Project FOMC5677 services include NGS sequencing of the V1V3 region of the 16S rRNA 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.
The complete report of your project, including all links in this report, can be downloaded by clicking the link provided below. The downloaded file is a compressed ZIP file and once unzipped, open the file “REPORT.html” (may only shown as "REPORT" in your computer) by double clicking it. Your default web browser will open it and you will see the exact content of this report.
Please download and save the file to your computer storage device. The download link will expire after 60 days upon your receiving of this report.
Complete report download link:
To view the report, please follow the following steps:
1.
Download the .zip file from the report link above.
2.
Extract all the contents of the downloaded .zip file to your desktop.
3.
Open the extracted folder and find the "REPORT.html" (may shown as only "REPORT").
4.
Open (double-clicking) the REPORT.html file. Your default browser will open the top age of the complete report. Within the
report, there are links to view all the analyses performed for the project.
The raw NGS sequence data is available for download with the link provided below. The data is a compressed ZIP file and can be unzipped to individual sequence files.
Since this is a pair-end sequencing, each of your samples is represented by two sequence files, one for READ 1,
with the file extension “*_R1.fastq.gz”, another READ 2, with the file extension “*_R1.fastq.gz”.
The files are in FASTQ format and are compressed. FASTQ format is a text-based data format for storing both a biological sequence
and its corresponding quality scores. Most sequence analysis software will be able to open them.
The Sample IDs associated with the R1 and R2 fastq files are listed in the table below:
Sample ID
Original Sample ID
Read 1 File Name
Read 2 File Name
S001
zr5677_100V1V3_R1.fastq.gz
zr5677_100V1V3_R2.fastq.gz
S002
zr5677_101V1V3_R1.fastq.gz
zr5677_101V1V3_R2.fastq.gz
S003
zr5677_102V1V3_R1.fastq.gz
zr5677_102V1V3_R2.fastq.gz
S004
zr5677_103V1V3_R1.fastq.gz
zr5677_103V1V3_R2.fastq.gz
S005
zr5677_104V1V3_R1.fastq.gz
zr5677_104V1V3_R2.fastq.gz
S006
zr5677_105V1V3_R1.fastq.gz
zr5677_105V1V3_R2.fastq.gz
S007
zr5677_106V1V3_R1.fastq.gz
zr5677_106V1V3_R2.fastq.gz
S008
zr5677_107V1V3_R1.fastq.gz
zr5677_107V1V3_R2.fastq.gz
S009
zr5677_108V1V3_R1.fastq.gz
zr5677_108V1V3_R2.fastq.gz
S010
zr5677_109V1V3_R1.fastq.gz
zr5677_109V1V3_R2.fastq.gz
S011
zr5677_10V1V3_R1.fastq.gz
zr5677_10V1V3_R2.fastq.gz
S012
zr5677_110V1V3_R1.fastq.gz
zr5677_110V1V3_R2.fastq.gz
S013
zr5677_111V1V3_R1.fastq.gz
zr5677_111V1V3_R2.fastq.gz
S014
zr5677_112V1V3_R1.fastq.gz
zr5677_112V1V3_R2.fastq.gz
S015
zr5677_113V1V3_R1.fastq.gz
zr5677_113V1V3_R2.fastq.gz
S016
zr5677_114V1V3_R1.fastq.gz
zr5677_114V1V3_R2.fastq.gz
S017
zr5677_115V1V3_R1.fastq.gz
zr5677_115V1V3_R2.fastq.gz
S018
zr5677_116V1V3_R1.fastq.gz
zr5677_116V1V3_R2.fastq.gz
S019
zr5677_117V1V3_R1.fastq.gz
zr5677_117V1V3_R2.fastq.gz
S020
zr5677_118V1V3_R1.fastq.gz
zr5677_118V1V3_R2.fastq.gz
S021
zr5677_119V1V3_R1.fastq.gz
zr5677_119V1V3_R2.fastq.gz
S022
zr5677_11V1V3_R1.fastq.gz
zr5677_11V1V3_R2.fastq.gz
S023
zr5677_120V1V3_R1.fastq.gz
zr5677_120V1V3_R2.fastq.gz
S024
zr5677_121V1V3_R1.fastq.gz
zr5677_121V1V3_R2.fastq.gz
S025
zr5677_122V1V3_R1.fastq.gz
zr5677_122V1V3_R2.fastq.gz
S026
zr5677_123V1V3_R1.fastq.gz
zr5677_123V1V3_R2.fastq.gz
S027
zr5677_124V1V3_R1.fastq.gz
zr5677_124V1V3_R2.fastq.gz
S028
zr5677_125V1V3_R1.fastq.gz
zr5677_125V1V3_R2.fastq.gz
S029
zr5677_126V1V3_R1.fastq.gz
zr5677_126V1V3_R2.fastq.gz
S030
zr5677_127V1V3_R1.fastq.gz
zr5677_127V1V3_R2.fastq.gz
S031
zr5677_128V1V3_R1.fastq.gz
zr5677_128V1V3_R2.fastq.gz
S032
zr5677_129V1V3_R1.fastq.gz
zr5677_129V1V3_R2.fastq.gz
S033
zr5677_12V1V3_R1.fastq.gz
zr5677_12V1V3_R2.fastq.gz
S034
zr5677_130V1V3_R1.fastq.gz
zr5677_130V1V3_R2.fastq.gz
S035
zr5677_131V1V3_R1.fastq.gz
zr5677_131V1V3_R2.fastq.gz
S036
zr5677_132V1V3_R1.fastq.gz
zr5677_132V1V3_R2.fastq.gz
S037
zr5677_133V1V3_R1.fastq.gz
zr5677_133V1V3_R2.fastq.gz
S038
zr5677_134V1V3_R1.fastq.gz
zr5677_134V1V3_R2.fastq.gz
S039
zr5677_135V1V3_R1.fastq.gz
zr5677_135V1V3_R2.fastq.gz
S040
zr5677_136V1V3_R1.fastq.gz
zr5677_136V1V3_R2.fastq.gz
S041
zr5677_137V1V3_R1.fastq.gz
zr5677_137V1V3_R2.fastq.gz
S042
zr5677_138V1V3_R1.fastq.gz
zr5677_138V1V3_R2.fastq.gz
S043
zr5677_139V1V3_R1.fastq.gz
zr5677_139V1V3_R2.fastq.gz
S044
zr5677_13V1V3_R1.fastq.gz
zr5677_13V1V3_R2.fastq.gz
S045
zr5677_140V1V3_R1.fastq.gz
zr5677_140V1V3_R2.fastq.gz
S046
zr5677_141V1V3_R1.fastq.gz
zr5677_141V1V3_R2.fastq.gz
S047
zr5677_142V1V3_R1.fastq.gz
zr5677_142V1V3_R2.fastq.gz
S048
zr5677_143V1V3_R1.fastq.gz
zr5677_143V1V3_R2.fastq.gz
S049
zr5677_144V1V3_R1.fastq.gz
zr5677_144V1V3_R2.fastq.gz
S050
zr5677_145V1V3_R1.fastq.gz
zr5677_145V1V3_R2.fastq.gz
S051
zr5677_146V1V3_R1.fastq.gz
zr5677_146V1V3_R2.fastq.gz
S052
zr5677_147V1V3_R1.fastq.gz
zr5677_147V1V3_R2.fastq.gz
S053
zr5677_148V1V3_R1.fastq.gz
zr5677_148V1V3_R2.fastq.gz
S054
zr5677_149V1V3_R1.fastq.gz
zr5677_149V1V3_R2.fastq.gz
S055
zr5677_14V1V3_R1.fastq.gz
zr5677_14V1V3_R2.fastq.gz
S056
zr5677_150V1V3_R1.fastq.gz
zr5677_150V1V3_R2.fastq.gz
S057
zr5677_151V1V3_R1.fastq.gz
zr5677_151V1V3_R2.fastq.gz
S058
zr5677_152V1V3_R1.fastq.gz
zr5677_152V1V3_R2.fastq.gz
S059
zr5677_153V1V3_R1.fastq.gz
zr5677_153V1V3_R2.fastq.gz
S060
zr5677_154V1V3_R1.fastq.gz
zr5677_154V1V3_R2.fastq.gz
S061
zr5677_155V1V3_R1.fastq.gz
zr5677_155V1V3_R2.fastq.gz
S062
zr5677_156V1V3_R1.fastq.gz
zr5677_156V1V3_R2.fastq.gz
S063
zr5677_157V1V3_R1.fastq.gz
zr5677_157V1V3_R2.fastq.gz
S064
zr5677_158V1V3_R1.fastq.gz
zr5677_158V1V3_R2.fastq.gz
S065
zr5677_159V1V3_R1.fastq.gz
