (Zymo Research, Irvine, CA).
DNA Extraction: If DNA extraction was performed, the following DNA
extraction kit was used according to the manufacturer’s instructions:
☑
ZymoBIOMICS®-96 MagBead DNA Kit (Zymo Research, Irvine, CA)
☐
N/A (DNA Extraction Not Performed)
Elution Volume: 50µL
Additional Notes: NA
Targeted Library Preparation: The DNA samples were prepared for targeted
sequencing with the Quick-16S™ NGS Library Prep Kit (Zymo Research, Irvine, CA).
These primers were custom designed by Zymo Research to provide the best coverage
of the 16S gene while maintaining high sensitivity. The primer sets used in this project
are marked below:
☐
Quick-16S™ Primer Set V1-V2 (Zymo Research, Irvine, CA)
☑
Quick-16S™ Primer Set V1-V3 (Zymo Research, Irvine, CA)
☐
Quick-16S™ Primer Set V3-V4 (Zymo Research, Irvine, CA)
☐
Quick-16S™ Primer Set V4 (Zymo Research, Irvine, CA)
☐
Quick-16S™ Primer Set V6-V8 (Zymo Research, Irvine, CA)
Additional Notes: NA
The sequencing library was prepared using an innovative library preparation process in
which PCR reactions were performed in real-time PCR machines to control cycles and
therefore limit PCR chimera formation. The final PCR products were quantified with
qPCR fluorescence readings and pooled together based on equal molarity. The final
pooled library was cleaned up with the Select-a-Size DNA Clean & Concentrator™
(Zymo Research, Irvine, CA), then quantified with TapeStation® (Agilent Technologies,
Santa Clara, CA) and Qubit® (Thermo Fisher Scientific, Waltham, WA).
Control Samples: The ZymoBIOMICS® Microbial Community Standard (Zymo
Research, Irvine, CA) was used as a positive control for each DNA extraction, if
performed. The ZymoBIOMICS® Microbial Community DNA Standard (Zymo Research,
Irvine, CA) was used as a positive control for each targeted library preparation.
Negative controls (i.e. blank extraction control, blank library preparation control) were
included to assess the level of bioburden carried by the wet-lab process.
Sequencing: The final library was sequenced on Illumina® NextSeq 2000™ with a p1
(Illumina, Sand Diego, CA) reagent kit (600 cycles). The sequencing was performed
with 25% PhiX spike-in.
Absolute Abundance Quantification*: A quantitative real-time PCR was set up with a
standard curve. The standard curve was made with plasmid DNA containing one copy
of the 16S gene and one copy of the fungal ITS2 region prepared in 10-fold serial
dilutions. The primers used were the same as those used in Targeted Library
Preparation. The equation generated by the plasmid DNA standard curve was used to
calculate the number of gene copies in the reaction for each sample. The PCR input
volume (2 µl) was used to calculate the number of gene copies per microliter in each
DNA sample.
The number of genome copies per microliter DNA sample was calculated by dividing
the gene copy number by an assumed number of gene copies per genome. The value
used for 16S copies per genome is 4. The value used for ITS copies per genome is 200.
The amount of DNA per microliter DNA sample was calculated using an assumed
genome size of 4.64 x 106 bp, the genome size of Escherichia coli, for 16S samples, or
an assumed genome size of 1.20 x 107 bp, the genome size of Saccharomyces
cerevisiae, for ITS samples. This calculation is shown below:
Calculated Total DNA = Calculated Total Genome Copies × Assumed Genome Size (4.64 × 106 bp) ×
Average Molecular Weight of a DNA bp (660 g/mole/bp) ÷ Avogadro’s Number (6.022 x 1023/mole)
* Absolute Abundance Quantification is only available for 16S and ITS analyses.
The absolute abundance standard curve data can be viewed in Excel here:
The absolute abundance standard curve is shown below:
The complete report of your project, including all links in this report, can be downloaded by clicking the link provided below. The downloaded file is a compressed ZIP file and once unzipped, open the file “REPORT.html” (may only shown as "REPORT" in your computer) by double clicking it. Your default web browser will open it and you will see the exact content of this report.
Please download and save the file to your computer storage device. The download link will expire after 60 days upon your receiving of this report.
Complete report download link:
To view the report, please follow the following steps:
1.
Download the .zip file from the report link above.
2.
Extract all the contents of the downloaded .zip file to your desktop.
3.
Open the extracted folder and find the "REPORT.html" (may shown as only "REPORT").
4.
Open (double-clicking) the REPORT.html file. Your default browser will open the top age of the complete report. Within the
report, there are links to view all the analyses performed for the project.
The raw NGS sequence data is available for download with the link provided below. The data is a compressed ZIP file and can be unzipped to individual sequence files.
Since this is a pair-end sequencing, each of your samples is represented by two sequence files, one for READ 1,
with the file extension “*_R1.fastq.gz”, another READ 2, with the file extension “*_R1.fastq.gz”.
The files are in FASTQ format and are compressed. FASTQ format is a text-based data format for storing both a biological sequence
and its corresponding quality scores. Most sequence analysis software will be able to open them.
