rebekah linder thesis v5

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Large Scale Screening of Publicly Available Metagenomes for Antibiotic Resistance

Introduction:

The term antibiotic began as a way to describe a laboratory effect or the action of a

chemical compound. Antibiotic can now be defined as a molecule that either kills or inhibits a

microbe. The first known detection of antibiotics was by Alexander Fleming with his discovery

of penicillin in 1928. Shortly after, in 1944, streptomycin was discovered to be the treatment for

tuberculosis. Once both of these antibiotics were being used universally, resistance strains began

to prevail [1]. Now, as the numbers of antibiotics in use increases, so does the prevalence of

antibiotic resistance.

Antibiotics, as a whole, are a huge leap in medicine. However, there are many downsides

to the overuse of antibiotics. If antibiotics are used for an extended amount of time, human gut

microbiota cannot fully recover, which leads to a change in the microbiota community [2].

According the cdc.gov, overuse of antibiotics can also lead to death or allergic reactions [3].

Finally, repeated use of antibiotics can lead to blindness [4], and perhaps worst of all, resistance.

Antibiotics use many different mechanisms and modes of actions. These can be seen in

Table 1 below (page 2).

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Table 1. Modes of action and resistance mechanisms of commonly used antibiotics [1].

Antibiotic class Example(s) Target Mode(s) of

resistance

-Lactams Penicillins

(ampicillin),

cephalosporins

(cephamycin),

penems

(meropenem),

monobactams

(aztreonam)

Peptidoglycan

biosynthesis

Hydrolysis, efflux,

altered target

Aminoglycosides Gentamicin,

streptomycin,

spectinomycin

Translation Phosphorylation,

acetylation,

nucleotidylation,

efflux, altered target

Glycopeptides Vancomycin,

teicoplanin

Peptidoglycan

biosynthesis

Reprogramming

peptidoglycan

biosynthesis

Tetracyclines Minocycline,

tigecycline

Translation Monooxygenation,


Macrolides Erythromycin,

azithromicin

Translation Hydrolysis,

glycosylation,

phosphorylation,


Lincosamides Clindamycin Translation Nucleotidylation,


Streptogramins Synercid Translation C-O lyase (type B

streptogramins),

acetylation (type A

streptogramins),


Oxazolidinones Linezolid Translation Efflux, altered target

Phenicols Chloramphenicol Translation Acetylation, efflux,

altered target

Quinolones Ciprofloxacin DNA replication Acetylation, efflux,

altered target

Pyrimidines Trimethoprim C1 metabolism Efflux, altered target

Sulfonamides Sulfamethoxazole C1 metabolism Efflux, altered target

Rifamycins Rifampin Transcription ADP-ribosylation,


Lipopeptides Daptomycin Cell membrane Altered target

Cationic peptides Colistin Cell membrane Altered target, efflux

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Antibiotic Resistance

Antibiotic resistance has spread rapidly throughout the years, and there are many factors

as to why. Antibiotic resistance genes (ARGs) occur naturally in bacteria and fungi across all

environments [5]. However, in many instances, the use of antibiotics greatly increases the

presence of these ARGs.

Agriculture is one of the most prominent sources of antibiotic resistance. Farmers often

spray antibiotics directly onto crops and/or use the manure from animals treated with antibiotics

as fertilizer for crops. This can absorb into the plant, which is then consumed by humans or

animals. The antibiotics can also absorb into the soil, which can then be washed off into rivers,

lakes, and streams, carrying ARGs to new places and affecting new populations [5]. The push for

more organic farming to replace inorganic nitrogen and phosphorus fertilizers is leading to an

increase in this transfer. Nonetheless, it has been found that ARGs are present in manure even if

the animal had not been treated with antibiotics [6]. In the food industry, antibiotics are used on

animals in order to increase size, production, and growth rates, as well as fight infection. Again,

these animals are then consumed, and the ARGs are transferred to humans [5].

Finally, humans use antibiotics in order to treat infections. The average American makes

outpatient visits twice a year, and 15.3% of those visits end in them receiving an antibiotic

prescription. About 100,000-200,000 tons of antibiotics are used annually, with 25,000 tons of

that total being used in China [7]. Even when a new antibiotic is introduced, its resistance strain

emerges within a few years [2]. All of the factors stated above lead to the spread of antibiotic

resistance genes and the rise of superbugs such as tuberculosis and methicillin-resistant

Staphylococcus aureus (MRSA).

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Antibiotic resistance genes work by inhibiting cell wall synthesis, DNA synthesis, RNA

synthesis, and protein synthesis [8]. They can also inactivate the antibiotic itself. A few more

pathways include alternating the binding site, modifying the metabolic pathway, and reducing

the intracellular antibiotic accumulation. Altering the binding site of the antibiotic reduces the

capacity at which the antibiotic binds. Modification of the metabolic pathway of the antibiotic

allows the cell to avoid the antibiotic effect. In order to reduce the intracellular antibiotic

accumulation, the permeability of the cell is decrease and/or the active efflux of the antibiotic is

increased [2].

The mechanisms of antibiotic resistance genes include genetic jugglery, intrinsic

resistance, the resistome, plasmid-mediated transmission, horizontal gene transfer, and cell-cell

fusion. Genetic jugglery includes random mutations of genes, leading to modified catalysts that

contain resistance. Intrinsic resistance includes genes in a bacterial genome that have a

possibility of developing a resistance phenotype. The resistome contains bacteria that produce

antibiotics as well as the resistance to those certain antibiotics [1]. In plasmid-mediated

transmission, the resistance genes are carried and spread via plasmids [9]. Horizontal gene

transfers involve neighboring bacteria swapping genetic information with each other. There are

three types of horizontal gene transfer: transformation, transduction, and conjugation.

Transformation occurs when bacteria obtains short fragments of naked DNA. Transduction

involves bacteriophages transferring DNA from one bacterium to another. Conjugation calls for

the transfer of DNA via sexual pilus [10]. Finally, cell-cell fusion involves the merging of two or

more cells in order to combine genomes [11].

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Metagenomics

Metagenomics allows for the characterization of microbial populations using its genetic

composition. It can also determine the function of microbes, as well as their evolutionary profile.

This is done by taking a sample, extracting, sequencing and then analyzing the DNA.

Metagenomics has quickly become the standard tool used by scientists in the microbial ecology

field, mostly due to its simplicity, along with its accuracy [12].

MG-RAST

MG-RAST stands for Metagenomic Rapid Annotation Using Subsystems Technology. It

is a SEED-based server hosted by Argonne National Laboratory that contains the metagenomic

information about various microbial populations. Users can upload raw metagenomic data for

analysis. According to mcs.anl.gov, MG-RAST provides the annotation of sequence fragments,

their phylogenetic classification, functional classification of samples, and comparison between

multiple metagenomes. MG-RAST also allows for the configuration of initial metabolic

reconstruction for the metagenome [13].