zr5677_159V1V3_R2.fastq.gz
S066
zr5677_15V1V3_R1.fastq.gz
zr5677_15V1V3_R2.fastq.gz
S067
zr5677_160V1V3_R1.fastq.gz
zr5677_160V1V3_R2.fastq.gz
S068
zr5677_161V1V3_R1.fastq.gz
zr5677_161V1V3_R2.fastq.gz
S069
zr5677_162V1V3_R1.fastq.gz
zr5677_162V1V3_R2.fastq.gz
S070
zr5677_163V1V3_R1.fastq.gz
zr5677_163V1V3_R2.fastq.gz
S071
zr5677_164V1V3_R1.fastq.gz
zr5677_164V1V3_R2.fastq.gz
S072
zr5677_165V1V3_R1.fastq.gz
zr5677_165V1V3_R2.fastq.gz
S073
zr5677_166V1V3_R1.fastq.gz
zr5677_166V1V3_R2.fastq.gz
S074
zr5677_167V1V3_R1.fastq.gz
zr5677_167V1V3_R2.fastq.gz
S075
zr5677_168V1V3_R1.fastq.gz
zr5677_168V1V3_R2.fastq.gz
S076
zr5677_169V1V3_R1.fastq.gz
zr5677_169V1V3_R2.fastq.gz
S077
zr5677_16V1V3_R1.fastq.gz
zr5677_16V1V3_R2.fastq.gz
S078
zr5677_170V1V3_R1.fastq.gz
zr5677_170V1V3_R2.fastq.gz
S079
zr5677_171V1V3_R1.fastq.gz
zr5677_171V1V3_R2.fastq.gz
S080
zr5677_172V1V3_R1.fastq.gz
zr5677_172V1V3_R2.fastq.gz
S081
zr5677_173V1V3_R1.fastq.gz
zr5677_173V1V3_R2.fastq.gz
S082
zr5677_174V1V3_R1.fastq.gz
zr5677_174V1V3_R2.fastq.gz
S083
zr5677_175V1V3_R1.fastq.gz
zr5677_175V1V3_R2.fastq.gz
S084
zr5677_176V1V3_R1.fastq.gz
zr5677_176V1V3_R2.fastq.gz
S085
zr5677_177V1V3_R1.fastq.gz
zr5677_177V1V3_R2.fastq.gz
S086
zr5677_178V1V3_R1.fastq.gz
zr5677_178V1V3_R2.fastq.gz
S087
zr5677_179V1V3_R1.fastq.gz
zr5677_179V1V3_R2.fastq.gz
S088
zr5677_17V1V3_R1.fastq.gz
zr5677_17V1V3_R2.fastq.gz
S089
zr5677_180V1V3_R1.fastq.gz
zr5677_180V1V3_R2.fastq.gz
S090
zr5677_181V1V3_R1.fastq.gz
zr5677_181V1V3_R2.fastq.gz
S091
zr5677_182V1V3_R1.fastq.gz
zr5677_182V1V3_R2.fastq.gz
S092
zr5677_183V1V3_R1.fastq.gz
zr5677_183V1V3_R2.fastq.gz
S093
zr5677_184V1V3_R1.fastq.gz
zr5677_184V1V3_R2.fastq.gz
S094
zr5677_185V1V3_R1.fastq.gz
zr5677_185V1V3_R2.fastq.gz
S095
zr5677_186V1V3_R1.fastq.gz
zr5677_186V1V3_R2.fastq.gz
S096
zr5677_187V1V3_R1.fastq.gz
zr5677_187V1V3_R2.fastq.gz
S097
zr5677_188V1V3_R1.fastq.gz
zr5677_188V1V3_R2.fastq.gz
S098
zr5677_189V1V3_R1.fastq.gz
zr5677_189V1V3_R2.fastq.gz
S099
zr5677_18V1V3_R1.fastq.gz
zr5677_18V1V3_R2.fastq.gz
S100
zr5677_190V1V3_R1.fastq.gz
zr5677_190V1V3_R2.fastq.gz
S101
zr5677_191V1V3_R1.fastq.gz
zr5677_191V1V3_R2.fastq.gz
S102
zr5677_192V1V3_R1.fastq.gz
zr5677_192V1V3_R2.fastq.gz
S103
zr5677_193V1V3_R1.fastq.gz
zr5677_193V1V3_R2.fastq.gz
S104
zr5677_194V1V3_R1.fastq.gz
zr5677_194V1V3_R2.fastq.gz
S105
zr5677_195V1V3_R1.fastq.gz
zr5677_195V1V3_R2.fastq.gz
S106
zr5677_196V1V3_R1.fastq.gz
zr5677_196V1V3_R2.fastq.gz
S107
zr5677_197V1V3_R1.fastq.gz
zr5677_197V1V3_R2.fastq.gz
S108
zr5677_198V1V3_R1.fastq.gz
zr5677_198V1V3_R2.fastq.gz
S109
zr5677_199V1V3_R1.fastq.gz
zr5677_199V1V3_R2.fastq.gz
S110
zr5677_19V1V3_R1.fastq.gz
zr5677_19V1V3_R2.fastq.gz
S111
zr5677_1V1V3_R1.fastq.gz
zr5677_1V1V3_R2.fastq.gz
S112
zr5677_200V1V3_R1.fastq.gz
zr5677_200V1V3_R2.fastq.gz
S113
zr5677_201V1V3_R1.fastq.gz
zr5677_201V1V3_R2.fastq.gz
S114
zr5677_202V1V3_R1.fastq.gz
zr5677_202V1V3_R2.fastq.gz
S115
zr5677_203V1V3_R1.fastq.gz
zr5677_203V1V3_R2.fastq.gz
S116
zr5677_204V1V3_R1.fastq.gz
zr5677_204V1V3_R2.fastq.gz
S117
zr5677_205V1V3_R1.fastq.gz
zr5677_205V1V3_R2.fastq.gz
S118
zr5677_206V1V3_R1.fastq.gz
zr5677_206V1V3_R2.fastq.gz
S119
zr5677_207V1V3_R1.fastq.gz
zr5677_207V1V3_R2.fastq.gz
S120
zr5677_208V1V3_R1.fastq.gz
zr5677_208V1V3_R2.fastq.gz
S121
zr5677_209V1V3_R1.fastq.gz
zr5677_209V1V3_R2.fastq.gz
S122
zr5677_20V1V3_R1.fastq.gz
zr5677_20V1V3_R2.fastq.gz
S123
zr5677_210V1V3_R1.fastq.gz
zr5677_210V1V3_R2.fastq.gz
S124
zr5677_211V1V3_R1.fastq.gz
zr5677_211V1V3_R2.fastq.gz
S125
zr5677_212V1V3_R1.fastq.gz
zr5677_212V1V3_R2.fastq.gz
S126
zr5677_213V1V3_R1.fastq.gz
zr5677_213V1V3_R2.fastq.gz
S127
zr5677_214V1V3_R1.fastq.gz
zr5677_214V1V3_R2.fastq.gz
S128
zr5677_215V1V3_R1.fastq.gz
zr5677_215V1V3_R2.fastq.gz
S129
zr5677_216V1V3_R1.fastq.gz
zr5677_216V1V3_R2.fastq.gz
S130
zr5677_217V1V3_R1.fastq.gz
zr5677_217V1V3_R2.fastq.gz
S131
zr5677_218V1V3_R1.fastq.gz
zr5677_218V1V3_R2.fastq.gz
S132
zr5677_219V1V3_R1.fastq.gz
zr5677_219V1V3_R2.fastq.gz
S133
zr5677_21V1V3_R1.fastq.gz
zr5677_21V1V3_R2.fastq.gz
S134
zr5677_220V1V3_R1.fastq.gz
zr5677_220V1V3_R2.fastq.gz
S135
zr5677_221V1V3_R1.fastq.gz
zr5677_221V1V3_R2.fastq.gz
S136
zr5677_222V1V3_R1.fastq.gz
zr5677_222V1V3_R2.fastq.gz
S137
zr5677_223V1V3_R1.fastq.gz
zr5677_223V1V3_R2.fastq.gz
S138
zr5677_224V1V3_R1.fastq.gz
zr5677_224V1V3_R2.fastq.gz
S139
zr5677_225V1V3_R1.fastq.gz
zr5677_225V1V3_R2.fastq.gz
S140
zr5677_226V1V3_R1.fastq.gz
zr5677_226V1V3_R2.fastq.gz
S141
zr5677_227V1V3_R1.fastq.gz
zr5677_227V1V3_R2.fastq.gz
S142
zr5677_228V1V3_R1.fastq.gz
zr5677_228V1V3_R2.fastq.gz
S143
zr5677_229V1V3_R1.fastq.gz
zr5677_229V1V3_R2.fastq.gz
S144
zr5677_22V1V3_R1.fastq.gz
zr5677_22V1V3_R2.fastq.gz
S145
zr5677_230V1V3_R1.fastq.gz
zr5677_230V1V3_R2.fastq.gz
S146
zr5677_231V1V3_R1.fastq.gz
zr5677_231V1V3_R2.fastq.gz
S147
zr5677_232V1V3_R1.fastq.gz
zr5677_232V1V3_R2.fastq.gz
S148
zr5677_233V1V3_R1.fastq.gz
zr5677_233V1V3_R2.fastq.gz
S149
zr5677_234V1V3_R1.fastq.gz
zr5677_234V1V3_R2.fastq.gz
S150
zr5677_235V1V3_R1.fastq.gz
zr5677_235V1V3_R2.fastq.gz
S151
zr5677_236V1V3_R1.fastq.gz
zr5677_236V1V3_R2.fastq.gz
S152
zr5677_237V1V3_R1.fastq.gz
zr5677_237V1V3_R2.fastq.gz
S153
zr5677_238V1V3_R1.fastq.gz
zr5677_238V1V3_R2.fastq.gz
S154
zr5677_239V1V3_R1.fastq.gz
zr5677_239V1V3_R2.fastq.gz
S155
zr5677_23V1V3_R1.fastq.gz
zr5677_23V1V3_R2.fastq.gz
S156
zr5677_240V1V3_R1.fastq.gz
zr5677_240V1V3_R2.fastq.gz
S157
zr5677_241V1V3_R1.fastq.gz
zr5677_241V1V3_R2.fastq.gz
S158
zr5677_242V1V3_R1.fastq.gz
zr5677_242V1V3_R2.fastq.gz
S159
zr5677_243V1V3_R1.fastq.gz
zr5677_243V1V3_R2.fastq.gz
S160
zr5677_244V1V3_R1.fastq.gz
zr5677_244V1V3_R2.fastq.gz
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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”.
