Project FOMC13027 services include NGS sequencing of the V1V3 region of the 16S rRNA gene amplicons from the samples. First and foremost, please
download this report, as well as the sequence raw data from the download links provided below.
These links will expire after 60 days. We cannot guarantee the availability of your data after 60 days.
Full Bioinformatics analysis service was requested. We provide many analyses, starting from the raw sequence quality and noise filtering, pair reads merging, as well as chimera filtering for the sequences, using the
DADA2 denosing algorithm and pipeline.
We also provide many downstream analyses such as taxonomy assignment, alpha and beta diversity analyses, and differential abundance analysis.
For taxonomy assignment, most informative would be the taxonomy barplots. We provide an interactive barplots to show the relative abundance of microbes at different taxonomy levels (from Phylum to species) that you can choose.
If you specify which groups of samples you want to compare for differential abundance, we provide both ANCOM and LEfSe differential abundance analysis.
The samples were processed and analyzed with the ZymoBIOMICS® Service: Targeted
Metagenomic Sequencing (Zymo Research, Irvine, CA).
DNA Extraction: If DNA extraction was performed, one of three different DNA
extraction kits was used depending on the sample type and sample volume and were
used according to the manufacturer’s instructions, unless otherwise stated. The kit used
in this project is marked below:
☐
ZymoBIOMICS® DNA Miniprep Kit (Zymo Research, Irvine, CA)
☐
ZymoBIOMICS® DNA Microprep Kit (Zymo Research, Irvine, CA)
☐
ZymoBIOMICS®-96 MagBead DNA Kit (Zymo Research, Irvine, CA)
☑
N/A (DNA Extraction Not Performed)
Elution Volume: 50µL
Additional Notes: NA
Targeted Library Preparation: The DNA samples were prepared for targeted
sequencing with the Quick-16S™ NGS Library Prep Kit (Zymo Research, Irvine, CA).
These primers were custom designed by Zymo Research to provide the best coverage
of the 16S gene while maintaining high sensitivity. The primer sets used in this project
are marked below:
☐
Quick-16S™ Primer Set V1-V2 (Zymo Research, Irvine, CA)
☑
Quick-16S™ Primer Set V1-V3 (Zymo Research, Irvine, CA)
☐
Quick-16S™ Primer Set V3-V4 (Zymo Research, Irvine, CA)
☐
Quick-16S™ Primer Set V4 (Zymo Research, Irvine, CA)
☐
Quick-16S™ Primer Set V6-V8 (Zymo Research, Irvine, CA)
☐
Other: NA
Additional Notes: NA
The sequencing library was prepared using an innovative library preparation process in
which PCR reactions were performed in real-time PCR machines to control cycles and
therefore limit PCR chimera formation. The final PCR products were quantified with
qPCR fluorescence readings and pooled together based on equal molarity. The final
pooled library was cleaned up with the Select-a-Size DNA Clean & Concentrator™
(Zymo Research, Irvine, CA), then quantified with TapeStation® (Agilent Technologies,
Santa Clara, CA) and Qubit® (Thermo Fisher Scientific, Waltham, WA).
Control Samples: The ZymoBIOMICS® Microbial Community Standard (Zymo
Research, Irvine, CA) was used as a positive control for each DNA extraction, if
performed. The ZymoBIOMICS® Microbial Community DNA Standard (Zymo Research,
Irvine, CA) was used as a positive control for each targeted library preparation.
Negative controls (i.e. blank extraction control, blank library preparation control) were
included to assess the level of bioburden carried by the wet-lab process.
Sequencing: The final library was sequenced on Illumina® MiSeq™ with a V3 reagent kit
(600 cycles). The sequencing was performed with 10% PhiX spike-in.
Absolute Abundance Quantification*: A quantitative real-time PCR was set up with a
standard curve. The standard curve was made with plasmid DNA containing one copy
of the 16S gene and one copy of the fungal ITS2 region prepared in 10-fold serial
dilutions. The primers used were the same as those used in Targeted Library
Preparation. The equation generated by the plasmid DNA standard curve was used to
calculate the number of gene copies in the reaction for each sample. The PCR input
volume (2 µl) was used to calculate the number of gene copies per microliter in each
DNA sample.
The number of genome copies per microliter DNA sample was calculated by dividing
the gene copy number by an assumed number of gene copies per genome. The value
used for 16S copies per genome is 4. The value used for ITS copies per genome is 200.
The amount of DNA per microliter DNA sample was calculated using an assumed
genome size of 4.64 x 106 bp, the genome size of Escherichia coli, for 16S samples, or
an assumed genome size of 1.20 x 107 bp, the genome size of Saccharomyces
cerevisiae, for ITS samples. This calculation is shown below:
Calculated Total DNA = Calculated Total Genome Copies × Assumed Genome Size (4.64 × 106 bp) ×
Average Molecular Weight of a DNA bp (660 g/mole/bp) ÷ Avogadro’s Number (6.022 x 1023/mole)
* Absolute Abundance Quantification is only available for 16S and ITS analyses.
The absolute abundance standard curve data can be viewed in Excel here:
The absolute abundance standard curve is shown below:
The complete report of your project, including all links in this report, can be downloaded by clicking the link provided below. The downloaded file is a compressed ZIP file and once unzipped, open the file “REPORT.html” (may only shown as "REPORT" in your computer) by double clicking it. Your default web browser will open it and you will see the exact content of this report.
Please download and save the file to your computer storage device. The download link will expire after 60 days upon your receiving of this report.
Complete report download link:
To view the report, please follow the following steps:
1.
Download the .zip file from the report link above.
2.
Extract all the contents of the downloaded .zip file to your desktop.
3.
Open the extracted folder and find the "REPORT.html" (may shown as only "REPORT").
4.
Open (double-clicking) the REPORT.html file. Your default browser will open the top age of the complete report. Within the
report, there are links to view all the analyses performed for the project.
The raw NGS sequence data is available for download with the link provided below. The data is a compressed ZIP file and can be unzipped to individual sequence files.
Since this is a pair-end sequencing, each of your samples is represented by two sequence files, one for READ 1,
with the file extension “*_R1.fastq.gz”, another READ 2, with the file extension “*_R1.fastq.gz”.
The files are in FASTQ format and are compressed. FASTQ format is a text-based data format for storing both a biological sequence
and its corresponding quality scores. Most sequence analysis software will be able to open them.
