Project FOMC3969 services include NGS sequencing of the V3V4 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.
Bioinformatics analysis service was not requested, however we still provide the sequence data quality trimming, noise-filtering, pair merging, as well as chimera filtering for the sequences, using the
DADA2 denoising algorithm and pipeline. The denoised, merged and chimera-free ASV (amplicon sequence variants) sequences allow you to perform
downstream analyses such as taxonomy assignment, diversity analysis and differential abundance analysis. If you need us help with these downstream bioinformatics analysis please contact us.
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
Read 1 File Name
R1 Read Count
S100
zr3969_100V3V4_R1.fastq.gz
57686
S101
zr3969_101V3V4_R1.fastq.gz
64213
S102
zr3969_102V3V4_R1.fastq.gz
57300
S103
zr3969_103V3V4_R1.fastq.gz
51363
S104
zr3969_104V3V4_R1.fastq.gz
51194
S105
zr3969_105V3V4_R1.fastq.gz
45951
S106
zr3969_106V3V4_R1.fastq.gz
58614
S107
zr3969_107V3V4_R1.fastq.gz
51632
S108
zr3969_108V3V4_R1.fastq.gz
44778
S109
zr3969_109V3V4_R1.fastq.gz
49961
S010
zr3969_10V3V4_R1.fastq.gz
35651
S110
zr3969_110V3V4_R1.fastq.gz
58633
S111
zr3969_111V3V4_R1.fastq.gz
44310
S112
zr3969_112V3V4_R1.fastq.gz
49783
S113
zr3969_113V3V4_R1.fastq.gz
46838
S114
zr3969_114V3V4_R1.fastq.gz
47509
S115
zr3969_115V3V4_R1.fastq.gz
46061
S116
zr3969_116V3V4_R1.fastq.gz
53797
S117
zr3969_117V3V4_R1.fastq.gz
45489
S118
zr3969_118V3V4_R1.fastq.gz
56119
S119
zr3969_119V3V4_R1.fastq.gz
45726
S011
zr3969_11V3V4_R1.fastq.gz
31501
S120
zr3969_120V3V4_R1.fastq.gz
35338
S121
zr3969_121V3V4_R1.fastq.gz
41004
S122
zr3969_122V3V4_R1.fastq.gz
54573
S123
zr3969_123V3V4_R1.fastq.gz
44927
S124
zr3969_124V3V4_R1.fastq.gz
54265
S125
zr3969_125V3V4_R1.fastq.gz
40743
S126
zr3969_126V3V4_R1.fastq.gz
52825
S127
zr3969_127V3V4_R1.fastq.gz
50548
S128
zr3969_128V3V4_R1.fastq.gz
58537
S129
zr3969_129V3V4_R1.fastq.gz
52716
S012
zr3969_12V3V4_R1.fastq.gz
37072
S130
zr3969_130V3V4_R1.fastq.gz
56772
S131
zr3969_131V3V4_R1.fastq.gz
50817
S132
zr3969_132V3V4_R1.fastq.gz
56193
S133
zr3969_133V3V4_R1.fastq.gz
66032
S134
zr3969_134V3V4_R1.fastq.gz
66323
S135
zr3969_135V3V4_R1.fastq.gz
54813
S136
zr3969_136V3V4_R1.fastq.gz
58607
S137
zr3969_137V3V4_R1.fastq.gz
48068
S138
zr3969_138V3V4_R1.fastq.gz
51823
S139
zr3969_139V3V4_R1.fastq.gz
53430
S013
zr3969_13V3V4_R1.fastq.gz
34157
S140
zr3969_140V3V4_R1.fastq.gz
55720
S141
zr3969_141V3V4_R1.fastq.gz
47995
S142
zr3969_142V3V4_R1.fastq.gz
52547
S143
zr3969_143V3V4_R1.fastq.gz
51597
S144
zr3969_144V3V4_R1.fastq.gz
55689
S145
zr3969_145V3V4_R1.fastq.gz
50968
S146
zr3969_146V3V4_R1.fastq.gz
44903
S147
zr3969_147V3V4_R1.fastq.gz
37927
S148
zr3969_148V3V4_R1.fastq.gz
49600
S149
zr3969_149V3V4_R1.fastq.gz
47608
S014
zr3969_14V3V4_R1.fastq.gz
40213
S150
zr3969_150V3V4_R1.fastq.gz
48138
S151
zr3969_151V3V4_R1.fastq.gz
46628
S152
zr3969_152V3V4_R1.fastq.gz
52770
S153
zr3969_153V3V4_R1.fastq.gz
58216
S154
zr3969_154V3V4_R1.fastq.gz
53012
S155
zr3969_155V3V4_R1.fastq.gz
52276
S156
zr3969_156V3V4_R1.fastq.gz
51293
S157
zr3969_157V3V4_R1.fastq.gz
38080
S158
zr3969_158V3V4_R1.fastq.gz
60956
S159
zr3969_159V3V4_R1.fastq.gz
47558
S015
zr3969_15V3V4_R1.fastq.gz
31833
S160
zr3969_160V3V4_R1.fastq.gz
50363
S161
zr3969_161V3V4_R1.fastq.gz
49670
S162
zr3969_162V3V4_R1.fastq.gz
56932
S163
zr3969_163V3V4_R1.fastq.gz
50274
S164
zr3969_164V3V4_R1.fastq.gz
53183
S165
zr3969_165V3V4_R1.fastq.gz
65877
S166
zr3969_166V3V4_R1.fastq.gz
46466
S167
zr3969_167V3V4_R1.fastq.gz
39934
S168
zr3969_168V3V4_R1.fastq.gz
54999
S169
zr3969_169V3V4_R1.fastq.gz
47943
S016
zr3969_16V3V4_R1.fastq.gz
40929
S170
zr3969_170V3V4_R1.fastq.gz
44212
S171
zr3969_171V3V4_R1.fastq.gz
36601
S172
zr3969_172V3V4_R1.fastq.gz
40639
S173
zr3969_173V3V4_R1.fastq.gz
57180
S174
zr3969_174V3V4_R1.fastq.gz
52818
S175
zr3969_175V3V4_R1.fastq.gz
55930
S176