The Sample IDs associated with the R1 and R2 fastq files are listed in the table below:
Sample ID
Original Sample ID
Read 1 File Name
Read 2 File Name
F16861.S100
original sample ID here
zr16861_100V1V3_R1.fastq.gz
zr16861_100V1V3_R2.fastq.gz
F16861.S101
original sample ID here
zr16861_101V1V3_R1.fastq.gz
zr16861_101V1V3_R2.fastq.gz
F16861.S102
original sample ID here
zr16861_102V1V3_R1.fastq.gz
zr16861_102V1V3_R2.fastq.gz
F16861.S103
original sample ID here
zr16861_103V1V3_R1.fastq.gz
zr16861_103V1V3_R2.fastq.gz
F16861.S104
original sample ID here
zr16861_104V1V3_R1.fastq.gz
zr16861_104V1V3_R2.fastq.gz
F16861.S105
original sample ID here
zr16861_105V1V3_R1.fastq.gz
zr16861_105V1V3_R2.fastq.gz
F16861.S106
original sample ID here
zr16861_106V1V3_R1.fastq.gz
zr16861_106V1V3_R2.fastq.gz
F16861.S107
original sample ID here
zr16861_107V1V3_R1.fastq.gz
zr16861_107V1V3_R2.fastq.gz
F16861.S108
original sample ID here
zr16861_108V1V3_R1.fastq.gz
zr16861_108V1V3_R2.fastq.gz
F16861.S109
original sample ID here
zr16861_109V1V3_R1.fastq.gz
zr16861_109V1V3_R2.fastq.gz
F16861.S010
original sample ID here
zr16861_10V1V3_R1.fastq.gz
zr16861_10V1V3_R2.fastq.gz
F16861.S110
original sample ID here
zr16861_110V1V3_R1.fastq.gz
zr16861_110V1V3_R2.fastq.gz
F16861.S111
original sample ID here
zr16861_111V1V3_R1.fastq.gz
zr16861_111V1V3_R2.fastq.gz
F16861.S112
original sample ID here
zr16861_112V1V3_R1.fastq.gz
zr16861_112V1V3_R2.fastq.gz
F16861.S113
original sample ID here
zr16861_113V1V3_R1.fastq.gz
zr16861_113V1V3_R2.fastq.gz
F16861.S114
original sample ID here
zr16861_114V1V3_R1.fastq.gz
zr16861_114V1V3_R2.fastq.gz
F16861.S115
original sample ID here
zr16861_115V1V3_R1.fastq.gz
zr16861_115V1V3_R2.fastq.gz
F16861.S116
original sample ID here
zr16861_116V1V3_R1.fastq.gz
zr16861_116V1V3_R2.fastq.gz
F16861.S117
original sample ID here
zr16861_117V1V3_R1.fastq.gz
zr16861_117V1V3_R2.fastq.gz
F16861.S118
original sample ID here
zr16861_118V1V3_R1.fastq.gz
zr16861_118V1V3_R2.fastq.gz
F16861.S119
original sample ID here
zr16861_119V1V3_R1.fastq.gz
zr16861_119V1V3_R2.fastq.gz
F16861.S011
original sample ID here
zr16861_11V1V3_R1.fastq.gz
zr16861_11V1V3_R2.fastq.gz
F16861.S120
original sample ID here
zr16861_120V1V3_R1.fastq.gz
zr16861_120V1V3_R2.fastq.gz
F16861.S121
original sample ID here
zr16861_121V1V3_R1.fastq.gz
zr16861_121V1V3_R2.fastq.gz
F16861.S122
original sample ID here
zr16861_122V1V3_R1.fastq.gz
zr16861_122V1V3_R2.fastq.gz
F16861.S123
original sample ID here
zr16861_123V1V3_R1.fastq.gz
zr16861_123V1V3_R2.fastq.gz
F16861.S124
original sample ID here
zr16861_124V1V3_R1.fastq.gz
zr16861_124V1V3_R2.fastq.gz
F16861.S125
original sample ID here
zr16861_125V1V3_R1.fastq.gz
zr16861_125V1V3_R2.fastq.gz
F16861.S126
original sample ID here
zr16861_126V1V3_R1.fastq.gz
zr16861_126V1V3_R2.fastq.gz
F16861.S127
original sample ID here
zr16861_127V1V3_R1.fastq.gz
zr16861_127V1V3_R2.fastq.gz
F16861.S128
original sample ID here
zr16861_128V1V3_R1.fastq.gz
zr16861_128V1V3_R2.fastq.gz
F16861.S129
original sample ID here
zr16861_129V1V3_R1.fastq.gz
zr16861_129V1V3_R2.fastq.gz
F16861.S012
original sample ID here
zr16861_12V1V3_R1.fastq.gz
zr16861_12V1V3_R2.fastq.gz
F16861.S130
original sample ID here
zr16861_130V1V3_R1.fastq.gz
zr16861_130V1V3_R2.fastq.gz
F16861.S131
original sample ID here
zr16861_131V1V3_R1.fastq.gz
zr16861_131V1V3_R2.fastq.gz
F16861.S132
original sample ID here
zr16861_132V1V3_R1.fastq.gz
zr16861_132V1V3_R2.fastq.gz
F16861.S133
original sample ID here
zr16861_133V1V3_R1.fastq.gz
zr16861_133V1V3_R2.fastq.gz
F16861.S134
original sample ID here
zr16861_134V1V3_R1.fastq.gz
zr16861_134V1V3_R2.fastq.gz
F16861.S135
original sample ID here
zr16861_135V1V3_R1.fastq.gz
zr16861_135V1V3_R2.fastq.gz
F16861.S136
original sample ID here
zr16861_136V1V3_R1.fastq.gz
zr16861_136V1V3_R2.fastq.gz
F16861.S137
original sample ID here
zr16861_137V1V3_R1.fastq.gz
zr16861_137V1V3_R2.fastq.gz
F16861.S138
original sample ID here
zr16861_138V1V3_R1.fastq.gz
zr16861_138V1V3_R2.fastq.gz
F16861.S139
original sample ID here
zr16861_139V1V3_R1.fastq.gz
zr16861_139V1V3_R2.fastq.gz
F16861.S013
original sample ID here
zr16861_13V1V3_R1.fastq.gz
zr16861_13V1V3_R2.fastq.gz
F16861.S140
original sample ID here
zr16861_140V1V3_R1.fastq.gz
zr16861_140V1V3_R2.fastq.gz
F16861.S141
original sample ID here
zr16861_141V1V3_R1.fastq.gz
zr16861_141V1V3_R2.fastq.gz
F16861.S142
original sample ID here
zr16861_142V1V3_R1.fastq.gz
zr16861_142V1V3_R2.fastq.gz
F16861.S143
original sample ID here
zr16861_143V1V3_R1.fastq.gz
zr16861_143V1V3_R2.fastq.gz
F16861.S144
original sample ID here
zr16861_144V1V3_R1.fastq.gz
zr16861_144V1V3_R2.fastq.gz
F16861.S145
original sample ID here
zr16861_145V1V3_R1.fastq.gz
zr16861_145V1V3_R2.fastq.gz
F16861.S146
original sample ID here
zr16861_146V1V3_R1.fastq.gz
zr16861_146V1V3_R2.fastq.gz
F16861.S147
original sample ID here
zr16861_147V1V3_R1.fastq.gz
zr16861_147V1V3_R2.fastq.gz
F16861.S148
original sample ID here
zr16861_148V1V3_R1.fastq.gz
zr16861_148V1V3_R2.fastq.gz
F16861.S149
original sample ID here
zr16861_149V1V3_R1.fastq.gz
zr16861_149V1V3_R2.fastq.gz
