Citizen Scienceand

the future of biology

Richard SpragueSept 25, 2015

I’m not a scientist

•A.B. Formal Linguistics

MBA, Concentration in MarketingM.A. International StudiesFellow, Lauder Institute

Find which microbes you have

{ "ubiome_bacteriacounts": [ { "count": 611672, "count_norm": 1008824, "parent": 0, "tax_rank": "root", "tax_color": null, "avg": null, "taxon": 1, "tax_name": "root" },

tax_name tax_rank May-14 Jun-14 Oct-14 Jan-15 Feb-15 21-Apr 28-Apr Jun-15 Aug-15

Firmicutes phylum 62.288% 46.338% 58.450% 71.836% 67.930% 79.980% 68.885% 67.761% 63.708%

Bacteroidetes phylum 25.573% 29.927% 27.485% 16.424% 14.900% 9.684% 13.602% 21.760% 18.289%

Proteobacteria phylum 2.976% 15.625% 0.359% 0.982% 7.514% 1.584% 3.853% 2.111% 1.280%

Actinobacteria phylum 1.097% 0.832% 6.562% 7.616% 0.771% 1.041% 0.382% 1.098% 0.967%

Tenericutes phylum 0.636% 0.249% 0.043% 0.065% 0.071% 0.098% 0.647% 0.530% 0.003%

Verrucomicrobia phylum 3.135% 1.965% 0.770% 0.664% 0.013% 1.155% 0.957% 0.236% 4.418%

Euryarchaeota phylum 0.000% 0.000% 0.000% 0.000% 0.000% 2.425% 0.227% 0.225% 0.637%

Lentisphaerae phylum 0.170% 0.000% 0.118% 0.017% 0.009% 0.018% 0.024% 0.107% 0.108%

Cyanobacteria phylum 0.001% 0.000% 0.003% 0.005% 0.031% 0.005% 0.000% 0.096% 0.012% { "count": 606322, "count_norm": 1000000, "parent": 131567, "tax_rank": "superkingdom", "tax_color": null, "avg": null, "taxon": 2, "tax_name": "Bacteria“},

May-14 Jun-14 Oct-14 Jan-15 Feb-15 21-Apr 28-Apr Jun-15 Aug-150%

10%

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30%

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60%

70%

80%

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100%

Phylum Composition

EuryarchaeotaVerrucomicrobiaTenericutesActinobacteriaProteobacteriaBacteroidetesFirmicutes

RuBiome/Python Utilities• uBiome_compare_samples <- function(sample1,sample2,rank="species")– Return all differences, including the difference in

count_norm• uBiome_sample_unique <- function (sample1,sample2,rank="species")–Which rows are the uniquely found in one but not the

other?• Diversity• def plotTaxa(taxName)

Study my microbiome with Excel

uBiome_sample_unique(husband,wife)

uBiome_compare_samples(before,after)

Bacteroides Plebeius

Jan-Hendrik Hehemann et al. Nature 464, 908-912 (8 April 2010) Transfer of carbohydrate-active enzymes from marine bacteria to Japanese gut microbiota.

plotTaxa(“Bacteroides plebeius”)

dentistChange <- uBiome_sample_unique(afterMouth,beforeMouth)

## count missing.tax_name## 1 3640 bacterium NLAE-zl-P562## 2 2725 Streptococcus sanguinis## 3 2075 Capnocytophaga gingivalis## 4 1969 Peptostreptococcus sp. oral clone FG014## 5 1618 Granulicatella adiacens

dentistCompare <- uBiome_compare_samples(beforeMouth,afterMouth)

## tax_name count_change## 64 Streptococcus pseudopneumoniae 62007## 68 Veillonella sp. oral taxon 780 8065## 41 Neisseria oralis 4693## 2 Abiotrophia sp. oral clone P4PA_155 P1 2308## 28 Granulicatella elegans 1987

Outcompetes the cavity-causing pathogen S. Mutans

Travel to Belize

2 weekbackpacking

Hacking my microbiome

1 2 3 4 50

10000

20000

30000

40000

50000

60000

70000

Bifidobacterium

Sample

Coun

t (no

rmal

ized)

Did it help my sleep?

No

Microbiome changes over time

David et al: Host lifestyle affects human microbiota on daily timescalesGenome Biology 2014, 15:R8

April 21

April 28

Date Calories Carbs Fat ProteinCholesterol Sodium Sugars Fibre

1-Apr-15 2136 351 50 81 140 1687 86 282-Apr-15 1098 148 42 67 155 1374 49 253-Apr-15 221 27 8 8 35 153 17 04-Apr-15 1643 133 231 89 235 3430 42 185-Apr-15 2210 193 430 92 363 3455 76 136-Apr-15 1621 184 76 54 169 1450 111 67-Apr-15 1499 106 65 148 423 4307 49 128-Apr-15 1406 111 63 74 240 2264 47 119-Apr-15 1522 149 52 76 399 2395 17 15

10-Apr-15 1376 109 266 85 288 2372 5 1911-Apr-15 1454 192 42 67 313 2455 43 1712-Apr-15 1245 156 34 86 235 2089 46 1913-Apr-15 1190 101 50 117 328 814 70 814-Apr-15 1774 209 70 82 110 981 75 2515-Apr-15 1765 140 70 112 312 931 27 1516-Apr-15 2107 185 88 93 232 1568 59 2217-Apr-15 2355 297 240 125 248 1284 147 1518-Apr-15 2673 343 78 130 295 4132 101 1619-Apr-15 2099 203 95 89 245 1974 72 1020-Apr-15 1716 180 57 133 283 1888 65 2021-Apr-15 2099 145 109 123 201 2831 42 1722-Apr-15 2605 226 114 115 216 4017 88 2023-Apr-15 1688 197 231 93 222 2825 40 1924-Apr-15 1893 242 68 114 302 2232 83 1525-Apr-15 2908 398 101 105 295 4069 200 1926-Apr-15 3119 294 200 123 401 3003 40 1527-Apr-15 1386 191 50 88 199 723 55 1328-Apr-15 1713 187 66 87 225 2484 48 8

Firmicu

tes

Bacteroidetes

Proteobacteria

Verruco

microbia

Unclassi

fied

Euryarch

aeota

Actinobact

eria0%

10%20%30%40%50%60%70%80%90%

One Week Change in my Microbiome

21-Apr 28-Apr

Macronutrients

Date Calories Carbs Fat Protein Cholesterol Sodium Sugars Fibre

Average (month) 1841.7 192.2 102.7 94.9 268.0 2298.5 64.1 15.2

Average (test period) 2242.6 241.9 124.7 108.7 262.3 2814.3 78.3 16.9

Difference from Ave 400.9 49.7 22.0 13.9 -5.7 515.7 14.2 1.6

% Diff from Ave 122% 126% 121% 115% 98% 122% 122% 111%

Microbes and cholesterol

Increased after one weekTax_name Change

Roseburia 41427

Faecalibacterium 33862Bacteroides 24346Lachnospira 13601Lactobacillus 9874

Same!

Next Steps

Conclusion: 5 Ways Science Will Change

1. Personalized results (n=1) and personal interpretations

2. Lower barrier to experimentation = more experiments

3. Rapid turnaround4. Researchers must focus on design, UX, and open

data5. Increased demands for accuracy and relevance

from “mainstream” science

Thank you

@sprague