zr3969_176V3V4_R1.fastq.gz
57383
S177
zr3969_177V3V4_R1.fastq.gz
54514
S178
zr3969_178V3V4_R1.fastq.gz
63196
S179
zr3969_179V3V4_R1.fastq.gz
32851
S017
zr3969_17V3V4_R1.fastq.gz
26838
S180
zr3969_180V3V4_R1.fastq.gz
41633
S181
zr3969_181V3V4_R1.fastq.gz
66099
S182
zr3969_182V3V4_R1.fastq.gz
59228
S183
zr3969_183V3V4_R1.fastq.gz
39017
S184
zr3969_184V3V4_R1.fastq.gz
42880
S185
zr3969_185V3V4_R1.fastq.gz
41603
S186
zr3969_186V3V4_R1.fastq.gz
34376
S187
zr3969_187V3V4_R1.fastq.gz
41611
S188
zr3969_188V3V4_R1.fastq.gz
51972
S189
zr3969_189V3V4_R1.fastq.gz
46941
S018
zr3969_18V3V4_R1.fastq.gz
32085
S190
zr3969_190V3V4_R1.fastq.gz
38301
S191
zr3969_191V3V4_R1.fastq.gz
67451
S192
zr3969_192V3V4_R1.fastq.gz
68273
S193
zr3969_193V3V4_R1.fastq.gz
64174
S194
zr3969_194V3V4_R1.fastq.gz
58156
S195
zr3969_195V3V4_R1.fastq.gz
56109
S196
zr3969_196V3V4_R1.fastq.gz
42529
S197
zr3969_197V3V4_R1.fastq.gz
45713
S198
zr3969_198V3V4_R1.fastq.gz
52031
S199
zr3969_199V3V4_R1.fastq.gz
59612
S019
zr3969_19V3V4_R1.fastq.gz
24967
S001
zr3969_1V3V4_R1.fastq.gz
29337
S200
zr3969_200V3V4_R1.fastq.gz
51467
S201
zr3969_201V3V4_R1.fastq.gz
53096
S202
zr3969_202V3V4_R1.fastq.gz
57595
S203
zr3969_203V3V4_R1.fastq.gz
51017
S204
zr3969_204V3V4_R1.fastq.gz
62912
S205
zr3969_205V3V4_R1.fastq.gz
46372
S206
zr3969_206V3V4_R1.fastq.gz
47607
S207
zr3969_207V3V4_R1.fastq.gz
58703
S208
zr3969_208V3V4_R1.fastq.gz
46178
S209
zr3969_209V3V4_R1.fastq.gz
49425
S020
zr3969_20V3V4_R1.fastq.gz
26467
S210
zr3969_210V3V4_R1.fastq.gz
47924
S211
zr3969_211V3V4_R1.fastq.gz
58269
S212
zr3969_212V3V4_R1.fastq.gz
52294
S213
zr3969_213V3V4_R1.fastq.gz
47759
S214
zr3969_214V3V4_R1.fastq.gz
51271
S215
zr3969_215V3V4_R1.fastq.gz
54279
S216
zr3969_216V3V4_R1.fastq.gz
43200
S217
zr3969_217V3V4_R1.fastq.gz
42887
S218
zr3969_218V3V4_R1.fastq.gz
54018
S219
zr3969_219V3V4_R1.fastq.gz
48233
S021
zr3969_21V3V4_R1.fastq.gz
19069
S220
zr3969_220V3V4_R1.fastq.gz
46877
S221
zr3969_221V3V4_R1.fastq.gz
48579
S222
zr3969_222V3V4_R1.fastq.gz
60803
S223
zr3969_223V3V4_R1.fastq.gz
75296
S224
zr3969_224V3V4_R1.fastq.gz
47004
S225
zr3969_225V3V4_R1.fastq.gz
63569
S226
zr3969_226V3V4_R1.fastq.gz
54171
S227
zr3969_227V3V4_R1.fastq.gz
55287
S228
zr3969_228V3V4_R1.fastq.gz
77356
S229
zr3969_229V3V4_R1.fastq.gz
49436
S022
zr3969_22V3V4_R1.fastq.gz
38424
S230
zr3969_230V3V4_R1.fastq.gz
56393
S231
zr3969_231V3V4_R1.fastq.gz
57384
S232
zr3969_232V3V4_R1.fastq.gz
49383
S233
zr3969_233V3V4_R1.fastq.gz
71153
S234
zr3969_234V3V4_R1.fastq.gz
49111
S235
zr3969_235V3V4_R1.fastq.gz
54257
S236
zr3969_236V3V4_R1.fastq.gz
68153
S237
zr3969_237V3V4_R1.fastq.gz
51559
S238
zr3969_238V3V4_R1.fastq.gz
50031
S239
zr3969_239V3V4_R1.fastq.gz
36482
S023
zr3969_23V3V4_R1.fastq.gz
28731
S240
zr3969_240V3V4_R1.fastq.gz
49291
S241
zr3969_241V3V4_R1.fastq.gz
44366
S242
zr3969_242V3V4_R1.fastq.gz
49309
S243
zr3969_243V3V4_R1.fastq.gz
46308
S244
zr3969_244V3V4_R1.fastq.gz
42626
S245
zr3969_245V3V4_R1.fastq.gz
39535
S246
zr3969_246V3V4_R1.fastq.gz
46239
S247
zr3969_247V3V4_R1.fastq.gz
58091
S248
zr3969_248V3V4_R1.fastq.gz
49309
S249
zr3969_249V3V4_R1.fastq.gz
43365
S024
zr3969_24V3V4_R1.fastq.gz
34616
S250
zr3969_250V3V4_R1.fastq.gz
47971
S251
zr3969_251V3V4_R1.fastq.gz
52868
S252
zr3969_252V3V4_R1.fastq.gz
57570
S253
zr3969_253V3V4_R1.fastq.gz
50748
S254
zr3969_254V3V4_R1.fastq.gz
48168
S255
zr3969_255V3V4_R1.fastq.gz
61936
S256
zr3969_256V3V4_R1.fastq.gz
50791
S257
zr3969_257V3V4_R1.fastq.gz
54746
S258
zr3969_258V3V4_R1.fastq.gz
58128
S259
zr3969_259V3V4_R1.fastq.gz
55424
S025
zr3969_25V3V4_R1.fastq.gz
25009
S260
zr3969_260V3V4_R1.fastq.gz
60352
S261
zr3969_261V3V4_R1.fastq.gz
42924
S262
zr3969_262V3V4_R1.fastq.gz
50320
S263
zr3969_263V3V4_R1.fastq.gz
51439
S264
zr3969_264V3V4_R1.fastq.gz
40764
S265
zr3969_265V3V4_R1.fastq.gz
49534
S266
zr3969_266V3V4_R1.fastq.gz
40610
S267
zr3969_267V3V4_R1.fastq.gz
50476
S268
zr3969_268V3V4_R1.fastq.gz