The Sample IDs associated with the R1 and R2 fastq files are listed in the table below:
Sample ID
Original Sample ID
Read 1 File Name
Read 2 File Name
S001
zr13027_100V1V3_R1.fastq.gz
zr13027_100V1V3_R2.fastq.gz
S002
zr13027_101V1V3_R1.fastq.gz
zr13027_101V1V3_R2.fastq.gz
S003
zr13027_102V1V3_R1.fastq.gz
zr13027_102V1V3_R2.fastq.gz
S004
zr13027_103V1V3_R1.fastq.gz
zr13027_103V1V3_R2.fastq.gz
S005
zr13027_104V1V3_R1.fastq.gz
zr13027_104V1V3_R2.fastq.gz
S006
zr13027_105V1V3_R1.fastq.gz
zr13027_105V1V3_R2.fastq.gz
S007
zr13027_106V1V3_R1.fastq.gz
zr13027_106V1V3_R2.fastq.gz
S008
zr13027_107V1V3_R1.fastq.gz
zr13027_107V1V3_R2.fastq.gz
S009
zr13027_108V1V3_R1.fastq.gz
zr13027_108V1V3_R2.fastq.gz
S010
zr13027_109V1V3_R1.fastq.gz
zr13027_109V1V3_R2.fastq.gz
S011
zr13027_10V1V3_R1.fastq.gz
zr13027_10V1V3_R2.fastq.gz
S012
zr13027_110V1V3_R1.fastq.gz
zr13027_110V1V3_R2.fastq.gz
S013
zr13027_111V1V3_R1.fastq.gz
zr13027_111V1V3_R2.fastq.gz
S014
zr13027_112V1V3_R1.fastq.gz
zr13027_112V1V3_R2.fastq.gz
S015
zr13027_113V1V3_R1.fastq.gz
zr13027_113V1V3_R2.fastq.gz
S016
zr13027_114V1V3_R1.fastq.gz
zr13027_114V1V3_R2.fastq.gz
S017
zr13027_115V1V3_R1.fastq.gz
zr13027_115V1V3_R2.fastq.gz
S018
zr13027_116V1V3_R1.fastq.gz
zr13027_116V1V3_R2.fastq.gz
S019
zr13027_117V1V3_R1.fastq.gz
zr13027_117V1V3_R2.fastq.gz
S020
zr13027_118V1V3_R1.fastq.gz
zr13027_118V1V3_R2.fastq.gz
S021
zr13027_119V1V3_R1.fastq.gz
zr13027_119V1V3_R2.fastq.gz
S022
zr13027_11V1V3_R1.fastq.gz
zr13027_11V1V3_R2.fastq.gz
S023
zr13027_120V1V3_R1.fastq.gz
zr13027_120V1V3_R2.fastq.gz
S024
zr13027_121V1V3_R1.fastq.gz
zr13027_121V1V3_R2.fastq.gz
S025
zr13027_122V1V3_R1.fastq.gz
zr13027_122V1V3_R2.fastq.gz
S026
zr13027_123V1V3_R1.fastq.gz
zr13027_123V1V3_R2.fastq.gz
S027
zr13027_124V1V3_R1.fastq.gz
zr13027_124V1V3_R2.fastq.gz
S028
zr13027_125V1V3_R1.fastq.gz
zr13027_125V1V3_R2.fastq.gz
S029
zr13027_126V1V3_R1.fastq.gz
zr13027_126V1V3_R2.fastq.gz
S030
zr13027_127V1V3_R1.fastq.gz
zr13027_127V1V3_R2.fastq.gz
S031
zr13027_128V1V3_R1.fastq.gz
zr13027_128V1V3_R2.fastq.gz
S032
zr13027_129V1V3_R1.fastq.gz
zr13027_129V1V3_R2.fastq.gz
S033
zr13027_12V1V3_R1.fastq.gz
zr13027_12V1V3_R2.fastq.gz
S034
zr13027_130V1V3_R1.fastq.gz
zr13027_130V1V3_R2.fastq.gz
S035
zr13027_131V1V3_R1.fastq.gz
zr13027_131V1V3_R2.fastq.gz
S036
zr13027_132V1V3_R1.fastq.gz
zr13027_132V1V3_R2.fastq.gz
S037
zr13027_133V1V3_R1.fastq.gz
zr13027_133V1V3_R2.fastq.gz
S038
zr13027_134V1V3_R1.fastq.gz
zr13027_134V1V3_R2.fastq.gz
S039
zr13027_135V1V3_R1.fastq.gz
zr13027_135V1V3_R2.fastq.gz
S040
zr13027_136V1V3_R1.fastq.gz
zr13027_136V1V3_R2.fastq.gz
S041
zr13027_137V1V3_R1.fastq.gz
zr13027_137V1V3_R2.fastq.gz
S042
zr13027_138V1V3_R1.fastq.gz
zr13027_138V1V3_R2.fastq.gz
S043
zr13027_139V1V3_R1.fastq.gz
zr13027_139V1V3_R2.fastq.gz
S044
zr13027_13V1V3_R1.fastq.gz
zr13027_13V1V3_R2.fastq.gz
S045
zr13027_140V1V3_R1.fastq.gz
zr13027_140V1V3_R2.fastq.gz
S046
zr13027_141V1V3_R1.fastq.gz
zr13027_141V1V3_R2.fastq.gz
S047
zr13027_142V1V3_R1.fastq.gz
zr13027_142V1V3_R2.fastq.gz
S048
zr13027_143V1V3_R1.fastq.gz
zr13027_143V1V3_R2.fastq.gz
S049
zr13027_144V1V3_R1.fastq.gz
zr13027_144V1V3_R2.fastq.gz
S050
zr13027_14V1V3_R1.fastq.gz
zr13027_14V1V3_R2.fastq.gz
S051
zr13027_15V1V3_R1.fastq.gz
zr13027_15V1V3_R2.fastq.gz
S052
zr13027_16V1V3_R1.fastq.gz
zr13027_16V1V3_R2.fastq.gz
S053
zr13027_17V1V3_R1.fastq.gz
zr13027_17V1V3_R2.fastq.gz
S054
zr13027_18V1V3_R1.fastq.gz
zr13027_18V1V3_R2.fastq.gz
S055
zr13027_19V1V3_R1.fastq.gz
zr13027_19V1V3_R2.fastq.gz
S056
zr13027_1V1V3_R1.fastq.gz
zr13027_1V1V3_R2.fastq.gz
S057
zr13027_20V1V3_R1.fastq.gz
zr13027_20V1V3_R2.fastq.gz
S058
zr13027_21V1V3_R1.fastq.gz
zr13027_21V1V3_R2.fastq.gz
S059
zr13027_22V1V3_R1.fastq.gz
zr13027_22V1V3_R2.fastq.gz
S060
zr13027_23V1V3_R1.fastq.gz
zr13027_23V1V3_R2.fastq.gz
S061
zr13027_24V1V3_R1.fastq.gz
zr13027_24V1V3_R2.fastq.gz
S062
zr13027_25V1V3_R1.fastq.gz
zr13027_25V1V3_R2.fastq.gz
S063
zr13027_26V1V3_R1.fastq.gz
zr13027_26V1V3_R2.fastq.gz
S064
zr13027_27V1V3_R1.fastq.gz
zr13027_27V1V3_R2.fastq.gz
S065
zr13027_28V1V3_R1.fastq.gz
zr13027_28V1V3_R2.fastq.gz
S066
zr13027_29V1V3_R1.fastq.gz
zr13027_29V1V3_R2.fastq.gz
S067
zr13027_2V1V3_R1.fastq.gz
zr13027_2V1V3_R2.fastq.gz
S068
zr13027_30V1V3_R1.fastq.gz
zr13027_30V1V3_R2.fastq.gz
S069
zr13027_31V1V3_R1.fastq.gz
zr13027_31V1V3_R2.fastq.gz
S070
zr13027_32V1V3_R1.fastq.gz
zr13027_32V1V3_R2.fastq.gz
S071
zr13027_33V1V3_R1.fastq.gz
zr13027_33V1V3_R2.fastq.gz