F16861.S014
original sample ID here
zr16861_14V1V3_R1.fastq.gz
zr16861_14V1V3_R2.fastq.gz
F16861.S150
original sample ID here
zr16861_150V1V3_R1.fastq.gz
zr16861_150V1V3_R2.fastq.gz
F16861.S151
original sample ID here
zr16861_151V1V3_R1.fastq.gz
zr16861_151V1V3_R2.fastq.gz
F16861.S152
original sample ID here
zr16861_152V1V3_R1.fastq.gz
zr16861_152V1V3_R2.fastq.gz
F16861.S153
original sample ID here
zr16861_153V1V3_R1.fastq.gz
zr16861_153V1V3_R2.fastq.gz
F16861.S154
original sample ID here
zr16861_154V1V3_R1.fastq.gz
zr16861_154V1V3_R2.fastq.gz
F16861.S155
original sample ID here
zr16861_155V1V3_R1.fastq.gz
zr16861_155V1V3_R2.fastq.gz
F16861.S156
original sample ID here
zr16861_156V1V3_R1.fastq.gz
zr16861_156V1V3_R2.fastq.gz
F16861.S157
original sample ID here
zr16861_157V1V3_R1.fastq.gz
zr16861_157V1V3_R2.fastq.gz
F16861.S158
original sample ID here
zr16861_158V1V3_R1.fastq.gz
zr16861_158V1V3_R2.fastq.gz
F16861.S159
original sample ID here
zr16861_159V1V3_R1.fastq.gz
zr16861_159V1V3_R2.fastq.gz
F16861.S015
original sample ID here
zr16861_15V1V3_R1.fastq.gz
zr16861_15V1V3_R2.fastq.gz
F16861.S160
original sample ID here
zr16861_160V1V3_R1.fastq.gz
zr16861_160V1V3_R2.fastq.gz
F16861.S161
original sample ID here
zr16861_161V1V3_R1.fastq.gz
zr16861_161V1V3_R2.fastq.gz
F16861.S162
original sample ID here
zr16861_162V1V3_R1.fastq.gz
zr16861_162V1V3_R2.fastq.gz
F16861.S016
original sample ID here
zr16861_16V1V3_R1.fastq.gz
zr16861_16V1V3_R2.fastq.gz
F16861.S017
original sample ID here
zr16861_17V1V3_R1.fastq.gz
zr16861_17V1V3_R2.fastq.gz
F16861.S018
original sample ID here
zr16861_18V1V3_R1.fastq.gz
zr16861_18V1V3_R2.fastq.gz
F16861.S019
original sample ID here
zr16861_19V1V3_R1.fastq.gz
zr16861_19V1V3_R2.fastq.gz
F16861.S001
original sample ID here
zr16861_1V1V3_R1.fastq.gz
zr16861_1V1V3_R2.fastq.gz
F16861.S020
original sample ID here
zr16861_20V1V3_R1.fastq.gz
zr16861_20V1V3_R2.fastq.gz
F16861.S021
original sample ID here
zr16861_21V1V3_R1.fastq.gz
zr16861_21V1V3_R2.fastq.gz
F16861.S022
original sample ID here
zr16861_22V1V3_R1.fastq.gz
zr16861_22V1V3_R2.fastq.gz
F16861.S023
original sample ID here
zr16861_23V1V3_R1.fastq.gz
zr16861_23V1V3_R2.fastq.gz
F16861.S024
original sample ID here
zr16861_24V1V3_R1.fastq.gz
zr16861_24V1V3_R2.fastq.gz
F16861.S025
original sample ID here
zr16861_25V1V3_R1.fastq.gz
zr16861_25V1V3_R2.fastq.gz
F16861.S026
original sample ID here
zr16861_26V1V3_R1.fastq.gz
zr16861_26V1V3_R2.fastq.gz
F16861.S027
original sample ID here
zr16861_27V1V3_R1.fastq.gz
zr16861_27V1V3_R2.fastq.gz
F16861.S028
original sample ID here
zr16861_28V1V3_R1.fastq.gz
zr16861_28V1V3_R2.fastq.gz
F16861.S029
original sample ID here
zr16861_29V1V3_R1.fastq.gz
zr16861_29V1V3_R2.fastq.gz
F16861.S002
original sample ID here
zr16861_2V1V3_R1.fastq.gz
zr16861_2V1V3_R2.fastq.gz
F16861.S030
original sample ID here
zr16861_30V1V3_R1.fastq.gz
zr16861_30V1V3_R2.fastq.gz
F16861.S031
original sample ID here
zr16861_31V1V3_R1.fastq.gz
zr16861_31V1V3_R2.fastq.gz
F16861.S032
original sample ID here
zr16861_32V1V3_R1.fastq.gz
zr16861_32V1V3_R2.fastq.gz
F16861.S033
original sample ID here
zr16861_33V1V3_R1.fastq.gz
zr16861_33V1V3_R2.fastq.gz
F16861.S034
original sample ID here
zr16861_34V1V3_R1.fastq.gz
zr16861_34V1V3_R2.fastq.gz
F16861.S035
original sample ID here
zr16861_35V1V3_R1.fastq.gz
zr16861_35V1V3_R2.fastq.gz
F16861.S036
original sample ID here
zr16861_36V1V3_R1.fastq.gz
zr16861_36V1V3_R2.fastq.gz
F16861.S037
original sample ID here
zr16861_37V1V3_R1.fastq.gz
zr16861_37V1V3_R2.fastq.gz
F16861.S038
original sample ID here
zr16861_38V1V3_R1.fastq.gz
zr16861_38V1V3_R2.fastq.gz
F16861.S039
original sample ID here
zr16861_39V1V3_R1.fastq.gz
zr16861_39V1V3_R2.fastq.gz
F16861.S003
original sample ID here
zr16861_3V1V3_R1.fastq.gz
zr16861_3V1V3_R2.fastq.gz
F16861.S040
original sample ID here
zr16861_40V1V3_R1.fastq.gz
zr16861_40V1V3_R2.fastq.gz
F16861.S041
original sample ID here
zr16861_41V1V3_R1.fastq.gz
zr16861_41V1V3_R2.fastq.gz
F16861.S042
original sample ID here
zr16861_42V1V3_R1.fastq.gz
zr16861_42V1V3_R2.fastq.gz
F16861.S043
original sample ID here
zr16861_43V1V3_R1.fastq.gz
zr16861_43V1V3_R2.fastq.gz
F16861.S044
original sample ID here
zr16861_44V1V3_R1.fastq.gz
zr16861_44V1V3_R2.fastq.gz
F16861.S045
original sample ID here
zr16861_45V1V3_R1.fastq.gz
zr16861_45V1V3_R2.fastq.gz
F16861.S046
original sample ID here
zr16861_46V1V3_R1.fastq.gz
zr16861_46V1V3_R2.fastq.gz
F16861.S047
original sample ID here
zr16861_47V1V3_R1.fastq.gz
zr16861_47V1V3_R2.fastq.gz
F16861.S048
original sample ID here
zr16861_48V1V3_R1.fastq.gz
zr16861_48V1V3_R2.fastq.gz
F16861.S049
original sample ID here
zr16861_49V1V3_R1.fastq.gz
zr16861_49V1V3_R2.fastq.gz
F16861.S004
original sample ID here
zr16861_4V1V3_R1.fastq.gz
zr16861_4V1V3_R2.fastq.gz
F16861.S050
original sample ID here
zr16861_50V1V3_R1.fastq.gz
zr16861_50V1V3_R2.fastq.gz
F16861.S051
original sample ID here
zr16861_51V1V3_R1.fastq.gz
zr16861_51V1V3_R2.fastq.gz
F16861.S052
original sample ID here
zr16861_52V1V3_R1.fastq.gz
zr16861_52V1V3_R2.fastq.gz
F16861.S053
original sample ID here
zr16861_53V1V3_R1.fastq.gz