65918
S269
zr3969_269V3V4_R1.fastq.gz
51437
S026
zr3969_26V3V4_R1.fastq.gz
28792
S270
zr3969_270V3V4_R1.fastq.gz
58627
S271
zr3969_271V3V4_R1.fastq.gz
60329
S272
zr3969_272V3V4_R1.fastq.gz
54631
S273
zr3969_273V3V4_R1.fastq.gz
65741
S274
zr3969_274V3V4_R1.fastq.gz
51977
S275
zr3969_275V3V4_R1.fastq.gz
54570
S276
zr3969_276V3V4_R1.fastq.gz
52315
S277
zr3969_277V3V4_R1.fastq.gz
45171
S278
zr3969_278V3V4_R1.fastq.gz
42346
S279
zr3969_279V3V4_R1.fastq.gz
22935
S027
zr3969_27V3V4_R1.fastq.gz
23076
S280
zr3969_280V3V4_R1.fastq.gz
40630
S281
zr3969_281V3V4_R1.fastq.gz
46606
S282
zr3969_282V3V4_R1.fastq.gz
37014
S283
zr3969_283V3V4_R1.fastq.gz
33767
S284
zr3969_284V3V4_R1.fastq.gz
43672
S285
zr3969_285V3V4_R1.fastq.gz
40662
S286
zr3969_286V3V4_R1.fastq.gz
44233
S287
zr3969_287V3V4_R1.fastq.gz
44782
S288
zr3969_288V3V4_R1.fastq.gz
38373
S289
zr3969_289V3V4_R1.fastq.gz
48221
S028
zr3969_28V3V4_R1.fastq.gz
30570
S290
zr3969_290V3V4_R1.fastq.gz
49571
S291
zr3969_291V3V4_R1.fastq.gz
54574
S292
zr3969_292V3V4_R1.fastq.gz
54601
S293
zr3969_293V3V4_R1.fastq.gz
49871
S294
zr3969_294V3V4_R1.fastq.gz
43083
S295
zr3969_295V3V4_R1.fastq.gz
42279
S296
zr3969_296V3V4_R1.fastq.gz
56385
S297
zr3969_297V3V4_R1.fastq.gz
45414
S298
zr3969_298V3V4_R1.fastq.gz
50795
S299
zr3969_299V3V4_R1.fastq.gz
38622
S029
zr3969_29V3V4_R1.fastq.gz
28089
S002
zr3969_2V3V4_R1.fastq.gz
34578
S300
zr3969_300V3V4_R1.fastq.gz
50782
S301
zr3969_301V3V4_R1.fastq.gz
59395
S302
zr3969_302V3V4_R1.fastq.gz
40517
S303
zr3969_303V3V4_R1.fastq.gz
44647
S304
zr3969_304V3V4_R1.fastq.gz
40817
S305
zr3969_305V3V4_R1.fastq.gz
47800
S306
zr3969_306V3V4_R1.fastq.gz
60196
S307
zr3969_307V3V4_R1.fastq.gz
39794
S308
zr3969_308V3V4_R1.fastq.gz
41100
S309
zr3969_309V3V4_R1.fastq.gz
45986
S030
zr3969_30V3V4_R1.fastq.gz
37150
S310
zr3969_310V3V4_R1.fastq.gz
44669
S311
zr3969_311V3V4_R1.fastq.gz
41889
S312
zr3969_312V3V4_R1.fastq.gz
48123
S313
zr3969_313V3V4_R1.fastq.gz
46459
S314
zr3969_314V3V4_R1.fastq.gz
44808
S315
zr3969_315V3V4_R1.fastq.gz
37835
S316
zr3969_316V3V4_R1.fastq.gz
52769
S317
zr3969_317V3V4_R1.fastq.gz
43577
S318
zr3969_318V3V4_R1.fastq.gz
44579
S319
zr3969_319V3V4_R1.fastq.gz
43467
S031
zr3969_31V3V4_R1.fastq.gz
32013
S320
zr3969_320V3V4_R1.fastq.gz
49868
S321
zr3969_321V3V4_R1.fastq.gz
52030
S322
zr3969_322V3V4_R1.fastq.gz
49082
S323
zr3969_323V3V4_R1.fastq.gz
55031
S324
zr3969_324V3V4_R1.fastq.gz
63518
S325
zr3969_325V3V4_R1.fastq.gz
53196
S326
zr3969_326V3V4_R1.fastq.gz
54850
S327
zr3969_327V3V4_R1.fastq.gz
57885
S328
zr3969_328V3V4_R1.fastq.gz
57875
S329
zr3969_329V3V4_R1.fastq.gz
60830
S032
zr3969_32V3V4_R1.fastq.gz
36067
S330
zr3969_330V3V4_R1.fastq.gz
63088
S331
zr3969_331V3V4_R1.fastq.gz
32616
S332
zr3969_332V3V4_R1.fastq.gz
43994
S333
zr3969_333V3V4_R1.fastq.gz
38912
S334
zr3969_334V3V4_R1.fastq.gz
45933
S335
zr3969_335V3V4_R1.fastq.gz
40273
S336
zr3969_336V3V4_R1.fastq.gz
41550
S337
zr3969_337V3V4_R1.fastq.gz
47445
S338
zr3969_338V3V4_R1.fastq.gz
51301
S339
zr3969_339V3V4_R1.fastq.gz
49303
S033
zr3969_33V3V4_R1.fastq.gz
26392
S340
zr3969_340V3V4_R1.fastq.gz
47230
S341
zr3969_341V3V4_R1.fastq.gz
58044
S342
zr3969_342V3V4_R1.fastq.gz
72338
S343
zr3969_343V3V4_R1.fastq.gz
56613
S034
zr3969_34V3V4_R1.fastq.gz
37493
S035
zr3969_35V3V4_R1.fastq.gz
31093
S036
zr3969_36V3V4_R1.fastq.gz
30991
S037
zr3969_37V3V4_R1.fastq.gz
33137
S038
zr3969_38V3V4_R1.fastq.gz
34726
S039
zr3969_39V3V4_R1.fastq.gz
35248
S003
zr3969_3V3V4_R1.fastq.gz
28946
S040
zr3969_40V3V4_R1.fastq.gz
34918
S041
zr3969_41V3V4_R1.fastq.gz
36179
S042
zr3969_42V3V4_R1.fastq.gz
35692
S043
zr3969_43V3V4_R1.fastq.gz
33954
S044
zr3969_44V3V4_R1.fastq.gz
37433
S045
zr3969_45V3V4_R1.fastq.gz
33826
S046
zr3969_46V3V4_R1.fastq.gz
38926
S047
zr3969_47V3V4_R1.fastq.gz
31500
S048
zr3969_48V3V4_R1.fastq.gz
37052
S049
zr3969_49V3V4_R1.fastq.gz
23482
S004
zr3969_4V3V4_R1.fastq.gz
32514
S050
zr3969_50V3V4_R1.fastq.gz
22930