Comprehensive Antibiotic Resistance Database

The Comprehensive Antibiotic Resistance Database, or CARD, is a database that

includes data about antibiotics as well as antibiotic resistance genes and their associated proteins.

The CARD helps to classify antibiotic resistance gene data. It contains tools that help identify

antibiotic resistance genes from either partial or whole sequence data [14].

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rpoB

rpoB is a gene that encodes the beta subunit of RNA polymerase [15]. It is a single-copy

protein encoding gene that is used as a marker for pyrosequencing-based assessments of

bacterial diversity in soil. [16].

USEARCH & UBLAST

USEARCH and UBLAST are algorithms that are used to search sequence databases.

They do this by pursuing high-scoring global and local alignments. UBLAST has been proven to

be up to 350 times faster than NCBI BLAST (another widely used algorithm) with similar or

even higher sensitivity [17].

Objectives

Because of the lack of consistency in using metagenomics to study antibiotic resistance

genes, this study researched any correlation there could be with the prevalence antibiotic

resistance in certain environment types. In order to do this, information from the MG-RAST

server will be compared to the CARD database.

The goal of this project is to create a reference between ARGs and MG-RAST available

datasets that could potentially give rise to a wider understanding of the source of ARGs in the

environment. This experiment could also create a profile of certain ARGs using environmental

information, ARG abundance, phylogenetic/taxonomic abundances, and metabolic pathways,

which could lead to new understandings of the distribution of ARGs in the environment.

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Materials and Methods:

Sampling and Data Sources

Data sets (1,056) were selected randomly, and downloaded directly from MG-RAST

using the MG-RAST API. The larger dataset was trimmed to 374 metagenomes based on the

ability to identify the sample type and quality of data (Table S1). There were 11 total

environment types: air (n=5), animal (n=50), coral reef (n=13), grassland (n=3), human (n=134),

mangrove (n=9), marine (n=23), soil (n=98), tundra (n=3), wastewater (n=11), and water (n=25).

The total amount of base pairs for this dataset was 2.15E11, and the total number of sequences

was 1.38E9. The largest sample had 7.04E9 base pairs and 6.98E7 sequences and belonged to a

human environment. The smallest sample had 3.10E6 base pairs and 9433 sequences and

belonged to a human environment as well.

Resistance Gene Detection

Because MG-RAST does not contain information on ARGs, the data sets had to be

compared against the Comprehensive Antibiotic Resistance Database (CARD). The screen-

passed data (DNA sequence data of acceptable quality and predicted as gene coding regions by

MG-RAST) from MG-RAST was searched against CARD in order to detect ARGs as well as

rpoB gene copies, or gene copy fragments. UBLAST and USEARCH algorithms [17] were used

to search the CARD database with the following search parameters: the e-value used was 1E-5,

with minimum 80% similarity using local alignments. Only 374 data sets were used in this

experiment, as samples were removed if the results were 5 kb or less (as they would only contain

a few hundred hits), or if there was only one sample from a certain environment type. The

samples were normalized by dividing the copy number of each ARG category by copy numbers

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of rpoB. This was done because it gives a more accurate representation of amount of ARGs in a

sample with respect to the number of prokaryotes. In the past, other studies [18], [19] divided by

the amount of 16s rRNA genes, but rpoB has a higher genetic resolution and reports more

species [16].

Statistical Analysis

R was used to analyze the data. The base R libraries, vegan, and gplots were used to

graph relative abundance heat maps (Figures 1 and 2), relative abundance of ARG categories

containing all samples (Figure 3), amount of rpoB copies per million base pairs (Figure 4), and

amount of ARG copies per rpoB copies for specific ARGs (Figures 5-8). Clustering of

environments by ARG categories was performed using Bray-Curtis dissimilarity with the

UPGMA algorithm and either median values by environment, or all data (Figures 1 and 2,

respectively).

Results and Discussion:

In Figure 1, the median value of the efflux pump conferring antibiotic resistance gene

was the most abundant across all environments, with mangroves, air, and wastewater having the

highest abundance. Animals, marine, and water environments had the lowest abundance. This is

because efflux pumps are in both Gram-negative and Gram-positive cells, along with eukaryotes.

In Figure 2, the beta-lactam resistance gene shows the highest abundance, followed by

the antibiotic inactivation enzyme. The least abundant gene was antibiotic sequestration. Human

saliva showed the highest amount of antibiotic resistance genes, with the beta-lactam resistance

gene being the highest, followed by the antibiotic inactivation enzyme.

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Figure 3 demonstrates the mean of each antibiotic resistance gene copy per rpoB copy.

The beta lactam resistance gene had the highest mean, but the antibiotic inactivation enzyme had

the greatest range. The efflux pump conferring antibiotic resistance contained the highest median

value. The resistance gene with the lowest amount of ARG copies/rpoB copies was the gene

involved in antibiotic sequestration.

The effective genome size is 6.3 million base pairs (mbp) for soil, 4.7 mbp for bacteria,

and 1.6 mbp for water [20]. The results in Figure 4 are lower due to the exclusion of DNA that

does not belong to ARGs. Air had the highest amount of rpoB copies per million base pairs,

while mangroves had the lowest. Soil had the greatest amount of variance amongst the

environments; however, humans had the highest number of copies per million bases pairs.

Although humans had the largest range of gene copies in Figures 5-8, humans had a much

larger ratio in Figure 6 because beta lactam are the most widely used group of antibiotics, which

gives rise to resistance genes. Mangroves had an unexpected high amount of resistance gene

copies per rpoB copies. This could be due to the fact that these particular samples were taken

from mangroves that had been exposed to long term fertilization, which undoubtedly contained a

massive amount of antibiotics. The resistance gene copies per rpoB copies was expected to be

high in air, as the source of these samples were pig farms in South Korea. Water had the lowest

range of gene copies throughout Figures 5-8, except for Figure 6, where animals had the lowest

ratio.

For future projects, in order to test the accuracy of the methods, a known sequence could

be taken, mutated, and then inserted into an MG-RAST sample to see if it can be detected. More

samples from more diverse environments could also be used in order to get a larger variety.

Finally, implementing an environmental organizational standard would greatly benefit future

10

researchers for projects like this one, so that results are more consistent and accurate across the

board.

