Updated: Dec 3, 2025 correcting some computations errors.

This document presents the results of statistical analysis on symptoms from viable, self-annotated Biomesight microbiome samples. The methodology for data acquisition is outlined in New Standards for Microbiome Analysis?.​

Tables have been refined to display only genus- and species-level taxa, the 20 most prominent entries per group, and associations achieving statistical significance (P < 0.01).​

The following sections provide the processed data, accompanied by guidance on interpretation and application. Counts of significant bacterial taxa are included, reflecting the application of non-standard but rigorously validated statistical approaches to extensive sample and reference populations, where statistical power derives from dataset scale

Significance	Genus
p < 0.01	219
p < 0.001	189
p < 0.0001	161
p < 0.00001	143

Averages and Medians

I prefer medians over averages. Medians are the values where half of the people have less and half has more. If the data was a bell-curve, then the values will almost be the same… with bacteria that happens rarely. Look at Bacteroides uniformis below, we see that the average is above and the median below.

If symptom median is higher than reference median, it means there is more of this bacteria. If lower, then less. This ignores how often the bacteria is seen (we average only over reports).

tax_name	Rank	Symptom Avarage	Reference Average	Symptom Median	Reference Median
Bacteroides	genus	29.336	25.739	23.905	29.302
Phocaeicola	genus	11.847	10.761	9.194	11.373
Phocaeicola vulgatus	species	6.735	5.719	3.351	4.42
Bacteroides uniformis	species	3.25	2.682	1.524	2.07
Coprococcus	genus	1.206	1.453	0.747	0.552
Bacteroides caccae	species	1.153	0.849	0.282	0.398
Pedobacter	genus	1.174	0.983	0.548	0.659
Bilophila	genus	0.425	0.343	0.203	0.309
Bilophila wadsworthia	species	0.412	0.335	0.193	0.29
Bifidobacterium	genus	0.653	0.961	0.132	0.055
Bacteroides rodentium	species	0.403	0.39	0.179	0.23
Sutterella wadsworthensis	species	0.642	0.66	0.059	0.012
Hathewaya	genus	0.35	0.272	0.153	0.191
Hathewaya histolytica	species	0.35	0.272	0.153	0.19
Phascolarctobacterium faecium	species	0.163	0.14	0.07	0.1
Lachnobacterium	genus	0.233	0.329	0.076	0.047
Butyricimonas	genus	0.194	0.186	0.107	0.133
Anaerofilum	genus	0.266	0.269	0.105	0.13
Oribacterium	genus	0.103	0.133	0.074	0.049
Anaerotruncus	genus	0.219	0.184	0.136	0.161

Bacteria Incidence – How often is it reported

The common sense belief is that if a bacteria is reported more often, then the amount should be higher. This is often not true. The microbiome is a complex thing. Look at Bacteroides uniformis below, we see that the average is above and the median below.

tax_name	Rank	Incidence Odds Ratio	Chi2	Symptoms %	Reference %
Shewanella upenei	species	1.46	13	35.8	24.4
Methanobrevibacter	genus	0.61	10.4	13.8	22.4
Methanobrevibacter smithii	species	0.62	10.1	13.5	21.9
Slackia isoflavoniconvertens	species	0.62	9	12.7	20.4
Prosthecobacter	genus	1.68	12.9	17	10.1
Bifidobacterium cuniculi	species	0.66	7.1	12.7	19.4
Desulfomonile tiedjei	species	1.45	7.8	20.2	13.9
Desulfomonile	genus	1.44	7.6	20.2	14

More or Less often based on Symptom Median All Incidence

This is a little more complex to understand. If we compute the mid point for people with the symptom, then if the bacteria was not involved then half of the reference should be above this value and half below this value. If not, it means that the symptom tends to over or under growth.