S051
zr3969_51V3V4_R1.fastq.gz
21277
S052
zr3969_52V3V4_R1.fastq.gz
21410
S053
zr3969_53V3V4_R1.fastq.gz
21040
S054
zr3969_54V3V4_R1.fastq.gz
26464
S055
zr3969_55V3V4_R1.fastq.gz
23669
S056
zr3969_56V3V4_R1.fastq.gz
28251
S057
zr3969_57V3V4_R1.fastq.gz
30505
S058
zr3969_58V3V4_R1.fastq.gz
33945
S059
zr3969_59V3V4_R1.fastq.gz
31995
S005
zr3969_5V3V4_R1.fastq.gz
27130
S060
zr3969_60V3V4_R1.fastq.gz
30468
S061
zr3969_61V3V4_R1.fastq.gz
33131
S062
zr3969_62V3V4_R1.fastq.gz
34883
S063
zr3969_63V3V4_R1.fastq.gz
39684
S064
zr3969_64V3V4_R1.fastq.gz
35687
S065
zr3969_65V3V4_R1.fastq.gz
28137
S066
zr3969_66V3V4_R1.fastq.gz
34281
S067
zr3969_67V3V4_R1.fastq.gz
27748
S068
zr3969_68V3V4_R1.fastq.gz
61720
S069
zr3969_69V3V4_R1.fastq.gz
29651
S006
zr3969_6V3V4_R1.fastq.gz
34737
S070
zr3969_70V3V4_R1.fastq.gz
34599
S071
zr3969_71V3V4_R1.fastq.gz
31285
S072
zr3969_72V3V4_R1.fastq.gz
33796
S073
zr3969_73V3V4_R1.fastq.gz
29615
S074
zr3969_74V3V4_R1.fastq.gz
31157
S075
zr3969_75V3V4_R1.fastq.gz
22282
S076
zr3969_76V3V4_R1.fastq.gz
26848
S077
zr3969_77V3V4_R1.fastq.gz
23770
S078
zr3969_78V3V4_R1.fastq.gz
31934
S079
zr3969_79V3V4_R1.fastq.gz
24665
S007
zr3969_7V3V4_R1.fastq.gz
28582
S080
zr3969_80V3V4_R1.fastq.gz
30256
S081
zr3969_81V3V4_R1.fastq.gz
29272
S082
zr3969_82V3V4_R1.fastq.gz
32746
S083
zr3969_83V3V4_R1.fastq.gz
29911
S084
zr3969_84V3V4_R1.fastq.gz
32481
S085
zr3969_85V3V4_R1.fastq.gz
26722
S086
zr3969_86V3V4_R1.fastq.gz
37859
S087
zr3969_87V3V4_R1.fastq.gz
26059
S088
zr3969_88V3V4_R1.fastq.gz
37173
S089
zr3969_89V3V4_R1.fastq.gz
29919
S008
zr3969_8V3V4_R1.fastq.gz
37168
S090
zr3969_90V3V4_R1.fastq.gz
38495
S091
zr3969_91V3V4_R1.fastq.gz
28195
S092
zr3969_92V3V4_R1.fastq.gz
38782
S093
zr3969_93V3V4_R1.fastq.gz
28481
S094
zr3969_94V3V4_R1.fastq.gz
38321
S095
zr3969_95V3V4_R1.fastq.gz
59239
S096
zr3969_96V3V4_R1.fastq.gz
62651
S097
zr3969_97V3V4_R1.fastq.gz
66421
S098
zr3969_98V3V4_R1.fastq.gz
48210
S099
zr3969_99V3V4_R1.fastq.gz
46984
S009
zr3969_9V3V4_R1.fastq.gz
29954
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 merging 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 analysis 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):
R1/R2
281
271
261
251
241
231
321
42.34%
54.39%
54.26%
54.24%
54.29%
54.18%
311
43.40%
58.15%
58.00%
57.96%
57.87%
57.81%
301
43.60%
57.98%
58.00%
58.20%
58.11%
58.12%
291
44.37%
58.53%
58.60%
58.74%
58.92%
58.74%
281
44.38%
58.58%
58.74%
59.02%
59.03%
58.91%
271
44.49%
58.78%
58.89%
59.13%
59.26%
59.06%
Based on the above result, the trim length combination of R1 = 271 bases and R2 = 241 bases (highlighted red above), was chosen for generating final ASVs for all sequences.
This combination generated highest number of merged non-chimeric ASVs and was used for downstream analyses, if requested.
3. Error plots from learning the error rates
After DADA2 building the error model for the set of data, it is always worthwhile, as a sanity check if nothing else, to visualize the estimated error rates.
The error rates for each possible transition (A→C, A→G, …) are shown below. Points are the observed error rates for each consensus quality score.
The black line shows the estimated error rates after convergence of the machine-learning algorithm.
The red line shows the error rates expected under the nominal definition of the Q-score.
The ideal result would be the estimated error rates (black line) are a good fit to the observed rates (points), and the error rates drop
with increased quality as expected.
Forward Read R1 Error Plot
Reverse Read R2 Error Plot
The PDF version of these plots are available here:
4. DADA2 Result Summary The table below shows the summary of the DADA2 analysis,
tracking paired read counts of each samples for all the steps during DADA2 denoising process -
including end-trimming (filtered), denoising (denoisedF, denoisedF), pair merging (merged) and chimera removal (nonchim).
