Project FOMC0000_Vivek services include NGS sequencing of the V4V4 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
F0000.S100
original sample ID here
zr0000_100V1V3_R1.fastq.gz
zr0000_100V1V3_R2.fastq.gz
F0000.S101
original sample ID here
zr0000_101V1V3_R1.fastq.gz
zr0000_101V1V3_R2.fastq.gz
F0000.S102
original sample ID here
zr0000_102V1V3_R1.fastq.gz
zr0000_102V1V3_R2.fastq.gz
F0000.S103
original sample ID here
zr0000_103V1V3_R1.fastq.gz
zr0000_103V1V3_R2.fastq.gz
F0000.S104
original sample ID here
zr0000_104V1V3_R1.fastq.gz
zr0000_104V1V3_R2.fastq.gz
F0000.S105
original sample ID here
zr0000_105V1V3_R1.fastq.gz
zr0000_105V1V3_R2.fastq.gz
F0000.S010
original sample ID here
zr0000_10V1V3_R1.fastq.gz
zr0000_10V1V3_R2.fastq.gz
F0000.S011
original sample ID here
zr0000_11V1V3_R1.fastq.gz
zr0000_11V1V3_R2.fastq.gz
F0000.S012
original sample ID here
zr0000_12V1V3_R1.fastq.gz
zr0000_12V1V3_R2.fastq.gz
F0000.S013
original sample ID here
zr0000_13V1V3_R1.fastq.gz
zr0000_13V1V3_R2.fastq.gz
F0000.S014
original sample ID here
zr0000_14V1V3_R1.fastq.gz
zr0000_14V1V3_R2.fastq.gz
F0000.S015
original sample ID here
zr0000_15V1V3_R1.fastq.gz
zr0000_15V1V3_R2.fastq.gz
F0000.S016
original sample ID here
zr0000_16V1V3_R1.fastq.gz
zr0000_16V1V3_R2.fastq.gz
F0000.S017
original sample ID here
zr0000_17V1V3_R1.fastq.gz
zr0000_17V1V3_R2.fastq.gz
F0000.S018
original sample ID here
zr0000_18V1V3_R1.fastq.gz
zr0000_18V1V3_R2.fastq.gz
F0000.S019
original sample ID here
zr0000_19V1V3_R1.fastq.gz
zr0000_19V1V3_R2.fastq.gz
F0000.S001
original sample ID here
zr0000_1V1V3_R1.fastq.gz
zr0000_1V1V3_R2.fastq.gz
F0000.S020
original sample ID here
zr0000_20V1V3_R1.fastq.gz
zr0000_20V1V3_R2.fastq.gz
F0000.S021
original sample ID here
zr0000_21V1V3_R1.fastq.gz
zr0000_21V1V3_R2.fastq.gz
F0000.S022
original sample ID here
zr0000_22V1V3_R1.fastq.gz
zr0000_22V1V3_R2.fastq.gz
F0000.S023
original sample ID here
zr0000_23V1V3_R1.fastq.gz
zr0000_23V1V3_R2.fastq.gz
F0000.S024
original sample ID here
zr0000_24V1V3_R1.fastq.gz
zr0000_24V1V3_R2.fastq.gz
F0000.S025
original sample ID here
zr0000_25V1V3_R1.fastq.gz
zr0000_25V1V3_R2.fastq.gz
F0000.S026
original sample ID here
zr0000_26V1V3_R1.fastq.gz
zr0000_26V1V3_R2.fastq.gz
F0000.S027
original sample ID here
zr0000_27V1V3_R1.fastq.gz
zr0000_27V1V3_R2.fastq.gz
F0000.S028
original sample ID here
zr0000_28V1V3_R1.fastq.gz
zr0000_28V1V3_R2.fastq.gz
F0000.S029
original sample ID here
zr0000_29V1V3_R1.fastq.gz
zr0000_29V1V3_R2.fastq.gz
F0000.S002
original sample ID here
zr0000_2V1V3_R1.fastq.gz
zr0000_2V1V3_R2.fastq.gz
F0000.S030
original sample ID here
zr0000_30V1V3_R1.fastq.gz
zr0000_30V1V3_R2.fastq.gz
F0000.S031
original sample ID here
zr0000_31V1V3_R1.fastq.gz
zr0000_31V1V3_R2.fastq.gz
F0000.S032
original sample ID here
zr0000_32V1V3_R1.fastq.gz
zr0000_32V1V3_R2.fastq.gz
F0000.S033
original sample ID here
zr0000_33V1V3_R1.fastq.gz
