Project FOMC21487_2 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
F21487.S100
original sample ID here
zr21487_100V1V3_R1.fastq.gz
zr21487_100V1V3_R2.fastq.gz
F21487.S101
original sample ID here
zr21487_101V1V3_R1.fastq.gz
zr21487_101V1V3_R2.fastq.gz
F21487.S102
original sample ID here
zr21487_102V1V3_R1.fastq.gz
zr21487_102V1V3_R2.fastq.gz
F21487.S103
original sample ID here
zr21487_103V1V3_R1.fastq.gz
zr21487_103V1V3_R2.fastq.gz
F21487.S104
original sample ID here
zr21487_104V1V3_R1.fastq.gz
zr21487_104V1V3_R2.fastq.gz
F21487.S105
original sample ID here
zr21487_105V1V3_R1.fastq.gz
zr21487_105V1V3_R2.fastq.gz
F21487.S106
original sample ID here
zr21487_106V1V3_R1.fastq.gz
zr21487_106V1V3_R2.fastq.gz
F21487.S107
original sample ID here
zr21487_107V1V3_R1.fastq.gz
zr21487_107V1V3_R2.fastq.gz
F21487.S108
original sample ID here
zr21487_108V1V3_R1.fastq.gz
zr21487_108V1V3_R2.fastq.gz
F21487.S109
original sample ID here
zr21487_109V1V3_R1.fastq.gz
zr21487_109V1V3_R2.fastq.gz
F21487.S010
original sample ID here
zr21487_10V1V3_R1.fastq.gz
zr21487_10V1V3_R2.fastq.gz
F21487.S110
original sample ID here
zr21487_110V1V3_R1.fastq.gz
zr21487_110V1V3_R2.fastq.gz
F21487.S111
original sample ID here
zr21487_111V1V3_R1.fastq.gz
zr21487_111V1V3_R2.fastq.gz
F21487.S112
original sample ID here
zr21487_112V1V3_R1.fastq.gz
zr21487_112V1V3_R2.fastq.gz
F21487.S113
original sample ID here
zr21487_113V1V3_R1.fastq.gz
zr21487_113V1V3_R2.fastq.gz
F21487.S114
original sample ID here
zr21487_114V1V3_R1.fastq.gz
zr21487_114V1V3_R2.fastq.gz
F21487.S115
original sample ID here
zr21487_115V1V3_R1.fastq.gz
zr21487_115V1V3_R2.fastq.gz
F21487.S116
original sample ID here
zr21487_116V1V3_R1.fastq.gz
zr21487_116V1V3_R2.fastq.gz
F21487.S117
original sample ID here
zr21487_117V1V3_R1.fastq.gz
zr21487_117V1V3_R2.fastq.gz
F21487.S118
original sample ID here
zr21487_118V1V3_R1.fastq.gz
zr21487_118V1V3_R2.fastq.gz
F21487.S119
original sample ID here
zr21487_119V1V3_R1.fastq.gz
zr21487_119V1V3_R2.fastq.gz
F21487.S011
original sample ID here
zr21487_11V1V3_R1.fastq.gz
zr21487_11V1V3_R2.fastq.gz
F21487.S120
original sample ID here
zr21487_120V1V3_R1.fastq.gz
zr21487_120V1V3_R2.fastq.gz
F21487.S121
original sample ID here
zr21487_121V1V3_R1.fastq.gz
zr21487_121V1V3_R2.fastq.gz
F21487.S012
original sample ID here
zr21487_12V1V3_R1.fastq.gz
zr21487_12V1V3_R2.fastq.gz
F21487.S013
original sample ID here
zr21487_13V1V3_R1.fastq.gz
zr21487_13V1V3_R2.fastq.gz
F21487.S014
original sample ID here
zr21487_14V1V3_R1.fastq.gz
zr21487_14V1V3_R2.fastq.gz
F21487.S015
original sample ID here
zr21487_15V1V3_R1.fastq.gz
zr21487_15V1V3_R2.fastq.gz
F21487.S016
original sample ID here
zr21487_16V1V3_R1.fastq.gz
zr21487_16V1V3_R2.fastq.gz
F21487.S017
original sample ID here
zr21487_17V1V3_R1.fastq.gz
zr21487_17V1V3_R2.fastq.gz
F21487.S018
original sample ID here
zr21487_18V1V3_R1.fastq.gz
zr21487_18V1V3_R2.fastq.gz
F21487.S019
original sample ID here
zr21487_19V1V3_R1.fastq.gz
zr21487_19V1V3_R2.fastq.gz
