This is a literal dumping of the results of processing all viable symptoms from the donated, self-annotated Biomesight samples. The methodology of getting the data is described in New Standards for Microbiome Analysis?. I am de-cluttering the tables using:
- Only Genus and Species results
- Only the top 20 items in each group.
- Only items with at least P < 0.01 are shown
It is just the data with suggestions on how to use the data at the bottom.
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.
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.
| tax_name | Rank | Symptom Avarage | Reference Average | Symptom Median | Reference Median |
| Bacteroides | genus | 27.956 | 25.653 | 26.986 | 23.764 |
| Phocaeicola vulgatus | species | 6.281 | 5.721 | 4.107 | 3.333 |
| Bacteroides uniformis | species | 3.014 | 2.676 | 1.917 | 1.498 |
| Coprococcus | genus | 1.356 | 1.449 | 0.614 | 0.744 |
| Pedobacter | genus | 1.09 | 0.994 | 0.62 | 0.549 |
| Bifidobacterium | genus | 0.727 | 0.974 | 0.071 | 0.139 |
| Bacteroides thetaiotaomicron | species | 1.11 | 1.056 | 0.513 | 0.453 |
| Bacteroides rodentium | species | 0.428 | 0.383 | 0.222 | 0.177 |
| Bilophila wadsworthia | species | 0.359 | 0.336 | 0.232 | 0.196 |
| Bilophila | genus | 0.368 | 0.344 | 0.24 | 0.206 |
| Bacteroides cellulosilyticus | species | 0.894 | 0.835 | 0.107 | 0.073 |
| Insolitispirillum | genus | 0.879 | 0.872 | 0.126 | 0.098 |
| Insolitispirillum peregrinum | species | 0.879 | 0.872 | 0.126 | 0.098 |
| Novispirillum | genus | 0.879 | 0.872 | 0.123 | 0.097 |
| Ruminococcus bromii | species | 0.791 | 0.782 | 0.189 | 0.164 |
| Hathewaya histolytica | species | 0.294 | 0.275 | 0.172 | 0.154 |
| Hathewaya | genus | 0.294 | 0.275 | 0.172 | 0.154 |
| Bifidobacterium longum | species | 0.258 | 0.337 | 0.035 | 0.051 |
| Bacteroides stercoris | species | 1.716 | 1.541 | 0.022 | 0.038 |
| Sutterella wadsworthensis | species | 0.687 | 0.67 | 0.045 | 0.061 |
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 Methanobrevibacter below, it is seen almost half as often with this condition. Bacteria like Desulfomonile are items to reduce, it is occurring too often.
| 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. Look at Isoalcanivorax below, almost 80% of the reference set has less than the median (and the symptom only has 50%); this implies that Isoalcanivorax is prone to overgrowth (“bumper harvest”) with the symptom.
Note: Many of these are rarely reported bacteria, but they are reported sufficiently to compute significance. Below, we restrict to common bacteria only.