zr0000_33V1V3_R2.fastq.gz
F0000.S034
original sample ID here
zr0000_34V1V3_R1.fastq.gz
zr0000_34V1V3_R2.fastq.gz
F0000.S035
original sample ID here
zr0000_35V1V3_R1.fastq.gz
zr0000_35V1V3_R2.fastq.gz
F0000.S036
original sample ID here
zr0000_36V1V3_R1.fastq.gz
zr0000_36V1V3_R2.fastq.gz
F0000.S037
original sample ID here
zr0000_37V1V3_R1.fastq.gz
zr0000_37V1V3_R2.fastq.gz
F0000.S038
original sample ID here
zr0000_38V1V3_R1.fastq.gz
zr0000_38V1V3_R2.fastq.gz
F0000.S039
original sample ID here
zr0000_39V1V3_R1.fastq.gz
zr0000_39V1V3_R2.fastq.gz
F0000.S003
original sample ID here
zr0000_3V1V3_R1.fastq.gz
zr0000_3V1V3_R2.fastq.gz
F0000.S040
original sample ID here
zr0000_40V1V3_R1.fastq.gz
zr0000_40V1V3_R2.fastq.gz
F0000.S041
original sample ID here
zr0000_41V1V3_R1.fastq.gz
zr0000_41V1V3_R2.fastq.gz
F0000.S042
original sample ID here
zr0000_42V1V3_R1.fastq.gz
zr0000_42V1V3_R2.fastq.gz
F0000.S043
original sample ID here
zr0000_43V1V3_R1.fastq.gz
zr0000_43V1V3_R2.fastq.gz
F0000.S044
original sample ID here
zr0000_44V1V3_R1.fastq.gz
zr0000_44V1V3_R2.fastq.gz
F0000.S045
original sample ID here
zr0000_45V1V3_R1.fastq.gz
zr0000_45V1V3_R2.fastq.gz
F0000.S046
original sample ID here
zr0000_46V1V3_R1.fastq.gz
zr0000_46V1V3_R2.fastq.gz
F0000.S047
original sample ID here
zr0000_47V1V3_R1.fastq.gz
zr0000_47V1V3_R2.fastq.gz
F0000.S048
original sample ID here
zr0000_48V1V3_R1.fastq.gz
zr0000_48V1V3_R2.fastq.gz
F0000.S049
original sample ID here
zr0000_49V1V3_R1.fastq.gz
zr0000_49V1V3_R2.fastq.gz
F0000.S004
original sample ID here
zr0000_4V1V3_R1.fastq.gz
zr0000_4V1V3_R2.fastq.gz
F0000.S050
original sample ID here
zr0000_50V1V3_R1.fastq.gz
zr0000_50V1V3_R2.fastq.gz
F0000.S051
original sample ID here
zr0000_51V1V3_R1.fastq.gz
zr0000_51V1V3_R2.fastq.gz
F0000.S052
original sample ID here
zr0000_52V1V3_R1.fastq.gz
zr0000_52V1V3_R2.fastq.gz
F0000.S053
original sample ID here
zr0000_53V1V3_R1.fastq.gz
zr0000_53V1V3_R2.fastq.gz
F0000.S054
original sample ID here
zr0000_54V1V3_R1.fastq.gz
zr0000_54V1V3_R2.fastq.gz
F0000.S055
original sample ID here
zr0000_55V1V3_R1.fastq.gz
zr0000_55V1V3_R2.fastq.gz
F0000.S056
original sample ID here
zr0000_56V1V3_R1.fastq.gz
zr0000_56V1V3_R2.fastq.gz
F0000.S057
original sample ID here
zr0000_57V1V3_R1.fastq.gz
zr0000_57V1V3_R2.fastq.gz
F0000.S058
original sample ID here
zr0000_58V1V3_R1.fastq.gz
zr0000_58V1V3_R2.fastq.gz
F0000.S059
original sample ID here
zr0000_59V1V3_R1.fastq.gz
zr0000_59V1V3_R2.fastq.gz
F0000.S005
original sample ID here
zr0000_5V1V3_R1.fastq.gz
zr0000_5V1V3_R2.fastq.gz
F0000.S060
original sample ID here
zr0000_60V1V3_R1.fastq.gz
zr0000_60V1V3_R2.fastq.gz
F0000.S061
original sample ID here
zr0000_61V1V3_R1.fastq.gz
zr0000_61V1V3_R2.fastq.gz
F0000.S062
original sample ID here
zr0000_62V1V3_R1.fastq.gz
zr0000_62V1V3_R2.fastq.gz
F0000.S063
original sample ID here
zr0000_63V1V3_R1.fastq.gz
zr0000_63V1V3_R2.fastq.gz