F21487.S001
original sample ID here
zr21487_1V1V3_R1.fastq.gz
zr21487_1V1V3_R2.fastq.gz
F21487.S020
original sample ID here
zr21487_20V1V3_R1.fastq.gz
zr21487_20V1V3_R2.fastq.gz
F21487.S021
original sample ID here
zr21487_21V1V3_R1.fastq.gz
zr21487_21V1V3_R2.fastq.gz
F21487.S022
original sample ID here
zr21487_22V1V3_R1.fastq.gz
zr21487_22V1V3_R2.fastq.gz
F21487.S023
original sample ID here
zr21487_23V1V3_R1.fastq.gz
zr21487_23V1V3_R2.fastq.gz
F21487.S024
original sample ID here
zr21487_24V1V3_R1.fastq.gz
zr21487_24V1V3_R2.fastq.gz
F21487.S025
original sample ID here
zr21487_25V1V3_R1.fastq.gz
zr21487_25V1V3_R2.fastq.gz
F21487.S026
original sample ID here
zr21487_26V1V3_R1.fastq.gz
zr21487_26V1V3_R2.fastq.gz
F21487.S027
original sample ID here
zr21487_27V1V3_R1.fastq.gz
zr21487_27V1V3_R2.fastq.gz
F21487.S028
original sample ID here
zr21487_28V1V3_R1.fastq.gz
zr21487_28V1V3_R2.fastq.gz
F21487.S029
original sample ID here
zr21487_29V1V3_R1.fastq.gz
zr21487_29V1V3_R2.fastq.gz
F21487.S002
original sample ID here
zr21487_2V1V3_R1.fastq.gz
zr21487_2V1V3_R2.fastq.gz
F21487.S030
original sample ID here
zr21487_30V1V3_R1.fastq.gz
zr21487_30V1V3_R2.fastq.gz
F21487.S031
original sample ID here
zr21487_31V1V3_R1.fastq.gz
zr21487_31V1V3_R2.fastq.gz
F21487.S032
original sample ID here
zr21487_32V1V3_R1.fastq.gz
zr21487_32V1V3_R2.fastq.gz
F21487.S033
original sample ID here
zr21487_33V1V3_R1.fastq.gz
zr21487_33V1V3_R2.fastq.gz
F21487.S034
original sample ID here
zr21487_34V1V3_R1.fastq.gz
zr21487_34V1V3_R2.fastq.gz
F21487.S035
original sample ID here
zr21487_35V1V3_R1.fastq.gz
zr21487_35V1V3_R2.fastq.gz
F21487.S036
original sample ID here
zr21487_36V1V3_R1.fastq.gz
zr21487_36V1V3_R2.fastq.gz
F21487.S037
original sample ID here
zr21487_37V1V3_R1.fastq.gz
zr21487_37V1V3_R2.fastq.gz
F21487.S038
original sample ID here
zr21487_38V1V3_R1.fastq.gz
zr21487_38V1V3_R2.fastq.gz
F21487.S039
original sample ID here
zr21487_39V1V3_R1.fastq.gz
zr21487_39V1V3_R2.fastq.gz
F21487.S003
original sample ID here
zr21487_3V1V3_R1.fastq.gz
zr21487_3V1V3_R2.fastq.gz
F21487.S040
original sample ID here
zr21487_40V1V3_R1.fastq.gz
zr21487_40V1V3_R2.fastq.gz
F21487.S041
original sample ID here
zr21487_41V1V3_R1.fastq.gz
zr21487_41V1V3_R2.fastq.gz
F21487.S042
original sample ID here
zr21487_42V1V3_R1.fastq.gz
zr21487_42V1V3_R2.fastq.gz
F21487.S043
original sample ID here
zr21487_43V1V3_R1.fastq.gz
zr21487_43V1V3_R2.fastq.gz
F21487.S044
original sample ID here
zr21487_44V1V3_R1.fastq.gz
zr21487_44V1V3_R2.fastq.gz
F21487.S045
original sample ID here
zr21487_45V1V3_R1.fastq.gz
zr21487_45V1V3_R2.fastq.gz
F21487.S046
original sample ID here
zr21487_46V1V3_R1.fastq.gz
zr21487_46V1V3_R2.fastq.gz
F21487.S047
original sample ID here
zr21487_47V1V3_R1.fastq.gz
zr21487_47V1V3_R2.fastq.gz
F21487.S048
original sample ID here
zr21487_48V1V3_R1.fastq.gz
zr21487_48V1V3_R2.fastq.gz
F21487.S049
original sample ID here
zr21487_49V1V3_R1.fastq.gz
zr21487_49V1V3_R2.fastq.gz
F21487.S004
original sample ID here