zr16861_53V1V3_R2.fastq.gz
F16861.S054
original sample ID here
zr16861_54V1V3_R1.fastq.gz
zr16861_54V1V3_R2.fastq.gz
F16861.S055
original sample ID here
zr16861_55V1V3_R1.fastq.gz
zr16861_55V1V3_R2.fastq.gz
F16861.S056
original sample ID here
zr16861_56V1V3_R1.fastq.gz
zr16861_56V1V3_R2.fastq.gz
F16861.S057
original sample ID here
zr16861_57V1V3_R1.fastq.gz
zr16861_57V1V3_R2.fastq.gz
F16861.S058
original sample ID here
zr16861_58V1V3_R1.fastq.gz
zr16861_58V1V3_R2.fastq.gz
F16861.S059
original sample ID here
zr16861_59V1V3_R1.fastq.gz
zr16861_59V1V3_R2.fastq.gz
F16861.S005
original sample ID here
zr16861_5V1V3_R1.fastq.gz
zr16861_5V1V3_R2.fastq.gz
F16861.S060
original sample ID here
zr16861_60V1V3_R1.fastq.gz
zr16861_60V1V3_R2.fastq.gz
F16861.S061
original sample ID here
zr16861_61V1V3_R1.fastq.gz
zr16861_61V1V3_R2.fastq.gz
F16861.S062
original sample ID here
zr16861_62V1V3_R1.fastq.gz
zr16861_62V1V3_R2.fastq.gz
F16861.S063
original sample ID here
zr16861_63V1V3_R1.fastq.gz
zr16861_63V1V3_R2.fastq.gz
F16861.S064
original sample ID here
zr16861_64V1V3_R1.fastq.gz
zr16861_64V1V3_R2.fastq.gz
F16861.S065
original sample ID here
zr16861_65V1V3_R1.fastq.gz
zr16861_65V1V3_R2.fastq.gz
F16861.S066
original sample ID here
zr16861_66V1V3_R1.fastq.gz
zr16861_66V1V3_R2.fastq.gz
F16861.S067
original sample ID here
zr16861_67V1V3_R1.fastq.gz
zr16861_67V1V3_R2.fastq.gz
F16861.S068
original sample ID here
zr16861_68V1V3_R1.fastq.gz
zr16861_68V1V3_R2.fastq.gz
F16861.S069
original sample ID here
zr16861_69V1V3_R1.fastq.gz
zr16861_69V1V3_R2.fastq.gz
F16861.S006
original sample ID here
zr16861_6V1V3_R1.fastq.gz
zr16861_6V1V3_R2.fastq.gz
F16861.S070
original sample ID here
zr16861_70V1V3_R1.fastq.gz
zr16861_70V1V3_R2.fastq.gz
F16861.S071
original sample ID here
zr16861_71V1V3_R1.fastq.gz
zr16861_71V1V3_R2.fastq.gz
F16861.S072
original sample ID here
zr16861_72V1V3_R1.fastq.gz
zr16861_72V1V3_R2.fastq.gz
F16861.S073
original sample ID here
zr16861_73V1V3_R1.fastq.gz
zr16861_73V1V3_R2.fastq.gz
F16861.S074
original sample ID here
zr16861_74V1V3_R1.fastq.gz
zr16861_74V1V3_R2.fastq.gz
F16861.S075
original sample ID here
zr16861_75V1V3_R1.fastq.gz
zr16861_75V1V3_R2.fastq.gz
F16861.S076
original sample ID here
zr16861_76V1V3_R1.fastq.gz
zr16861_76V1V3_R2.fastq.gz
F16861.S077
original sample ID here
zr16861_77V1V3_R1.fastq.gz
zr16861_77V1V3_R2.fastq.gz
F16861.S078
original sample ID here
zr16861_78V1V3_R1.fastq.gz
zr16861_78V1V3_R2.fastq.gz
F16861.S079
original sample ID here
zr16861_79V1V3_R1.fastq.gz
zr16861_79V1V3_R2.fastq.gz
F16861.S007
original sample ID here
zr16861_7V1V3_R1.fastq.gz
zr16861_7V1V3_R2.fastq.gz
F16861.S080
original sample ID here
zr16861_80V1V3_R1.fastq.gz
zr16861_80V1V3_R2.fastq.gz
F16861.S081
original sample ID here
zr16861_81V1V3_R1.fastq.gz
zr16861_81V1V3_R2.fastq.gz
F16861.S082
original sample ID here
zr16861_82V1V3_R1.fastq.gz
zr16861_82V1V3_R2.fastq.gz
F16861.S083
original sample ID here
zr16861_83V1V3_R1.fastq.gz
zr16861_83V1V3_R2.fastq.gz
F16861.S084
original sample ID here
zr16861_84V1V3_R1.fastq.gz
zr16861_84V1V3_R2.fastq.gz
F16861.S085
original sample ID here
zr16861_85V1V3_R1.fastq.gz
zr16861_85V1V3_R2.fastq.gz
F16861.S086
original sample ID here
zr16861_86V1V3_R1.fastq.gz
zr16861_86V1V3_R2.fastq.gz
F16861.S087
original sample ID here
zr16861_87V1V3_R1.fastq.gz
zr16861_87V1V3_R2.fastq.gz
F16861.S088
original sample ID here
zr16861_88V1V3_R1.fastq.gz
zr16861_88V1V3_R2.fastq.gz
F16861.S089
original sample ID here
zr16861_89V1V3_R1.fastq.gz
zr16861_89V1V3_R2.fastq.gz
F16861.S008
original sample ID here
zr16861_8V1V3_R1.fastq.gz
zr16861_8V1V3_R2.fastq.gz
F16861.S090
original sample ID here
zr16861_90V1V3_R1.fastq.gz
zr16861_90V1V3_R2.fastq.gz
F16861.S091
original sample ID here
zr16861_91V1V3_R1.fastq.gz
zr16861_91V1V3_R2.fastq.gz
F16861.S092
original sample ID here
zr16861_92V1V3_R1.fastq.gz
zr16861_92V1V3_R2.fastq.gz
F16861.S093
original sample ID here
zr16861_93V1V3_R1.fastq.gz
zr16861_93V1V3_R2.fastq.gz
F16861.S094
original sample ID here
zr16861_94V1V3_R1.fastq.gz
zr16861_94V1V3_R2.fastq.gz
F16861.S095
original sample ID here
zr16861_95V1V3_R1.fastq.gz
zr16861_95V1V3_R2.fastq.gz
F16861.S096
original sample ID here
zr16861_96V1V3_R1.fastq.gz
zr16861_96V1V3_R2.fastq.gz
F16861.S097
original sample ID here
zr16861_97V1V3_R1.fastq.gz
zr16861_97V1V3_R2.fastq.gz
F16861.S098
original sample ID here
zr16861_98V1V3_R1.fastq.gz
zr16861_98V1V3_R2.fastq.gz
F16861.S099
original sample ID here
zr16861_99V1V3_R1.fastq.gz
zr16861_99V1V3_R2.fastq.gz
F16861.S009
original sample ID here
zr16861_9V1V3_R1.fastq.gz
zr16861_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
281
271
261
251
241
231
321
79.75%
80.26%
80.68%
81.16%
80.44%
72.17%
311
79.69%
80.26%
80.69%
80.12%
72.22%
54.67%
301
79.72%
80.31%
79.65%
71.86%
54.70%
28.44%
291
79.79%
79.30%
71.42%
54.34%
28.52%
20.64%
281
78.89%
71.13%
54.13%
28.27%
20.60%
7.21%
271
70.97%
54.11%
28.17%
20.42%
7.19%
3.37%
Based on the above result, the trim length combination of R1 = 321 bases and R2 = 251 bases (highlighted red above), was chosen for generating final ASVs for all sequences.
