Same Sample – 2 Labs: 16s vs Shotgun

Back Story

Latest microbiome results are in. Seems like my microbiome is stubborn and stuck these last few tests. Wondering if I should just use this test suggestions exclusively or combine with my prior Thorne test 

We have two sample – one via BiomeSight and one via Thorne. This post is going to do two things:

  • Look at Suggestions – by combining both sets of suggestions using the Uber Consensus
  • Look at the differences between the reports.

We also review “which is better”. My focus is clinical application to individuals — not research papers; answer at bottom.

Uber Consensus

The process has become very simple — “Just give me Suggestions!” on both samples and then going to uber consensus as illustrated by the video below.

The result was excellent agreement on suggestion between each set of results. The CSV files are attached below.

Differences between Reports

I compared two things between the reports:

  • Percentage of the bacteria in the microbiome
  • Percentile of the bacteria in the microbiome

At the Phylum Level

Items less than 100 should be ignored (accuracy of measurement limits). There are a few dramatic differences.

Bacteria NameThorne CountBiomeSight Count
Firmicutes396799529540
Actinobacteria606102100
Bacteroidetes461289448230
Proteobacteria609518150
Chlorobi36429
Acidobacteria35100
Cyanobacteria8320
Spirochaetes8530
Verrucomicrobia5910
Chloroflexi7750
Tenericutes5430
Deinococcus-Thermus4830
Fibrobacteres410
Synergistetes1720
By Count

Looking at Percentiles next

Bacteria NameThorne %ileBiomeSight %ile
Chlorobi2584
Actinobacteria8533
Acidobacteria3481
Spirochaetes8136
Cyanobacteria311
Deinococcus-Thermus5529
Firmicutes1437
Chloroflexi6750
Verrucomicrobia141
Tenericutes132
Proteobacteria1018
Synergistetes64
Bacteroidetes5556
Fibrobacteres10
By Percentile ranking

We have Bacteroidetes in agreement with both — but for the rest…

At the genus level

Bacteria NameThorne CountBiomeSight Count
Bacteroides180054397640
Blautia16470107220
Roseburia1679373640
Faecalibacterium109196152890
Corynebacterium43413820
Ruminococcus917744170
Phocaeicola223209199669
Parabacteroides1185531940
Phascolarctobacterium610123980
Dorea3613000
Sutterella1611339
Oscillospira08250
Coprococcus612012589
Eggerthella6491760
Pseudobutyrivibrio1495790
Lachnospira115936230
Prevotella9544260
Anaerostipes93036310
Clostridium20394960
Pedobacter462410
Odoribacter40772060
Bifidobacterium27831019
Escherichia751610
Porphyromonas1372150
Mediterraneibacter1483113629
Bilophila61110
Veillonella751160
Desulfovibrio19001250
Streptococcus1477840
Acetivibrio33470
Chlorobaculum6429
Finegoldia1339920
Gemella17400
Enterococcus585220
Paenibacillus37620
Mogibacterium39370
Acetobacterium15340
Serratia47350
Eubacterium517240
Megasphaera35290
Selenomonas52290
Bacillus24810
Caldicellulosiruptor11240
Campylobacter23510
Slackia16240
Sphingobacterium48270
Caloramator10190
Staphylococcus18110
Hathewaya8170
Peptoniphilus656800
Peptostreptococcus6150
Microbacterium12510
Adlercreutzia525620
Rhodothermus690
Erysipelothrix1290
Acidaminococcus1290
Hymenobacter8010
Negativicoccus11550
Collinsella7410
Rhodococcus6710
Dialister2580
Anaerococcus336390
Pseudoclostridium860
Moorella960
Vibrio6010
Caldilinea150
Brochothrix250
Mycobacterium6720
Neisseria5710
Pectinatus750
Thermoclostridium1650
Alkaliphilus940
Shewanella3160
Lactobacillus5730
Leptospira430
Deinococcus3510
Tetragenococcus530
Ethanoligenens3410
Weissella1030
Gulosibacter120
Pseudoclavibacter220
Kocuria2810
Meiothermus220
Stenotrophomonas2810
Symbiobacterium320
Devosia420
Dysgonomonas3420
Azoarcus2110
Leuconostoc920
Glaciecola110
Turicibacter2130
Pelotomaculum110
Parascardovia210
Lentibacillus210
Actinopolyspora210
Kitasatospora210
MLOs310
Ochrobactrum310
Rickettsia310
Luteibacter310
Fibrobacter410
Pediococcus1420
Halanaerobium610
Dyadobacter1410
Mycoplasma1720
Thauera910
Lysobacter1110
By Counts