zr21487_4V1V3_R1.fastq.gz
zr21487_4V1V3_R2.fastq.gz
F21487.S050
original sample ID here
zr21487_50V1V3_R1.fastq.gz
zr21487_50V1V3_R2.fastq.gz
F21487.S051
original sample ID here
zr21487_51V1V3_R1.fastq.gz
zr21487_51V1V3_R2.fastq.gz
F21487.S052
original sample ID here
zr21487_52V1V3_R1.fastq.gz
zr21487_52V1V3_R2.fastq.gz
F21487.S053
original sample ID here
zr21487_53V1V3_R1.fastq.gz
zr21487_53V1V3_R2.fastq.gz
F21487.S054
original sample ID here
zr21487_54V1V3_R1.fastq.gz
zr21487_54V1V3_R2.fastq.gz
F21487.S055
original sample ID here
zr21487_55V1V3_R1.fastq.gz
zr21487_55V1V3_R2.fastq.gz
F21487.S056
original sample ID here
zr21487_56V1V3_R1.fastq.gz
zr21487_56V1V3_R2.fastq.gz
F21487.S057
original sample ID here
zr21487_57V1V3_R1.fastq.gz
zr21487_57V1V3_R2.fastq.gz
F21487.S058
original sample ID here
zr21487_58V1V3_R1.fastq.gz
zr21487_58V1V3_R2.fastq.gz
F21487.S059
original sample ID here
zr21487_59V1V3_R1.fastq.gz
zr21487_59V1V3_R2.fastq.gz
F21487.S005
original sample ID here
zr21487_5V1V3_R1.fastq.gz
zr21487_5V1V3_R2.fastq.gz
F21487.S060
original sample ID here
zr21487_60V1V3_R1.fastq.gz
zr21487_60V1V3_R2.fastq.gz
F21487.S061
original sample ID here
zr21487_61V1V3_R1.fastq.gz
zr21487_61V1V3_R2.fastq.gz
F21487.S062
original sample ID here
zr21487_62V1V3_R1.fastq.gz
zr21487_62V1V3_R2.fastq.gz
F21487.S063
original sample ID here
zr21487_63V1V3_R1.fastq.gz
zr21487_63V1V3_R2.fastq.gz
F21487.S064
original sample ID here
zr21487_64V1V3_R1.fastq.gz
zr21487_64V1V3_R2.fastq.gz
F21487.S065
original sample ID here
zr21487_65V1V3_R1.fastq.gz
zr21487_65V1V3_R2.fastq.gz
F21487.S066
original sample ID here
zr21487_66V1V3_R1.fastq.gz
zr21487_66V1V3_R2.fastq.gz
F21487.S067
original sample ID here
zr21487_67V1V3_R1.fastq.gz
zr21487_67V1V3_R2.fastq.gz
F21487.S068
original sample ID here
zr21487_68V1V3_R1.fastq.gz
zr21487_68V1V3_R2.fastq.gz
F21487.S069
original sample ID here
zr21487_69V1V3_R1.fastq.gz
zr21487_69V1V3_R2.fastq.gz
F21487.S006
original sample ID here
zr21487_6V1V3_R1.fastq.gz
zr21487_6V1V3_R2.fastq.gz
F21487.S070
original sample ID here
zr21487_70V1V3_R1.fastq.gz
zr21487_70V1V3_R2.fastq.gz
F21487.S071
original sample ID here
zr21487_71V1V3_R1.fastq.gz
zr21487_71V1V3_R2.fastq.gz
F21487.S072
original sample ID here
zr21487_72V1V3_R1.fastq.gz
zr21487_72V1V3_R2.fastq.gz
F21487.S073
original sample ID here
zr21487_73V1V3_R1.fastq.gz
zr21487_73V1V3_R2.fastq.gz
F21487.S074
original sample ID here
zr21487_74V1V3_R1.fastq.gz
zr21487_74V1V3_R2.fastq.gz
F21487.S075
original sample ID here
zr21487_75V1V3_R1.fastq.gz
zr21487_75V1V3_R2.fastq.gz
F21487.S076
original sample ID here
zr21487_76V1V3_R1.fastq.gz
zr21487_76V1V3_R2.fastq.gz
F21487.S077
original sample ID here
zr21487_77V1V3_R1.fastq.gz
zr21487_77V1V3_R2.fastq.gz
F21487.S078
original sample ID here
zr21487_78V1V3_R1.fastq.gz
zr21487_78V1V3_R2.fastq.gz
F21487.S079
original sample ID here
zr21487_79V1V3_R1.fastq.gz
zr21487_79V1V3_R2.fastq.gz
F21487.S007
original sample ID here
zr21487_7V1V3_R1.fastq.gz