F0000.S064
original sample ID here
zr0000_64V1V3_R1.fastq.gz
zr0000_64V1V3_R2.fastq.gz
F0000.S065
original sample ID here
zr0000_65V1V3_R1.fastq.gz
zr0000_65V1V3_R2.fastq.gz
F0000.S066
original sample ID here
zr0000_66V1V3_R1.fastq.gz
zr0000_66V1V3_R2.fastq.gz
F0000.S067
original sample ID here
zr0000_67V1V3_R1.fastq.gz
zr0000_67V1V3_R2.fastq.gz
F0000.S068
original sample ID here
zr0000_68V1V3_R1.fastq.gz
zr0000_68V1V3_R2.fastq.gz
F0000.S069
original sample ID here
zr0000_69V1V3_R1.fastq.gz
zr0000_69V1V3_R2.fastq.gz
F0000.S006
original sample ID here
zr0000_6V1V3_R1.fastq.gz
zr0000_6V1V3_R2.fastq.gz
F0000.S070
original sample ID here
zr0000_70V1V3_R1.fastq.gz
zr0000_70V1V3_R2.fastq.gz
F0000.S071
original sample ID here
zr0000_71V1V3_R1.fastq.gz
zr0000_71V1V3_R2.fastq.gz
F0000.S072
original sample ID here
zr0000_72V1V3_R1.fastq.gz
zr0000_72V1V3_R2.fastq.gz
F0000.S073
original sample ID here
zr0000_73V1V3_R1.fastq.gz
zr0000_73V1V3_R2.fastq.gz
F0000.S074
original sample ID here
zr0000_74V1V3_R1.fastq.gz
zr0000_74V1V3_R2.fastq.gz
F0000.S075
original sample ID here
zr0000_75V1V3_R1.fastq.gz
zr0000_75V1V3_R2.fastq.gz
F0000.S076
original sample ID here
zr0000_76V1V3_R1.fastq.gz
zr0000_76V1V3_R2.fastq.gz
F0000.S077
original sample ID here
zr0000_77V1V3_R1.fastq.gz
zr0000_77V1V3_R2.fastq.gz
F0000.S078
original sample ID here
zr0000_78V1V3_R1.fastq.gz
zr0000_78V1V3_R2.fastq.gz
F0000.S079
original sample ID here
zr0000_79V1V3_R1.fastq.gz
zr0000_79V1V3_R2.fastq.gz
F0000.S007
original sample ID here
zr0000_7V1V3_R1.fastq.gz
zr0000_7V1V3_R2.fastq.gz
F0000.S080
original sample ID here
zr0000_80V1V3_R1.fastq.gz
zr0000_80V1V3_R2.fastq.gz
F0000.S081
original sample ID here
zr0000_81V1V3_R1.fastq.gz
zr0000_81V1V3_R2.fastq.gz
F0000.S082
original sample ID here
zr0000_82V1V3_R1.fastq.gz
zr0000_82V1V3_R2.fastq.gz
F0000.S083
original sample ID here
zr0000_83V1V3_R1.fastq.gz
zr0000_83V1V3_R2.fastq.gz
F0000.S084
original sample ID here
zr0000_84V1V3_R1.fastq.gz
zr0000_84V1V3_R2.fastq.gz
F0000.S085
original sample ID here
zr0000_85V1V3_R1.fastq.gz
zr0000_85V1V3_R2.fastq.gz
F0000.S086
original sample ID here
zr0000_86V1V3_R1.fastq.gz
zr0000_86V1V3_R2.fastq.gz
F0000.S087
original sample ID here
zr0000_87V1V3_R1.fastq.gz
zr0000_87V1V3_R2.fastq.gz
F0000.S088
original sample ID here
zr0000_88V1V3_R1.fastq.gz
zr0000_88V1V3_R2.fastq.gz
F0000.S089
original sample ID here
zr0000_89V1V3_R1.fastq.gz
zr0000_89V1V3_R2.fastq.gz
F0000.S008
original sample ID here
zr0000_8V1V3_R1.fastq.gz
zr0000_8V1V3_R2.fastq.gz
F0000.S090
original sample ID here
zr0000_90V1V3_R1.fastq.gz
zr0000_90V1V3_R2.fastq.gz
F0000.S091
original sample ID here
zr0000_91V1V3_R1.fastq.gz
zr0000_91V1V3_R2.fastq.gz
F0000.S092
original sample ID here
zr0000_92V1V3_R1.fastq.gz
zr0000_92V1V3_R2.fastq.gz
F0000.S093
original sample ID here
zr0000_93V1V3_R1.fastq.gz
zr0000_93V1V3_R2.fastq.gz
F0000.S094
original sample ID here