11

Figures and Tables:

Figure 1: Relative Abundance of ARGs by Environment Type

12

Figure 2: Relative Abundance of ARGs by Sample ID

13

Figure 3: Relative Abundance of ARG Categories Containing All Samples

14

Figure 4: Amount of rpoB Copies per Million Base Pairs

15

Figure 5: Amount of Macrolide Resistance Gene Copies per rpoB Copies

16

Figure 6: Amount of Beta Lactam Resistance Gene Copies per rpoB Copies

17

Figure 7: Amount of Erythromycin Resistance Efflux Pump Gene Copies per rpoB Copies

18

Figure 8: Amount of Efflux Pump Conferring Antibiotic Resistance Gene Copies per rpoB

Copies

Additional File: Table S1

Table S1

mgm_IDaminocoumarin.resistance.geneaminoglycoside.resistance.geneantibiotic.inactivation.enzymeantibiotic.resistance.gene.cluster..cassette..or.operonantibiotic.resistant.gene.variant.or.mutantantibiotic.target.modifying.enzymeantibiotic.target.protection.proteinantibiotic.target.replacement.proteinbeta.lactam.resistance.genechloramphenicol.resistance.geneefflux.pump.conferring.antibiotic.resistanceelfamycin.resistance.geneerythromycin.resistance.efflux.pumpethambutol.resistance.genefluoroquinolone.resistance.genefosfomycin.resistance.genefusidic.acid.resistance.genegene.altering.cell.wall.charge.conferring.antibiotic.resistancegene.conferring.antibiotic.resistance.via.molecular.bypassgene.conferring.resistance.via.absencegene.involved.in.antibiotic.sequestrationgene.involved.in.self.resistance.to.antibioticgene.modulating.antibiotic.effluxgene.modulating.beta.lactam.resistancegene.modulating.permeability.to.antibioticglycopeptide.resistance.geneisoniazid.resistance.genelincosamide.resistance.genelinezolid.resistance.genelipopeptide.antibiotic.resistance.genemacrolide.resistance.genemupirocin.resistance.genenitrofuratoin.resistance.genepeptide.antibiotic.resistance.genephenicol.resistance.genepleuromutilin.resistance.genepolyamine.resistance.genepolymyxin.resistance.genepyrazinamide.resistance.generifampin.resistance.genestreptogramin.resistance.genestreptothricin.resistance.genesulfonamide.resistance.genetetracycline.resistance.genetriclosan.resistance.genetrimethoprim.resistance.genetunicamycin.resistance.generpoBrpo_mbpinterestdownloadedidprojectnamebpssequencesEnvironment_combinedbiomefeaturematerialenviroment.nbsp.packagedisease_statussequencing.nbsp.typelocationphcountrytemperaturesequencing.nbsp.methodavg.nbsp.seq.nbsp.lengthNA.

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mgm44409435.5909090919.0909090910.5909090916.18181818216.454545450.3181818181.40909090909.1363636366.13636363622.636363641.6363636360.3181818180.18181818214.31818182002.8181818185.7272727270.68181818202.4090909097.77272727300.4090909095.1363636360.1363636360.272727273006.6363636360.2272727271.3636363641.272727273000.5454545453.7272727270.0454545453.4545454550.3181818180.0454545450.7727272738.0454545450.6818181821.9090909090220.5489552281426524440943.3Human Faeces - Kurokawahuman F2-W4007612830198Humananimal-associated habitatanimal-associated habitatanimal-associated habitathuman-gutNAWGSunknownNAJapanNAother1327.112public

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mgm44409454.66.912.710.60.30.30.16.4416.50.60.209.3001.73.20.201.8500.42.800.30060.60.40.800.10.42.702.10.200.44.602.10100.6896994081426524440945.3Human Faeces - Kurokawahuman In-B144990709958Humananimal-associated habitatanimal-associated habitatanimal-associated habitathuman-gutNAWGSunknownNAJapanNAother1456.022public

mgm44409462.52.951.12.24.90.30.6504.14.210.250.650.407.1000.91.950.2500.753.100.11.800.35003.650.150.50.300.050.151.9501.10.400.254.70.252.350200.6826818361426524440946.3Human Faeces - Kurokawahuman In-A2929622420226Humananimal-associated habitatanimal-associated habitatanimal-associated habitathuman-gutNAWGSunknownNAJapanNAother1448.444public

mgm44409484.1071428576.4285714290.9285714294.28571428611.821428570.2857142861.1428571430.0357142866.1071428574.32142857115.0357142910.1785714290.2857142869.857142857001.8214285713.8214285710.501.5714285715.14285714300.1428571433.3928571430.2142857140.214285714004.3214285710.14285714310.96428571400.0357142860.3928571432.5357142860.0714285712.4642857140.14285714300.5357142865.6428571430.1785714291.4642857140280.6034861371426524440948.3Human Faeces - Kurokawahuman In-D4639708937296Humananimal-associated habitatanimal-associated habitatanimal-associated habitathuman-gutNAWGSunknownNAJapanNAother1244.023public

mgm444094917.2142857138.428571431.7857142869.42857142960.785714290.3571428572.07142857103418.0714285776.785714292.1428571430.7857142860.28571428650.142857140011.071428577.1428571431.57142857103.64285714327.428571430.1428571432.0714285714.50.2142857140.21428571400.07142857116.357142860.2142857143.1428571435.285714286002.513.785714290.07142857112.714285710.35714285702.525.785714290.6428571434.4285714290140.539669631426524440949.3Human Faeces - Kurokawahuman In-M2594179716164Humananimal-associated habitatanimal-associated habitatanimal-associated habitathuman-gutNAWGSunknownNAJapanNAother1604.912public

mgm44409841.9166666671.0833333330.250.6666666672.79166666700.33333333302.4583333333.57.8333333331.1666666670.54166666705.291666667000.7916666670.3750.291666667012.79166666700.2083333330.37500003.0833333330.1250.58333333300001.62500.708333333000.2916666672.8750.9166666671.7916666670241.3397244891426644440984.3Jeffries et al PLoS OneAustralian Coorong lowest salinity murray mouth 36psu M11791413177061Marineestuarine biomemarine benthic featuresandy sedimentsedimentNAWGSCoorong LagoonNAAustraliaNA454232.467public

mgm44410221.8297872341.2765957450.3191489360.2765957452.17021276600.17021276602.5744680853.3829787237.4893617020.8085106380.48936170205.127659574000.6170212770.1063829790.1489361700.9361702132.25531914900.1914893620.10638297900002.7446808510.0212765960.2978723400001.63829787200.404255319000.148936172.4255319150.8297872342.1702127660471.6259537651426644441022.3Jeffries et al PLoS OneAustralian Coorong high salinity 136psu M428906111124220Marineestuarine biomemarine benthic featuresandy sedimentsedimentNAWGSCoorong LagoonNAAustraliaNA454232.701public

mgm44411340.4666666670.5777777780.2666666670.5333333331.2888888890.0444444440.0888888890.0666666670.7555555560.3111111111.40.177777778000.666666667000.1555555560.5111111110.02222222200.3333333330.35555555600.0222222220.46666666700.088888889000.3111111110.0222222220.0444444440.08888888900.0444444440.0444444440.15555555600.2444444440.08888888900.0222222220.42222222200.0666666670450.8394382731426584441134.3Global Ocean Sampling ExpeditionGS110b Shotgun - Open Ocean - Indian Ocean - Indian Ocean - International5360727749597Marinemarine habitatmarine habitatmarine habitatwaterNAWGSIndian OceanNAunknown27other1080.857public