zr21487_7V1V3_R2.fastq.gz
F21487.S080
original sample ID here
zr21487_80V1V3_R1.fastq.gz
zr21487_80V1V3_R2.fastq.gz
F21487.S081
original sample ID here
zr21487_81V1V3_R1.fastq.gz
zr21487_81V1V3_R2.fastq.gz
F21487.S082
original sample ID here
zr21487_82V1V3_R1.fastq.gz
zr21487_82V1V3_R2.fastq.gz
F21487.S083
original sample ID here
zr21487_83V1V3_R1.fastq.gz
zr21487_83V1V3_R2.fastq.gz
F21487.S084
original sample ID here
zr21487_84V1V3_R1.fastq.gz
zr21487_84V1V3_R2.fastq.gz
F21487.S085
original sample ID here
zr21487_85V1V3_R1.fastq.gz
zr21487_85V1V3_R2.fastq.gz
F21487.S086
original sample ID here
zr21487_86V1V3_R1.fastq.gz
zr21487_86V1V3_R2.fastq.gz
F21487.S087
original sample ID here
zr21487_87V1V3_R1.fastq.gz
zr21487_87V1V3_R2.fastq.gz
F21487.S088
original sample ID here
zr21487_88V1V3_R1.fastq.gz
zr21487_88V1V3_R2.fastq.gz
F21487.S089
original sample ID here
zr21487_89V1V3_R1.fastq.gz
zr21487_89V1V3_R2.fastq.gz
F21487.S008
original sample ID here
zr21487_8V1V3_R1.fastq.gz
zr21487_8V1V3_R2.fastq.gz
F21487.S090
original sample ID here
zr21487_90V1V3_R1.fastq.gz
zr21487_90V1V3_R2.fastq.gz
F21487.S091
original sample ID here
zr21487_91V1V3_R1.fastq.gz
zr21487_91V1V3_R2.fastq.gz
F21487.S092
original sample ID here
zr21487_92V1V3_R1.fastq.gz
zr21487_92V1V3_R2.fastq.gz
F21487.S093
original sample ID here
zr21487_93V1V3_R1.fastq.gz
zr21487_93V1V3_R2.fastq.gz
F21487.S094
original sample ID here
zr21487_94V1V3_R1.fastq.gz
zr21487_94V1V3_R2.fastq.gz
F21487.S095
original sample ID here
zr21487_95V1V3_R1.fastq.gz
zr21487_95V1V3_R2.fastq.gz
F21487.S096
original sample ID here
zr21487_96V1V3_R1.fastq.gz
zr21487_96V1V3_R2.fastq.gz
F21487.S097
original sample ID here
zr21487_97V1V3_R1.fastq.gz
zr21487_97V1V3_R2.fastq.gz
F21487.S098
original sample ID here
zr21487_98V1V3_R1.fastq.gz
zr21487_98V1V3_R2.fastq.gz
F21487.S099
original sample ID here
zr21487_99V1V3_R1.fastq.gz
zr21487_99V1V3_R2.fastq.gz
F21487.S009
original sample ID here
zr21487_9V1V3_R1.fastq.gz
zr21487_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
67.74%
68.39%
68.83%
69.39%
69.62%
63.02%
311
67.72%
68.43%
68.92%
68.90%
63.14%
48.28%
301
67.78%
68.50%
68.34%
62.33%
48.20%
28.20%
291
67.86%
67.93%
61.80%
47.37%
28.21%
19.76%
281
67.49%
61.57%
47.29%
27.69%
19.67%
8.85%
271
61.38%
47.37%
27.64%
19.16%
8.72%
4.33%
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
F21487.S001
F21487.S002
F21487.S003
F21487.S004
F21487.S005
F21487.S006
F21487.S007
F21487.S008
F21487.S009
F21487.S010
F21487.S011
F21487.S012
F21487.S013
F21487.S014
F21487.S015
F21487.S016
F21487.S017
F21487.S018
F21487.S019
F21487.S020
F21487.S021
F21487.S022
F21487.S023
F21487.S024
F21487.S025
F21487.S026
F21487.S027
F21487.S028
F21487.S029
F21487.S030
F21487.S031
F21487.S032
F21487.S033
F21487.S034
F21487.S035
F21487.S036
F21487.S037
F21487.S038
F21487.S039
F21487.S040
F21487.S041
F21487.S042
F21487.S043
F21487.S044
F21487.S045
F21487.S046
F21487.S047
F21487.S048
F21487.S049
F21487.S050
F21487.S051
F21487.S052
F21487.S053