zr0000_94V1V3_R1.fastq.gz
zr0000_94V1V3_R2.fastq.gz
F0000.S095
original sample ID here
zr0000_95V1V3_R1.fastq.gz
zr0000_95V1V3_R2.fastq.gz
F0000.S096
original sample ID here
zr0000_96V1V3_R1.fastq.gz
zr0000_96V1V3_R2.fastq.gz
F0000.S097
original sample ID here
zr0000_97V1V3_R1.fastq.gz
zr0000_97V1V3_R2.fastq.gz
F0000.S098
original sample ID here
zr0000_98V1V3_R1.fastq.gz
zr0000_98V1V3_R2.fastq.gz
F0000.S099
original sample ID here
zr0000_99V1V3_R1.fastq.gz
zr0000_99V1V3_R2.fastq.gz
F0000.S009
original sample ID here
zr0000_9V1V3_R1.fastq.gz
zr0000_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
250
240
230
220
210
200
250
40.85%
40.39%
7.39%
5.57%
0.66%
0.65%
240
42.18%
7.35%
5.06%
0.68%
0.65%
0.65%
230
8.74%
6.61%
0.67%
0.66%
0.66%
0.65%
220
7.49%
0.66%
0.66%
0.67%
0.66%
0.64%
210
0.67%
0.66%
0.66%
0.66%
0.65%
0.64%
200
0.65%
0.65%
0.65%
0.65%
0.64%
0.63%
Based on the above result, the trim length combination of R1 = 240 bases and R2 = 250 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
F0000.S001
F0000.S002
F0000.S003
F0000.S004
F0000.S005
F0000.S006
F0000.S007
F0000.S008
F0000.S009
F0000.S010
F0000.S011
F0000.S012
F0000.S013
F0000.S014
F0000.S015
F0000.S016
F0000.S017
F0000.S018
F0000.S019
F0000.S020
F0000.S021
F0000.S022
F0000.S023
F0000.S024
F0000.S025
F0000.S026
F0000.S027
F0000.S028
F0000.S029
F0000.S030
F0000.S031
F0000.S032
F0000.S033
F0000.S034
F0000.S035
F0000.S036
F0000.S037
F0000.S038
F0000.S039
F0000.S040
F0000.S041
F0000.S042
F0000.S043
F0000.S044
F0000.S045
F0000.S046
F0000.S047
F0000.S048
F0000.S049
F0000.S050
F0000.S051
F0000.S052
F0000.S053
F0000.S054
F0000.S055
F0000.S056
F0000.S057
F0000.S058
F0000.S059
F0000.S060
F0000.S061
F0000.S062
F0000.S063
F0000.S064
F0000.S065
F0000.S066
F0000.S067
F0000.S068
F0000.S069
F0000.S070
F0000.S071
F0000.S072
F0000.S073
F0000.S074
F0000.S075
F0000.S076
F0000.S077
F0000.S078
F0000.S079
F0000.S080
F0000.S081
F0000.S082
F0000.S083
F0000.S084
F0000.S085
F0000.S086
F0000.S087
F0000.S088
F0000.S089
F0000.S090
F0000.S091
F0000.S092
F0000.S093
F0000.S094
F0000.S095
F0000.S096
F0000.S097
F0000.S098
F0000.S099
F0000.S100
F0000.S101
F0000.S102
F0000.S103
F0000.S104
F0000.S105
Row Sum
Percentage
input
98,100
118,116
125,863
112,826
116,689
117,149
106,718
114,565
168,393
141,664
199,782
101,512
100,060
123,433
120,900
137,471
119,428
128,971
184,978
117,666
163,815
129,881
207,764
111,941
196,445
118,180
221,574
108,054
121,319
102,112
223,272
210,534
125,096
168,850
147,231
185,132
209,874
203,948
201,181
208,098
202,721
219,896
214,996
208,121
207,807
206,407
206,586
214,660
205,775
213,249
204,970
204,190
219,730
203,439
203,790
216,142
219,777
201,139
211,364
204,660
205,861
214,758
210,818
211,217
218,611
205,717
205,048
205,334
208,768
214,799
205,743
216,122
208,184
218,462
211,851
202,663
212,850
202,267
209,557
204,452
206,914
203,866
200,493
204,983
204,335
217,036
212,089