| tax_name | Rank | Symptom Median | Odds Ratio | Chi2 | Below | Above |
| Bilophila | genus | 0.3095 | 0.55 | 30.3 | 2147 | 1184 |
| Bilophila wadsworthia | species | 0.2905 | 0.58 | 24.8 | 2096 | 1222 |
| Bacteroides | genus | 29.302 | 0.62 | 20.2 | 2318 | 1428 |
| Ruminococcus callidus | species | 0.003 | 1.63 | 19.1 | 817 | 1331 |
| Bifidobacterium | genus | 0.055 | 1.57 | 17.3 | 1433 | 2245 |
| Caloramator uzoniensis | species | 0.005 | 0.63 | 16.8 | 1224 | 772 |
| Oribacterium | genus | 0.049 | 1.56 | 16.6 | 1315 | 2045 |
| Phocaeicola | genus | 11.3735 | 0.65 | 15.9 | 2268 | 1476 |
| Peptoniphilus methioninivorax | species | 0.005 | 0.65 | 15.8 | 1590 | 1027 |
| Bacteroides fluxus | species | 0.015 | 0.65 | 15.7 | 1875 | 1218 |
| Acidaminococcus | genus | 0.029 | 0.65 | 15.1 | 1703 | 1112 |
| Limnobacter | genus | 0.02 | 0.65 | 15.1 | 1678 | 1096 |
| Turicibacter | genus | 0.005 | 1.54 | 15.1 | 961 | 1476 |
| Limnobacter litoralis | species | 0.02 | 0.65 | 15 | 1676 | 1096 |
| Oribacterium sinus | species | 0.049 | 1.52 | 14.9 | 1328 | 2018 |
| Acidaminococcus fermentans | species | 0.028 | 0.67 | 13.2 | 1499 | 1004 |
| Luteibacter anthropi | species | 0.0075 | 1.5 | 13.1 | 820 | 1230 |
| Candidatus Amoebophilus asiaticus | species | 0.013 | 0.68 | 12.8 | 2064 | 1402 |
| Candidatus Amoebophilus | genus | 0.013 | 0.68 | 12.8 | 2064 | 1402 |
| Shewanella | genus | 0.007 | 0.67 | 12.7 | 1421 | 957 |
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 |
| Deferribacter | genus | 0.002 | 0.1 | 36.3 | 50 | 5 |
| Deferribacter autotrophicus | species | 0.002 | 0.1 | 35.4 | 49 | 5 |
| Sutterella stercoricanis | species | 0.005 | 0.41 | 33.3 | 143 | 59 |
| Helicobacter | genus | 0.002 | 0.35 | 32.4 | 108 | 38 |
| Lentibacillus salinarum | species | 0.002 | 0.19 | 32.4 | 59 | 11 |
| Lentibacillus | genus | 0.002 | 0.19 | 32.4 | 59 | 11 |
| Helicobacter suncus | species | 0.002 | 0.33 | 30.6 | 92 | 30 |
| Isoalcanivorax | genus | 0.002 | 0.14 | 27.6 | 43 | 6 |
| Isoalcanivorax indicus | species | 0.002 | 0.14 | 27.6 | 43 | 6 |
| Bilophila | genus | 0.203 | 1.89 | 27.4 | 108 | 204 |
| Viridibacillus neidei | species | 0.002 | 0.22 | 27.1 | 55 | 12 |
| Alcanivorax | genus | 0.002 | 0.16 | 26.5 | 44 | 7 |
| Bilophila wadsworthia | species | 0.1925 | 1.86 | 26.3 | 109 | 203 |
| Devosia | genus | 0.002 | 0.41 | 26.2 | 110 | 45 |
| Niabella | genus | 0.002 | 0.28 | 26.1 | 65 | 18 |
| Bacteroides | genus | 23.9055 | 1.78 | 25.6 | 129 | 229 |
| Coraliomargarita akajimensis | species | 0.002 | 0.32 | 25.6 | 75 | 24 |
| Coraliomargarita | genus | 0.002 | 0.32 | 25.6 | 75 | 24 |
| Actinopolyspora | genus | 0.002 | 0.24 | 25.5 | 55 | 13 |
| Niabella aurantiaca | species | 0.002 | 0.27 | 25 | 60 | 16 |
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. For example, Bilophila is more often high than with the reference.