F21487.S054
F21487.S055
F21487.S056
F21487.S057
F21487.S058
F21487.S059
F21487.S060
F21487.S061
F21487.S062
F21487.S063
F21487.S064
F21487.S065
F21487.S066
F21487.S067
F21487.S068
F21487.S069
F21487.S070
F21487.S071
F21487.S072
F21487.S073
F21487.S074
F21487.S075
F21487.S076
F21487.S077
F21487.S078
F21487.S079
F21487.S080
F21487.S081
F21487.S082
F21487.S083
F21487.S084
F21487.S085
F21487.S086
F21487.S087
F21487.S088
F21487.S089
F21487.S090
F21487.S091
F21487.S092
F21487.S093
F21487.S094
F21487.S095
F21487.S096
F21487.S097
F21487.S098
F21487.S099
F21487.S100
F21487.S101
F21487.S102
F21487.S103
F21487.S104
F21487.S105
F21487.S106
F21487.S107
F21487.S108
F21487.S109
F21487.S110
F21487.S111
F21487.S112
F21487.S113
F21487.S114
F21487.S115
F21487.S116
F21487.S117
F21487.S118
F21487.S119
F21487.S120
F21487.S121
Row Sum
Percentage
input
201,637
241,666
288,287
174,750
203,707
200,415
193,776
171,690
174,083
183,789
173,109
179,368
230,421
194,473
233,325
206,855
203,283
173,921
198,456
201,258
273,830
211,715
268,490
208,968
177,196
212,676
212,430
151,822
250,150
184,131
162,037
77,768
173,976
168,264
249,934
250,478
237,972
201,062
180,934
85,637
240,770
177,900
175,109
208,533
262,261
223,081
263,270
128,798
283,703
229,603
266,574
236,636
281,635
271,826
4,454
99,784
268,555
183,577
319,016
165,742
202,976
230,138
183,515
99,061
208,918
187,386
280,572
252,338
396,281
228,970
186,782
104,034
260,578
198,989
202,689
173,282
174,460
148,800
194,330
89,225
256,710
234,237
181,332
169,411
245,075
228,855
80,506
73,412
187,958
271,734
164,095
212,528
174,666
217,291
226,593
209,347
166,136
218,509
153,787
102,462
172,701
179,302
199,937
205,225
191,680
243,087
290,368
106,779
201,351
201,620
217,043
268,668
175,443
190,817
182,911
110,538
225,033
177,374
259,644
169,819
174,106
24,179,980
100.00%
filtered
165,561
198,724
237,374
143,434
167,942
165,045
159,412
140,977
142,512
151,445
142,290
147,480
189,639
159,929
191,585
170,073
166,857
143,056
163,032
165,565
224,720
173,961
220,716
172,270
145,651
174,683
174,540
124,581
205,365
151,324
133,167
77,768
142,948
138,152
205,189
206,016
195,502
165,122
148,298
85,635
197,984
146,629
144,406
171,375
215,947
183,737
215,738
128,794
233,203
188,800
218,782
194,352
231,525
223,789
4,454
99,784
220,791
150,926
261,878
136,385
166,947
189,097
150,940
99,061
171,793
154,465
230,688
208,128
325,513
188,663
153,733
104,034
214,023
163,595
166,468
142,269
143,595
122,029
159,647
89,225
210,212
192,920
149,003
139,242
201,459
188,169
80,506
73,411
154,675
223,167
134,485
175,100
143,302
178,825
186,251
172,379
136,617
179,503
126,502
102,459
142,352
147,791
164,532
168,113
158,073
200,180
238,806
106,776
165,052
166,164
178,376
220,603
144,540
156,990
150,178
110,536
185,166
145,738
213,340
139,611
143,193
20,085,029
83.06%
denoisedF
163,543
196,676
234,877
142,038
166,496
163,478
157,798
139,424
141,649
149,563
140,535
145,877
188,568
158,215
190,134
168,587
165,331
141,553
161,847
163,790
222,667
172,060