Below is the link to a PDF file for viewing the quality plots for all samples:
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):
 
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
F5677.S101
F5677.S102
F5677.S103
F5677.S104
F5677.S105
F5677.S106
F5677.S107
F5677.S108
F5677.S109
F5677.S110
F5677.S111
F5677.S112
F5677.S113
F5677.S114
F5677.S115
F5677.S116
F5677.S117
F5677.S118
F5677.S119
F5677.S120
F5677.S121
F5677.S122
F5677.S123
F5677.S124
F5677.S125
F5677.S126
F5677.S127
F5677.S128
F5677.S129
F5677.S130
F5677.S131
F5677.S132
F5677.S133
F5677.S134
F5677.S135
F5677.S136
F5677.S137
F5677.S138
F5677.S139
F5677.S140
F5677.S141
F5677.S142
F5677.S143
F5677.S144
F5677.S145
F5677.S146
F5677.S147
F5677.S148
F5677.S149
F5677.S150
F5677.S001
F5677.S002
F5677.S003
F5677.S004
F5677.S005
F5677.S006
F5677.S007
F5677.S008
F5677.S009
F5677.S010
F5677.S011
F5677.S012
F5677.S013
F5677.S014
F5677.S015
F5677.S016
F5677.S017
F5677.S018
F5677.S019
F5677.S020
F5677.S021
F5677.S022
F5677.S023
F5677.S024
F5677.S025
F5677.S026
F5677.S027
F5677.S028
F5677.S029
F5677.S030
F5677.S031
F5677.S032
F5677.S033
F5677.S034
F5677.S035
F5677.S036
F5677.S037
F5677.S038
F5677.S039
F5677.S040
F5677.S041
F5677.S042
F5677.S043
F5677.S044
F5677.S045
F5677.S046
F5677.S047
F5677.S048
F5677.S049
F5677.S050
F5677.S151
F5677.S152
F5677.S153
F5677.S154
F5677.S155
F5677.S156
F5677.S157
F5677.S158
F5677.S159
F5677.S160
F5677.S161
F5677.S162
F5677.S163
F5677.S164
F5677.S165
F5677.S166
F5677.S167
F5677.S168
F5677.S169
F5677.S170
F5677.S171
F5677.S172
F5677.S173
F5677.S174
F5677.S175
F5677.S176
F5677.S177
F5677.S178
F5677.S179
F5677.S180
F5677.S181
F5677.S182
F5677.S183
F5677.S184
F5677.S185
F5677.S186
F5677.S187
F5677.S188
F5677.S189
F5677.S190
F5677.S191
F5677.S192
F5677.S193
F5677.S194
F5677.S195
F5677.S196
F5677.S197
F5677.S198
F5677.S199
F5677.S200
F5677.S201
F5677.S202
F5677.S203
F5677.S204
F5677.S205
F5677.S206
F5677.S207
F5677.S208
F5677.S209
F5677.S210
F5677.S211
F5677.S212
F5677.S213
F5677.S214
F5677.S215
F5677.S216
F5677.S217
F5677.S218
F5677.S219
F5677.S220
F5677.S221
F5677.S222
F5677.S223
F5677.S224
F5677.S225
F5677.S226
F5677.S227
F5677.S228
F5677.S229
F5677.S230
F5677.S231
F5677.S232
F5677.S233
F5677.S234
F5677.S235
F5677.S236
F5677.S237
F5677.S238
F5677.S239
F5677.S240
F5677.S241
F5677.S242
F5677.S243
F5677.S244
F5677.S245
F5677.S246
F5677.S247
F5677.S248
F5677.S249
F5677.S250
F5677.S251
F5677.S252
F5677.S253
F5677.S254
F5677.S255
F5677.S256
F5677.S257
F5677.S258
F5677.S259
F5677.S260
F5677.S261
F5677.S262
F5677.S263
F5677.S264
F5677.S265
F5677.S266
F5677.S267
F5677.S268
F5677.S269
F5677.S270
F5677.S271
F5677.S272
F5677.S273
F5677.S274
F5677.S275
F5677.S276
F5677.S277
F5677.S278
F5677.S279
F5677.S280
F5677.S281
F5677.S282
F5677.S283
F5677.S284
F5677.S285
F5677.S286
F5677.S287
F5677.S288
F5677.S289
F5677.S290
F5677.S291
F5677.S292
F5677.S293
F5677.S294
F5677.S295
F5677.S296
F5677.S297
F5677.S298
F5677.S299
F5677.S300
F5677.S301
F5677.S302
F5677.S303
F5677.S304
F5677.S305
F5677.S306
F5677.S307
F5677.S308
F5677.S309
F5677.S310
F5677.S311
F5677.S312
F5677.S313
F5677.S314
F5677.S315
F5677.S316
F5677.S317
F5677.S318
F5677.S319
F5677.S320
F5677.S321
F5677.S322
F5677.S323
F5677.S324
F5677.S325
F5677.S326
F5677.S327
F5677.S328
F5677.S329
F5677.S330
F5677.S331
F5677.S332
F5677.S333
F5677.S334
F5677.S335
F5677.S336
F5677.S337
F5677.S338
F5677.S339
F5677.S340
F5677.S341
F5677.S342
F5677.S343
F5677.S344
F5677.S345
F5677.S346
F5677.S347
F5677.S348
F5677.S349
F5677.S350
F5677.S351
F5677.S352
F5677.S353
F5677.S354
F5677.S355
F5677.S356
F5677.S357
F5677.S358
F5677.S359
F5677.S360
F5677.S361
F5677.S362
F5677.S363
F5677.S364
F5677.S365
F5677.S366
F5677.S367
F5677.S368
F5677.S369
F5677.S370
F5677.S371
F5677.S372
F5677.S373
F5677.S374
F5677.S375
F5677.S376
F5677.S377
F5677.S378
F5677.S379
F5677.S380
F5677.S381
F5677.S382
F5677.S383
F5677.S384
F5677.S385
F5677.S386
F5677.S387
F5677.S388
F5677.S389
F5677.S390
F5677.S391
F5677.S392
F5677.S393
F5677.S394
F5677.S395
F5677.S396
F5677.S397
F5677.S398
F5677.S399
F5677.S400
F5677.S401
F5677.S402
F5677.S403
F5677.S404
F5677.S405
F5677.S406
F5677.S407
F5677.S408
F5677.S409
F5677.S410
F5677.S411
F5677.S412
F5677.S413
F5677.S414
F5677.S415
F5677.S416
F5677.S417
F5677.S418
F5677.S419
F5677.S420
F5677.S421
F5677.S422
F5677.S423
F5677.S424
F5677.S425
F5677.S426
F5677.S427
F5677.S428
F5677.S429
F5677.S430
F5677.S431
F5677.S432
F5677.S433
F5677.S434
F5677.S435
F5677.S436
F5677.S437
F5677.S438
F5677.S439
F5677.S440
F5677.S441
F5677.S442
F5677.S443
F5677.S444
F5677.S445
F5677.S446
F5677.S447
F5677.S448
F5677.S449
F5677.S450
F5677.S451
F5677.S452
F5677.S453
F5677.S454
F5677.S455
F5677.S456
F5677.S457
F5677.S458
F5677.S459
F5677.S460
F5677.S461
F5677.S462
F5677.S463
F5677.S464
F5677.S465
F5677.S466
F5677.S467
F5677.S468
F5677.S469
F5677.S470
F5677.S471
F5677.S472
F5677.S473
F5677.S474
F5677.S475
F5677.S476
F5677.S477
F5677.S478
F5677.S479
F5677.S480
F5677.S481
F5677.S482
F5677.S483
F5677.S484
F5677.S485
F5677.S486
F5677.S487
F5677.S488
F5677.S489
F5677.S490
F5677.S491
F5677.S492
F5677.S493
F5677.S494
F5677.S495
F5677.S496
F5677.S497
F5677.S498
F5677.S499
F5677.S500
F5677.S501
F5677.S502
F5677.S503
F5677.S504
F5677.S505
F5677.S506
F5677.S507
F5677.S508
F5677.S509
F5677.S510
F5677.S511
F5677.S512
F5677.S513
F5677.S514
F5677.S515
F5677.S516
F5677.S517
F5677.S518
F5677.S519
F5677.S520
F5677.S521
F5677.S522
F5677.S523
F5677.S524
F5677.S525
F5677.S526
F5677.S527
F5677.S528
F5677.S529
F5677.S530
F5677.S531
F5677.S532
F5677.S533
F5677.S534
F5677.S535
F5677.S536
F5677.S537
F5677.S538
F5677.S539
F5677.S540
F5677.S541
F5677.S542
F5677.S543
F5677.S544
F5677.S545
F5677.S546
F5677.S547
F5677.S548
F5677.S549
F5677.S550
F5677.S051
F5677.S052
F5677.S053
F5677.S054
F5677.S055
F5677.S056
F5677.S057
F5677.S058
F5677.S059
F5677.S060
F5677.S061
F5677.S062
F5677.S063
F5677.S064
F5677.S065
F5677.S066
F5677.S067
F5677.S068
F5677.S069
F5677.S070
F5677.S071
F5677.S072
F5677.S073
F5677.S074
F5677.S075
F5677.S076
F5677.S077
F5677.S078
F5677.S079
F5677.S080
F5677.S081
F5677.S082
F5677.S083
F5677.S084
F5677.S085
F5677.S086
F5677.S087
F5677.S088
F5677.S089
F5677.S090
F5677.S091
F5677.S092
F5677.S093
F5677.S094
F5677.S095
F5677.S096
F5677.S097
F5677.S098
F5677.S099
F5677.S100
F5677.S551
F5677.S552
F5677.S553
F5677.S554