Sample ID
F3969.S001
F3969.S002
F3969.S003
F3969.S004
F3969.S005
F3969.S006
F3969.S007
F3969.S008
F3969.S009
F3969.S010
F3969.S011
F3969.S012
F3969.S013
F3969.S014
F3969.S015
F3969.S016
F3969.S017
F3969.S018
F3969.S019
F3969.S020
F3969.S021
F3969.S022
F3969.S023
F3969.S024
F3969.S025
F3969.S026
F3969.S027
F3969.S028
F3969.S029
F3969.S030
F3969.S031
F3969.S032
F3969.S033
F3969.S034
F3969.S035
F3969.S036
F3969.S037
F3969.S038
F3969.S039
F3969.S040
F3969.S041
F3969.S042
F3969.S043
F3969.S044
F3969.S045
F3969.S046
F3969.S047
F3969.S048
F3969.S049
F3969.S050
F3969.S051
F3969.S052
F3969.S053
F3969.S054
F3969.S055
F3969.S056
F3969.S057
F3969.S058
F3969.S059
F3969.S060
F3969.S061
F3969.S062
F3969.S063
F3969.S064
F3969.S065
F3969.S066
F3969.S067
F3969.S068
F3969.S069
F3969.S070
F3969.S071
F3969.S072
F3969.S073
F3969.S074
F3969.S075
F3969.S076
F3969.S077
F3969.S078
F3969.S079
F3969.S080
F3969.S081
F3969.S082
F3969.S083
F3969.S084
F3969.S085
F3969.S086
F3969.S087
F3969.S088
F3969.S089
F3969.S090
F3969.S091
F3969.S092
F3969.S093
F3969.S094
F3969.S095
F3969.S096
F3969.S097
F3969.S098
F3969.S099
F3969.S100
F3969.S101
F3969.S102
F3969.S103
F3969.S104
F3969.S105
F3969.S106
F3969.S107
F3969.S108
F3969.S109
F3969.S110
F3969.S111
F3969.S112
F3969.S113
F3969.S114
F3969.S115
F3969.S116
F3969.S117
F3969.S118
F3969.S119
F3969.S120
F3969.S121
F3969.S122
F3969.S123
F3969.S124
F3969.S125
F3969.S126
F3969.S127
F3969.S128
F3969.S129
F3969.S130
F3969.S131
F3969.S132
F3969.S133
F3969.S134
F3969.S135
F3969.S136
F3969.S137
F3969.S138
F3969.S139
F3969.S140
F3969.S141
F3969.S142
F3969.S143
F3969.S144
F3969.S145
F3969.S146
F3969.S147
F3969.S148
F3969.S149
F3969.S150
F3969.S151
F3969.S152
F3969.S153
F3969.S154
F3969.S155
F3969.S156
F3969.S157
F3969.S158
F3969.S159
F3969.S160
F3969.S161
F3969.S162
F3969.S163
F3969.S164
F3969.S165
F3969.S166
F3969.S167
F3969.S168
F3969.S169
F3969.S170
F3969.S171
F3969.S172
F3969.S173
F3969.S174
F3969.S175
F3969.S176
F3969.S177
F3969.S178
F3969.S179
F3969.S180
F3969.S181
F3969.S182
F3969.S183
F3969.S184
F3969.S185
F3969.S186
F3969.S187
F3969.S188
F3969.S189
F3969.S190
F3969.S191
F3969.S192
F3969.S193
F3969.S194
F3969.S195
F3969.S196
F3969.S197
F3969.S198
F3969.S199
F3969.S200
F3969.S201
F3969.S202
F3969.S203
F3969.S204
F3969.S205
F3969.S206
F3969.S207
F3969.S208
F3969.S209
F3969.S210
F3969.S211
F3969.S212
F3969.S213
F3969.S214
F3969.S215
F3969.S216
F3969.S217
F3969.S218
F3969.S219
F3969.S220
F3969.S221
F3969.S222
F3969.S223
F3969.S224
F3969.S225
F3969.S226
F3969.S227
F3969.S228
F3969.S229
F3969.S230
F3969.S231
F3969.S232
F3969.S233
F3969.S234
F3969.S235
F3969.S236
F3969.S237
F3969.S238
F3969.S239
F3969.S240
F3969.S241
F3969.S242
F3969.S243
F3969.S244
F3969.S245
F3969.S246
F3969.S247
F3969.S248
F3969.S249
F3969.S250
F3969.S251
F3969.S252
F3969.S253
F3969.S254
F3969.S255
F3969.S256
F3969.S257
F3969.S258
F3969.S259
F3969.S260
F3969.S261
F3969.S262
F3969.S263
F3969.S264
F3969.S265
F3969.S266
F3969.S267
F3969.S268
F3969.S269
F3969.S270
F3969.S271
F3969.S272
F3969.S273
F3969.S274
F3969.S275
F3969.S276
F3969.S277
F3969.S278
F3969.S279
F3969.S280
F3969.S281
F3969.S282
F3969.S283
F3969.S284
F3969.S285
F3969.S286
F3969.S287
F3969.S288
F3969.S289
F3969.S290
F3969.S291
F3969.S292
F3969.S293
F3969.S294
F3969.S295
F3969.S296
F3969.S297
F3969.S298
F3969.S299
F3969.S300
F3969.S301
F3969.S302
F3969.S303
F3969.S304
F3969.S305
F3969.S306
F3969.S307
F3969.S308
F3969.S309
F3969.S310
F3969.S311
F3969.S312
F3969.S313
F3969.S314
F3969.S315
F3969.S316
F3969.S317
F3969.S318
F3969.S319
F3969.S320
F3969.S321
F3969.S322
F3969.S323
F3969.S324
F3969.S325
F3969.S326
F3969.S327
F3969.S328
F3969.S329
F3969.S330
F3969.S331
F3969.S332
F3969.S333
F3969.S334
F3969.S335
F3969.S336
F3969.S337
F3969.S338
F3969.S339
F3969.S340
F3969.S341
F3969.S342
F3969.S343
Row Sum
Percentage
input
29,337
34,578
28,946
32,514
27,130
34,737
28,582
37,168
29,954
35,651
31,501
37,072
34,157
40,213
31,833
40,929
26,838
32,085
24,967
26,467
19,069
38,424
28,731
34,616
25,009
28,792
23,076
30,570
28,089
37,150
32,013
36,067
26,392
37,493
31,093
30,991
33,137
34,726
35,248
34,918
36,179
35,692
33,954
37,433
33,826
38,926
31,500
37,052
23,482
22,930
21,277
21,410
21,040
26,464
23,669
28,251
30,505
33,945
31,995
30,468
33,131
34,883
39,684
35,687
28,137
34,281
27,748
61,720
29,651
34,599
31,285
33,796
29,615
31,157
22,282
26,848
23,770
31,934
24,665
30,256
29,272
32,746
29,911
32,481
26,722
37,859
26,059
37,173
29,919
38,495
28,195
38,782
28,481
38,321
59,239
62,651
66,421
48,210
46,984
57,686
64,213
57,300
51,363
51,194
45,951
58,614
51,632
44,778
49,961