219,444
170,686
144,499
172,988
172,825
123,417
203,679
149,741
132,060
76,890
141,933
136,317
203,840
204,345
193,832
163,277
146,118
84,985
196,247
145,118
142,999
169,429
214,449
181,893
213,975
128,009
231,386
187,276
216,713
192,922
229,788
222,156
4,344
99,112
218,169
149,654
260,550
135,189
164,877
187,353
149,515
97,844
169,955
152,909
228,873
205,964
322,889
186,993
153,021
103,363
212,543
161,970
164,896
140,306
141,834
120,777
158,267
88,457
207,247
190,731
147,817
138,081
199,809
185,915
79,529
72,504
152,851
221,116
133,080
172,794
141,527
177,167
184,243
170,834
135,065
177,944
124,939
101,074
140,782
146,300
162,647
166,749
156,212
198,522
236,937
105,924
163,787
164,461
176,599
219,137
142,629
155,170
148,532
109,513
183,560
144,031
211,604
137,959
141,696
19,894,584
82.28%
denoisedR
161,885
194,291
232,244
140,087
165,021
161,318
155,824
138,215
140,377
147,287
138,232
143,959
186,773
155,804
188,272
166,755
163,734
139,734
160,257
161,370
220,875
169,647
217,317
168,790
142,992
170,707
170,829
121,145
201,348
146,608
129,942
76,351
140,369
134,613
201,786
201,932
191,718
160,793
144,294
84,365
193,794
142,390
140,599
167,702
211,731
179,532
211,369
127,608
229,171
184,759
214,141
191,013
227,379
219,619
4,399
98,921
216,775
147,545
258,532
133,576
163,451
184,964
147,816
97,707
167,877
151,081
226,760
203,540
320,299
184,421
151,471
102,883
210,808
159,552
162,930
138,165
140,218
119,219
156,332
88,041
205,902
188,057
145,939
136,348
197,581
184,152
79,122
72,029
151,120
218,821
130,949
170,967
139,403
174,736
181,883
168,486
133,096
175,982
122,860
100,922
138,090
144,581
160,213
164,684
154,466
196,403
235,006
106,122
162,169
162,430
174,683
216,873
140,661
153,419
146,262
109,784
181,981
141,570
208,787
135,579
139,109
19,667,177
81.34%
merged
150,023
183,382
222,040
130,607
156,047
153,530
143,604
131,328
137,327
138,694
126,210
135,525
182,195
147,405
178,017
159,866
158,709
131,274
155,042
150,754
214,483
155,485
212,149
161,887
132,854
160,202
162,883
113,932
192,080
136,821
122,862
70,806
132,807
125,675
195,579
193,215
183,099
151,461
132,707
78,716
148,939
131,527
132,563
158,997
203,454
163,501
202,100
122,575
215,935
176,912
203,461
182,471
218,397
212,257
4,121
95,767
209,779
141,359
251,434
126,968
155,491
176,014
139,798
91,741
159,519
141,394
219,237
193,717
311,572
175,944
148,419
98,743
204,857
152,023
133,486
129,011
130,537
112,606
149,133
83,276
195,266
175,525
137,293
130,240
190,942
175,241
73,318
66,773
141,885
210,120
122,248
161,390
130,904
166,768
174,339
160,523
124,424
165,926
112,495
94,725
129,443
136,756
150,895
158,485
143,927
188,996
228,437
102,827
156,055
154,481
166,406
213,800
132,224
144,006
136,062
105,860
170,318
133,263
200,637
127,321
129,272
18,630,133
77.05%
nonchim
139,991
164,018
210,582
121,960
139,793
138,086
126,289
118,150
136,818
126,150
119,680
127,852
165,471
134,445
167,580
146,806
137,764
126,461
139,190
142,477
186,996
143,077
187,386
152,415
123,034
152,752
152,139