tax_name	Rank	Symptom Median	Odds Ratio	Chi2	Below	Above
Alcanivorax	genus	0.002	0.28	74.1	365	101
Isoalcanivorax	genus	0.002	0.28	73.2	355	98
Isoalcanivorax indicus	species	0.002	0.28	73.2	355	98
Nostoc flagelliforme	species	0.002	0.27	68.3	305	83
Pelagicoccus croceus	species	0.002	0.31	63.6	366	114
Psychroflexus	genus	0.002	0.31	63.1	348	107
Psychroflexus gondwanensis	species	0.002	0.31	63.1	348	107
Niabella aurantiaca	species	0.002	0.35	62.5	507	177
Salidesulfovibrio	genus	0.002	0.33	59.7	370	121
Salidesulfovibrio brasiliensis	species	0.002	0.33	59.7	370	121
Deferribacter autotrophicus	species	0.002	0.32	59.2	355	115
Psychrobacter glacialis	species	0.002	0.38	59.2	629	238
Deferribacter	genus	0.002	0.33	58.4	357	117
Bacillus ferrariarum	species	0.002	0.34	56	354	119
Rickettsia marmionii Stenos et al. 2005	species	0.002	0.36	52.7	374	133
Segetibacter aerophilus	species	0.002	0.35	51.8	356	126
Thiorhodococcus pfennigii	species	0.002	0.36	51.7	392	143
Niabella	genus	0.002	0.4	51.4	543	215
Pontibacillus halophilus	species	0.002	0.37	50.7	397	147
Pontibacillus	genus	0.002	0.37	50.6	401	149

More or Less often based on Reference Median All Incidence

This is like the above, but with a different line in the sand. Instead of the median of those with the condition, we use the median of the reference set.

tax_name	Rank	Reference Median	Odds Ratio	Chi2	Below	Above
Bilophila	genus	0.3095	0.55	154.4	2147	1184
Psychrobacter glacialis	species	0.002	0.38	145.8	629	238
Niabella aurantiaca	species	0.002	0.35	136.6	507	177
Alcanivorax	genus	0.002	0.28	134.4	365	101
Isoalcanivorax	genus	0.002	0.28	131.4	355	98
Isoalcanivorax indicus	species	0.002	0.28	131.4	355	98
Bilophila wadsworthia	species	0.2905	0.58	127.5	2096	1222
Bacteroides heparinolyticus	species	0.003	0.49	122	921	449
Niabella	genus	0.002	0.4	119.8	543	215
Pelagicoccus croceus	species	0.002	0.31	118.5	366	114
Nostoc flagelliforme	species	0.002	0.27	116.1	305	83
Psychroflexus	genus	0.002	0.31	114.9	348	107
Psychroflexus gondwanensis	species	0.002	0.31	114.9	348	107
Salidesulfovibrio	genus	0.002	0.33	112.8	370	121
Salidesulfovibrio brasiliensis	species	0.002	0.33	112.8	370	121
Actinopolyspora	genus	0.002	0.4	111.3	501	199
Chromatium	genus	0.002	0.39	111.3	491	193
Chromatium weissei	species	0.002	0.39	110.7	490	193
Bacteroides	genus	29.302	0.62	110.5	2318	1428
Thiorhodococcus	genus	0.002	0.42	110	551	231

More or Less often based on Symptom Median High Incidence

Above we see that many of the top bacteria identified are sparse, that is not reported often. We then restrict them to those that occur above 50% or the time.

tax_name	Rank	Symptom Median Freq	Odds Ratio	Chi2	Below	Above
Clostridium taeniosporum	species	0.003	0.61	19	1266	777
Dethiosulfovibrio	genus	0.004	0.68	12.3	1414	958
Tetragenococcus doogicus	species	0.003	0.69	11.3	1280	880
Hydrocarboniphaga daqingensis	species	0.004	0.7	10.4	1525	1069
Mycoplasmopsis	genus	0.005	0.71	10.2	1703	1201
Pediococcus	genus	0.004	0.75	6.6	1225	919

More or Less often based on Reference Median High Incidence

Above we see that many of the top bacteria identified are sparse, that is not reported often. We then restrict them to those that occur above 50% or the time.

Bilophila	genus	0.3095	0.55	154.4	2147	1184
Psychrobacter glacialis	species	0.002	0.38	145.8	629	238
Niabella aurantiaca	species	0.002	0.35	136.6	507	177
Alcanivorax	genus	0.002	0.28	134.4	365	101
Isoalcanivorax indicus	species	0.002	0.28	131.4	355	98
Isoalcanivorax	genus	0.002	0.28	131.4	355	98
Bilophila wadsworthia	species	0.2905	0.58	127.5	2096	1222
Bacteroides heparinolyticus	species	0.003	0.49	122	921	449
Niabella	genus	0.002	0.4	119.8	543	215
Pelagicoccus croceus	species	0.002	0.31	118.5	366	114
Nostoc flagelliforme	species	0.002	0.27	116.1	305	83
Psychroflexus gondwanensis	species	0.002	0.31	114.9	348	107
Psychroflexus	genus	0.002	0.31	114.9	348	107
Salidesulfovibrio brasiliensis	species	0.002	0.33	112.8	370	121
Salidesulfovibrio	genus	0.002	0.33	112.8	370	121
Actinopolyspora	genus	0.002	0.4	111.3	501	199
Chromatium	genus	0.002	0.39	111.3	491	193
Chromatium weissei	species	0.002	0.39	110.7	490	193
Bacteroides	genus	29.302	0.62	110.5	2318	1428
Thiorhodococcus	genus	0.002	0.42	110	551	231