S072
zr13027_34V1V3_R1.fastq.gz
zr13027_34V1V3_R2.fastq.gz
S073
zr13027_35V1V3_R1.fastq.gz
zr13027_35V1V3_R2.fastq.gz
S074
zr13027_36V1V3_R1.fastq.gz
zr13027_36V1V3_R2.fastq.gz
S075
zr13027_37V1V3_R1.fastq.gz
zr13027_37V1V3_R2.fastq.gz
S076
zr13027_38V1V3_R1.fastq.gz
zr13027_38V1V3_R2.fastq.gz
S077
zr13027_39V1V3_R1.fastq.gz
zr13027_39V1V3_R2.fastq.gz
S078
zr13027_3V1V3_R1.fastq.gz
zr13027_3V1V3_R2.fastq.gz
S079
zr13027_40V1V3_R1.fastq.gz
zr13027_40V1V3_R2.fastq.gz
S080
zr13027_41V1V3_R1.fastq.gz
zr13027_41V1V3_R2.fastq.gz
S081
zr13027_42V1V3_R1.fastq.gz
zr13027_42V1V3_R2.fastq.gz
S082
zr13027_43V1V3_R1.fastq.gz
zr13027_43V1V3_R2.fastq.gz
S083
zr13027_44V1V3_R1.fastq.gz
zr13027_44V1V3_R2.fastq.gz
S084
zr13027_45V1V3_R1.fastq.gz
zr13027_45V1V3_R2.fastq.gz
S085
zr13027_46V1V3_R1.fastq.gz
zr13027_46V1V3_R2.fastq.gz
S086
zr13027_47V1V3_R1.fastq.gz
zr13027_47V1V3_R2.fastq.gz
S087
zr13027_48V1V3_R1.fastq.gz
zr13027_48V1V3_R2.fastq.gz
S088
zr13027_49V1V3_R1.fastq.gz
zr13027_49V1V3_R2.fastq.gz
S089
zr13027_4V1V3_R1.fastq.gz
zr13027_4V1V3_R2.fastq.gz
S090
zr13027_50V1V3_R1.fastq.gz
zr13027_50V1V3_R2.fastq.gz
S091
zr13027_51V1V3_R1.fastq.gz
zr13027_51V1V3_R2.fastq.gz
S092
zr13027_52V1V3_R1.fastq.gz
zr13027_52V1V3_R2.fastq.gz
S093
zr13027_53V1V3_R1.fastq.gz
zr13027_53V1V3_R2.fastq.gz
S094
zr13027_54V1V3_R1.fastq.gz
zr13027_54V1V3_R2.fastq.gz
S095
zr13027_55V1V3_R1.fastq.gz
zr13027_55V1V3_R2.fastq.gz
S096
zr13027_56V1V3_R1.fastq.gz
zr13027_56V1V3_R2.fastq.gz
S097
zr13027_57V1V3_R1.fastq.gz
zr13027_57V1V3_R2.fastq.gz
S098
zr13027_58V1V3_R1.fastq.gz
zr13027_58V1V3_R2.fastq.gz
S099
zr13027_59V1V3_R1.fastq.gz
zr13027_59V1V3_R2.fastq.gz
S100
zr13027_5V1V3_R1.fastq.gz
zr13027_5V1V3_R2.fastq.gz
S101
zr13027_60V1V3_R1.fastq.gz
zr13027_60V1V3_R2.fastq.gz
S102
zr13027_61V1V3_R1.fastq.gz
zr13027_61V1V3_R2.fastq.gz
S103
zr13027_62V1V3_R1.fastq.gz
zr13027_62V1V3_R2.fastq.gz
S104
zr13027_63V1V3_R1.fastq.gz
zr13027_63V1V3_R2.fastq.gz
S105
zr13027_64V1V3_R1.fastq.gz
zr13027_64V1V3_R2.fastq.gz
S106
zr13027_65V1V3_R1.fastq.gz
zr13027_65V1V3_R2.fastq.gz
S107
zr13027_66V1V3_R1.fastq.gz
zr13027_66V1V3_R2.fastq.gz
S108
zr13027_67V1V3_R1.fastq.gz
zr13027_67V1V3_R2.fastq.gz
S109
zr13027_68V1V3_R1.fastq.gz
zr13027_68V1V3_R2.fastq.gz
S110
zr13027_69V1V3_R1.fastq.gz
zr13027_69V1V3_R2.fastq.gz
S111
zr13027_6V1V3_R1.fastq.gz
zr13027_6V1V3_R2.fastq.gz
S112
zr13027_70V1V3_R1.fastq.gz
zr13027_70V1V3_R2.fastq.gz
S113
zr13027_71V1V3_R1.fastq.gz
zr13027_71V1V3_R2.fastq.gz
S114
zr13027_72V1V3_R1.fastq.gz
zr13027_72V1V3_R2.fastq.gz
S115
zr13027_73V1V3_R1.fastq.gz
zr13027_73V1V3_R2.fastq.gz
S116
zr13027_74V1V3_R1.fastq.gz
zr13027_74V1V3_R2.fastq.gz
S117
zr13027_75V1V3_R1.fastq.gz
zr13027_75V1V3_R2.fastq.gz
S118
zr13027_76V1V3_R1.fastq.gz
zr13027_76V1V3_R2.fastq.gz
S119
zr13027_77V1V3_R1.fastq.gz
zr13027_77V1V3_R2.fastq.gz
S120
zr13027_78V1V3_R1.fastq.gz
zr13027_78V1V3_R2.fastq.gz
S121
zr13027_79V1V3_R1.fastq.gz
zr13027_79V1V3_R2.fastq.gz
S122
zr13027_7V1V3_R1.fastq.gz
zr13027_7V1V3_R2.fastq.gz
S123
zr13027_80V1V3_R1.fastq.gz
zr13027_80V1V3_R2.fastq.gz
S124
zr13027_81V1V3_R1.fastq.gz
zr13027_81V1V3_R2.fastq.gz
S125
zr13027_82V1V3_R1.fastq.gz
zr13027_82V1V3_R2.fastq.gz
S126
zr13027_83V1V3_R1.fastq.gz
zr13027_83V1V3_R2.fastq.gz
S127
zr13027_84V1V3_R1.fastq.gz
zr13027_84V1V3_R2.fastq.gz
S128
zr13027_85V1V3_R1.fastq.gz
zr13027_85V1V3_R2.fastq.gz
S129
zr13027_86V1V3_R1.fastq.gz
zr13027_86V1V3_R2.fastq.gz
S130
zr13027_87V1V3_R1.fastq.gz
zr13027_87V1V3_R2.fastq.gz
S131
zr13027_88V1V3_R1.fastq.gz
zr13027_88V1V3_R2.fastq.gz
S132
zr13027_89V1V3_R1.fastq.gz
zr13027_89V1V3_R2.fastq.gz
S133
zr13027_8V1V3_R1.fastq.gz
zr13027_8V1V3_R2.fastq.gz
S134
zr13027_90V1V3_R1.fastq.gz
zr13027_90V1V3_R2.fastq.gz
S135
zr13027_91V1V3_R1.fastq.gz
zr13027_91V1V3_R2.fastq.gz
S136
zr13027_92V1V3_R1.fastq.gz
zr13027_92V1V3_R2.fastq.gz
S137
zr13027_93V1V3_R1.fastq.gz
zr13027_93V1V3_R2.fastq.gz
S138
zr13027_94V1V3_R1.fastq.gz
zr13027_94V1V3_R2.fastq.gz
S139
zr13027_95V1V3_R1.fastq.gz
zr13027_95V1V3_R2.fastq.gz
S140
zr13027_96V1V3_R1.fastq.gz
zr13027_96V1V3_R2.fastq.gz
S141
zr13027_97V1V3_R1.fastq.gz
zr13027_97V1V3_R2.fastq.gz
S142
zr13027_98V1V3_R1.fastq.gz
zr13027_98V1V3_R2.fastq.gz
S143
zr13027_99V1V3_R1.fastq.gz
zr13027_99V1V3_R2.fastq.gz
S144
zr13027_9V1V3_R1.fastq.gz
zr13027_9V1V3_R2.fastq.gz
Please download and save the file to your computer storage device. The download link will expire after 60 days upon your receiving of this report.