58,633
44,310
49,783
46,838
47,509
46,061
53,797
45,489
56,119
45,726
35,338
41,004
54,573
44,927
54,265
40,743
52,825
50,548
58,537
52,716
56,772
50,817
56,193
66,032
66,323
54,813
58,607
48,068
51,823
53,430
55,720
47,995
52,547
51,597
55,689
50,968
44,903
37,927
49,600
47,608
48,138
46,628
52,770
58,216
53,012
52,276
51,293
38,080
60,956
47,558
50,363
49,670
56,932
50,274
53,183
65,877
46,466
39,934
54,999
47,943
44,212
36,601
40,639
57,180
52,818
55,930
57,383
54,514
63,196
32,851
41,633
66,099
59,228
39,017
42,880
41,603
34,376
41,611
51,972
46,941
38,301
67,451
68,273
64,174
58,156
56,109
42,529
45,713
52,031
59,612
51,467
53,096
57,595
51,017
62,912
46,372
47,607
58,703
46,178
49,425
47,924
58,269
52,294
47,759
51,271
54,279
43,200
42,887
54,018
48,233
46,877
48,579
60,803
75,296
47,004
63,569
54,171
55,287
77,356
49,436
56,393
57,384
49,383
71,153
49,111
54,257
68,153
51,559
50,031
36,482
49,291
44,366
49,309
46,308
42,626
39,535
46,239
58,091
49,309
43,365
47,971
52,868
57,570
50,748
48,168
61,936
50,791
54,746
58,128
55,424
60,352
42,924
50,320
51,439
40,764
49,534
40,610
50,476
65,918
51,437
58,627
60,329
54,631
65,741
51,977
54,570
52,315
45,171
42,346
22,935
40,630
46,606
37,014
33,767
43,672
40,662
44,233
44,782
38,373
48,221
49,571
54,574
54,601
49,871
43,083
42,279
56,385
45,414
50,795
38,622
50,782
59,395
40,517
44,647
40,817
47,800
60,196
39,794
41,100
45,986
44,669
41,889
48,123
46,459
44,808
37,835
52,769
43,577
44,579
43,467
49,868
52,030
49,082
55,031
63,518
53,196
54,850
57,885
57,875
60,830
63,088
32,616
43,994
38,912
45,933
40,273
41,550
47,445
51,301
49,303
47,230
58,044
72,338
56,613
15,591,941
100.00%
filtered
26,400
31,440
26,217
29,884
23,533
31,105
24,932
33,026
27,566
32,382
28,553
33,180
31,581
36,305
28,682
36,465
24,578
29,110
22,098
23,623
16,451
34,873
24,987
30,724
22,690
26,277
20,691
27,603
25,461
33,778
28,748
32,204
23,383
34,070
28,259
27,849
30,270
31,233
31,703
31,637
32,388
32,641
30,784
34,297
30,460
35,095
27,190
33,283
21,298
20,740
19,076
19,351
18,947
24,004
21,482
25,394
26,981
30,528
28,663
27,080
29,633
29,917
36,066
31,702
24,978
30,802
24,521
55,602
26,919
30,622
27,948
30,069
26,833
28,537
19,691
23,725
21,270
28,734
21,777
26,914
26,239
29,795
27,050
29,643
24,023
34,479
23,084
33,537
26,699
34,547
25,577
35,194
25,542
34,674
52,180
56,308
59,023
41,508
42,557
51,008
58,325
50,761
45,259
45,091
39,980
52,353
46,254
39,800
44,809
53,011
39,000
43,513
41,040
41,880
40,689
48,357
39,806
50,187
40,564
30,142
35,051
47,979
39,807
47,938
35,360
47,292
44,283
50,973
46,103
49,578
44,414
49,571
58,981
59,477
48,155
52,759
41,443
43,189
47,635
49,133
41,209
46,080
45,954
49,856
44,834
38,346
32,414
43,975
42,377
41,819
40,546
47,002
52,851
48,023
46,385
44,605
32,538
55,372
42,299
45,859
42,978
51,253
46,003
47,869
58,662
40,535
34,826
49,610
42,720
38,372
33,359
35,364
51,108
47,021
49,927
50,799
47,743
56,461
27,398
35,697
59,086
52,128
34,525
38,563
37,174
30,377
37,443
46,629
41,673
34,155
59,646
61,055
57,535
52,428
49,255
36,194
40,531
45,597
52,355
45,877
47,872
51,236
45,558
55,939
40,527
41,427
51,461
40,985
44,230
42,825
52,026
46,256
42,559
46,193
47,353
38,237
37,573
48,663
42,217
41,205
42,175
54,425
67,755
41,042
56,109
49,148
48,287
69,395
44,863
50,252
51,005
43,176
65,166
44,978
49,054
60,909
46,031
44,784
32,243
43,341
40,412
43,163
39,855
38,558
35,226
41,592
51,054
44,864
39,089
42,092
47,100
51,963
44,895
42,754
54,573
44,760
48,600
52,784
50,050
54,049
38,047
44,909
45,881
35,290
44,143
36,602
44,767
59,054
46,581
52,319
52,410
47,621
59,444
47,519
47,331
46,265
40,434
38,316
19,266
36,240
41,466
33,654
29,588
38,871
35,726
38,855
40,358
34,422
42,453
43,741
48,391
49,424
44,001
38,558
36,712
50,918
40,885
44,681
34,129
45,401
52,958
36,354
39,083
36,566
41,620
53,580
35,726
35,732
40,473
40,179
36,974
41,813
41,404
39,109
34,027
47,236
39,029
39,286
38,811
44,971
45,275
43,142
49,046
56,941
48,243
48,853
51,535
51,575
54,715
55,894
28,153
39,349
34,262
41,486
36,048
36,855
42,571
45,772
42,728
41,710
51,348
64,809
50,217
13,870,421
88.96%
denoisedF
25,601
29,924
25,833
29,688
22,739
29,613
23,994
32,124
27,238
31,828
27,205
32,042
31,079
35,200
27,189
35,057
23,444
27,757
21,195
21,955
16,197
33,469
24,184
29,157
21,392
25,038
19,832
26,337
23,801
32,774
27,953
30,484
22,584
33,441
27,457
26,320
29,122
29,652
30,403
30,064
30,999
31,564
30,307
33,626
29,148
33,400
26,759
32,560
20,569
19,906
18,187
18,482
18,064
23,327
20,647
24,357
25,919
29,981
26,921
26,066
28,926
28,230
35,538
30,470
23,125
29,538
23,362
53,644