This combination generated highest number of merged non-chimeric ASVs and was used for downstream analyses, if requested.
3. Error plots from learning the error rates
After DADA2 building the error model for the set of data, it is always worthwhile, as a sanity check if nothing else, to visualize the estimated error rates.
The error rates for each possible transition (A→C, A→G, …) are shown below. Points are the observed error rates for each consensus quality score.
The black line shows the estimated error rates after convergence of the machine-learning algorithm.
The red line shows the error rates expected under the nominal definition of the Q-score.
The ideal result would be the estimated error rates (black line) are a good fit to the observed rates (points), and the error rates drop
with increased quality as expected.
Forward Read R1 Error Plot
Reverse Read R2 Error Plot
The PDF version of these plots are available here:
4. DADA2 Result Summary The table below shows the summary of the DADA2 analysis,
tracking paired read counts of each samples for all the steps during DADA2 denoising process -
including end-trimming (filtered), denoising (denoisedF, denoisedF), pair merging (merged) and chimera removal (nonchim).
Sample ID
F16861.S001
F16861.S002
F16861.S003
F16861.S004
F16861.S005
F16861.S006
F16861.S007
F16861.S008
F16861.S009
F16861.S010
F16861.S011
F16861.S012
F16861.S013
F16861.S014
F16861.S015
F16861.S016
F16861.S017
F16861.S018
F16861.S019
F16861.S020
F16861.S021
F16861.S022
F16861.S023
F16861.S024
F16861.S025
F16861.S026
F16861.S027
F16861.S028
F16861.S029
F16861.S030
F16861.S031
F16861.S032
F16861.S033
F16861.S034
F16861.S035
F16861.S036
F16861.S037
F16861.S038
F16861.S039
F16861.S040
F16861.S041
F16861.S042
F16861.S043
F16861.S044
F16861.S045
F16861.S046
F16861.S047
F16861.S048
F16861.S049
F16861.S050
F16861.S051
F16861.S052
F16861.S053
F16861.S054
F16861.S055
F16861.S056
F16861.S057
F16861.S058
F16861.S059
F16861.S060
F16861.S061
F16861.S062
F16861.S063
F16861.S064
F16861.S065
F16861.S066
F16861.S067
F16861.S068
F16861.S069
F16861.S070
F16861.S071
F16861.S072
F16861.S073
F16861.S074
F16861.S075
F16861.S076
F16861.S077
F16861.S078
F16861.S079
F16861.S080
F16861.S081
F16861.S082
F16861.S083
F16861.S084
F16861.S085
F16861.S086
F16861.S087
F16861.S088
F16861.S089
F16861.S090
F16861.S091
F16861.S092
F16861.S093
F16861.S094
F16861.S095
F16861.S096
F16861.S097
F16861.S098
F16861.S099
F16861.S100
F16861.S101
F16861.S102
F16861.S103
F16861.S104
F16861.S105
F16861.S106
F16861.S107
F16861.S108
F16861.S109
F16861.S110
F16861.S111
F16861.S112
F16861.S113
F16861.S114
F16861.S115
F16861.S116
F16861.S117
F16861.S118
F16861.S119
F16861.S120
F16861.S121
F16861.S122
F16861.S123
F16861.S124
F16861.S125
F16861.S126
F16861.S127
F16861.S128
F16861.S129
F16861.S130
F16861.S131
F16861.S132
F16861.S133
F16861.S134
F16861.S135
F16861.S136
F16861.S137
F16861.S138
F16861.S139
F16861.S140
F16861.S141
F16861.S142
F16861.S143
F16861.S144
F16861.S145
F16861.S146
F16861.S147
F16861.S148
F16861.S149
F16861.S150
F16861.S151
F16861.S152
F16861.S153
F16861.S154
F16861.S155
F16861.S156
F16861.S157
F16861.S158
F16861.S159
F16861.S160
F16861.S161
F16861.S162
Row Sum
Percentage
input
140,724
192,385
154,192
136,235
144,024
179,184
176,563
129,144
183,118
185,526
126,061
155,592
161,237
160,087
197,540
199,443
183,618
132,646
115,202
147,375
229,541
160,941
1,964
132,259
196,381
235,282
77,550
227,226
226,971
213,729
195,121
202,951
110,580
129,628
149,978
135,932
154,889
154,114
187,395
155,423
191,491
128,469
128,855
147,686
185,308
193,721
161,649
142,852
177,375
175,889
146,688
144,833
163,133
168,835
193,002
156,210
179,353
161,175
179,523
178,035
165,475
212,243
145,413
148,476
188,809
154,335
203,951
202,380
330,314
188,337
133,771
191,395
235,342
165,377
176,312
196,072
162,522
121,797
164,491
150,111
185,546
208,158
139,320
157,503
184,679
200,305
187,130
183,557
176,944
183,128
122,350
170,047
121,377
168,062
182,300
151,974
136,313
130,817
159,758
151,759
121,442
155,233
175,057
143,556
176,469
184,495
201,051
163,011
165,466
144,270
178,815
183,983
122,676
154,743
155,506
138,091
205,433
146,591
155,921
169,134
128,601
136,846
172,498
158,016
159,131
167,979
135,756
177,756
188,537
149,992
152,237
179,062
192,854
143,507
192,200
148,846
166,433
166,173
178,129
153,843
167,310
179,095
151,775
149,561
186,396
162,563
164,116
169,497
179,844
162,777
142,627
175,724
131,237
162,814
171,591
173,188
181,166
147,481
172,894
131,257
154,958
158,006
26,784,999
100.00%
filtered
136,208
186,508
149,642
132,033
139,699
173,621
171,351
125,224
177,435
180,005
122,272
150,739
156,405
155,035
191,367
193,401
177,944
128,544
111,671
142,872
222,443
155,943
1,913
128,261
190,334
227,957
75,328
220,127
219,983
207,046
189,343
196,829
107,274
125,550
145,415
131,817
150,092
149,360
181,621
150,636
185,516
124,610
124,822
143,243
179,533
187,866
156,565
138,397
171,828
170,389
142,266
140,420
158,040
163,754
187,144
151,441
173,955
156,130
173,883
172,587
160,395
205,712
140,995
143,953
182,943
149,682
197,642
196,106
320,412
182,662
129,747
185,398
228,216
160,499
170,946
190,029
157,478
117,941
159,543
145,499
179,848
201,822
134,927
152,717
179,201
194,250
181,525
177,989
171,475
177,410
118,568
164,827
117,818
163,080
176,722
147,411
132,230
126,769
154,699
147,122
117,783
150,579
169,737
139,169
171,064
178,921
195,071
157,935
160,423
139,839
173,219
178,394
118,956