F5677.S555
F5677.S556
F5677.S557
F5677.S558
F5677.S559
F5677.S560
F5677.S561
F5677.S562
F5677.S563
F5677.S564
F5677.S565
F5677.S566
F5677.S567
F5677.S568
F5677.S569
F5677.S570
F5677.S571
F5677.S572
F5677.S573
F5677.S574
F5677.S575
F5677.S576
F5677.S577
F5677.S578
F5677.S579
F5677.S580
F5677.S581
F5677.S582
F5677.S583
F5677.S584
F5677.S585
F5677.S586
F5677.S587
F5677.S588
F5677.S589
F5677.S590
F5677.S591
F5677.S592
F5677.S593
F5677.S594
F5677.S595
F5677.S596
F5677.S597
F5677.S598
F5677.S599
F5677.S600
F5677.S601
F5677.S602
F5677.S603
F5677.S604
F5677.S605
F5677.S606
F5677.S607
F5677.S608
F5677.S609
F5677.S610
F5677.S611
F5677.S612
F5677.S613
F5677.S614
F5677.S615
F5677.S616
F5677.S617
F5677.S618
F5677.S619
F5677.S620
F5677.S621
F5677.S622
F5677.S623
F5677.S624
F5677.S625
F5677.S626
F5677.S627
F5677.S628
F5677.S629
F5677.S630
F5677.S631
F5677.S632
F5677.S633
F5677.S634
F5677.S635
F5677.S636
F5677.S637
F5677.S638
F5677.S639
F5677.S640
F5677.S641
F5677.S642
F5677.S643
F5677.S644
F5677.S645
F5677.S646
F5677.S647
F5677.S648
F5677.S649
F5677.S650
F5677.S651
F5677.S652
F5677.S653
F5677.S654
F5677.S655
F5677.S656
F5677.S657
F5677.S658
F5677.S659
F5677.S660
F5677.S661
F5677.S662
F5677.S663
F5677.S664
F5677.S665
F5677.S666
F5677.S667
F5677.S668
F5677.S669
F5677.S670
F5677.S671
F5677.S672
F5677.S673
F5677.S674
F5677.S675
F5677.S676
F5677.S677
F5677.S678
F5677.S679
F5677.S680
F5677.S681
F5677.S682
Row Sum
Percentage
input
55,051
62,599
50,744
51,394
51,485
55,930
53,517
63,812
50,687
44,679
51,534
60,418
54,971
55,320
55,797
58,635
50,427
63,991
63,088
54,195
51,214
58,553
61,282
66,216
66,152
43,865
47,137
54,028
44,510
46,172
51,619
51,102
45,952
47,941
54,614
52,961
43,370
48,750
46,420
61,000
49,941
56,431
49,142
47,827
38,969
59,990
43,893
54,090
48,039
57,779
62,947
71,418
75,420
46,493
56,417
55,342
51,941
55,660
58,776
59,241
54,200
66,823
43,995
57,574
46,621
43,333
59,752
57,377
52,436
54,748
49,779
46,676
54,194
51,723
55,269
57,677
48,721
56,732
52,937
65,173
79,602
61,639
53,962
52,688
70,873
54,804
61,782
64,236
56,037
53,674
58,596
64,192
49,375
51,525
51,016
58,354
59,637
38,452
51,842
50,085
43,435
40,456
52,432
46,611
57,779
50,523
42,203
52,757
50,936
38,806
45,330
40,351
46,039
47,091
50,405
63,808
46,391
40,452
45,192
42,354
45,057
43,408
39,886
50,237
38,199
42,581
58,547
46,175
51,143
44,945
46,963
55,260
50,128
51,069
51,951
41,577
46,761
51,935
48,042
43,811
46,161
60,082
53,482
49,583
45,270
41,942
37,630
47,000
51,219
39,490
45,302
44,946
51,458
45,565
45,734
50,670
51,052
44,868
49,357
49,083
45,979
52,047
40,973
43,004
50,092
53,200
45,691
56,092
57,666
47,407
54,243
61,932
43,171
42,778
41,761
44,545
40,893
44,419
43,605
39,310
52,277
49,028
65,504
45,998
46,686
52,527
58,710
52,540
54,921
38,782
34,049
39,327
36,493
61,015
30,892
48,511
39,168
58,495
57,880
49,508
49,428
51,987
47,695
64,010
57,215
39,397
52,077
49,387
47,165
46,497
45,271
48,372
52,158
42,078
48,973
49,716
49,486
42,806
50,701
36,049
50,163
43,457
39,872
44,157
41,187
46,798
61,404
38,257
32,356
39,026
37,839
51,698
50,324
47,379
51,222
48,291
44,958
49,452
53,694
53,990
42,991
42,963
41,859
44,275
52,113
39,939
50,294
49,760
60,198
38,223
48,359
47,501
33,919
45,066
34,726
39,090
48,894
46,877
43,393
54,180
46,705
46,148
48,797
55,488
50,579
43,340
51,112
40,469
49,203
46,457
67,918
45,240
45,590
43,324
48,928
37,803
36,785
34,144
40,153
38,765
38,264
64,147
59,014
43,560
56,856
46,628
50,471
51,581
51,317
53,883
42,979
49,148
47,669
60,502
33,008
32,236
38,039
38,380
29,543
33,493
44,472
49,718
36,386
37,391
59,543
42,321
48,378
41,055
50,740
40,745
45,850
41,849
48,956
45,910
55,315
63,849
45,271
55,980
44,063
45,598
38,583
52,098
39,696
39,838
37,644
43,101
61,445
34,643
42,425
33,132
44,946
37,784
38,832
46,083
44,904
45,588
45,201
83,912
37,126
37,240
34,648
51,175
44,606
23,977
34,681
29,382
17,738
45,165
59,751
25,845
26,267
32,042
19,810
18,454
32,386
30,846
28,859
32,839
32,001
67,160
20,268
37,196
26,866
23,500
23,200
27,265
30,952
39,744
26,622
23,241
66,555
22,007
18,007
32,454
18,909
37,218
23,273
39,020
19,621
18,988
31,534
54,997
36,750
12,034
35,055
24,414
32,598
38,414
24,347
24,924
17,388
30,257
40,540
64,322
34,930
30,736
26,335
31,440
28,619
16,168
31,948
28,917
29,063
30,310
51,077
37,893
28,921
24,206
26,906
34,672
31,393
32,025
29,912
30,867
38,437
55,929
25,941
34,549
25,376
22,163
27,725
31,951
34,865
34,940
45,276
25,301
51,490
21,952
33,193
29,153
36,686
25,320
34,456
48,038
32,966
29,162
32,023
52,282
19,064
17,109
34,094
42,663
30,036
27,118
23,790
22,201
28,687
24,808
53,346
28,554
43,272
35,301
31,060
23,747
28,508
28,221
32,837
38,294
33,311
52,504
26,417
45,368
27,020
25,074
39,562
33,494
36,780
27,813
35,572
24,790
51,713
40,300
38,751
37,382
29,994
27,110
31,171
41,243
41,444
32,204
32,908
58,898
40,161
38,232
42,476
29,491
25,502
20,516
34,340
34,133
32,872
43,215
54,802
36,059
29,518
39,032
32,265
33,101
32,373
40,588
51,972
50,496
51,177
56,852
62,701
57,133
43,950
59,993
58,785
57,205
46,421
59,100
56,591
67,471
44,776
60,859
45,818
67,420
58,867
55,144
55,630
62,153
58,753
52,298
53,446
42,071
60,352
37,618
44,577
57,149
54,424
61,675
59,411
77,519
64,767
53,113
55,345
48,941
56,937
59,732
67,647
45,913
38,572
40,478
35,577
37,268
39,160
50,770
56,496
54,770
34,736
36,512
21,320
60,676
50,124
34,948
28,684
26,198
33,965
29,633
38,392
33,370
30,758
26,201
25,148
56,840
31,734
33,988
35,872
37,133
29,098
32,595
35,970
38,010
32,922
38,972
57,224
27,381
31,961
33,805
34,349
45,989
32,568
23,195
27,173
30,921
32,912
68,364
28,093
57,050
37,397
35,396
34,269
31,162
36,719
40,364
23,559
30,371
55,137
28,735
30,354
31,242
23,714
43,000
33,263
24,923
21,398
33,703
40,797
55,557
30,031
26,331
44,240
35,986
32,846
30,455
32,215
15,945
33,547
37,716
57,344
27,975
48,949
30,068
38,448
41,049
37,123
36,894
31,967
28,467
27,419
50,175
36,924
33,681
32,695
27,785
45,436
20,302
30,412
34,346
28,215
28,775
53,658
22,851
33,440
41,941
63,267
49,326
61,134
58,208
56,956
59,924
53,367
47,386
49,679
50,738
41,964
56,721
45,326
54,373
47,574
53,985
69,191
60,455
82,729
56,098
88,270
59,814
72,375
48,263
63,823
47,736
40,324
45,020
34,126
40,648
46,127
82,972
59,479
56,033
61,556
52,771
30,089,286
100.00%
filtered
54,265
61,752
50,029
50,601
50,707
55,152