26,628
29,051
26,178
29,303
26,343
27,461
19,209
23,114
19,915
27,604
20,375
26,016
24,722
29,301
26,410
27,980
23,763
34,276
22,579
32,889
25,227
33,263
24,497
33,301
24,230
33,527
49,811
55,262
56,205
40,268
41,858
48,511
57,652
50,215
42,619
43,021
38,077
50,754
43,513
38,398
43,084
52,287
37,760
40,941
39,016
39,670
38,834
47,074
37,873
49,076
39,500
29,491
34,379
45,213
38,078
45,610
33,220
45,195
42,131
49,603
44,257
46,977
41,867
46,679
58,087
57,554
46,485
50,743
40,091
40,641
45,639
46,878
39,318
44,091
44,994
47,753
42,587
36,725
30,876
42,482
40,162
39,532
39,167
45,876
52,168
45,939
44,899
43,497
31,198
54,551
40,656
44,891
42,007
50,366
45,353
46,342
58,236
39,779
33,440
49,076
40,642
36,599
32,940
33,887
50,042
45,894
48,169
49,650
45,732
55,075
26,038
34,349
58,583
49,701
33,430
36,815
35,473
29,629
34,904
45,157
39,578
33,060
57,972
58,742
55,766
50,599
47,176
35,405
38,571
44,395
50,669
44,105
46,205
48,835
44,246
52,664
38,640
39,899
49,190
39,992
42,155
40,581
50,192
44,412
42,036
44,815
45,748
37,394
36,404
46,928
40,368
39,809
39,964
52,757
66,726
39,534
53,841
47,561
47,217
68,621
43,444
47,998
49,883
41,951
64,808
43,677
47,569
58,742
44,497
43,651
31,394
41,028
40,325
41,645
38,691
37,438
34,021
40,684
49,148
44,298
38,053
41,079
45,292
50,050
42,840
41,365
52,036
43,380
48,160
52,384
48,773
52,307
37,232
42,664
45,011
34,087
42,718
35,338
44,089
56,411
45,161
50,352
51,200
45,513
57,943
46,260
45,375
44,955
38,161
37,260
18,518
34,973
40,045
32,557
28,550
37,301
34,591
38,101
39,252
33,670
41,288
42,188
47,067
48,008
42,745
37,425
35,418
50,407
39,856
43,520
33,023
44,128
51,821
35,609
38,065
35,820
40,081
52,179
34,352
34,258
39,083
39,163
35,621
40,181
40,905
38,664
32,995
45,873
37,734
38,299
37,524
43,512
43,855
41,342
47,762
55,811
47,052
46,994
50,065
50,125
53,813
54,795
27,573
37,907
32,919
40,585
35,742
34,908
40,915
45,264
41,174
39,400
49,605
63,088
49,530
13,407,624
85.99%
denoisedR
25,710
30,203
25,979
29,383
22,683
30,091
24,025
32,203
26,803
31,694
27,924
32,077
31,212
35,062
27,780
35,260
23,697
27,642
21,378
22,668
16,341
33,794
24,121
29,693
22,079
25,464
19,953
26,424
24,422
32,685
27,807
31,286
22,681
33,134
27,485
27,067
29,609
29,855
30,239
30,307
31,325
31,310
29,731
33,367
29,191
33,773
26,711
32,071
20,369
19,914
18,289
18,597
18,102
23,380
20,376
24,439
26,026
29,571
27,362
26,170
29,119
28,739
35,419
30,233
24,108
29,608
23,669
54,189
26,433
29,465
27,020
29,119
25,959
27,386
18,931
23,300
20,504
27,431
21,081
26,201
25,444
29,295
26,188
28,274
23,767
34,062
22,713
33,093
25,713
33,391
24,656
33,704
24,301
33,358
50,294
54,542
57,295
40,388
41,957
49,104
57,866
50,057
43,608
43,695
38,396
50,823
44,315
38,677
43,909
50,706
37,874
42,318
39,211
40,484
39,264
47,147
38,157
49,485
39,741
29,158
34,443
46,014
38,171
46,187
34,151
45,555
42,974
49,769
44,695
47,781
43,013
48,087
57,796
57,361
46,697
51,094
40,259
40,491
46,273
47,226
39,570
44,292
45,229
48,119
43,390
37,099
31,153
42,713
40,480
40,311
39,272
46,210
51,861
46,557
44,531
43,625
31,574
54,546
40,708
44,668
41,788
50,126
45,263
46,201
57,988
39,742
33,444
48,947
41,436
37,010
33,197
34,149
50,337
45,919
48,538
49,715
45,836
54,770
26,581
34,421
58,002
49,968
33,257
36,998
35,687
29,394
36,028
45,478
40,127
32,845
58,568
58,669
56,219
50,766
47,295
35,060
39,441
43,974
50,704
44,692
46,153
49,620
44,290
53,833
39,289
40,135
49,662
40,125
42,534
41,160
49,991
44,533
41,353
45,099
46,155
36,924
36,390
47,194
40,404
39,699
40,597
53,263
66,843
39,857
53,971
48,245
47,262
68,249
44,209
48,746
49,450
42,162
64,439
44,232
47,987
58,914
44,946
43,398
31,739
41,586
40,327
41,980
38,457
37,818
34,264
40,921
49,268
44,593
38,364
40,737
45,819
50,031
43,804
41,150
52,865
43,815
48,255
51,911
48,921
52,242
36,626
43,002
44,584
34,497
42,469
35,707
44,243
56,909
45,246
51,304
50,667
46,345
58,391
46,851
46,131
45,137
39,117
36,736
18,741
35,363
40,072
32,875
28,862
37,722
34,261
37,955
39,381
33,751
41,349
42,333
47,103
48,565
42,626
37,395
35,687
50,339
40,280
43,315
33,382
44,341
52,109
35,693
37,862
35,707
40,243
51,682
34,854
34,722
39,000
39,545
36,001
40,290
40,761
38,852
33,321
46,142
38,232
38,168
37,483
43,595
43,955
41,291
47,594
55,609
47,119
47,247
49,898
49,867
53,532
54,429
27,359
38,293
33,039
40,666
35,320
35,525
40,918
44,974
41,231
40,475
49,377
63,840
48,974
13,469,497
86.39%
merged
24,232
28,029
25,413
28,882
20,578
28,121
22,128
31,121
25,702
30,264
25,804
30,545
30,381
33,875
25,511
33,490
21,407
25,931
19,220
19,074
15,642
31,834
22,535
26,347
19,008
23,430
18,125