213,053
208,800
300,278
263,899
212,703
200,426
158,686
222,389
180,733
209,151
219,856
168,451
150,595
187,915
205,211
185,303
190,273
152,305
19,380,829
100.00%
filtered
97,299
117,014
124,795
111,883
115,657
116,113
105,804
113,579
167,058
140,521
198,209
100,632
99,250
122,409
119,938
136,383
118,377
127,869
184,126
116,706
162,344
128,737
207,653
110,884
195,549
117,061
220,638
107,129
120,270
101,151
222,390
209,662
123,976
167,963
145,901
184,462
209,872
203,944
201,181
208,096
202,720
219,893
214,995
208,119
207,806
206,406
206,585
214,659
205,774
213,247
204,967
204,186
219,728
203,437
203,789
216,142
219,768
201,128
211,363
204,659
205,858
214,737
210,811
211,214
218,609
205,712
205,043
205,321
208,762
214,794
205,734
216,120
208,182
218,460
211,841
202,652
212,848
202,264
209,556
204,445
206,912
203,864
200,490
204,981
204,333
217,035
212,085
212,403
208,024
299,413
263,163
211,854
199,692
158,016
221,615
180,682
209,110
219,803
168,419
150,559
187,880
205,153
185,261
190,242
152,270
19,338,078
99.78%
denoisedF
90,484
109,479
116,824
103,947
107,010
108,430
99,039
105,920
160,618
133,819
189,844
95,141
96,608
119,152
117,106
133,287
115,001
124,896
179,741
113,833
155,262
122,056
199,147
105,190
187,845
114,261
205,998
104,283
117,158
98,509
213,052
203,139
118,696
160,517
139,819
167,751
204,754
198,728
195,451
202,775
197,011
214,261
209,960
203,771
204,448
201,740
201,695
210,416
201,910
208,912
200,102
199,252
215,154
195,988
194,073
208,155
213,096
195,483
201,376
197,003
198,151
206,829
201,005
203,783
210,709
198,498
195,898
196,609
201,670
209,607
200,854
209,514
202,893
214,183
207,105
197,682
209,814
197,194
203,906
198,938
201,660
198,950
196,106
200,117
200,424
212,738
207,860
167,681
162,961
230,275
206,525
169,658
162,909
134,070
178,321
171,170
202,252
204,779
159,278
141,631
177,690
193,892
175,878
181,904
146,343
18,358,290
94.72%
denoisedR
90,867
109,625
117,176
103,990
107,541
108,918
99,269
105,881
157,848
131,533
186,486
92,960
96,479
119,071
116,470
132,948
114,906
124,757
179,183
113,358
151,730
119,042
195,613
102,639
187,083
113,510
196,122
103,252
116,808
98,225
213,425
203,173
115,587
157,561
136,220
165,281
203,347
196,900
193,579
201,294
195,038
212,697
208,080
202,912
203,217
199,814
200,330
208,391
200,140
207,277
197,841
197,819
213,813
195,618
193,416
207,640
212,314
195,634
200,075
195,772
197,591
205,904
200,432
202,736
210,597
197,835
196,431
195,537
200,741
209,624
200,273
209,008
202,845
212,687
207,182
198,041
209,629
197,356
203,229
197,607
201,068
197,944
194,964
199,905
199,821
212,127
207,911
161,145
159,686
221,322
196,612
159,696
156,321
127,807
168,999
169,423
200,438
200,439
154,812
139,826
172,761
188,458
173,192
178,591
146,297
18,172,345
93.76%
merged
76,167
92,035
99,862
85,130
87,492
91,808
86,350
87,876
139,914
112,275
163,215
78,935
88,531
108,387
106,588
122,374
103,864