52,723
62,879
49,950
44,033
50,798
59,556
54,212
54,507
54,947
57,782
49,699
63,046
62,200
53,382
50,472
57,695
60,385
65,213
65,242
43,265
46,482
53,288
43,904
45,507
50,846
50,334
45,329
47,309
53,859
52,135
42,760
48,038
45,742
60,092
49,232
55,994
48,750
47,102
38,683
59,517
43,610
53,702
47,697
57,322
62,002
70,367
74,322
45,838
55,569
54,557
51,150
54,838
57,885
58,394
53,440
65,845
43,355
56,708
45,928
42,767
58,960
56,587
51,720
53,924
49,134
46,056
53,408
50,938
54,528
56,823
47,997
55,875
52,159
64,323
78,426
60,778
53,214
51,965
69,813
54,002
60,896
63,275
55,248
52,871
57,687
63,322
48,661
50,775
50,309
57,471
58,819
37,892
51,079
49,289
43,089
40,145
52,060
46,264
56,943
50,194
41,840
52,371
50,557
38,505
44,987
40,071
45,630
46,753
50,041
62,870
46,049
40,169
44,852
41,946
44,743
43,063
39,634
49,845
37,938
42,272
57,721
45,828
50,756
44,615
46,530
54,862
49,720
50,748
51,608
41,281
46,431
51,148
47,712
43,483
45,829
59,612
53,107
49,209
44,892
41,638
37,393
46,628
50,501
39,253
44,965
44,618
51,101
45,217
45,372
50,304
50,638
44,558
48,976
48,357
45,682
51,661
40,632
42,656
49,733
52,807
45,381
55,673
57,292
47,072
53,488
61,038
42,851
42,435
41,484
44,189
40,642
44,065
43,261
39,058
51,908
48,669
64,499
45,685
46,304
52,186
58,245
52,218
54,532
38,505
33,769
39,053
36,196
60,079
30,699
48,144
38,862
58,086
57,400
49,118
49,070
51,658
47,316
63,512
56,338
39,152
51,675
49,031
46,799
46,145
45,005
48,037
51,791
41,792
48,593
49,006
49,102
42,443
50,348
35,831
49,778
43,121
39,565
43,821
40,856
46,440
60,478
37,994
32,188
38,748
37,546
51,325
49,961
46,992
50,846
47,975
44,655
48,779
53,313
53,530
42,676
42,637
41,553
43,922
51,768
39,693
49,923
49,328
59,297
37,949
48,028
47,125
33,686
44,768
34,498
38,816
48,517
46,543
43,062
53,413
46,344
45,780
48,450
55,119
50,171
43,022
50,734
40,195
48,823
46,081
66,901
44,894
45,284
42,978
48,567
37,511
36,517
33,873
39,824
38,475
38,034
63,203
58,191
43,250
56,388
46,294
50,094
51,160
50,893
53,468
42,715
48,771
47,291
59,584
32,785
32,001
37,760
38,074
29,334
33,291
44,161
49,349
36,119
37,110
58,717
42,048
48,003
40,798
50,344
40,419
45,477
41,539
48,576
45,569
54,901
62,933
44,944
55,572
43,749
45,221
38,348
51,681
39,426
39,519
37,400
42,760
60,562
34,393
42,099
32,892
44,612
37,503
38,488
45,740
44,542
45,257
44,846
82,730
36,873
36,900
34,406
50,774
44,281
23,781
34,380
29,149
17,595
44,747
58,893
25,641
26,068
31,774
19,647
18,267
32,142
30,571
28,652
32,547
31,741
66,171
20,115
36,923
26,636
23,335
23,038
27,032
30,711
39,361
26,363
23,033
65,518
21,799
17,838
32,191
18,771
36,884
23,094
38,658
19,423
18,813
31,247
54,227
36,406
11,927
34,752
24,218
32,295
38,010
24,136
24,717
17,250
29,984
39,942
63,376
34,561
30,471
26,084
31,167
28,380
16,027
31,690
28,633
28,798
30,053
50,335
37,581
28,653
23,995
26,671
34,388
31,126
31,741
29,660
30,611
38,118
55,180
25,711
34,248
25,176
21,946
27,497
31,690
34,529
34,648
44,899
25,064
50,727
21,749
32,933
28,893
36,359
25,108
34,154
47,677
32,659
28,910
31,710
51,555
18,909
16,976
33,801
42,302
29,753
26,873
23,626
22,046
28,413
24,574
52,614
28,304
42,921
35,027
30,764
23,551
28,240
27,968
32,555
37,952
33,018
51,731
26,171
44,973
26,784
24,840
39,217
33,219
36,479
27,561
35,276
24,561
50,966
39,927
38,441
37,086
29,732
26,878
30,883
40,878
41,081
31,926
32,622
58,010
39,817
37,908
42,130
29,245
25,292
20,335
34,041
33,820
32,574
42,838
53,951
35,700
29,257
38,672
31,991
32,807
32,112
40,225
51,138
49,816
50,473
56,085
61,787
56,252
43,387
59,039
57,937
56,389
45,785
58,218
55,730
66,507
44,146
60,028
45,184
66,449
58,021
54,352
54,782
61,300
57,900
51,571
52,639
41,415
59,431
37,092
43,943
56,322
53,701
60,774
58,493
76,320
63,829
52,386
54,575
48,228
56,129
58,881
66,611
45,312
37,968
39,833
35,051
36,726
38,550
50,033
55,619
53,984
34,476
36,177
21,146
59,815
49,406
34,610
28,455
25,968
33,724
29,354
38,069
33,074
30,458
25,990
24,956
55,987
31,449
33,717
35,530
36,819
28,838
32,305
35,654
37,652
32,603
38,606
56,436
27,128
31,691
33,480
34,094
45,577
32,295
23,010
26,948
30,691
32,650
67,247
27,843
56,541
37,044
35,052
33,982
30,871
36,410
40,011
23,332
30,105
54,326
28,500
30,086
31,000
23,476
42,621
32,953
24,765
21,241
33,426
40,476
54,780
29,775
26,081
43,875
35,665
32,605
30,210
31,945
15,843
33,246
37,367
56,577
27,753
48,494
29,791
38,120
40,695
36,812
36,576
31,710
28,224
27,146
49,467
36,555
33,396
32,412
27,545
45,089
20,146
30,155
34,009
27,954
28,528
52,929
22,691
33,149
41,580
62,340
48,650
60,283
57,347
56,157
59,028
52,589
46,682
48,992
49,984
41,354
55,877
44,619
53,585
46,875
53,211
68,187
59,569
81,446
55,321
87,015
58,937
71,240
47,597
62,940
47,075
39,664
44,370
33,693
40,114
45,515
81,807
58,564
55,189
60,672
52,006
29,768,430
98.93%
denoisedF
49,416
57,150
46,744
46,510
47,617
50,729
48,931
59,073
45,502
39,754
46,731
55,390
50,517
50,414
51,241
54,654
45,881
59,183
58,730
49,725
46,670
53,448
57,295
61,945
61,082
39,631
43,280
49,422
41,451
41,857
47,304
47,134
40,985
44,567
50,092
48,150
38,855
44,902
41,841
56,998
45,527
53,689
45,697
43,192
36,519
56,411
40,852
51,307
45,409
54,172
57,431
66,098
68,643
41,674
52,692
50,678
46,752
50,789
54,250
54,580
49,745
61,305
39,979
51,789
42,129
38,494
54,507
53,753
48,013
49,701
44,708
42,266
49,450
46,438
49,641
52,246
44,438
52,379
48,083
59,539
73,028
56,200
48,769
46,879
65,595
50,400
56,832
57,799
51,018
48,428
53,938
58,715
44,388
46,920
46,116
53,341
54,452
34,146
47,321
45,536
40,515
37,863
49,647
43,769
53,095
47,473
39,006
49,624
48,130
35,770
42,500
37,300
43,269
43,999
46,983
58,834
43,816
37,976
42,204
39,422
42,029
40,327
36,779
47,163
35,655
39,624
52,738
43,020
47,746
41,647
43,704
51,967
46,893
47,825
48,538
38,639
44,252
47,500
44,620
40,892
43,244
56,190
50,565
46,628
42,545
39,111
35,162
44,360
46,248
36,915
42,938
42,527
48,413
41,942
43,091
47,643
48,255
41,680
46,418
44,340
42,893
48,428
38,040
40,376
47,193
49,808
43,152
53,557
54,277
44,293
48,941
55,940
40,148
40,092
38,843
42,621
38,213
41,781
40,664
37,110
48,824
46,127
60,975
43,106
44,111
48,820
54,523
48,839
51,538
36,300
31,316
36,840
34,592
55,834
28,633
45,885
35,819
54,322
54,610
46,207
46,253
48,897
44,805
60,117
52,096
36,747
48,889
46,683
44,441
44,119
43,033
45,576
48,515
39,142
46,284
43,849
46,245
39,091