108,466
182,019
130,328
117,633
67,897
124,044
112,343
184,995
184,784
177,094
143,273
117,760
75,880
137,209
126,384
124,710
146,660
190,246
148,565
177,281
118,892
206,356
165,435
194,258
172,562
203,842
201,692
2,393
85,413
193,067
131,122
243,317
108,914
142,733
167,105
125,662
81,920
150,577
120,722
189,852
184,393
269,131
162,162
138,583
92,266
182,783
140,964
123,937
121,939
122,233
106,185
135,084
75,806
183,333
157,118
128,563
123,023
176,291
165,198
67,123
62,472
130,318
196,863
115,024
152,355
126,614
162,503
169,928
152,158
118,931
151,945
104,757
84,615
117,881
129,209
142,200
149,516
135,272
176,837
223,123
95,149
116,003
140,848
145,977
198,858
124,048
125,003
116,470
94,272
150,497
124,711
192,136
115,912
122,161
17,231,448
71.26%
This table can be downloaded as an Excel table below:
5. DADA2 Amplicon Sequence Variants (ASVs). A total of 18248 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%(>=1711 reads)
A
Total reads
17,231,448
17,231,448
B
Total assigned reads
17,115,202
17,115,202
C
Assigned reads in species with read count < MPC
0
166,536
D
Assigned reads in samples with read count < 500
0
0
E
Total samples
120
120
F
Samples with reads >= 500
120
120
G
Samples with reads < 500
0
0
H
Total assigned reads used for analysis (B-C-D)
17,115,202
16,948,666
I
Reads assigned to single species
15,966,710
15,888,637
J
Reads assigned to multiple species
415,344
398,480
K
Reads assigned to novel species
733,148
661,549
L
Total number of species
1,303
416
M
Number of single species
527
343
N
Number of multi-species
59
14
O
Number of novel species
717
59
P
Total unassigned reads
116,246
116,246
Q
Chimeric reads
5,029
5,029
R
Reads without BLASTN hits
29,941
29,941
S
Others: short, low quality, singletons, etc.
81,276
81,276
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 at Species level
Comparison 1
Female Diseased Baseline vs Female Healthy Baseline
To test whether the alpha diversity among different comparison groups are different statistically, we use the Kruskal Wallis H test
provided the "alpha-group-significance" fucntion in the QIIME 2 "diversity" package. Kruskal Wallis H test is the non-parametric alternative
to the One Way ANOVA. Non-parametric means that the test doesn’t assume your data comes from a particular distribution. The H test is used
when the assumptions for ANOVA aren’t met (like the assumption of normality). It is sometimes called the one-way ANOVA on ranks,
as the ranks of the data values are used in the test rather than the actual data points. The H test determines whether the medians of two
or more groups are different.
Below are the Kruskal Wallis H test results for each comparison based on three different alpha diversity measures: 1) Observed species (features),
2) Shannon index, and 3) Simpson index.
Comparison 1.
Female Diseased Baseline vs Female Healthy Baseline
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:
NMDS and PCoA Plots for Individual Comparisons at Species level
Comparison No.