DADA2 is a software package that models and corrects Illumina-sequenced amplicon errors.
DADA2 infers sample sequences exactly, without coarse-graining into OTUs,
and resolves differences of as little as one nucleotide. DADA2 identified more real variants
and output fewer spurious sequences than other methods.
DADA2’s advantage is that it uses more of the data. The DADA2 error model incorporates quality information,
which is ignored by all other methods after filtering. The DADA2 error model incorporates quantitative abundances,
whereas most other methods use abundance ranks if they use abundance at all.
The DADA2 error model identifies the differences between sequences, eg. A->C,
whereas other methods merely count the mismatches. DADA2 can parameterize its error model from the data itself,
rather than relying on previous datasets that may or may not reflect the PCR and sequencing protocols used in your study.
DADA2 pipeline includes several tools for read quality control, including quality filtering, trimming, denoising, pair merging and chimera filtering. Below are the major processing steps of DADA2:
Step 1. Read trimming based on sequence quality
The quality of NGS Illumina sequences often decreases toward the end of the reads.
DADA2 allows to trim off the poor quality read ends in order to improve the error
model building and pair mergicing performance.
Step 2. Learn the Error Rates
The DADA2 algorithm makes use of a parametric error model (err) and every
amplicon dataset has a different set of error rates. The learnErrors method
learns this error model from the data, by alternating estimation of the error
rates and inference of sample composition until they converge on a jointly
consistent solution. As in many machine-learning problems, the algorithm must
begin with an initial guess, for which the maximum possible error rates in
this data are used (the error rates if only the most abundant sequence is
correct and all the rest are errors).
Step 3. Infer amplicon sequence variants (ASVs) based on the error model built in previous step. This step is also called sequence "denoising".
The outcome of this step is a list of ASVs that are the equivalent of oligonucleotides.
Step 4. Merge paired reads. If the sequencing products are read pairs, DADA2 will merge the R1 and R2 ASVs into single sequences.
Merging is performed by aligning the denoised forward reads with the reverse-complement of the corresponding
denoised reverse reads, and then constructing the merged “contig” sequences.
By default, merged sequences are only output if the forward and reverse reads overlap by
at least 12 bases, and are identical to each other in the overlap region (but these conditions can be changed via function arguments).
Step 5. Remove chimera.
The core dada method corrects substitution and indel errors, but chimeras remain. Fortunately, the accuracy of sequence variants
after denoising makes identifying chimeric ASVs simpler than when dealing with fuzzy OTUs.
Chimeric sequences are identified if they can be exactly reconstructed by
combining a left-segment and a right-segment from two more abundant “parent” sequences. The frequency of chimeric sequences varies substantially
from dataset to dataset, and depends on on factors including experimental procedures and sample complexity.
Results
1. Read Quality Plots NGS sequence analaysis starts with visualizing the quality of the sequencing. Below are the quality plots of the first
sample for the R1 and R2 reads separately. In gray-scale is a heat map of the frequency of each quality score at each base position. The mean
quality score at each position is shown by the green line, and the quartiles of the quality score distribution by the orange lines.
The forward reads are usually of better quality. It is a common practice to trim the last few nucleotides to avoid less well-controlled errors
that can arise there. The trimming affects the downstream steps including error model building, merging and chimera calling. FOMC uses an empirical
approach to test many combinations of different trim length in order to achieve best final amplicon sequence variants (ASVs), see the next
section “Optimal trim length for ASVs”.
2. Optimal trim length for ASVs The final number of merged and chimera-filtered ASVs depends on the quality filtering (hence trimming) in the very beginning of the DADA2 pipeline.