mgm44411440.250.3790322580.0322580650.3870967741.17741935500.1693548390.0403225810.4516129030.2822580651.1774193550.4435483870.02419354800.725806452000.1612903230.3225806450.06451612900.4435483870.47580645200.0161290320.29838709700.024193548000.2500.1290322580.04838709700.0403225810.0241935480.16129032300.2258064520.04838709700.0645161290.5322580650.0564516130.04032258101241.5044084141426584441144.3Global Ocean Sampling ExpeditionGS010 Shotgun - Coastal - North American East Coast - Cape May, NJ - USA8242442678304Marinemarine habitatmarine habitatmarine habitatwaterNAWGSCape May, NJNAUnitedNAStatesNAofNAAmerica12other1052.621public

mgm44411480.6341463410.6829268290.3414634150.6829268292.8292682930.2439024390.2926829270.0487804881.121951220.6585365852.9024390240.6585365850.0487804880.2926829271.707317073000.3902439020.5121951220.17073170700.7317073171.24390243900.0731707320.4878048780.2195121950.12195122000.92682926800.3414634150.1219512200.0487804880.0243902440.3902439020.0731707320.5365853660.14634146300.1707317070.9512195120.2439024390.0487804880410.7488296981426584441148.3Global Ocean Sampling ExpeditionGS117b Shotgun - Coastal sample - Indian Ocean - St. Anne Island, Seychelles - Seychelles5475210250609Marinemarine habitatmarine habitatmarine habitatwaterNAWGSIndian Ocean - St. Anne Island, SeychellesNASeychelles26.5other1081.865public

mgm44411530.5227272730.6590909090.4318181820.3863636361.8409090910.0681818180.3636363640.2954545451.4090909091.0454545453.5227272730.7727272730.0909090910.0909090912.227272727000.4090909090.1818181820.15909090900.6136363641.45454545500.1136363640.1818181820.0681818180.090909091000.95454545500.3181818180.02272727300.02272727300.5227272730.0227272730.4318181820.06818181800.1590909091.3409090910.3409090910.5909090910440.7937802271426584441153.3Global Ocean Sampling ExpeditionGS007 Shotgun - Coastal - North American East Coast - Northern Gulf of Maine - Canada5543096050980Marinemarine habitatmarine habitatmarine habitatwaterNAWGSNorthern Gulf of MaineNACanada17.9other1087.308public

mgm44415680.06250.2291666670.0833333330.3854166670.531250.0104166670.156250.0104166670.2916666670.0729166670.6250.270833333000.3020833330.01041666700.0520833330.3750.01041666700.2708333330.14583333300.0208333330.36458333300.03125000.0937500.0208333330.02083333300.0104166670.0104166670.05208333300.0729166670.02083333300.0208333330.36458333300.0104166670961.4756546041426584441568.3Global Ocean Sampling ExpeditionGS119 Shotgun - Open Ocean - Indian Ocean - International Water Outside of Reunion Island - International6505587460987Marinemarine habitatmarine habitatmarine habitatwaterNAWGSIndian Ocean - International Water Outside of Reunion IslandNAunknown23.8other1066.717public

mgm444157014.3911620320.4108019610.504091652.66121112924.764320790.4975450081.1603927990.04255319132.9148936226.9574468160.772504091.3126022913.1440261870.66121112944.90343699004.6252045831.5450081830.81833060601.37643207917.5073649800.6268412440.7103109660.4959083470.1947626840.0016366610.00163666119.687397710.0049099841.6366612111.8281505730.0441898530.0180032730.83142389512.918166940.1653027825.1653027820.22749590800.84779050721.184942722.35679214417.0654664506110.9275062121426584441570.3Global Ocean Sampling ExpeditionGS000a Shotgun - Open Ocean - Sargasso Sea - Sargasso Station 11 - Bermuda658755696644551Marinemarine habitatmarine habitatmarine habitatwaterNAWGSSargasso SeaNABermuda20.5other1022.038public

mgm444157114.3911620320.4108019610.504091652.66121112924.764320790.4975450081.1603927990.04255319132.9148936226.9574468160.772504091.3126022913.1440261870.66121112944.90343699004.6252045831.5450081830.81833060601.37643207917.5073649800.6268412440.7103109660.4959083470.1947626840.0016366610.00163666119.687397710.0049099841.6366612111.8281505730.0441898530.0180032730.83142389512.918166940.1653027825.1653027820.22749590800.84779050721.184942722.35679214417.0654664506110.9275062121426584441571.3Global Ocean Sampling ExpeditionGS000a Shotgun - Open Ocean - Sargasso Sea - Sargasso Station 13 - Bermuda658755696644551Marinemarine habitatmarine habitatmarine habitatwaterNAWGSSargasso SeaNABermuda20other1022.038public

mgm44415851.0808823531.0661764710.2058823530.4338235292.8602941180.0735294120.3529411760.1029411761.6176470591.3161764714.0147058820.7867647060.0955882350.0882352942.91911764700.0147058820.4632352940.1838235290.25735294100.6911764711.78676470600.0220588240.1397058820.0661764710.051470588001.2867647060.0514705880.5147058820.11029411800.0294117650.0441176470.7205882350.0220588240.6691176470.05882352900.2573529411.5073529410.1102941180.63970588201360.9127052831426584441585.3Global Ocean Sampling ExpeditionGS013 Shotgun - Coastal - North American East Coast - Off Nags Head, NC - USA149007574138033Marinemarine habitatmarine habitatmarine habitatwaterNAWGSOff Nags Head, NCNAUnitedNAStatesNAofNAAmerica9.3other1079.507public

mgm44415890.2369668250.5260663510.0663507110.64928911.2180094790.0047393360.1658767770.080568720.4976303320.2369668251.2985781990.3033175360.00473933600.744075829000.1848341230.5924170620.05687203800.2938388630.45497630300.0047393360.5545023700.02369668200.0047393360.2322274880.0047393360.1137440760.07582938400.0284360190.0379146920.18483412300.2274881520.03317535500.0568720380.6398104270.0236966820.0805687202111.4411279761426584441589.3Global Ocean Sampling ExpeditionGS019 Shotgun - Coastal - Caribbean Sea - Northeast of Colon - Panama146413090135325Marinemarine habitatmarine habitatmarine habitatwaterNAWGSCaribbean Sea, Northeast of ColonNAPanama27.7other1081.937public