47,690
33,880
47,764
40,179
37,446
41,254
37,993
43,278
55,528
35,754
30,203
36,353
35,814
48,084
47,020
43,866
46,607
44,984
42,282
44,000
51,299
50,300
40,040
40,724
38,746
41,401
48,676
37,564
47,148
46,033
54,803
35,578
45,538
45,217
31,186
42,553
32,736
36,542
45,578
43,977
39,782
49,214
43,264
42,753
46,128
52,251
46,911
40,000
47,840
37,604
44,934
42,632
62,197
42,454
42,813
40,317
45,453
35,325
33,941
31,438
36,695
36,405
35,673
57,871
54,500
40,687
52,786
43,597
47,279
48,215
47,425
50,733
41,110
45,490
44,047
54,733
31,265
29,872
36,395
35,511
27,489
31,617
41,724
47,096
34,161
34,528
54,973
39,611
45,591
37,998
47,274
37,917
42,816
38,922
45,566
42,929
51,880
59,159
42,472
52,367
40,848
42,876
35,882
49,577
36,421
37,712
35,026
40,284
56,794
32,150
40,432
30,579
42,455
35,646
36,298
43,428
41,355
42,414
42,687
77,911
34,022
35,462
32,140
48,237
42,141
22,700
33,076
28,475
16,671
43,889
54,302
24,867
24,692
31,026
18,592
17,324
30,516
29,465
27,583
31,298
30,516
61,893
19,020
35,867
25,705
22,265
21,347
25,740
29,699
38,251
25,023
21,856
62,283
20,726
16,678
30,913
17,599
35,911
21,789
37,537
18,337
17,859
30,356
50,066
35,144
10,944
33,037
23,007
30,985
36,607
22,982
23,557
16,024
28,424
37,081
59,206
32,978
29,071
24,882
30,016
26,961
14,873
29,972
26,897
27,634
28,636
46,517
35,872
27,362
22,493
25,237
33,757
29,558
30,592
28,127
29,133
36,232
51,403
24,463
32,969
23,179
21,192
26,519
30,110
33,012
32,769
43,457
24,031
47,378
20,313
31,890
27,303
34,425
23,983
33,251
46,574
31,285
27,774
30,861
48,424
17,431
16,289
32,714
40,669
28,630
25,570
22,406
21,103
27,387
23,290
47,540
27,046
41,940
33,526
29,541
22,214
27,436
26,689
31,411
36,750
31,595
48,625
25,172
43,637
25,873
23,695
38,187
31,686
35,208
26,664
33,951
23,550
47,050
38,313
36,991
35,822
28,775
25,620
30,148
40,255
39,425
30,711
31,248
53,415
38,873
36,550
41,317
27,528
24,045
19,291
32,727
32,517
31,258
40,721
49,286
34,093
27,891
37,223
30,816
31,193
30,821
38,628
46,859
45,896
46,980
52,169
57,948
52,794
38,961
54,221
53,252
51,689
41,431
55,431
51,937
62,996
40,488
56,131
41,813
63,376
52,190
50,085
51,534
57,295
53,004
48,285
48,839
37,615
55,800
32,999
40,417
53,389
50,174
57,439
54,150
72,205
59,017
47,330
50,656
43,962
51,915
55,419
62,183
41,917
35,024
37,068
31,374
34,073
36,598
45,694
51,985
50,533
32,618
34,864
20,037
54,825
45,667
33,475
27,303
25,039
32,295
27,871
36,401
32,004
29,314
24,755
23,865
51,681
30,526
32,147
33,932
35,219
27,481
30,865
34,532
36,325
30,680
37,479
52,822
26,110
30,014
32,503
32,596
44,863
31,093
21,681
25,799
29,365
31,273
62,402
26,534
55,203
35,554
33,795
32,443
29,710
35,552
38,494
22,658
29,236
50,751
27,621
28,670
30,140
22,118
41,337
31,467
23,047
20,543
31,945
38,994
51,213
28,831
25,122
42,922
33,674
31,090
28,729
30,540
14,874
31,653
36,285
52,713
26,909
47,210
28,876
36,751
39,568
35,707
35,017
30,294
26,358
26,050
45,331
35,136
31,688
31,033
25,814
42,778
18,997
28,757
32,649
26,858
27,020
47,965
21,679
31,745
39,322
58,749
44,984
55,722
53,315
53,255
55,009
48,025
43,143
45,253
46,683
38,088
51,985
40,963
49,727
43,953
49,454
64,113
55,649
77,326
51,029
82,603
54,591
68,031
43,878
59,187
44,029
36,275
41,134
30,982
37,409
40,972
76,595
54,513
48,971
56,258
47,028
27,987,116
93.01%
denoisedR
51,795
58,857
47,881
47,890
48,307
52,062
50,441
59,914
47,421
41,845
48,152
56,324
51,995
51,308
52,557
55,019
47,184
60,475
59,810
50,508
48,362
54,785
57,958
62,542
61,702
40,998
43,997
50,983
41,994
43,359
48,110
47,855
43,179
44,699
51,549
49,425
40,486
45,940
43,686
58,039
46,523
53,820
46,116
44,335
36,484
56,376
41,201
51,290
45,561
54,453
58,610
67,137
70,689
43,530
53,522
51,243
48,919
52,258
55,153
55,349
50,621
62,646
41,391
53,459
43,401
40,870
56,159
54,238
48,984
51,173
46,586
43,526
50,215
48,102
51,979
53,515
45,383
53,207
49,230
61,956
74,959
57,937
49,957
49,542
67,066
51,398
58,060
59,993
52,684
50,178
54,950
60,968
46,141
48,229
47,912
54,426
56,111
35,698
48,130
46,313
40,795
37,795
49,477
43,626
54,032
47,880
39,483
49,827
48,141
36,197
42,747
37,775
43,396
44,473
47,258
59,704
43,623
38,139
42,814
39,552
42,393
40,808
37,506
47,609
35,863
40,008
54,819
43,850
48,394
41,975
43,919
52,581
47,415
48,344
48,694
39,132
44,144
48,592
45,015
41,087
43,862
56,986
50,405
47,032
42,410
38,932
34,927
43,444
47,800
37,175
42,580
42,493
47,948
42,361
43,204
47,559
48,151
42,304
46,764
45,782
42,871
48,704
38,475
40,412
47,327
49,892
43,546
53,453
54,294
44,405
51,050
57,775
40,248
40,131
39,094
42,118
38,664
41,685
40,926
37,293
49,039
46,040
62,098
43,371
43,874
49,450
54,985
49,168
51,313
36,581
31,700
37,152
34,569
56,762
29,053
46,008
36,937
54,995
54,658
47,292
46,278
48,977
44,768
60,554
53,646
37,029
48,975
46,673
44,911
43,913
42,938
45,593
49,038
39,974
46,201
46,317
46,374
39,906
47,670
33,713
47,234
40,762
37,890
41,562
38,374
43,973
57,132
35,577
30,331
36,064
35,957
49,029
47,532
44,192
48,125
45,490
42,227
46,315
51,060
50,920
40,265
40,825
39,068
42,050
48,995
37,755
47,190
46,893
56,418
35,490
45,418
44,957
31,914
42,722
32,195
35,904
45,427
44,538
40,284
51,228
43,634
43,670
45,975
52,023
47,179
40,708
48,269
38,011
45,930
43,661
63,782
42,391
42,751
40,276
46,433
35,796
34,363
31,663
37,733
36,474
35,385
59,703
55,731
40,511
53,294
44,271
47,380
48,422
48,465
50,830
40,726
45,472
44,686
56,446
31,336
29,826
36,333
36,502
27,788
31,855
42,149
47,086
34,296
34,924
56,307
40,134
45,843
38,144
47,816
38,171
43,011
39,189
45,554
43,546
52,139
60,212
42,442
52,580
41,504
43,422
36,180
49,547
37,236
37,894
35,267
40,416
58,250
32,480
40,160
30,793
42,363
36,042
36,431
43,342
41,740
42,937
42,555
79,268
34,625
35,317
32,694
48,584
42,039
22,933
33,146
28,420
16,608
43,624
56,489
24,954
25,096
31,001
18,676
17,407
30,767
29,728
27,674
31,322
30,823
62,843
19,286
35,850
25,743
22,468
22,063
26,054
29,807
38,267
25,208
22,079
62,813
21,027
17,117
31,168
17,963
35,843
22,198
37,593
18,498
18,001
30,475
51,419
35,238
11,369
33,462
23,281
30,956
36,446
23,094
23,713
16,473
28,817
38,137
60,592
33,195
29,390
25,106
30,081
27,312
15,236
30,538
27,489
27,956
28,821
47,392
35,969
27,642
22,937
25,639
33,468
29,986
30,766
28,715
29,413
36,584
52,660
24,767
33,247
24,202
21,329
26,504
30,216
32,957
33,739
43,563
24,132
47,687
20,850
32,175