In order to achieve highest number of ASVs, an empirical approach was used -
Create a random subset of each sample consisting of 5,000 R1 and 5,000 R2 (to reduce computation time)
Trim 10 bases at a time from the ends of both R1 and R2 up to 50 bases
For each combination of trimmed length (e.g., 300x300, 300x290, 290x290 etc), the trimmed reads are
subject to the entire DADA2 pipeline for chimera-filtered merged ASVs
The combination with highest percentage of the input reads becoming final ASVs is selected for the complete set of data
Below is the result of such operation, showing ASV percentages of total reads for all trimming combinations (1st Column = R1 lengths in bases; 1st Row = R2 lengths in bases):
R1/R2
281
271
261
251
241
231
321
55.63%
55.58%
55.42%
56.49%
60.77%
56.01%
311
55.45%
55.67%
55.11%
56.05%
55.90%
43.98%
301
55.42%
55.57%
55.25%
51.62%
43.52%
24.96%
291
55.53%
55.56%
50.93%
39.95%
24.91%
15.83%
281
55.72%
51.34%
39.78%
23.47%
15.54%
11.42%
271
52.21%
40.66%
23.11%
14.51%
11.26%
5.47%
Based on the above result, the trim length combination of R1 = 321 bases and R2 = 241 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
F13027.S001
F13027.S002
F13027.S003
F13027.S004
F13027.S005
F13027.S006
F13027.S007
F13027.S008
F13027.S009
F13027.S010
F13027.S011
F13027.S012
F13027.S013
F13027.S014
F13027.S015
F13027.S016
F13027.S017
F13027.S018
F13027.S019
F13027.S020
F13027.S021
F13027.S022
F13027.S023
F13027.S024
F13027.S025
F13027.S026
F13027.S027
F13027.S028
F13027.S029
F13027.S030
F13027.S031
F13027.S032
F13027.S033
F13027.S034
F13027.S035
F13027.S036
F13027.S037
F13027.S038
F13027.S039
F13027.S040
F13027.S041
F13027.S042
F13027.S043
F13027.S044
F13027.S045
F13027.S046
F13027.S047
F13027.S048
F13027.S049
F13027.S050
F13027.S051
F13027.S052
F13027.S053
F13027.S054
F13027.S055
F13027.S056
F13027.S057
F13027.S058
F13027.S059
F13027.S060
F13027.S061
F13027.S062
F13027.S063
F13027.S064
F13027.S065
F13027.S066
F13027.S067
F13027.S068
F13027.S069
F13027.S070
F13027.S071
F13027.S072
F13027.S073
F13027.S074
F13027.S075
F13027.S076
F13027.S077
F13027.S078
F13027.S079
F13027.S080
F13027.S081
F13027.S082
F13027.S083
F13027.S084
F13027.S085
F13027.S086
F13027.S087
F13027.S088
F13027.S089
F13027.S090
F13027.S091
F13027.S092
F13027.S093
F13027.S094
F13027.S095
F13027.S096
F13027.S097
F13027.S098
F13027.S099
F13027.S100
F13027.S101
F13027.S102
F13027.S103
F13027.S104
F13027.S105
F13027.S106
F13027.S107
F13027.S108
F13027.S109
F13027.S110
F13027.S111
F13027.S112
F13027.S113
F13027.S114
F13027.S115
F13027.S116
F13027.S117
F13027.S118
F13027.S119
F13027.S120
F13027.S121
F13027.S122
F13027.S123
F13027.S124
F13027.S125
F13027.S126
F13027.S127
F13027.S128
F13027.S129
F13027.S130
F13027.S131
F13027.S132
F13027.S133
F13027.S134
F13027.S135
F13027.S136
F13027.S137
F13027.S138
F13027.S139
F13027.S140
F13027.S141
F13027.S142
F13027.S143
F13027.S144
Row Sum
Percentage
input
13,171
8,985
16,336
11,874
10,125
11,884
13,567
10,255
15,571
11,121
9,853
13,330
11,392
11,037
14,910
14,195
15,661
14,205
11,682
10,695
15,699
15,430
13,303
14,685
10,156
15,612
14,298
16,182
11,173
13,320
14,029
15,521
10,825
16,512
19,847
13,546
10,826
16,101
14,730
13,994
15,048
9,808
15,046
14,336
12,043
13,168
12,091
16,690
16,985
10,294
10,265
16,199
13,159
9,160
14,016
11,087
21,872
12,062
13,945
11,785
12,861
7,118
15,392
12,278
17,323
14,800
12,711
15,881
11,816
11,034
9,346
10,729
8,332
14,835
15,794
8,628
16,602
16,271
12,867
10,774
14,720
13,193
13,537
17,280
13,583
15,265
13,845
13,943
14,918
18,341
16,191
10,370
14,687
22,628
13,791
12,004
10,874
18,720
16,046
10,190
13,202
13,014
16,426
14,500
12,418
15,887
15,627
15,021
19,932
18,655
12,381
11,764
11,324
11,892
13,221
11,369
17,787
8,992
13,520
12,811
14,524
11,808
12,876
13,745
15,755
13,079
15,429
17,861
14,769
13,095
14,117
11,959
7,934
14,992
15,765
14,808
15,265
10,797
12,134
16,774
9,821
12,579
10,221
11,878
1,955,943
100.00%
filtered
12,797
8,714
15,864
11,515
9,797
11,508
13,177
9,929
15,027
10,782
9,556
12,947
11,022
10,695
14,434
13,708
15,163
13,738
11,320
10,380
15,194
14,936
12,917
14,204
9,824
15,095
13,845
15,725
10,831
12,924
13,580
15,013
10,511
16,024
19,225
13,146
10,515
15,654
14,285
13,539
14,568
9,495
14,562
13,892
11,658
12,794
11,725
16,196
16,437
9,974
9,944
15,735
12,766
8,881
13,562
10,732
21,157
11,694
13,498
11,449
12,488
6,897
14,869
11,953
16,768
14,329
12,327
15,393
11,445
10,704
9,047
10,416
8,058
14,375
15,297
8,365
16,079
15,795
12,526
10,452
14,258
12,793
13,130
16,729
13,178
14,776
13,404
13,490
14,446
17,782
15,715
10,046
14,211
21,926
13,367
11,637
10,546
18,163
15,558
9,884
12,820
12,641
15,928
14,040
12,025
15,387
15,124
14,542
19,307
18,094
11,996
11,388
10,973
11,526
12,830
11,007
17,232
8,725
13,117
12,438
14,065
11,440
12,493
13,312
15,246
12,714
14,896
17,293
14,315
12,738
13,676
11,596
7,678
14,523
15,278
14,374
14,776
10,480
11,762
16,235
9,506
12,192
9,892
11,513
1,895,534
96.91%
denoisedF
12,231
8,428
15,073
11,000
9,358
10,963
12,714
9,573
14,104
10,365
9,077
12,289
10,718
10,224
13,872
13,028
14,616
13,130
10,653
9,946
14,704
14,332
12,348
13,495
9,457
14,364
13,177
15,111
10,214
12,310
12,952
14,431
10,056
15,205
18,641
12,575
10,227
14,932
13,527
12,829
14,126
9,071
13,927
13,379
11,181
12,061
11,197
15,791
15,646
9,592
9,556
14,918
12,208
8,487
12,977
10,216
20,451
11,317
12,983
11,066
12,012
6,569
14,218
11,239
15,968
13,823
11,860
14,738
10,990
10,288
8,595
9,850
7,730
13,678
14,650
8,021
15,436
15,104
12,007
10,030
13,598
12,334
12,540
15,908
12,490
14,189
12,710
12,641
13,823
17,116
14,972
9,569
13,675