mgm44415940.5816993460.856209150.4640522880.7777777781.6993464050.0065359480.2875816990.0392156861.52941176512.7973856210.4379084970.07189542501.954248366000.3006535950.6535947710.12418300700.3660130721.06535947700.0392156860.61437908500.013071895000.78431372500.2483660130.07843137300.0196078430.0392156860.46405228800.4248366010.02614379100.1241830071.2091503270.0980392160.41176470601531.4030338931426584441594.3Global Ocean Sampling ExpeditionGS026 Shotgun - Open Ocean - Galapagos Islands - 134 miles NE of Galapagos - Ecuador109049397102708Marinemarine habitatmarine habitatmarine habitatwaterNAWGSOpen Ocean, 134 miles NE of GalapagosNAEcuador27.8other1061.742public

mgm44415960.2210884350.3299319730.0680272110.248299320.9149659860.0102040820.1768707480.0680272110.4693877550.2312925171.064625850.3265306120.0034013610.0136054420.724489796000.122448980.1768707480.07142857100.2823129250.448979592000.1598639460.0102040820.023809524000.2653061220.0102040820.1428571430.03741496600.0136054420.0170068030.1394557820.0034013610.1972789120.01700680300.0714285710.51700680300.10204081602941.4340848091426584441596.4Global Ocean Sampling ExpeditionGS028 Shotgun - Coastal - Galapagos Islands - Coastal Floreana - Ecuador205008796189052Marinemarine habitatmarine habitatmarine habitatwaterNAWGSGalapagos Islands, Coastal FloreanaNAEcuador25other1084.404public

mgm44416010.3520408160.811224490.0867346940.2193877551.78061224500.1734693880.1071428570.750.3520408161.7346938780.4336734690.00510204101.137755102000.2704081630.1326530610.08673469400.4132653060.68367346900.0051020410.07653061200.035714286000.3112244900.1734693880.11224489800.0357142860.0561224490.27040816300.3418367350.04591836700.0867346940.7653061220.0255102040.10714285701961.290830161426584441601.3Global Ocean Sampling ExpeditionGS035 Shotgun - Coastal - Galapagos Islands - Wolf Island - Ecuador151840270140814Marinemarine habitatmarine habitatmarine habitatwaterNAWGSGalapagos Islands - Wolf IslandNAEcuador21.8other1078.304public

mgm44416060.50.5675675680.0810810810.2837837841.13513513500.1891891890.0270270270.8378378380.7432432432.0270270270.2837837840.09459459501.5000.1891891890.1756756760.09459459500.2162162160.797297297000.14864864900000.58108108100.1891891890.05405405400.0270270270.0270270270.39189189200.270270270.02702702700.0945945950.8243243240.0540540540.4324324320741.454132811426584441606.3Global Ocean Sampling ExpeditionGS108a Shotgun - Lagoon Reef - Indian Ocean - Coccos Keeling, Inside Lagoon - Australia5088943751787Marinemarine habitatmarine habitatmarine habitatwaterNAWGSIndian Ocean - Coccos Keeling, Inside LagoonNAAustralia25.8other982.668public

mgm44416110.2068273090.244979920.094377510.3594377510.6626506020.0180722890.1626506020.0321285140.4076305220.2248995980.9317269080.2650602410.0120481930.0080321290.60240963900.0020080320.0903614460.2971887550.06224899600.2289156630.39759036100.0080321290.2891566270.0060240960.030120482000.2349397590.0080321290.1244979920.01807228900.0240963860.0080321290.1204819280.0020080320.1566265060.04216867500.066265060.43574297200.09839357404981.442283971426584441611.3Global Ocean Sampling ExpeditionGS114 Shotgun - Open Ocean - Indian Ocean - 500 Miles west of the Seychelles in the Indian Ocean - International345285679348823Marinemarine habitatmarine habitatmarine habitatwaterNAWGSIndian Ocean - 500 Miles west of the Seychelles in the Indian OceanNAunknown28.2other989.859public

mgm44416160.2685714290.440.120.361.120.040.1657142860.0342857140.5257142860.2742857141.2514285710.2971428570.0057142860.0228571430.754285714000.1485714290.2971428570.06285714300.2857142860.457142857000.2685714290.0171428570.074285714000.2857142860.0057142860.1257142860.06285714300.0457142860.0285714290.1771428570.0057142860.2171428570.0800.0628571430.57714285700.09142857101751.5136887541426584441616.3Global Ocean Sampling ExpeditionGS123 Shotgun - Open Ocean - Indian Ocean - International water between Madagascar and South Africa - International115611614107966Marinemarine habitatmarine habitatmarine habitatwaterNAWGSIndian Ocean - International water between Madagascar and South AfricaNAunknown20.4other1070.815public

mgm44416170.4761904760.5918367350.1088435370.5510204081.7414965990.0748299320.251700680.061224490.809523810.5986394562.1564625850.4693877550.0340136050.0816326531.408163265000.2653061220.4285714290.1224489800.4421768710.90476190500.0340136050.4013605440.061224490.0748299320.01360544200.61224489800.2448979590.0748299320.0136054420.0272108840.0272108840.3333333330.0204081630.374149660.06802721100.122448980.8979591840.1020408160.19727891201471.3659651571426584441617.3Global Ocean Sampling ExpeditionGS148 Shotgun - Fringing Reef - Indian Ocean - East coast Zanzibar (Tanzania), offshore Paje lagoon - Tanzania107616215107741Marinemarine habitatmarine habitatmarine habitatwaterNAWGSIndian Ocean - East coast Zanzibar (Tanzania), offshore Paje lagoonNATanzaniaNAother998.842public

mgm44416790.8615384621.9846153850.7230769230.6769230773.07692307703.2153846150.0153846151.2153846150.6923076923.3692307690.60.01538461501.892307692000.3846153850.6461538460.06153846200.8923076920.876923077000.50769230800.0769230770010.0461538460.1230769230.30769230800.0461538460.1076923080.46153846200.4615384620.06153846200.0769230774.16923076900.20652.4394988341426524441679.3Cow RumenCow rumen -- 640F626644817264849Animalanimal-associated habitatanimal-associated habitatanimal-associated habitathost-associatedNAWGSBloomington, ILNAUnitedNAStatesNAofNAAmericaNA454100.604public

mgm44416800.6111111111.6481481480.8518518520.4259259262.40740740702.4259259260.0370370370.7962962960.4629629632.6111111110.5370370370.07407407401.314814815000.2962962960.4074074070.07407407400.7962962960.722222222000.31481481500.18518518500.0370370370.9074074070.0185185190.1481481480.24074074100.0925925930.0740740740.29629629600.37037037000.0740740743.2407407410.0740740740.0370370370542.9746541291426524441680.3Cow RumenCow rumen -- 80F618153371178713Animalanimal-associated habitatanimal-associated habitatanimal-associated habitathost-associatedNAWGSBloomington, ILNAUnitedNAStatesNAofNAAmericaNA454101.578public

mgm44416811.1718752.2031250.75781250.67968754.0468750.0468750.9218750.3281252.218751.43755.52343750.609375003.546875000.66406250.4843750.210937500.656251.914062500.08593750.351562500.3281250.02343750.031251.17187500.4218750.2968750.03906250.031250.13281250.664062501.11718750.0781250.00781250.57031253.351562500.367187501283.6451104521424714441681.3Cow RumenCow rumen -- 710F635115534345317Animalanimal-associated habitatanimal-associated habitatanimal-associated habitathost-associatedNAWGSBloomington, ILNAUnitedNAStatesNAofNAAmericaNA454101.691public