Looking at the percentile rankings — the absolute numbers may vary greatly, but what about relative percentiles?

Bacteria NameThorne %ileBiomesight %ile
Ochrobactrum22
Actinopolyspora11
Halanaerobium11
MLOs11
Glaciecola11
Lentibacillus11
Pelotomaculum11
Parascardovia11
Luteibacter11
Phocaeicola8989
Rickettsia10
Pediococcus109
Fibrobacter20
Mycoplasma53
Alkaliphilus13
Finegoldia8588
Kitasatospora30
Thauera51
Streptococcus5550
Turicibacter1217
Peptoniphilus6458
Hathewaya18
Clostridium1811
Desulfovibrio6169
Eubacterium3846
Symbiobacterium19
Enterococcus8879
Sphingobacterium1323
Pseudoclavibacter111
Anaerococcus7283
Eggerthella9886
Gulosibacter012
Lactobacillus2311
Bifidobacterium5543
Leuconostoc214
Shewanella3547
Prevotella5063
Corynebacterium9986
Collinsella130
Oscillospira016
Faecalibacterium4965
Meiothermus117
Caloramator119
Coprococcus3957
Lysobacter180
Odoribacter8163
Adlercreutzia6381
Pedobacter1331
Dyadobacter201
Dysgonomonas244
Mediterraneibacter6990
Devosia122
Acetivibrio527
Thermoclostridium932
Ethanoligenens251
Dialister1135
Veillonella1641
Pectinatus127
Porphyromonas8862
Moorella128
Negativicoccus6639
Lachnospira5121
Rhodothermus132
Tetragenococcus132
Acetobacterium334
Anaerostipes6596
Bilophila133
Ruminococcus1447
Weissella235
Parabacteroides4275
Acidaminococcus439
Pseudoclostridium137
Leptospira142
Serratia3475
Slackia445
Phascolarctobacterium5697
Erysipelothrix446
Sutterella146
Bacteroides3987
Roseburia4391
Escherichia2877
Selenomonas2173
Deinococcus541
Megasphaera1872
Brochothrix156
Kocuria582
Mogibacterium1774
Stenotrophomonas633
Azoarcus610
Caldilinea061
Caldicellulosiruptor264
Mycobacterium8724
Hymenobacter681
Blautia573
Paenibacillus8719
Neisseria690
Pseudobutyrivibrio2595
Campylobacter751
Gemella482
Peptostreptococcus181
Chlorobaculum184
Staphylococcus850
Vibrio912
Bacillus921
Rhodococcus910
Dorea193
Microbacterium941
By Percentile

Bottom Line

I have never had much belief in the absolute accuracy of the bacteria named or the count. Why? Simple, I understand the statistical process being used and its weakness. I will leave arguments over “which is better” and “which is accurate” to others.

See The taxonomy nightmare before Christmas… for more information.

My focus and concern is to improve the microbiome. With sparse data and the great complexity involved, I am actually very pleased that the suggestions are in agreement. The suggestions are computed using fuzzy logic expert systems. The noise in the data and the statistical processes involved seem to be smoothed out by this Artificial Intelligence engine approach.

Score: Labs: -2, Microbiome Prescription 2

Which is better?. My focus is clinical application to individuals, both give similar suggestions using the Fuzzy Logic Expert System. There is no difference in that sense.

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