20,825
12,667
11,156
10,104
17,508
14,934
9,491
12,281
12,241
15,219
13,589
11,427
14,770
14,442
13,925
18,709
17,330
11,492
10,848
10,628
10,998
12,292
10,470
16,514
8,360
12,642
11,725
13,607
11,015
11,872
12,412
14,553
12,359
14,200
16,402
13,697
12,172
13,058
11,023
7,327
13,920
14,899
13,794
14,104
10,068
11,326
15,597
9,125
11,647
9,395
10,854
1,813,783
92.73%
denoisedR
12,325
8,413
15,190
11,113
9,520
10,989
12,620
9,578
14,424
10,439
9,068
12,541
10,646
10,340
13,911
13,224
14,685
13,328
10,845
10,020
14,741
14,350
12,395
13,618
9,355
14,575
13,312
15,284
10,422
12,555
13,091
14,464
10,206
15,486
18,719
12,660
10,101
14,986
13,550
13,061
14,088
9,099
13,939
13,444
11,139
12,193
11,244
15,790
15,842
9,487
9,541
15,161
12,320
8,558
13,135
10,279
20,500
11,343
12,967
11,043
11,932
6,543
14,360
11,468
16,182
13,803
11,827
14,952
11,131
10,239
8,684
9,987
7,805
13,698
14,786
8,039
15,470
15,295
12,062
10,050
13,509
12,330
12,643
16,147
12,608
14,263
12,952
12,875
13,868
17,252
15,062
9,595
13,823
21,186
12,920
11,274
10,113
17,526
15,161
9,500
12,247
12,234
15,388
13,578
11,499
14,978
14,555
14,094
18,713
17,480
11,569
10,986
10,659
11,018
12,305
10,641
16,719
8,448
12,599
11,928
13,679
11,160
12,024
12,771
14,639
12,265
14,450
16,670
13,781
12,172
13,228
11,283
7,350
14,011
14,889
13,875
14,206
10,146
11,396
15,647
9,128
11,707
9,487
11,089
1,826,873
93.40%
merged
10,840
7,465
13,301
9,876
8,356
9,429
11,070
8,504
12,524
9,271
7,766
10,991
9,645
9,147
12,096
11,776
13,218
11,909
9,558
8,915
13,146
12,867
10,855
11,803
8,253
12,653
11,699
13,786
9,104
11,032
11,616
12,938
9,007
13,736
17,014
11,285
9,096
13,315
11,810
11,622
12,742
8,045
12,172
12,050
9,806
10,485
9,870
14,551
14,107
8,465
8,364
13,058
11,027
7,577
11,779
8,995
18,428
10,094
11,601
9,723
10,468
5,764
12,710
9,903
14,111
12,381
10,461
13,364
10,035
8,975
7,535
8,708
6,884
12,062
12,972
7,113
13,644
13,717
10,497
8,892
11,891
11,274
11,118
14,361
11,235
12,640
11,458
11,234
12,228
15,458
13,217
8,373
12,392
18,580
11,272
9,738
8,767
15,505
13,519
8,354
11,019
11,041
13,670
12,161
9,926
13,185
12,712
12,550
16,881
15,374
10,339
9,537
9,637
9,727
10,937
9,384
14,960
7,340
11,256
10,404
12,587
9,855
10,741
11,085
12,698
11,067
12,686
14,865
12,316
10,619
11,653
9,837
6,489
12,448
13,624
12,209
12,283
9,045
10,112
13,792
8,334
10,347
8,170
9,818
1,616,763
82.66%
nonchim
6,372
5,321
8,619
7,167
5,666
6,329
7,457
6,121
7,926
6,948
5,224
7,978
7,331
6,132
8,209
8,273
7,750
8,575
6,728
5,975
8,749
8,944
7,410
7,711
5,729
8,618
7,616
9,652
6,254
6,624
8,506
9,488
6,052
8,321
10,913
8,257
5,321
8,927
7,870
7,069
8,999
5,796
7,754
7,301
7,129
6,941
6,158
9,882
8,920
5,974
6,207
7,631
7,927
5,326
8,548
6,863
12,031
7,166
7,836
6,757
7,724
3,953
9,310
7,524
8,053
7,463
8,181
8,023
7,344
6,171
5,664
5,566
5,063
7,963
8,784
4,972
9,022
9,327
7,398
6,750
8,053
7,288
8,338
9,163
7,098
8,680
7,085
7,920
8,115
10,603
8,696
5,499
8,380
11,115
7,812
7,118
6,852
10,098
9,719
6,279
7,991
7,162
9,146
7,963
6,074
9,508
7,971
8,100
10,214
9,915
7,378
6,338
6,656
6,735
7,699
6,291
10,104
5,464
7,886
7,752
8,048
5,921
7,890
7,454
7,973
8,083
8,109
8,997
7,581
7,251
6,731
6,435
5,045
8,042
8,926
8,269
8,641
6,378
7,479
9,609
5,123
7,149
5,717
6,393
1,091,035
55.78%
This table can be downloaded as an Excel table below:
5. DADA2 Amplicon Sequence Variants (ASVs). A total of 11991 unique merged and chimera-free ASV sequences were identified, and their corresponding
read counts for each sample are available in the "ASV Read Count Table" with rows for the ASV sequences and columns for sample. This read count table can be used for
microbial profile comparison among different samples and the sequences provided in the table can be used to taxonomy assignment.
The species-level, open-reference 16S rRNA NGS reads taxonomy assignment pipeline
Version 20210310
1. Raw sequences reads in FASTA format were BLASTN-searched against a combined set of 16S rRNA reference sequences.
It consists of MOMD (version 0.1), the HOMD (version 15.2 http://www.homd.org/index.php?name=seqDownload&file&type=R ),
HOMD 16S rRNA RefSeq Extended Version 1.1 (EXT), GreenGene Gold (GG)
(http://greengenes.lbl.gov/Download/Sequence_Data/Fasta_data_files/gold_strains_gg16S_aligned.fasta.gz) ,
and the NCBI 16S rRNA reference sequence set (https://ftp.ncbi.nlm.nih.gov/blast/db/16S_ribosomal_RNA.tar.gz).
These sequences were screened and combined to remove short sequences (<1000nt), chimera, duplicated and sub-sequences,
as well as sequences with poor taxonomy annotation (e.g., without species information).
This process resulted in 1,015 from HOMD V15.22, 495 from EXT, 3,940 from GG and 18,044 from NCBI, a total of 25,120 sequences.
Altogether these sequence represent a total of 15,601 oral and non-oral microbial species.
The NCBI BLASTN version 2.7.1+ (Zhang et al, 2000) was used with the default parameters.
Reads with ≥ 98% sequence identity to the matched reference and ≥ 90% alignment length
(i.e., ≥ 90% of the read length that was aligned to the reference and was used to calculate
the sequence percent identity) were classified based on the taxonomy of the reference sequence
with highest sequence identity. If a read matched with reference sequences representing
more than one species with equal percent identity and alignment length, it was subject
to chimera checking with USEARCH program version v8.1.1861 (Edgar 2010). Non-chimeric reads with multi-species
best hits were considered valid and were assigned with a unique species
notation (e.g., spp) denoting unresolvable multiple species.