Summary

A large number of bacterial taxa exhibit shifts with P < 0.01 in association with this condition. The subsequent challenge is determining how to modulate these taxa, since the volume of candidates exceeds what most individuals can practically consider. Moreover, for many of the taxa identified, there is no published evidence in the U.S. National Library of Medicine describing how to alter their abundance.

A deep optimization model, such as the one implemented on the Microbiome Taxa R2 site, can be used to inform probiotic selection. This model provides coverage for each identified taxon and infers which probiotics are most likely to shift their levels. Its output may then be integrated with more conventional recommendations derived from literature indexed in the U.S. National Library of Medicine where such evidence exists, with the two recommendation sets reconciled by giving priority to probiotic-based suggestions.

Development of a dedicated database based on Biomesight samples is in progress. The current model uses data contributed by PrecisionBiome, and datasets generated with differing laboratory processing pipelines cannot be safely combined, as discussed in The taxonomy nightmare before Christmas…. Once the Biomesight-specific database is complete, an option for generating (offline-only) personalized suggestions will be added to the Microbiome Prescription website.

Probiotics Suggestions

The following are based on a simplified algorithm using R2 data for Biomesight. These are tentative numbers subject to future refinements. Bacteria listed are only for probiotics detected with Biomesight tests. Probiotics include some that are available only in some countries and some that are pending approval for retail sale.

• Good Count: Number of bacteria expected to shift in desired direction
• Bad Count: Number of bacteria expected to shift in wrong direction
• Impact: Estimator of impact based on Chi-2, Slope and R2 vectors

Probiotic Species	Impact	Good Count	Bad Count
Faecalibacterium prausnitzii	128.43	7	2
Segatella copri	79.14	10	0
Bifidobacterium breve	66.59	16	1
Bifidobacterium longum	59.74	16	0
Bifidobacterium adolescentis	44.18	14	0
Lactobacillus helveticus	37.56	105	31
Bifidobacterium bifidum	13.45	15	2
Akkermansia muciniphila	12.69	16	14
Bifidobacterium catenulatum	12.37	15	0
Enterococcus faecalis	10.76	70	27
Bifidobacterium animalis	6.11	7	0
Enterococcus faecium	3.69	38	18
Streptococcus thermophilus	3.24	8	0
Enterococcus durans	1.81	36	8
Clostridium butyricum	1.46	36	4
Escherichia coli	1.28	2	2
Bacillus subtilis	0.7	44	23
Limosilactobacillus vaginalis	0.69	42	19
Pediococcus acidilactici	0.68	32	42
Lactococcus lactis	0.57	11	2
Bifidobacterium pseudocatenulatum	0.53	34	11
Limosilactobacillus fermentum	0.4	14	8
Veillonella atypica	0.38	11	0
Lacticaseibacillus paracasei	0.35	17	5
Limosilactobacillus reuteri	0.34	26	11
Ligilactobacillus salivarius	0.15	7	12
Lactobacillus acidophilus	0.14	33	12
Heyndrickxia coagulans	0.09	28	13
Leuconostoc mesenteroides	0.06	17	13
Lacticaseibacillus rhamnosus	0.02	4	2
Lacticaseibacillus casei	-0.04	4	8
Lactobacillus crispatus	-0.2	13	23
Lactobacillus jensenii	-0.21	37	29
Lactiplantibacillus plantarum	-0.24	0	1
Lactiplantibacillus pentosus	-0.28	2	5
Odoribacter laneus	-0.3	0	1
Parabacteroides distasonis	-0.89	3	2
Lactobacillus johnsonii	-4.71	55	26
Parabacteroides goldsteinii	-8.88	0	11
Blautia wexlerae	-21.96	3	2
Blautia hansenii	-25.06	2	3
Bacteroides uniformis	-458.22	0	14
Bacteroides thetaiotaomicron	-489.69	0	13