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mgm44471011.5150214592.2918454940.3991416311.4506437774.8197424890.1416309010.4721030040.2789699572.7854077251.5321888416.6137339060.6995708150.0600858370.0686695283.9399141630.0171673820.0042918450.695278971.218884120.24034334800.9313304722.12446351900.0386266091.0729613730.0515021460.36909871200.0042918451.9270386270.0987124460.4806866950.30901287600.1244635190.1459227470.8454935620.0171673821.0343347640.2575107300.2403433482.2145922750.04721030.32188841202331.7943547471426644447101.3The oral metagenome in health and diseaseCA1_02P129851692295072Humanhuman-associated habitathuman-associated habitathuman-associated habitathuman-oralNAWGSValenciaNASpain37454440.068public

mgm44471921.7087912091.719780220.3131868130.890109894.5109890110.2087912090.50.5329670333.1483516482.0164835168.3736263741.0714285710.0879120880.0879120884.7857142860.02747252700.7527472530.4780219780.40659340701.0989010992.9120879120.0054945050.0769230770.3846153850.0659340660.291208791002.5989010990.0824175820.8131868130.17582417600.0219780220.0769230771.0164835160.0219780221.1813186810.19780219800.4120879122.5164835160.1483516480.53846153801822.3472215121426644447192.3The oral metagenome in health and diseaseNOCA_01P77538485204218Humanhuman-associated habitathuman-associated habitathuman-associated habitathuman-oralNAWGSValenciaNASpain37454379.685public

mgm44479430.843751.86250.4531250.58753.7250.3093750.3781250.1406251.8593750.7281253.9281250.58750.08750.21252.381250.0062500.4468750.51250.1062500.8156251.237500.006250.38750.1593750.2687500.0031251.18750.0906250.21250.32187500.08750.106250.51250.0531250.58750.24687500.106251.4468750.081250.1503202.247597711426644447943.3The oral metagenome in health and diseaseCA_04P142374233339503Humanhuman-associated habitathuman-associated habitathuman-associated habitathuman-oralNAWGSValenciaNASpain37454419.361public

mgm44479701.3529411761.8235294120.2941176472.1960784314.2745098040.5686274510.6078431370.1372549021.7450980390.372549024.9803921570.5490196080.0392156860.7058823532.4509803920.01960784300.2352941182.0980392160.11764705901.1764705881.294117647002.0392156860.5294117650.215686275001.8627450980.3529411760.2352941180.31372549000.0392156860.2352941180.1764705880.5490196080.25490196100.1176470591.0980392160.03921568600511.843156491426644447970.3The oral metagenome in health and diseaseCA_05_4.62766992470503Humanhuman-associated habitathuman-associated habitathuman-associated habitathuman-oralNAWGSValenciaNASpain37454392.464public

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mgm44492845.9166666676.4444444440.8888888893.0555555568.250.1388888890.97222222208.8055555568.47222222223.222222221.6111111110.7777777780.11111111115.861111110.02777777801.6666666672.4166666670.501.9722222226.86111111100.252.250.0833333330.027777778008.0555555560.05555555610.361111111000.1666666674.1944444440.0277777781.7222222220.02777777800.5277777787.1944444440.9444444445.1944444440361.164542591426584449284.3 Comparative metagenomic, phylogenetic, and physiological analyses of soil microbial communities across nitrogen gradients1_52_RL83091342577306soilgrassland biomemeadow soilbulk soilsoilNAWGSCedar Creek Ecosystem Science Reserve, Bethel, MN, USANAUSANA454399.884public

mgm44493587.4827586218.8275862071.6896551722.2068965528.1724137930.241379310.75862069012.9310344814.8965517232.344827591.3448275861.9310344830.27586206923.344827590.1034482760.0689655171.4827586211.7931034480.44827586201.3793103458.89655172400.3103448281.6206896550.2068965520.0689655170011.896551720.1379310340.8965517240.448275862000.1379310346.7241379310.0689655171.758620690.06896551700.4482758629.9655172411.31034482810.827586210290.9356387341426584449358.3 Comparative metagenomic, phylogenetic, and physiological analyses of soil microbial communities across nitrogen gradients2_204_RL23099486990765soilgrassland biomemeadow soilbulk soilsoilNAWGSKellog Biological Station, Hickory Corners, MI, USANAUSANA454341.485public

mgm44493596.768.481.881.3610.160.960.96012.3613.0429.61.041.241.1221.4400.041.520.920.4801.28.7200.440.720.840.40010.320.120.960.76000.125.840.282.20.400.528.561.088.720251.1715238361426584449359.3 Comparative metagenomic, phylogenetic, and physiological analyses of soil microbial communities across nitrogen gradients2_209_RL32133972862232soilgrassland biomemeadow soilbulk soilsoilNAWGSKellog Biological Station, Hickory Corners, MI, USANAUSANA454342.906public

mgm44493606.0370370379.2592592591.481481481210.222222220.2222222220.62962963011.8518518512.1481481530.29629631.4444444441.8518518520.29629629619.7777777800.0370370372.2222222221.7037037040.3703703701.8518518528.44444444400.6666666671.4074074070.2222222220.0740740740010.148148150.0740740740.7407407410.74074074100.0370370370.2222222225.7407407410.0740740741.9629629630.11111111100.4444444448.74074074127.5925925930270.9189732441426584449360.3 Comparative metagenomic, phylogenetic, and physiological analyses of soil microbial communities across nitrogen gradients2_301_RL42938061684770soilgrassland biomemeadow soilbulk soilsoilNAWGSKellog Biological Station, Hickory Corners, MI, USANAUSANA454346.592public

mgm44493627.9354838718.0322580651.8709677423.03225806510.258064520.6129032260.838709677011.5161290313.9677419429.387096770.9677419351.3225806450.77419354821.0967741900.0322580651.3225806452.7419354840.29032258101.5161290327.38709677400.2258064522.4838709680.5806451610.2258064520011.193548390.1290322580.5806451610.741935484000.2258064526.1612903230.1935483871.8064516130.22580645200.3225806459.1935483870.483870968100311.0174173981426584449362.3 Comparative metagenomic, phylogenetic, and physiological analyses of soil microbial communities across nitrogen gradients2_309_RL63046930488074soilgrassland biomemeadow soilbulk soilsoilNAWGSKellog Biological Station, Hickory Corners, MI, USANAUSANA454345.951public