2. Unassigned reads (i.e., reads with < 98% identity or < 90% alignment length) were pooled together and reads < 200 bases were
removed. The remaining reads were subject to the de novo
operational taxonomy unit (OTU) calling and chimera checking using the USEARCH program version v8.1.1861 (Edgar 2010).
The de novo OTU calling and chimera checking was done using 98% as the sequence identity cutoff, i.e., the species-level OTU.
The output of this step produced species-level de novo clustered OTUs with 98% identity.
Representative reads from each of the OTUs/species were then BLASTN-searched
against the same reference sequence set again to determine the closest species for
these potential novel species. These potential novel species were pooled together with the reads that were signed to specie-level in
the previous step, for down-stream analyses.
Reference:
Edgar RC. Search and clustering orders of magnitude faster than BLAST.
Bioinformatics. 2010 Oct 1;26(19):2460-1. doi: 10.1093/bioinformatics/btq461. Epub 2010 Aug 12. PubMed PMID: 20709691.
3. Designations used in the taxonomy:
1) Taxonomy levels are indicated by these prefixes:
k__: domain/kingdom
p__: phylum
c__: class
o__: order
f__: family
g__: genus
s__: species
Example:
k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Lachnospiraceae;g__Blautia;s__faecis
2) Unique level identified – known species:
k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Lachnospiraceae;g__Roseburia;s__hominis
The above example shows some reads match to a single species (all levels are unique)
3) Non-unique level identified – known species:
k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Lachnospiraceae;g__Roseburia;s__multispecies_spp123_3
The above example “s__multispecies_spp123_3” indicates certain reads equally match to 3 species of the
genus Roseburia; the “spp123” is a temporally assigned species ID.
k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Lachnospiraceae;g__multigenus;s__multispecies_spp234_5
The above example indicates certain reads match equally to 5 different species, which belong to multiple genera.;
the “spp234” is a temporally assigned species ID.
4) Unique level identified – unknown species, potential novel species:
k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Lachnospiraceae;g__Roseburia;s__ hominis_nov_97%
The above example indicates that some reads have no match to any of the reference sequences with
sequence identity ≥ 98% and percent coverage (alignment length) ≥ 98% as well. However this groups
of reads (actually the representative read from a de novo OTU) has 96% percent identity to
Roseburia hominis, thus this is a potential novel species, closest to Roseburia hominis.
(But they are not the same species).
5) Multiple level identified – unknown species, potential novel species:
k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Lachnospiraceae;g__Roseburia;s__ multispecies_sppn123_3_nov_96%
The above example indicates that some reads have no match to any of the reference sequences
with sequence identity ≥ 98% and percent coverage (alignment length) ≥ 98% as well.
However this groups of reads (actually the representative read from a de novo OTU)
has 96% percent identity equally to 3 species in Roseburia. Thus this is no single
closest species, instead this group of reads match equally to multiple species at 96%.
Since they have passed chimera check so they represent a novel species. “sppn123” is a
temporary ID for this potential novel species.
4. The taxonomy assignment algorithm is illustrated in this flow char below:
Read Taxonomy Assignment - Result Summary *
Code
Category
MPC=0% (>=1 read)
MPC=0.01%(>=109 reads)
A
Total reads
1,091,035
1,091,035
B
Total assigned reads
1,090,200
1,090,200
C
Assigned reads in species with read count < MPC
0
2,383
D
Assigned reads in samples with read count < 500
0
0
E
Total samples
144
144
F
Samples with reads >= 500
144
144
G
Samples with reads < 500
0
0
H
Total assigned reads used for analysis (B-C-D)
1,090,200
1,087,817
I
Reads assigned to single species
1,050,685
1,049,459
J
Reads assigned to multiple species
15,540
15,230
K
Reads assigned to novel species
23,975
23,128
L
Total number of species
287
144
M
Number of single species
173
118
N
Number of multi-species
14
4
O
Number of novel species
100
22
P
Total unassigned reads
835
835
Q
Chimeric reads
17
17
R
Reads without BLASTN hits
3
3
S
Others: short, low quality, singletons, etc.
815
815
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[1][2] together with the terms beta diversity (β-diversity)
and gamma diversity (γ-diversity). Whittaker's idea was that the total species diversity in a landscape
(gamma diversity) is determined by two different things, the mean species diversity in sites or habitats
at a more local scale (alpha diversity) and the differentiation among those habitats (beta diversity).
Diversity measures are affected by the sampling depth. Rarefaction is a technique to assess species richness from the results of sampling. Rarefaction allows
the calculation of species richness for a given number of individual samples, based on the construction
of so-called rarefaction curves. This curve is a plot of the number of species as a function of the
number of samples. Rarefaction curves generally grow rapidly at first, as the most common species are found,
but the curves plateau as only the rarest species remain to be sampled.
The two main factors taken into account when measuring diversity are richness and evenness.
Richness is a measure of the number of different kinds of organisms present in a particular area.
Evenness compares the similarity of the population size of each of the species present. There are
many different ways to measure the richness and evenness. These measurements are called "estimators" or "indices".
Below is a diversity of 3 commonly used indices showing the values for all the samples (dots) and in groups (boxes).
Alpha Diversity Box Plots for All Groups
Alpha Diversity Box Plots for Individual Comparisons
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.
In general, they can be quantitative (using sequence abundance, e.g., Bray-Curtis or weighted UniFrac)
or binary (considering only presence-absence of sequences, e.g., binary Jaccard or unweighted UniFrac).
They can be even based on phylogeny (e.g., UniFrac metrics) or not (non-UniFrac metrics, such as Bray-Curtis, etc.).
For microbiome studies, species profiles of samples can be compared with the Bray-Curtis dissimilarity,
which is based on the count data type. The pair-wise Bray-Curtis dissimilarity matrix of all samples can then be
subject to either multi-dimensional scaling (MDS, also known as PCoA) or non-metric MDS (NMDS).
MDS/PCoA is a
scaling or ordination method that starts with a matrix of similarities or dissimilarities
between a set of samples and aims to produce a low-dimensional graphical plot of the data
in such a way that distances between points in the plot are close to original dissimilarities.
NMDS is similar to MDS, however it does not use the dissimilarities data, instead it converts them into
the ranks and use these ranks in the calculation.
In our beta diversity analysis, Bray-Curtis dissimilarity matrix was first calculated and then plotted by the PCoA and
NMDS separately. Below are beta diveristy results for all groups together:
NMDS and PCoA Plots for All Groups
The above PCoA and NMDS plots are based on count data. The count data can also be transformed into centered log ratio (CLR)
for each species. The CLR data is no longer count data and cannot be used in Bray-Curtis dissimilarity calculation. Instead
CLR can be compared with Euclidean distances. When CLR data are compared by Euclidean distance, the distance is also called
Aitchison distance.
Below are the NMDS and PCoA plots of the Aitchison distances of the samples:
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 (http://www.compositionaldata.com/).