| tax_name | Rank | Symptom Median Freq | Odds Ratio | Chi2 | Below | Above |
| Bilophila | genus | 0.3095 | 0.55 | 30.3 | 2147 | 1184 |
| Bilophila wadsworthia | species | 0.2905 | 0.58 | 24.8 | 2096 | 1222 |
| Bacteroides | genus | 29.302 | 0.62 | 20.2 | 2318 | 1428 |
| Ruminococcus callidus | species | 0.003 | 1.63 | 19.1 | 817 | 1331 |
| Bifidobacterium | genus | 0.055 | 1.57 | 17.3 | 1433 | 2245 |
| Caloramator uzoniensis | species | 0.005 | 0.63 | 16.8 | 1224 | 772 |
| Oribacterium | genus | 0.049 | 1.56 | 16.6 | 1315 | 2045 |
| Phocaeicola | genus | 11.3735 | 0.65 | 15.9 | 2268 | 1476 |
| Peptoniphilus methioninivorax | species | 0.005 | 0.65 | 15.8 | 1590 | 1027 |
| Bacteroides fluxus | species | 0.015 | 0.65 | 15.7 | 1875 | 1218 |
| Acidaminococcus | genus | 0.029 | 0.65 | 15.1 | 1703 | 1112 |
| Limnobacter | genus | 0.02 | 0.65 | 15.1 | 1678 | 1096 |
| Turicibacter | genus | 0.005 | 1.54 | 15.1 | 961 | 1476 |
| Limnobacter litoralis | species | 0.02 | 0.65 | 15 | 1676 | 1096 |
| Oribacterium sinus | species | 0.049 | 1.52 | 14.9 | 1328 | 2018 |
| Acidaminococcus fermentans | species | 0.028 | 0.67 | 13.2 | 1499 | 1004 |
| Luteibacter anthropi | species | 0.0075 | 1.5 | 13.1 | 820 | 1230 |
| Candidatus Amoebophilus asiaticus | species | 0.013 | 0.68 | 12.8 | 2064 | 1402 |
| Candidatus Amoebophilus | genus | 0.013 | 0.68 | 12.8 | 2064 | 1402 |
| Shewanella | genus | 0.007 | 0.67 | 12.7 | 1421 | 957 |
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.
| tax_name | Rank | Reference Median Freq | Odds Ratio | Chi2 | Below | Above |
| Sutterella stercoricanis | species | 0.005 | 0.41 | 33.3 | 143 | 59 |
| Bilophila | genus | 0.203 | 1.89 | 27.4 | 108 | 204 |
| Bilophila wadsworthia | species | 0.1925 | 1.86 | 26.3 | 109 | 203 |
| Bacteroides | genus | 23.9055 | 1.78 | 25.6 | 129 | 229 |
| Bacteroides rodentium | species | 0.179 | 1.75 | 24.2 | 130 | 227 |
| Tetragenococcus doogicus | species | 0.003 | 0.53 | 18.9 | 135 | 71 |
| Dethiosulfovibrio | genus | 0.004 | 0.55 | 18.6 | 155 | 86 |
| Ruminococcus callidus | species | 0.008 | 0.52 | 18.4 | 127 | 66 |
| Bifidobacterium | genus | 0.132 | 0.62 | 17.2 | 216 | 135 |
| Bacteroides uniformis | species | 1.524 | 1.59 | 16.9 | 138 | 219 |
| Candidatus Phytoplasma prunorum | species | 0.006 | 0.59 | 16.9 | 171 | 101 |
| Clostridium taeniosporum | species | 0.003 | 0.55 | 16.6 | 137 | 76 |
| Coprococcus | genus | 0.747 | 0.63 | 16.4 | 219 | 139 |
| Phocaeicola | genus | 9.194 | 1.54 | 14.8 | 141 | 217 |
| Clostridium akagii | species | 0.003 | 0.57 | 14.5 | 127 | 72 |
| Bacteroides gallinarum | species | 0.003 | 0.57 | 14 | 122 | 69 |
| Tetragenococcus | genus | 0.003 | 0.59 | 13.2 | 132 | 78 |
| Bifidobacterium adolescentis | species | 0.013 | 0.63 | 12.7 | 163 | 103 |
| Luteibacter anthropi | species | 0.016 | 0.59 | 12.3 | 122 | 72 |
| Coprococcus eutactus | species | 0.01 | 0.62 | 12.2 | 149 | 93 |
Summary
Above, we have identified a ton of bacteria that have P < 0.01 shifts with this condition. The next issue is how to adjust them. A deep modification model such as that illustrated on the Microbiome Taxa R2 Site may be used for probiotics. Once probiotics are suggested, then more conventional US National Library of Medicine based suggestion could be done base on the probiotics selected. Most of the bacteria above lack any literature on how to modify.
This is pending work because I must create a database using Biomesight samples. The above site used data shared from PrecisionBiome. We cannot safely mix data from different sample processing methods (see The taxonomy nightmare before Christmas… ). Once that database is built, I will add its probiotics suggestions below.
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