27,570
34,670
24,193
33,195
46,350
31,642
27,803
30,862
49,306
18,191
16,356
32,596
40,813
28,809
25,607
22,950
21,335
27,426
23,422
49,352
27,404
41,814
33,693
29,579
22,726
27,285
26,904
31,623
36,692
32,001
49,306
25,183
43,740
26,081
23,797
37,846
32,135
34,990
26,556
33,931
23,756
47,692
38,719
37,193
35,806
28,664
25,936
29,763
39,989
39,840
30,807
31,407
55,218
38,595
36,381
41,088
27,729
24,379
19,441
33,026
32,439
31,414
41,177
50,646
34,472
28,204
37,177
30,705
31,542
30,901
38,815
48,755
47,150
47,885
52,701
58,846
53,961
41,298
56,110
54,926
54,111
42,887
55,930
52,803
63,999
41,993
57,283
43,139
64,110
55,634
51,476
52,400
58,170
54,982
49,322
49,726
38,973
56,941
34,974
41,498
53,934
50,697
58,116
55,734
73,152
60,298
49,779
51,366
45,611
53,135
56,287
63,574
42,965
36,024
37,926
33,152
35,031
37,101
47,457
52,805
51,728
33,001
34,969
20,483
56,839
47,161
33,537
27,493
25,036
32,324
28,167
36,601
32,072
29,347
24,970
24,180
53,004
30,461
32,587
34,395
35,447
27,598
31,075
34,516
36,641
31,331
37,390
54,465
26,114
30,570
32,224
33,160
44,557
31,239
22,137
25,941
29,388
31,432
64,085
26,791
55,096
35,806
34,000
32,978
29,717
35,466
38,808
22,658
29,186
51,599
27,496
29,261
29,992
22,551
41,355
31,559
23,890
20,589
32,207
39,296
51,772
28,798
25,251
42,946
33,976
31,215
29,178
30,986
15,031
31,707
36,325
54,054
26,861
47,297
28,800
37,041
39,613
35,700
34,980
30,409
27,060
26,307
46,737
35,316
32,083
31,120
26,533
43,798
19,539
28,975
32,760
26,982
27,116
50,099
22,047
31,836
39,435
59,858
46,828
56,573
54,303
53,975
56,221
50,023
44,089
46,531
47,291
39,524
53,203
42,067
50,940
44,870
50,702
65,162
57,133
78,267
52,772
83,456
55,400
68,367
45,085
60,189
44,803
37,816
42,205
32,193
37,988
43,576
78,365
55,859
52,247
57,920
49,236
28,383,867
94.33%
merged
47,842
55,370
45,343
44,725
46,124
48,759
47,593
57,173
44,035
38,491
45,232
53,387
49,114
48,423
49,728
52,970
44,494
57,535
57,272
48,011
45,404
51,687
55,783
60,259
58,771
38,295
41,793
48,076
40,251
40,505
45,545
45,534
39,747
42,888
48,625
46,521
37,488
43,545
40,605
55,712
43,780
52,321
43,870
41,427
35,204
54,185
39,207
49,602
43,961
52,184
55,148
64,056
66,405
40,410
51,372
48,521
45,656
49,191
52,336
52,559
47,905
59,371
38,960
49,868
40,639
37,587
52,849
52,201
46,294
47,978
43,205
40,757
47,510
44,808
48,191
50,188
42,667
50,685
46,413
58,127
70,998
54,492
46,653
45,602
64,019
48,825
55,029
55,802
49,443
46,807
52,172
57,355
42,863
45,297
44,576
51,328
52,744
33,002
45,510
43,739
39,006
36,196
47,918
41,900
51,171
45,877
37,325
47,786
46,410
34,304
41,105
35,830
41,734
42,512
45,003
56,809
42,180
36,630
40,882
37,779
40,590
38,791
35,397
45,715
34,296
38,083
51,154
41,811
46,147
39,961
41,857
50,486
45,306
46,164
46,593
37,177
42,588
45,965
42,727
39,104
41,882
54,338
48,645
45,102
40,772
37,152
33,412
41,911
44,636
35,524
41,347
40,967
46,131
40,059
41,717
45,816
46,590
40,226
44,932
42,787
40,881
46,347
36,653
38,819
45,560
47,600
41,930
51,975
52,076
42,468
47,519
53,972
38,368
38,507
37,236
41,146
36,847
40,184
39,027
35,915
46,959
44,325
59,463
41,648
42,349
47,010
52,249
46,654
49,267
34,967
29,821
35,563
33,434
53,680
27,466
44,384
34,500
52,289
52,695
45,112
44,343
46,894
43,128
58,266
50,476
35,290
47,108
45,043
43,276
42,578
41,505
43,840
46,586
38,018
44,763
42,346
44,485
37,484
45,888
32,238
46,008
38,685
36,405
39,726
36,367
41,657
53,380
34,056
28,833
34,445
34,845
46,559
45,463
42,003
44,843
43,289
40,558
42,621
49,786
48,505
38,343
39,568
36,985
40,157
46,627
36,103
45,165
44,451
53,084
33,778
43,739
43,708
30,050
41,098
30,930
34,386
43,460
42,784
37,914
47,909
41,470
41,428
44,436
49,888
44,887
38,525
46,084
36,199
42,955
41,092
60,250
40,646
40,794
38,401
44,028
34,263
32,571
29,956
35,429
35,079
33,664
55,631
53,021
38,698
50,681
42,354
45,431
46,407
45,927
48,844
39,669
43,165
42,275
52,991
30,256
28,311
35,397
34,472
26,371
30,543
40,451
45,553
32,919
33,003
53,568
38,278
44,028
36,044
45,486
36,487
41,107
37,405
43,498
41,570
50,017
57,421
40,727
50,224
39,344
41,682
34,563
48,097
34,948
36,582
33,519
38,643
55,307
30,815
39,070
29,147
40,836
34,672
34,924
41,662
39,399
40,921
41,069
75,649
32,571
34,397
31,015
46,719
40,588
21,991
32,075
27,878
15,859
42,951
52,838
24,311
23,896
30,369
17,828
16,624
29,381
28,779
26,887
30,272
29,850
59,663
18,376
34,953
24,942
21,609
20,610
24,948
28,943
37,336
24,063
21,093
60,520
20,102
16,088
30,088
16,908
35,050
21,035
36,669
17,641
17,204
29,684
48,248
34,184
10,554
32,002
22,241
29,884
35,350
22,136
22,778
15,394
27,466
36,085
57,551
31,948
28,187
24,095
29,122
26,099
14,233
29,035
25,981
26,972
27,597
44,792
34,557
26,549
21,649
24,396
32,983
28,642
29,821
27,367
28,138
34,945
49,778
23,697
32,181
22,432
20,717
25,683
28,856
31,687
32,021
42,341
23,238
45,361
19,576
31,264
26,177
33,036
23,242
32,419
45,451
30,478
26,870
30,168
46,994
16,846
15,796
31,715
39,403
27,846
24,554
21,882
20,546
26,551
22,337
45,511
26,330
41,015
32,410
28,511
21,567
26,655
25,812
30,696
35,700
30,755
46,999
24,395
42,652
25,297
22,859
37,109
30,858
33,950
25,797
32,859
22,886
44,943
37,301
35,939
34,741
27,887
24,843
29,219
39,473
38,382
29,787
30,245
51,846
37,906
35,256
40,465
26,259
23,291
18,530
31,884
31,346
30,261
39,351
47,115
33,130
27,024
36,010
29,736
30,119
29,751
37,478
45,703
44,365
45,285
50,087
56,057
51,382
37,845
52,465
51,549
50,367
39,649
53,912
50,011
61,502
39,165
54,279
40,541
61,864
50,939
48,320
49,845
55,105
51,190
46,969
47,025
36,113
54,332
31,775
38,986
51,689
48,097
55,674
52,447
70,216
56,828
45,805
48,575
42,341
49,940
53,818
60,302
40,342
33,800
35,963
30,280
32,986
35,618
44,052
50,210
49,068
31,382
33,836
19,516
53,230
44,457
32,616
26,502
24,249
31,144
26,902
35,176
31,205
28,354
23,927
23,237
49,916
29,723
31,209
32,955
34,092
26,422
29,863
33,559
35,475
29,620
36,447
51,692
25,280
29,131
31,408
31,842
43,970
30,228
20,969
24,993
28,298
30,283
60,365
25,740
53,966
34,566
32,955
31,619
28,738
34,775
37,473
22,120
28,478
49,073
26,853
28,022
29,254
21,349
40,262
30,327
22,335
19,998
30,929
38,053
49,521
28,012
24,399
42,206
32,260
29,930
27,892
29,764
14,202
30,354
35,378
51,255
26,188
46,218
28,044
35,856
38,642
34,791
33,698
29,227
25,457
25,421
43,586
34,104
30,575
29,943
24,988
41,734
18,519
27,738
31,608
26,069