115,246
169,500
103,537
131,279
99,085
171,432
88,419
169,999
103,544
177,776
92,004
105,922
88,656
193,007
181,939
103,097
138,333
121,155
144,325
184,614
179,253
170,712
181,800
170,570
191,685
182,054
187,647
190,936
182,920
183,410
192,272
186,277
192,137
180,304
182,587
197,622
183,765
181,016
193,027
202,229
186,732
180,235
184,019
181,604
193,905
186,533
189,327
199,844
188,568
176,651
179,977
181,376
201,463
183,917
198,529
193,852
204,169
193,924
188,317
204,992
185,733
190,275
187,885
192,050
189,781
181,620
189,733
191,291
203,514
199,247
142,642
138,642
197,232
173,430
139,549
137,700
115,747
150,107
159,474
188,707
178,696
139,747
126,880
155,227
172,574
159,819
161,579
138,315
16,611,025
85.71%
nonchim
30,248
33,832
40,879
33,965
39,652
42,892
44,214
46,039
67,767
55,051
89,056
40,142
43,447
58,149
53,382
60,847
42,181
49,080
93,902
46,263
57,598
48,631
81,661
43,939
74,326
42,074
90,372
42,716
43,086
37,555
87,326
71,729
43,636
59,480
57,599
70,813
76,163
61,212
60,065
68,257
68,069
68,330
71,997
74,814
98,746
78,738
69,510
76,966
84,479
58,007
47,697
64,018
78,922
112,323
104,220
116,580
120,592
117,921
83,394
120,489
130,745
120,450
111,442
101,820
119,833
124,872
116,311
125,415
105,503
121,502
94,098
94,000
107,056
118,537
98,554
113,846
123,339
98,682
90,214
60,892
51,766
101,417
72,401
112,851
104,790
110,479
94,042
84,939
62,986
122,820
108,097
69,539
70,599
62,911
84,744
98,522
108,929
114,355
90,191
75,691
78,775
108,279
104,006
88,640
83,070
8,386,988
43.27%
This table can be downloaded as an Excel table below:
5. DADA2 Amplicon Sequence Variants (ASVs). A total of 136938 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%(>=661 reads)
A
Total reads
8,386,988
8,386,988
B
Total assigned reads
6,611,159
6,611,159
C
Assigned reads in species with read count < MPC
0
333,262
D
Assigned reads in samples with read count < 500
0
0
E
Total samples
105
105
F
Samples with reads >= 500
105
105
G
Samples with reads < 500
0
0
H
Total assigned reads used for analysis (B-C-D)
6,611,159
6,277,897
I
Reads assigned to single species
3,804,804
3,754,963
J
Reads assigned to multiple species
1,872,993
1,857,264
K
Reads assigned to novel species
933,362
665,670
L
Total number of species
7,020
395
M
Number of single species
550
97
N
Number of multi-species
172
27
O
Number of novel species
6,298
271
P
Total unassigned reads
1,775,829
1,775,829
Q
Chimeric reads
557,227
557,227
R
Reads without BLASTN hits
189,496
189,496
S
Others: short, low quality, singletons, etc.
1,029,106
1,029,106
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 statisticall, 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 sifgnificane 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 also 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.
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.
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.