Comparison Name
NMDA
PCoA
Bray-Curtis
CLR Euclidean
Bray-Curtis
CLR Euclidean
Comparison 1
Female Diseased Baseline vs Female Healthy Baseline
Interactive 3D PCoA Plots - Bray-Curtis Dissimilarity
Interactive 3D PCoA Plots - Euclidean Distance
Interactive 3D PCoA Plots - Correlation Coefficients
Group Significance of Beta-diversity Indices
To test whether the between-group dissimilarities are significantly greater than the within-group dissimilarities,
the "beta-group-significance" function provided in the QIIME 2 "diversity" package was used with PERMANOVA
(permutational multivariate analysis of variance) as the group significant testing method.
Three beta diversity matrics were used: 1) Bray–Curtis dissimilarity 2) Correlation coefficient matrix , and 3) Aitchison distance
(Euclidean distance between clr-transformed compositions).
Comparison 1.
Female Diseased Baseline vs Female Healthy Baseline
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.
"ALDEx2 is a compositional data analysis tool designed to enhance the statistical analysis of high-throughput sequencing datasets,
including RNA-seq, ChIP-seq, 16S rRNA gene sequencing, metagenomic analysis, and selective growth experiments.
Despite the fundamental similarities in data structure across these various experimental designs—namely,
counts of sequencing reads mapped to numerous features—traditional data analysis methods
have remained disparate and non-transferable between experiment types.
ALDEx2 addresses this challenge by employing compositional data analysis methods from the physical and geological sciences,
which convert raw data into relative abundances. This transformation leads to analyses that are more robust and reproducible.
Utilizing Bayesian methods to infer technical and statistical errors, ALDEx2 has demonstrated its applicability and effectiveness
across diverse datasets. It accurately identifies differential abundance and the direction of changes in selective growth experiments,
aligns closely with leading tools in identifying differentially expressed genes in RNA-seq datasets,
and successfully distinguishes differential taxa in the Human Microbiome Project 16S rRNA gene abundance dataset."
In this paired-sample differential abundance test, ALDEx2 was used with the Wilcoxon rank-sum test to identify features at different taxonomy ranks (from Phylum to Species)
that are significantly differentially abundant between two conditions. p-values were adjusted using "Holm" or "Benjamini-Hochberg" (BH) method to control the false discovery rate (FDR).
The simplest but strict p-value adjustment method is the Bonferroni method in which the p-values are multiplied by the number of comparisons.
Both Holm (1979) and Benjamini & Hochberg (1995) ("BH" or its alias "fdr") provide less conservative corrections.
In the below ALDEx2 result folder, comparisons were done with these two adjustment methods. Also, analyses were done with and without "paired sample" options for comparison.
Reference:
Fernandes AD, Macklaim JM, Linn TG, Reid G, Gloor GB. ANOVA-like differential expression (ALDEx) analysis for mixed population RNA-Seq. PLoS One. 2013 Jul 2;8(7):e67019. doi: 10.1371/journal.pone.0067019. PMID: 23843979; PMCID: PMC3699591.
Fernandes AD, Reid JN, Macklaim JM, McMurrough TA, Edgell DR, Gloor GB. Unifying the analysis of high-throughput sequencing datasets: characterizing RNA-seq, 16S rRNA gene sequencing and selective growth experiments by compositional data analysis. Microbiome. 2014 May 5;2:15. doi: 10.1186/2049-2618-2-15. PMID: 24910773; PMCID: PMC4030730.
Bonferroni, C. E., Teoria statistica delle classi e calcolo delle probabilità, Pubblicazioni del R Istituto Superiore di Scienze Economiche e Commerciali di Firenze 1936
Holm, S. (1979). A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics, 6, 65--70. http://www.jstor.org/stable/4615733.
Benjamini, Y., and Hochberg, Y. (1995). Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society Series B, 57, 289--300. http://www.jstor.org/stable/2346101.
LEfSe (Linear Discriminant Analysis Effect Size) is an alternative method to find "organisms, genes, or
pathways that consistently explain the differences between two or more microbial communities" (Segata et al., 2011) [19].
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).
References:
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.
Female Diseased Baseline vs Female Healthy Baseline
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) [20].
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)[21], 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.