25,809
46,326
21,212
30,637
37,498
57,115
43,928
53,291
51,311
51,765
53,301
46,548
41,562
43,690
44,928
36,905
50,264
39,239
48,188
42,669
48,077
62,225
54,204
75,269
49,532
80,306
52,252
66,211
42,364
57,443
42,595
35,143
39,722
30,080
36,091
39,795
74,540
52,977
47,277
54,547
45,443
27,046,304
89.89%
nonchim
23,327
24,108
20,268
20,343
23,156
20,460
23,327
27,282
21,787
19,273
28,986
24,229
26,188
23,509
19,653
25,460
18,993
30,040
36,926
22,287
22,860
23,301
29,676
30,354
23,489
22,999
20,683
21,070
16,506
18,010
20,837
20,586
17,751
21,202
24,235
20,479
18,957
21,314
23,431
18,692
17,021
31,734
21,206
19,912
16,117
24,439
22,971
19,551
16,724
22,001
25,875
32,679
28,481
25,489
23,451
20,715
22,826
21,524
22,313
24,353
23,365
27,783
19,933
24,307
17,928
18,301
25,598
28,599
21,483
18,134
23,546
21,748
24,474
21,249
22,859
25,929
20,581
18,710
18,826
26,139
32,769
22,283
22,567
21,037
27,753
23,681
23,524
25,920
24,207
21,686
23,416
31,395
20,330
20,823
20,643
24,114
21,165
23,806
26,009
22,410
17,801
16,154
19,637
19,638
19,577
19,629
17,947
19,005
20,163
16,661
16,137
17,677
17,218
20,482
23,701
26,186
18,469
12,925
14,084
15,713
20,766
16,982
18,243
19,148
16,246
16,936
24,480
15,873
19,769
23,660
21,755
24,568
17,739
20,662
22,780
21,489
18,298
20,218
21,476
18,075
16,906
24,790
19,613
17,381
17,211
17,366
17,628
15,586
21,003
16,301
18,847
17,859
20,213
18,368
20,444
25,662
17,709
18,427
22,514
17,098
22,222
22,405
16,677
16,162
19,669
22,541
15,404
24,732
23,977
19,797
25,576
27,422
19,195
17,807
17,560
24,070
17,208
19,833
16,594
14,693
19,633
21,749
25,426
20,102
20,450
20,260
26,400
22,236
23,862
15,418
15,502
14,716
12,712
22,063
13,582
20,473
17,997
22,331
23,268
21,144
23,835
20,405
17,612
26,535
25,926
16,219
18,794
21,524
23,006
24,447
20,865
22,112
21,427
17,573
20,097
20,310
18,855
19,324
22,703
17,073
22,529
19,298
16,766
17,274
15,018
20,628
24,425
17,231
13,192
15,176
19,394
21,036
17,839
19,017
24,412
20,741
20,189
20,353
27,774
22,877
19,838
14,346
19,382
20,291
20,672
16,570
19,394
21,742
27,087
16,104
19,990
16,926
13,870
16,241
15,116
15,924
18,232
20,797
17,847
18,866
21,256
17,433
17,496
26,414
19,682
16,813
19,652
18,102
21,532
20,441
28,473
16,889
20,096
18,441
20,462
14,971
13,370
16,972
16,930
17,217
20,133
25,246
22,282
20,450
25,429
18,632
21,004
21,610
22,031
18,130
23,031
23,956
20,062
21,452
13,008
14,521
15,744
13,949
11,779
11,754
20,666
20,900
14,403
17,940
26,393
17,903
18,461
17,074
19,977
17,520
18,749
15,341
22,140
17,310
22,608
30,065
17,738
22,052
17,742
27,909
16,708
27,652
18,577
22,965
15,093
16,512
25,722
15,358
21,029
14,482
18,116
21,552
20,265
22,805
18,794
17,526
18,193
46,642
16,146
24,277
20,822
22,738
19,674
10,050
13,828
18,271
8,211
30,571
23,908
12,419
11,984
12,719
8,990
8,109
15,230
13,292
9,055
14,566
15,584
30,810
9,254
18,128
8,350
12,188
11,877
11,341
12,000
16,840
11,738
9,216
26,601
7,867
8,350
12,546
9,003
17,989
11,711
16,005
7,502
7,146
11,806
21,874
14,771
5,744
14,020
10,158
16,793
14,849
8,646
9,032
8,160
12,408
14,018
22,777
11,801
11,174
9,842
11,609
12,140
7,671
13,095
10,834
12,091
13,804
24,064
15,063
11,372
11,680
12,268
17,609
13,570
12,220
12,986
12,272
16,963
23,193
10,156
13,034
10,742
8,933
12,983
13,904
14,936
13,441
18,793
9,873
21,048
10,156
16,304
12,420
13,220
9,725
15,388
17,491
13,114
13,435
11,103
25,134
8,091
5,848
15,439
16,488
10,716
12,669
8,021
8,144
11,233
11,021
24,568
11,624
16,318
14,446
14,388
9,473
13,307
10,924
10,030
14,165
13,313
19,084
12,696
18,987
11,947
11,256
16,481
12,430
13,493
10,524
12,496
8,501
19,291
14,309
14,385
15,481
9,522
11,432
12,987
16,423
16,533
11,702
13,085
25,718
18,579
15,511
17,742
13,522
9,855
9,205
12,133
19,611
13,017
19,833
25,997
14,763
12,343
17,112
11,770
13,711
12,938
17,857
18,109
19,458
19,627
21,577
23,326
28,387
21,443
23,512
23,588
22,323
22,009
25,921
24,409
29,410
19,765
30,137
19,453
26,699
27,271
25,142
21,912
25,243
24,514
24,361
21,346
16,998
25,724
16,401
20,496
23,724
25,162
26,552
21,546
34,440
26,660
22,442
24,935
23,898
25,199
21,817
24,721
19,064
15,050
17,125
15,653
15,167
18,402
23,207
19,014
22,212
17,140
16,461
8,689
22,620
21,892
11,954
11,389
11,765
12,716
13,504
16,055
14,573
13,564
10,053
10,119
20,273
17,278
12,247
14,260
14,475
10,790
12,710
17,089
17,634
15,158
13,836
27,416
12,822
12,967
12,686
12,828
21,421
13,339
9,371
10,785
15,121
13,404
26,249
11,248
20,796
13,917
13,988
15,436
11,375
18,089
15,236
7,689
15,911
16,380
14,926
10,742
12,785
11,230
17,822
14,671
12,926
8,582
15,333
23,010
27,481
12,139
11,192
30,034
16,260
16,058
13,339
13,611
7,925
13,461
18,193
21,690
13,246
20,352
12,710
16,228
20,366
17,884
16,705
15,751
12,917
9,160
22,899
14,759
14,690
15,548
13,315
21,956
10,480
12,647
16,104
11,759
12,649
26,954
7,844
14,500
18,921
23,843
23,998
31,033
28,350
27,641
23,929
20,068
21,375
26,844
27,195
16,699
25,456
21,961
22,528
17,039
28,913
33,918
24,801
44,840
24,796
46,385
25,669
33,139
24,284
28,347
18,676
16,746
19,273
19,858
16,466
22,222
35,855
23,468
27,087
27,901
22,456
12,764,166
42.42%
This table can be downloaded as an Excel table below:
5. DADA2 Amplicon Sequence Variants (ASVs). A total of 56023 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=0.1%(>=5154 reads)
A
Total reads
8,525,372
8,525,372
B
Total assigned reads
5,154,794
5,154,794
C
Assigned reads in species with read count < MPC
0
416,942
D
Assigned reads in samples with read count < 500
0
0
E
Total samples
682
682
F
Samples with reads >= 500
682
682
G
Samples with reads < 500
0
0
H
Total assigned reads used for analysis (B-C-D)
5,154,794
4,737,852
I
Reads assigned to single species
1,515,894
1,411,778
J
Reads assigned to multiple species
13,845
10,824
K
Reads assigned to novel species
3,625,055
3,315,250
L
Total number of species
1,003
140
M
Number of single species
176
37
N
Number of multi-species
22
1
O
Number of novel species
805
102
P
Total unassigned reads
3,370,578
3,370,578
Q
Chimeric reads
120,840
120,840
R
Reads without BLASTN hits
2,823,799
2,823,799
S
Others: short, low quality, singletons, etc.
425,939
425,939
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 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) chosen s the group significan 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.