mgm44493657.2307692319.2307692311.6153846151.76923076910.923076920.6923076920.384615385011.0769230813.15384615291.3076923081.3846153850.61538461519.69230769001.3076923081.6923076920.076923077026.46153846200.3076923081.3846153850.4615384620.3846153850011.153846150.0769230770.1538461540.769230769000.3076923085.7692307690.1538461541.6923076920.38461538500.1538461548.2307692310.3846153859.3076923080130.9463667051426584449365.3 Comparative metagenomic, phylogenetic, and physiological analyses of soil microbial communities across nitrogen gradients2_409_RL91373674739365soilgrassland biomemeadow soilbulk soilsoilNAWGSKellog Biological Station, Hickory Corners, MI, USANAUSANA454348.958public

mgm44506800.4736842111.7368421051.2631578950.2105263161.6842105260.1052631581.1052631580.1052631580.8947368421.0526315792.5263157890.3684210530.10526315801.473684211000.3157894740.210526316000.4736842110.68421052600.0526315790.15789473700.157894737000.6315789470.05263157900.210526316000.0526315790.57894736800.1578947370.1052631580.10526315802.0526315790.2631578950.6315789470.105263158190.1910740871426524450680.3Project for: Cow 7887 Virome ReadsCow 7887 Virome Reads99437869298062Animalanimal-associated habitatanimal-associated habitatanimal-associated habitatunknownNAWGSUrbana, ILNAUnitedNAStatesNAofNAAmericanotNAdeterminedNA;NACelsius454333.615public

mgm44532467.98214285711.885714292.0392857142.215.042857140.2928571430.5714285710.02515.3821428617.5821428639.010714291.0857142862.4607142860.31428571426.2750.0250.0214285713.0928571431.9071428570.31785714301.58571428610.6678571400.8857142861.4107142860.2357142860.1392857140.0107142860.00714285712.4250.0464285710.6357142861.1178571430.01071428600.4607142867.9107142860.0785714293.0142857140.12857142900.36785714312.952.63214285710.4607142902800.7812643541426654453246.3Soil microbial community from Rothamsted, Park Grass (METASOIL project)METASOIL F1 Rothamsted 2009 February 0-21cm Direct MPBIO1O1358393415976268soilTemperate grasslandsprairiesoilsoilNAWGSRothamstedNAEnglandNA454367.106public

mgm445325411.7455830416.360424032.8515901062.3215547718.452296820.321554770.710247350.00706713822.1660777426.1766784555.671378091.2473498233.8021201410.40989399338.752650180.0141342760.0247349823.9858657241.9116607770.43109540601.83392226115.2226148401.1731448761.3356890460.3074204950.1095406360.003533569018.409893990.031802120.8621908131.3144876330.0035335690.0035335690.53003533611.724381630.1024734983.6749116610.11307420500.49823321618.303886933.8374558316.519434630.0035335692830.8806791231426654453254.3Soil microbial community from Rothamsted, Park Grass (METASOIL project)METASOIL F2b Rothamsted 2009 February 0-21cm Indirect MPBIO1O1321342919890966soilTemperate grasslandsprairiesoilsoilNAWGSRothamstedNAEnglandNA454360.668public

mgm44533800.8285714290.57142857100.3714285712.37142857100.2285714290.0571428570.8285714290.5428571432.3714285710.8571428570.02857142901.60.05714285700.3714285710.1714285710.2011.200.0285714290.14285714300000.7142857140.0285714290.40.057142857000.0285714290.37142857100.485714286000.20.9142857140.1428571430.0571428570351.3936554671426644453380.3Guanabara Bay MetagenomeGB Site 7-22511381160464Waterestuarine biomeoceancoastal waterwaterNAWGSRio de JaneiroNABrazil27454415.351public

mgm44534069.062513.447916672.0729166672.70833333316.871527780.3576388890.6944444440.01388888916.5347222218.5590277842.506944441.2777777782.4166666670.43055555629.006944440.0451388890.0173611113.2638888892.3402777780.3437501.91319444411.4791666700.7951388891.8020833330.3229166670.1597222220013.520833330.0381944440.68751.194444444000.531258.656250.1076388893.2222222220.12500.39930555613.940972222.56597222211.3784722202880.8508004241426654453406.3Soil microbial community from Rothamsted, Park Grass (METASOIL project)METASOIL J7 Rothamsted 2009 July 0-21cm Direct MoBIO338504768938860soilTemperate grasslandsprairiesoilsoilNAWGSRothamstedNAEnglandNA454360.549public

mgm44534339.20454545510.996753252.2272727271.84415584412.253246750.3538961040.6071428570.00649350616.5064935120.3376623441.324675321.0032467532.7792207790.45454545528.996753250.0227272730.0227272732.3831168831.5324675320.32467532501.47402597411.191558440.0032467530.7792207791.2045454550.3409090910.1331168830014.178571430.0551948050.6493506490.814935065000.2857142868.7922077920.1136363642.4675324680.1201298700.35714285713.181818182.26298701313.7207792203080.9974336941426654453433.3Soil microbial community from Rothamsted, Park Grass (METASOIL project)METASOIL F6 Rothamsted 2009 February 11-21cm Indirect in plug308792456826513soilTemperate grasslandsprairiesoilsoilNAWGSRothamstedNAEnglandNA454373.609public

mgm44534358.38187702311.695792882.1067961172.82847896414.210355990.3754045310.695792880.02588996815.203883517.3462783239.472491911.2071197412.2750809060.4401294526.268608410.0355987060.0129449842.7993527512.559870550.29126213601.83495145610.3915857600.8155339812.11974110.3300970870.15857605200.00647249212.799352750.1262135920.5825242721.038834951000.4012944987.8349514560.1100323622.8867313920.14886731400.35922330112.631067962.41423948210.7055016203090.8319727751426654453435.3Soil microbial community from Rothamsted, Park Grass (METASOIL project)METASOIL J1rhizo-10 Rothamsted Rhizosphere 2010 July 0-21cm Direct MPBIO1O13714063841068770soilTemperate grasslandsprairiesoilsoilNAWGSRothamstedNAEnglandNA454347.508public

mgm446112215.332.60.76.452.701.4027.114.264.92.40.7040.6009.84.61.104.422.401.82.40000150.32.24.4002.212.1010.6001.922.41.230101.0520143231426584461122.3Human gut microbiome viewed across age and geography (WGS)Amz1teenF950557423342Humanfecesfecesfecesplant-associatedNAWGSunknownNAVenezuelaNApyrosequencing407.23public