The problem of microbiome data being compositional arises when comparing two groups of samples for
identifying “differentially abundant” species. A species with the same absolute abundance between two
conditions, its relative abundances in the two conditions (e.g., percent abundance) can become different
if the relative abundance of other species change greatly. This problem can lead to incorrect conclusion
in terms of differential abundance for microbial species in the samples.
When studying differential abundance (DA), the current better approach is to transform the read count
data into log ratio data. The ratios are calculated between read counts of all species in a sample to
a “reference” count (e.g., mean read count of the sample). The log ratio data allow the detection of DA
species without being affected by percentage bias mentioned above
In this report, a compositional DA analysis tool “ANCOM” (analysis of composition of microbiomes)
was used. ANCOM transforms the count data into log-ratios and thus is more suitable for comparing
the composition of microbiomes in two or more populations. "ANCOM" generates a table of features with
W-statistics and whether the null hypothesis is rejected. The “W” is the W-statistic, or number of
features that a single feature is tested to be significantly different against. Hence the higher the "W"
the more statistical sifgnificant that a feature/species is differentially abundant.
References:
Gloor GB, Macklaim JM, Pawlowsky-Glahn V, Egozcue JJ. Microbiome Datasets Are Compositional: And This Is Not Optional. Front Microbiol.
2017 Nov 15;8:2224. doi: 10.3389/fmicb.2017.02224. PMID: 29187837; PMCID: PMC5695134.
Mandal S, Van Treuren W, White RA, Eggesbø M, Knight R, Peddada SD. Analysis of composition of
microbiomes: a novel method for studying microbial composition. Microb Ecol Health Dis.
2015 May 29;26:27663. doi: 10.3402/mehd.v26.27663. PMID: 26028277; PMCID: PMC4450248.
Lin H, Peddada SD. Analysis of compositions of microbiomes with bias correction.
Nat Commun. 2020 Jul 14;11(1):3514. doi: 10.1038/s41467-020-17041-7.
PMID: 32665548; PMCID: PMC7360769.
Starting with version V1.2, we include the results of ANCOM-BC (Analysis of Compositions of
Microbiomes with Bias Correction) (Lin and Peddada 2020). ANCOM-BC is an updated version of "ANCOM" that:
(a) provides statistically valid test with appropriate p-values,
(b) provides confidence intervals for differential abundance of each taxon,
(c) controls the False Discovery Rate (FDR),
(d) maintains adequate power, and
(e) is computationally simple to implement.
The bias correction (BC) addresses a challenging problem of the bias introduced by differences in
the sampling fractions across samples. This bias has been a major hurdle in performing DA analysis of microbiome data.
ANCOM-BC estimates the unknown sampling fractions and corrects the bias induced by their differences among samples.
The absolute abundance data are modeled using a linear regression framework.
Starting with version V1.43, ANCOM-BC2 is used instead of ANCOM-BC, So that multiple pairwise directional test can be performed (if there are more than two gorups in a comparison).
When performing pairwise directional test, the mixed directional false discover rate (mdFDR) is taken into account. The mdFDR
is the combination of false discovery rate due to multiple testing, multiple pairwise comparisons, and directional tests within
each pairwise comparison. The mdFDR is adopted from (Guo, Sarkar, and Peddada 2010; Grandhi, Guo, and Peddada 2016). For more detail
explanation and additional features of ANCOM-BC2 please see author's documentation.
References:
Lin H, Peddada SD. Analysis of compositions of microbiomes with bias correction.
Nat Commun. 2020 Jul 14;11(1):3514. doi: 10.1038/s41467-020-17041-7.
PMID: 32665548; PMCID: PMC7360769.
Guo W, Sarkar SK, Peddada SD. Controlling false discoveries in multidimensional directional decisions, with applications to gene expression data on ordered categories. Biometrics. 2010 Jun;66(2):485-92. doi: 10.1111/j.1541-0420.2009.01292.x. Epub 2009 Jul 23. PMID: 19645703; PMCID: PMC2895927.
Grandhi A, Guo W, Peddada SD. A multiple testing procedure for multi-dimensional pairwise comparisons with application to gene expression studies. BMC Bioinformatics. 2016 Feb 25;17:104. doi: 10.1186/s12859-016-0937-5. PMID: 26917217; PMCID: PMC4768411.
LEfSe (Linear Discriminant Analysis Effect Size) is an alternative method to find "organisms, genes, or
pathways that consistently explain the differences between two or more microbial communities" (Segata et al., 2011).
Specifically, LEfSe uses rank-based Kruskal-Wallis (KW) sum-rank test to detect features with significant
differential (relative) abundance with respect to the class of interest. Since it is rank-based, instead of proportional based,
the differential species identified among the comparison groups is less biased (than percent abundance based).
Reference:
Segata N, Izard J, Waldron L, Gevers D, Miropolsky L, Garrett WS, Huttenhower C. Metagenomic biomarker discovery and explanation. Genome Biol. 2011 Jun 24;12(6):R60. doi: 10.1186/gb-2011-12-6-r60. PMID: 21702898; PMCID: PMC3218848.
To analyze the co-occurrence or co-exclusion between microbial species among different samples, network correlation
analysis tools are usually used for this purpose. However, microbiome count data are compositional. If count data are normalized to the total number of counts in the
sample, the data become not independent and traditional statistical metrics (e.g., correlation) for the detection
of specie-species relationships can lead to spurious results. In addition, sequencing-based studies typically
measure hundreds of OTUs (species) on few samples; thus, inference of OTU-OTU association networks is severely
under-powered. Here we use SPIEC-EASI (SParse InversECovariance Estimation
for Ecological Association Inference), a statistical method for the inference of microbial
ecological networks from amplicon sequencing datasets that addresses both of these issues (Kurtz et al., 2015).
SPIEC-EASI combines data transformations developed for compositional data analysis with a graphical model
inference framework that assumes the underlying ecological association network is sparse. SPIEC-EASI provides
two algorithms for network inferencing – 1) Meinshausen-Bühlmann's neighborhood selection (MB method) and inverse covariance selection
(GLASSO method, i.e., graphical least absolute shrinkage and selection operator). This is fundamentally distinct from SparCC, which essentially estimate pairwise correlations. In addition
to these two methods, we provide the results of a third method - SparCC (Sparse Correlations for Compositional Data)(Friedman & Alm 2012), which
is also a method for inferring correlations from compositional data. SparCC estimates the linear Pearson correlations between
the log-transformed components.
References:
Kurtz ZD, Müller CL, Miraldi ER, Littman DR, Blaser MJ, Bonneau RA. Sparse and compositionally robust inference of microbial ecological networks. PLoS Comput Biol. 2015 May 7;11(5):e1004226. doi: 10.1371/journal.pcbi.1004226. PMID: 25950956; PMCID: PMC4423992.
The results of this analysis are for research purpose only. They are not intended to diagnose, treat, cure, or prevent any disease. Forsyth and FOMC
are not responsible for use of information provided in this report outside the research area.