23,866
22,025
31,547
26,478
28,468
20,867
32,136
26,472
24,316
27,904
27,423
28,852
27,544
27,888
28,771
29,210
32,474
26,558
31,280
25,923
30,020
19,057
18,521
16,700
16,915
16,769
22,247
18,633
22,406
24,160
27,789
25,147
23,278
28,185
25,128
33,304
27,926
20,492
27,554
21,125
51,086
26,088
26,515
24,676
27,642
25,125
25,418
18,037
21,844
18,046
25,674
18,305
24,539
22,351
28,164
24,973
25,301
23,480
33,748
21,852
32,179
22,649
31,487
23,123
30,426
21,895
30,530
45,260
52,430
53,724
37,353
40,921
43,825
56,683
49,326
38,638
40,579
34,386
46,598
40,480
35,995
40,643
47,913
35,414
37,393
35,816
36,698
35,683
45,127
33,991
47,127
37,634
27,865
33,187
42,655
35,898
43,262
29,293
42,808
38,433
47,054
42,095
42,085
38,806
42,878
56,605
54,155
43,624
47,250
37,364
38,380
43,007
41,863
35,752
40,312
44,105
45,165
38,890
34,580
27,829
40,923
37,616
35,322
36,803
43,476
50,186
43,557
41,460
40,661
29,693
53,281
36,720
42,748
39,594
48,294
42,718
43,925
57,438
38,221
30,375
48,149
37,675
33,211
32,788
31,628
48,933
44,074
43,719
46,943
41,418
52,382
24,189
31,872
57,499
44,937
30,265
34,048
32,337
28,089
32,512
42,808
37,299
30,894
55,580
54,354
53,540
47,009
43,527
33,965
36,625
42,030
48,031
41,465
43,335
45,408
41,777
48,771
35,149
37,380
45,024
38,417
39,289
36,753
47,256
40,374
40,367
42,313
43,103
35,134
34,147
44,658
37,582
37,253
36,525
50,055
64,399
37,594
49,567
44,749
45,266
66,543
40,405
44,387
46,732
40,015
63,380
40,835
43,530
54,290
42,114
41,561
29,702
37,087
40,309
39,419
36,337
34,784
31,262
38,858
44,198
43,268
35,752
38,004
41,613
46,568
41,199
39,108
48,035
41,646
47,143
51,052
45,518
47,293
34,155
39,308
43,088
32,752
38,904
33,032
43,295
51,566
41,974
47,510
47,599
42,545
56,416
43,391
41,602
42,071
35,468
34,808
16,808
32,697
37,921
29,819
26,815
34,602
32,530
36,528
36,191
31,686
38,878
39,446
45,052
45,034
40,526
34,939
33,152
49,607
37,467
40,142
30,759
42,571
50,518
33,224
35,615
33,366
35,795
49,714
32,237
32,066
36,212
36,829
33,082
36,884
39,641
38,013
31,002
42,766
35,189
35,860
35,017
40,666
41,196
37,669
44,581
53,560
44,066
43,713
47,536
46,350
51,533
51,761
26,194
35,763
29,772
39,203
34,390
31,563
36,880
43,861
37,094
36,680
46,233
61,430
47,595
12,587,348
80.73%
nonchim
6,003
7,179
3,151
3,080
4,723
7,093
5,537
5,805
3,085
6,601
8,519
6,033
4,501
6,349
7,269
6,632
6,394
5,886
5,556
9,144
3,498
7,212
4,484
10,288
7,000
8,732
8,001
6,060
10,063
5,985
5,587
12,177
6,761
4,718
3,958
8,954
8,493
9,869
6,603
9,364
9,116
6,423
4,224
6,604
6,907
7,823
3,412
4,828
3,577
5,593
5,434
4,587
5,082
4,484
4,973
8,211
5,790
3,420
6,312
6,614
5,403
8,987
3,995
4,981
8,727
10,183
8,375
8,686
2,585
10,222
10,744
6,217
3,637
5,091
3,442
5,400
7,993
5,261
8,077
6,345
8,050
5,056
6,002
8,762
4,063
3,286
4,582
5,918
9,500
6,392
5,909
7,377
5,424
7,663
14,870
6,237
10,973
7,437
5,852
14,634
6,390
2,902
15,719
13,847
10,466
5,608
9,353
8,375
10,334
4,717
6,899
12,312
8,514
10,121
14,144
8,464
11,365
6,755
6,333
5,052
5,517
9,077
8,260
9,444
10,658
7,852
11,721
9,248
5,734
15,697
15,434
15,793
8,689
8,634
13,513
8,663
7,260
7,206
10,474
10,240
10,872
10,909
6,173
9,681
18,370
8,868
9,242
6,627
7,373
14,307
7,173
10,137
5,408
9,407
9,604
7,234
7,558
6,383
9,203
6,378
7,194
6,382
8,106
7,076
4,487
7,059
8,527
6,771
11,201
10,116
6,110
10,127
7,823
8,614
12,487
9,132
14,638
6,336
7,361
7,480
4,825
11,820
8,288
7,739
7,972
4,857
11,669
8,174
8,522
7,907
10,397
13,984
7,320
7,650
12,541
5,114
9,277
6,977
8,897
11,104
7,125
13,535
10,509
14,967
10,813
8,850
15,307
6,965
8,721
13,638
8,233
11,067
4,642
9,992
12,702
6,875
9,739
10,756
7,152
9,593
7,918
11,352
7,731
7,301
12,675
10,929
9,946
5,175
6,595
13,624
7,864
10,063
4,266
6,590
10,551
12,780
7,744
5,029
4,015
14,648
3,019
11,131
7,699
6,793
8,099
6,442
13,303
1,604
6,934
9,056
10,562
7,379
11,399
9,001
15,719
8,727
6,946
5,551
8,966
10,802
7,696
11,695
5,598
6,548
11,794
8,577
5,914
11,208
7,363
11,994
7,579
13,279
7,340
7,838
12,164
7,536
14,285
5,178
2,794
9,436
7,310
6,865
6,216
8,733
6,200
5,425
8,564
5,255
10,303
11,961
10,380
9,681
10,323
7,475
7,531
5,602
7,875
9,574
6,643
9,341
7,369
5,606
9,114
6,170
11,308
8,597
8,037
10,650
5,885
7,246
11,087
10,537
6,170
6,724
5,313
9,549
8,374
6,317
6,022
8,486
10,620
9,417
10,902
6,193
5,898
12,270
8,697
11,513
6,939
8,401
3,984
8,679
8,113
5,412
4,223
13,130
12,342
5,159
10,489
11,521
9,858
8,594
6,394
2,803,342
17.98%
This table can be downloaded as an Excel table below:
5. DADA2 Amplicon Sequence Variants (ASVs). A total of 59050 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.