150,013
150,675
133,892
199,209
141,880
150,976
163,784
124,642
132,547
167,136
153,171
154,146
163,028
131,686
172,489
182,866
145,293
147,442
173,734
187,013
139,113
186,418
144,120
161,522
161,021
172,829
149,231
162,178
173,670
147,264
144,923
180,534
157,547
158,985
164,443
174,396
157,730
138,218
170,239
127,210
157,674
166,295
167,902
175,611
143,031
167,630
127,187
150,321
153,113
25,964,912
96.94%
denoisedF
135,588
185,101
148,484
130,887
138,648
172,588
170,343
124,385
176,120
178,241
121,234
149,177
155,429
153,963
189,841
191,553
177,082
127,550
110,214
141,410
220,498
155,282
1,592
127,520
188,542
225,983
74,969
218,590
218,474
205,163
188,067
195,300
106,565
124,464
144,381
131,210
149,079
147,694
180,558
149,833
184,149
123,842
123,310
141,896
178,068
186,123
155,722
137,589
169,681
168,354
140,930
139,488
156,616
162,223
185,607
150,436
172,817
155,077
172,469
171,561
159,346
204,142
139,901
142,665
181,523
148,450
195,777
194,334
318,461
181,349
129,025
182,203
226,766
159,667
169,325
189,144
156,678
116,986
158,286
144,391
178,186
199,993
133,722
151,214
178,001
192,713
179,818
176,058
170,038
175,673
117,227
162,728
116,392
161,528
174,240
145,751
130,484
125,609
152,898
145,177
116,798
149,273
168,224
137,935
169,137
177,401
193,696
156,114
158,635
138,473
171,899
177,071
117,967
149,062
149,260
132,582
197,301
140,467
149,616
162,996
123,785
131,743
165,979
152,075
153,379
162,251
130,592
170,944
181,831
144,455
145,913
171,915
185,742
138,018
184,681
142,899
160,151
160,090
171,188
147,703
160,743
172,684
145,808
144,091
179,647
156,589
157,785
163,772
172,856
156,339
136,920
168,960
125,922
156,362
165,033
166,767
174,505
141,715
166,259
125,822
149,522
151,968
25,752,739
96.15%
denoisedR
133,993
182,960
146,567
128,519
136,853
170,600
168,822
122,882
174,044
176,042
119,428
146,912
153,422
151,417
186,757
188,300
174,699
125,788
108,367
139,300
216,616
153,137
1,797
126,085
186,775
223,713
74,335
216,858
215,112
203,793
185,600
193,412
105,453
122,560
142,669
129,493
146,618
144,968
178,179
148,379
181,820
121,697
121,215
140,213
175,810
183,419
153,498
135,870
167,573
165,004
138,840
137,062
153,677
160,177
183,276
148,582
170,779
153,244
169,660
169,404
157,585
202,071
138,081
140,359
179,280
146,671
193,219
192,059
314,476
178,922
127,311
181,070
224,249
157,879
166,578
186,797
155,004
115,747
156,472
142,426
175,213
197,518
131,797
148,610
176,114
190,190
176,280
171,876
166,996
172,287
114,812
160,016
114,408
159,162
172,271
143,872
127,617
123,161
149,968
142,241
114,475
147,353
166,142
135,434
166,261
174,598
191,156
152,908
155,034
135,979
169,139
174,829
115,869
147,128
146,818
130,615
194,596
137,557
147,218
161,061
122,043
130,021
163,681
150,183
151,357
161,029
128,675
168,283
180,314
142,657
143,451
168,935
184,363
136,093
181,940
140,664
158,306
158,327
168,503
145,432
158,470
171,143
143,730
142,013
177,772
154,826
156,118
162,009
170,691
153,257
134,513
166,908
124,078
154,407
162,905
164,275
172,378
139,181
163,609
124,014
147,576
150,184
25,395,193
94.81%
merged
131,144
176,099
141,383
121,698
130,120
166,403
165,639
118,529
168,371
169,153
113,619
139,096
149,793
145,650
179,795
179,468
170,921
121,725
102,194
132,077
207,413
129,933
555
111,600
181,592
216,885
73,160
212,314
209,644
198,957
180,275
188,914
102,482
116,178
137,446
127,303
141,034
137,097
171,849
145,015
175,829
117,135
114,837
133,858
168,788
174,916
149,578
132,410
158,725
155,036
132,659
131,446
145,562
153,239
175,023
144,337
164,201
148,367
162,326
165,019
152,909
197,157
132,305
133,714
173,130
141,456
184,272
182,867
304,267
171,267
117,426
174,679
218,016
154,679
158,649
182,889
151,724
111,482
149,425
137,509
165,900
188,982
126,514
140,101
170,432
183,616
166,976
162,524
159,541
162,643
107,487
151,609
107,679
151,593
162,191
135,434
117,939
116,643
142,410
133,206
109,098
142,064
159,364
129,844
156,935
167,593
185,715
143,781
143,973
128,675
163,154
167,274
108,977
143,156
138,846
124,164
186,221
128,368
140,027
156,949
118,173
126,237
157,963
145,366
148,496
158,216
122,117
160,844
177,185
139,509
136,108
157,367
179,878
129,293
173,915
134,236
151,831
155,064
159,579
138,620
151,956
167,365
137,360
137,681
174,606
151,044
151,575
158,898
161,664
145,329
127,209
160,761
117,989
148,265
156,466
158,276
167,728
132,529
156,345
117,451
143,755
145,144
24,362,627
90.96%
nonchim
118,977
146,760
125,681
110,283
112,968
157,770
158,334
79,584
159,883
155,011
98,037
130,048
143,961
133,369
173,924
170,615
154,357
110,619
95,674
121,697
195,875
111,568
555
106,209
165,208
195,588
73,150
210,137
193,073
190,859
157,535
172,559
84,515
104,265
123,190
119,146
134,687
130,477
136,250
135,065
162,453
106,669
108,967
114,073
147,654
155,516
134,303
122,043
147,567
139,893
122,387
120,784
132,832
134,722
160,862
120,281
145,834
127,816
151,012
146,170
138,373
182,619
108,913
123,978
148,822
122,022
172,517
162,110
245,670
155,925
99,239
163,012
192,588
133,588
148,457
162,855
127,355
97,405
127,772
127,780
143,175
164,656
114,893
129,682
155,434
172,095
156,913
152,143
144,593
153,758
97,785
144,418
99,775
142,875
143,868
127,194
111,641
108,935
136,013
125,483
103,119
135,906
151,793
122,328
145,427
157,542
177,779
132,393
135,644
120,488
151,051
152,859
100,284
118,989
128,962
115,154
171,141
119,765
130,507
135,939
102,738
108,247
148,943
128,049
136,771
157,689
107,680
148,647
172,099
122,334
128,235
144,334
171,467
111,481
159,670