mgm44611242.1458333330.9791666670.1250.4895833333.2708333330.0729166670.3750.0104166671.343750.9166666675.4479166670.7395833330.11458333302.1979166670.01041666700.531250.3229166670.13541666701.7604166671.33333333300.0833333330.2812500.072916667002.4166666670.7395833330.2708333330.083333333000.0416666670.63541666700.43750.07291666700.1354166671.3229166670.3854166670.2395833330962.4918286841424504461124.3Human gut microbiome viewed across age and geography (WGS)Amz2infF3852592394521Humanfecesfecesfecesplant-associatedNAWGSunknownNAVenezuelaNApyrosequencing407.591public

mgm44611251.6226415093.1886792450.4465408812.0314465415.8113207550.0440251570.5031446540.0314465412.9245283021.7861635226.5723270440.723270440.0628930820.0251572334.283018868000.8742138361.8867924530.17610062900.9496855352.21383647800.0754716981.6603773580.0188679250.06289308200.0062893081.6792452830.0440251570.3522012580.47169811300.0125786160.2138364781.069182390.0062893081.1006289310.04402515700.1949685532.7421383650.0314465410.46540880501591.7602822791424504461125.3Human gut microbiome viewed across age and geography (WGS)Amz4chldM190326422219458Humanfecesfecesfecesplant-associatedNAWGSunknownNAVenezuelaNApyrosequencing411.589public

mgm44611262.725.920.81.6611.060.140.780.15.443.0411.60.820.10.167.7001.721.260.3801.144.2400.140.820.120.14002.680.040.760.8800.020.421.920.042.280.0800.484.6800.50501.9909450231424504461126.3Human gut microbiome viewed across age and geography (WGS)Amz4chldM22511370263788Humanfecesfecesfecesplant-associatedNAWGSunknownNAVenezuelaNApyrosequencing393.706public

mgm446112714.155963333.321100924.8807339455.2844036752.899082570.0366972481.5412844040.07339449529.9908256914.1376146861.990825691.8165137610.28440367041.00917431009.5779816513.2752293581.21100917403.14678899122.0917431201.4587155960.97247706400.0642201830014.688073390.0183486242.4220183494.56880733900.0366972482.2844036711.3853211010.853211010.11009174301.93577981721.5779816502.87155963301091.9751894821424504461127.3Human gut microbiome viewed across age and geography (WGS)Amz5chldF155184579137296Humanfecesfecesfecesplant-associatedNAWGSunknownNAVenezuelaNApyrosequencing401.939public

mgm44611281.3448275862.3448275860.1379310341.6034482764.7413793100.3275862070.0344827591.9482758621.0862068974.8103448280.741379310.01724137903.068965517000.6724137931.4310344830.155172414011.6206896550.01724137901.2586206900001.3793103450.0172413790.3103448280.344827586000.1724137930.84482758600.844827586000.1551724141.91379310300.2068965520582.4631112991424504461128.3Human gut microbiome viewed across age and geography (WGS)Amz5chldF22354745459051Humanfecesfecesfecesplant-associatedNAWGSunknownNAVenezuelaNApyrosequencing398.765public

mgm44611311.96254.03750.58752.03756.90.03750.56250.01253.78751.91258.450.5750.1250.055.4001.2251.76250.200.775300.1751.48750.03750.0625002.10.050.40.57500.0250.26251.4750.01251.33750.02500.2753.18750.1750.51250802.3810209771426584461131.3Human gut microbiome viewed across age and geography (WGS)Amz7eldM3359903296450Humanfecesfecesfecesplant-associatedNAWGSunknownNAVenezuelaNApyrosequencing348.357public

mgm44611353.1351351355.2522522520.6486486491.2432432439.360360360.1891891890.6486486490.0450450455.0540540542.71171171211.981981980.8738738740.11711711707.225225225001.5585585590.8828828830.26126126101.7027027033.87387387400.2522522520.5495495500.198198198003.4324324320.5045045050.5225225230.675675676000.333333333201.7927927930.19819819800.360360364.0540540540.1981981980.7027027030.0090090091112.3264451971426584461135.3Human gut microbiome viewed across age and geography (WGS)AmzC1babyF247712278136378Humanfecesfecesfecesplant-associatedNAWGSunknownNAVenezuelaNApyrosequencing349.853public

mgm44611393.2480620165.4651162790.306201551.63178294610.042635660.155038760.6240310080.2170542645.1782945742.58527131812.003875970.8720930230.04263565907.651162791001.6782945741.1666666670.37596899201.5193798454.41472868200.2403100780.8062015500.36821705400.0697674422.961240310.4961240310.7519379840.7519379840.0038759690.0310077520.3527131781.89922480602.116279070.18604651200.4883720934.1201550390.0077519380.47674418602582.7132015081426584461139.3Human gut microbiome viewed across age and geography (WGS)AmzC5chldF95090615259910Humanfecesfecesfecesplant-associatedNAWGSunknownNAVenezuelaNApyrosequencing365.86public

mgm44611402.8108108115.0108108110.1891891890.8972972979.3567567570.2054054050.4756756760.2270270273.8594594591.89189189210.075675680.6972972970.06486486505.745945946001.5189189190.7081081080.18918918901.8648648652.98918918900.129729730.29729729700.302702703002.7027027030.5837837840.3783783780.8540540540.0108108110.0054054050.3621621621.601.5675675680.23783783800.2378378383.4216216220.2432432430.37297297301852.341121321426584461140.3Human gut microbiome viewed across age and geography (WGS)AmzC5chldM79021962213186Humanfecesfecesfecesplant-associatedNAWGSunknownNAVenezuelaNApyrosequencing370.671public

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mgm44613411.2769230771.6307692310.1538461540.4153846152.76923076900.5384615380.0153846151.9692307692.0461538465.1076923080.7384615380.18461538503.584615385000.4461538460.3692307690.07692307700.8461538461.44615384600.0461538460.29230769200001.9230769230.0461538460.1538461540.153846154000.0769230771.13846153800.384615385000.0769230772.0615384620.2307692311.40652.7176138711424714461341.3muegge_mammals_paper_mammal_samples_MGAfElphSD3.MG23918041140869Humanfecesfecesfeceshost-associatedNAWGSSan Diego ZooNAUnitedNAStatesNAofNAAmericaNApyrosequencing169.789public

mgm44613431.0227272730.750.2954545450.5454545452.0681818180.0909090910.40909090901.2272727271.2530.4772727270.0454545450.0909090911.95454545500.0227272730.2045454550.4090909090.13636363600.6818181821.11363636400.0454545450.3863636360.0681818180.022727273001.0227272730.0454545450.2727272730.068181818000.0227272730.4318181820.0227272730.4545454550.02272727300.1363636361.11363636400.50443.7516122341426584461343.3muegge_mammals_paper_mammal_samples_MGBaboonSTL.MG1172829152293Humanfecesfecesfeceshost-associatedNAWGSSt Louis ZooNAUnitedNAStatesNAofNAAmericaNApyrosequencing224.28public

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rebekah linder thesis v5

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