129,598
135,011
150,299
148,745
127,588
136,910
160,105
125,385
127,584
166,416
135,771
132,230
142,862
148,389
135,833
112,853
144,384
105,099
121,334
143,368
139,379
153,256
122,725
143,192
107,131
127,662
130,281
22,174,474
82.79%
This table can be downloaded as an Excel table below:
5. DADA2 Amplicon Sequence Variants (ASVs). A total of 17896 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%(>=2206 reads)
A
Total reads
22,174,474
22,174,474
B
Total assigned reads
22,069,226
22,069,226
C
Assigned reads in species with read count < MPC
0
240,306
D
Assigned reads in samples with read count < 500
0
0
E
Total samples
161
161
F
Samples with reads >= 500
161
161
G
Samples with reads < 500
0
0
H
Total assigned reads used for analysis (B-C-D)
22,069,226
21,828,920
I
Reads assigned to single species
20,741,908
20,607,686
J
Reads assigned to multiple species
559,075
541,208
K
Reads assigned to novel species
768,243
680,026
L
Total number of species
1,447
392
M
Number of single species
579
322
N
Number of multi-species
55
12
O
Number of novel species
813
58
P
Total unassigned reads
105,248
105,248
Q
Chimeric reads
990
990
R
Reads without BLASTN hits
30,530
30,530
S
Others: short, low quality, singletons, etc.
73,728
73,728
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).
Alpha Diversity Box Plots for All Groups
Alpha Diversity Box Plots for Individual Comparisons
To test whether the alpha diversity among different comparison groups are different statistically, we use the Kruskal Wallis H test
provided the "alpha-group-significance" fucntion in the QIIME 2 "diversity" package. Kruskal Wallis H test is the non-parametric alternative
to the One Way ANOVA. Non-parametric means that the test doesn’t assume your data comes from a particular distribution. The H test is used
when the assumptions for ANOVA aren’t met (like the assumption of normality). It is sometimes called the one-way ANOVA on ranks,
as the ranks of the data values are used in the test rather than the actual data points. The H test determines whether the medians of two
or more groups are different.
Below are the Kruskal Wallis H test results for each comparison based on three different alpha diversity measures: 1) Observed species (features),
2) Shannon index, and 3) Simpson index.
Beta diversity compares the similarity (or dissimilarity) of microbial profiles between different
groups of samples. There are many different similarity/dissimilarity metrics [8].
In general, they can be quantitative (using sequence abundance, e.g., Bray-Curtis or weighted UniFrac)
or binary (considering only presence-absence of sequences, e.g., binary Jaccard or unweighted UniFrac).
They can be even based on phylogeny (e.g., UniFrac metrics) or not (non-UniFrac metrics, such as Bray-Curtis, etc.).
For microbiome studies, species profiles of samples can be compared with the Bray-Curtis dissimilarity,
which is based on the count data type. The pair-wise Bray-Curtis dissimilarity matrix of all samples can then be
subject to either multi-dimensional scaling (MDS, also known as PCoA) or non-metric MDS (NMDS).
MDS/PCoA is a
scaling or ordination method that starts with a matrix of similarities or dissimilarities
between a set of samples and aims to produce a low-dimensional graphical plot of the data
in such a way that distances between points in the plot are close to original dissimilarities.
NMDS is similar to MDS, however it does not use the dissimilarities data, instead it converts them into
the ranks and use these ranks in the calculation.
In our beta diversity analysis, Bray-Curtis dissimilarity matrix was first calculated and then plotted by the PCoA and
NMDS separately. Below are beta diveristy results for all groups together:
The above PCoA and NMDS plots are based on count data. The count data can also be transformed into centered log ratio (CLR)
for each species. The CLR data is no longer count data and cannot be used in Bray-Curtis dissimilarity calculation. Instead
CLR can be compared with Euclidean distances. When CLR data are compared by Euclidean distance, the distance is also called
Aitchison distance.
Below are the NMDS and PCoA plots of the Aitchison distances of the samples:
Interactive 3D PCoA Plots - Bray-Curtis Dissimilarity
Interactive 3D PCoA Plots - Euclidean Distance
Interactive 3D PCoA Plots - Correlation Coefficients
Group Significance of Beta-diversity Indices
To test whether the between-group dissimilarities are significantly greater than the within-group dissimilarities,
the "beta-group-significance" function provided in the QIIME 2 "diversity" package was used with PERMANOVA
(permutational multivariate analysis of variance) as the group significant testing method.
Three beta diversity matrics were used: 1) Bray–Curtis dissimilarity 2) Correlation coefficient matrix , and 3) Aitchison distance
(Euclidean distance between clr-transformed compositions).
16S rRNA next generation sequencing (NGS) generates a fixed number of reads that reflect the proportion of different
species in a sample, i.e., the relative abundance of species, instead of the absolute abundance.
In Mathematics, measurements involving probabilities, proportions, percentages, and ppm can all
be thought of as compositional data. This makes the microbiome read count data “compositional”
(Gloor et al, 2017). In general, compositional data represent parts of a whole which only
carry relative information [9].
The problem of microbiome data being compositional arises when comparing two groups of samples for
identifying “differentially abundant” species. A species with the same absolute abundance between two
conditions, its relative abundances in the two conditions (e.g., percent abundance) can become different
if the relative abundance of other species change greatly. This problem can lead to incorrect conclusion
in terms of differential abundance for microbial species in the samples.
When studying differential abundance (DA), the current better approach is to transform the read count
data into log ratio data. The ratios are calculated between read counts of all species in a sample to
a “reference” count (e.g., mean read count of the sample). The log ratio data allow the detection of DA
species without being affected by percentage bias mentioned above
In this report, a compositional DA analysis tool “ANCOM” (analysis of composition of microbiomes)
was used [10]. ANCOM transforms the count data into log-ratios and thus is more suitable for comparing
the composition of microbiomes in two or more populations. "ANCOM" generates a table of features with
W-statistics and whether the null hypothesis is rejected. The “W” is the W-statistic, or number of
features that a single feature is tested to be significantly different against. Hence the higher the "W"
the more statistical sifgnificant that a feature/species is differentially abundant.
References:
Gloor GB, Macklaim JM, Pawlowsky-Glahn V, Egozcue JJ. Microbiome Datasets Are Compositional: And This Is Not Optional. Front Microbiol.
2017 Nov 15;8:2224. doi: 10.3389/fmicb.2017.02224. PMID: 29187837; PMCID: PMC5695134.
Mandal S, Van Treuren W, White RA, Eggesbø M, Knight R, Peddada SD. Analysis of composition of
microbiomes: a novel method for studying microbial composition. Microb Ecol Health Dis.
2015 May 29;26:27663. doi: 10.3402/mehd.v26.27663. PMID: 26028277; PMCID: PMC4450248.
Starting with version V1.2, we include the results of ANCOM-BC (Analysis of Compositions of
Microbiomes with Bias Correction) (Lin and Peddada 2020) [11]. ANCOM-BC is an updated version of "ANCOM" that:
(a) provides statistically valid test with appropriate p-values,
(b) provides confidence intervals for differential abundance of each taxon,
(c) controls the False Discovery Rate (FDR),
(d) maintains adequate power, and
(e) is computationally simple to implement.
The bias correction (BC) addresses a challenging problem of the bias introduced by differences in
the sampling fractions across samples. This bias has been a major hurdle in performing DA analysis of microbiome data.
ANCOM-BC estimates the unknown sampling fractions and corrects the bias induced by their differences among samples.
The absolute abundance data are modeled using a linear regression framework.
Starting with version V1.43, ANCOM-BC2 is used instead of ANCOM-BC, So that multiple pairwise directional test can be performed (if there are more than two gorups in a comparison).
When performing pairwise directional test, the mixed directional false discover rate (mdFDR) is taken into account. The mdFDR
is the combination of false discovery rate due to multiple testing, multiple pairwise comparisons, and directional tests within
each pairwise comparison. The mdFDR is adopted from (Guo, Sarkar, and Peddada 2010 [12]; Grandhi, Guo, and Peddada 2016 [13]). For more detail
explanation and additional features of ANCOM-BC2 please see author's documentation.
References:
Lin H, Peddada SD. Analysis of compositions of microbiomes with bias correction.
Nat Commun. 2020 Jul 14;11(1):3514. doi: 10.1038/s41467-020-17041-7.
PMID: 32665548; PMCID: PMC7360769.
Guo W, Sarkar SK, Peddada SD. Controlling false discoveries in multidimensional directional decisions, with applications to gene expression data on ordered categories. Biometrics. 2010 Jun;66(2):485-92. doi: 10.1111/j.1541-0420.2009.01292.x. Epub 2009 Jul 23. PMID: 19645703; PMCID: PMC2895927.
Grandhi A, Guo W, Peddada SD. A multiple testing procedure for multi-dimensional pairwise comparisons with application to gene expression studies. BMC Bioinformatics. 2016 Feb 25;17:104. doi: 10.1186/s12859-016-0937-5. PMID: 26917217; PMCID: PMC4768411.
LEfSe (Linear Discriminant Analysis Effect Size) is an alternative method to find "organisms, genes, or
pathways that consistently explain the differences between two or more microbial communities" (Segata et al., 2011) [14].
Specifically, LEfSe uses rank-based Kruskal-Wallis (KW) sum-rank test to detect features with significant
differential (relative) abundance with respect to the class of interest. Since it is rank-based, instead of proportional based,
the differential species identified among the comparison groups is less biased (than percent abundance based).
Reference:
Segata N, Izard J, Waldron L, Gevers D, Miropolsky L, Garrett WS, Huttenhower C. Metagenomic biomarker discovery and explanation. Genome Biol. 2011 Jun 24;12(6):R60. doi: 10.1186/gb-2011-12-6-r60. PMID: 21702898; PMCID: PMC3218848.
To analyze the co-occurrence or co-exclusion between microbial species among different samples, network correlation
analysis tools are usually used for this purpose. However, microbiome count data are compositional. If count data are normalized to the total number of counts in the
sample, the data become not independent and traditional statistical metrics (e.g., correlation) for the detection
of specie-species relationships can lead to spurious results. In addition, sequencing-based studies typically
measure hundreds of OTUs (species) on few samples; thus, inference of OTU-OTU association networks is severely
under-powered. Here we use SPIEC-EASI (SParse InversECovariance Estimation
for Ecological Association Inference), a statistical method for the inference of microbial
ecological networks from amplicon sequencing datasets that addresses both of these issues (Kurtz et al., 2015) [15].
SPIEC-EASI combines data transformations developed for compositional data analysis with a graphical model
inference framework that assumes the underlying ecological association network is sparse. SPIEC-EASI provides
two algorithms for network inferencing – 1) Meinshausen-Bühlmann's neighborhood selection (MB method) and inverse covariance selection
(GLASSO method, i.e., graphical least absolute shrinkage and selection operator). This is fundamentally distinct from SparCC, which essentially estimate pairwise correlations. In addition
to these two methods, we provide the results of a third method - SparCC (Sparse Correlations for Compositional Data)(Friedman & Alm 2012)[16], which
is also a method for inferring correlations from compositional data. SparCC estimates the linear Pearson correlations between
the log-transformed components.
References:
Kurtz ZD, Müller CL, Miraldi ER, Littman DR, Blaser MJ, Bonneau RA. Sparse and compositionally robust inference of microbial ecological networks. PLoS Comput Biol. 2015 May 7;11(5):e1004226. doi: 10.1371/journal.pcbi.1004226. PMID: 25950956; PMCID: PMC4423992.
The results of this analysis are for research purpose only. They are not intended to diagnose, treat, cure, or prevent any disease. Forsyth and FOMC
are not responsible for use of information provided in this report outside the research area.
