An ontology for microbial phenotypes

Chibucos, Marcus C.; Zweifel, Adrienne E.; Herrera, Jonathan C; Meza, William; Eslamfam, Shabnam; Uetz, Peter; Siegele, Deborah A.; Hu, James C.; Giglio, Michelle

doi:10.1186/s12866-014-0294-3

Cited by 37 publications

(35 citation statements)

References 33 publications

(35 reference statements)

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“…The EMP Ontology (EMPO) for microbial environments was devised to facilitate the present analysis while preserving interoperability. Coordinated by the ENVO team, annotations from ENVO 11,51 , UBERON (metazoan anatomy) 57 , PO (Plant Ontology) 58 , FAO (Fungal Anatomy Ontology, purl.obolibrary.org/obo/fao.owl), and OMP (Ontology of Microbial Phenotypes) 59 were mapped to our EMPO levels 2 and 3 (empo_2 and empo_3). Additionally, the free-living or host-associated lifestyles were captured in level 1 categories (empo_1).…”

Section: Methodsmentioning

confidence: 99%

A communal catalogue reveals Earth’s multiscale microbial diversity

Thompson¹,

Sanders²,

McDonald³

et al. 2017

Nature

2,014

1,758

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A primary aim of microbial ecology is to determine patterns and drivers of community distribution, interaction, and assembly amidst complexity and uncertainty. Microbial community composition has been shown to change across gradients of environment, geographic distance, salinity, temperature, oxygen, nutrients, pH, day length, and biotic factors 1-6 . These patterns have been identified mostly by focusing on one sample type and region at a time, with insights extra polated across environments and geography to produce generalized principles. To assess how microbes are distributed across environments globally-or whether microbial community dynamics follow funda mental ecological 'laws' at a planetary scale-requires either a massive monolithic cross environment survey or a practical methodology for coordinating many independent surveys. New studies of microbial environments are rapidly accumulating; however, our ability to extract meaningful information from across datasets is outstripped by the rate of data generation. Previous meta analyses have suggested robust gen eral trends in community composition, including the importance of salinity 1 and animal association 2 . These findings, although derived from relatively small and uncontrolled sample sets, support the util ity of meta analysis to reveal basic patterns of microbial diversity and suggest that a scalable and accessible analytical framework is needed.The Earth Microbiome Project (EMP, http://www.earthmicrobiome. org) was founded in 2010 to sample the Earth's microbial communities at an unprecedented scale in order to advance our understanding of the organizing biogeographic principles that govern microbial commu nity structure 7,8 . We recognized that open and collaborative science, including scientific crowdsourcing and standardized methods 8 , would help to reduce technical variation among individual studies, which can overwhelm biological variation and make general trends difficult to detect 9 . Comprising around 100 studies, over half of which have yielded peer reviewed publications (Supplementary Table 1), the EMP has now dwarfed by 100 fold the sampling and sequencing depth of earlier meta analysis efforts 1,2 ; concurrently, powerful analysis tools have been developed, opening a new and larger window into the distri bution of microbial diversity on Earth. In establishing a scalable frame work to catalogue microbiota globally, we provide both a resource for the exploration of myriad questions and a starting point for the guided acquisition of new data to answer them. As an example of using this Our growing awareness of the microbial world's importance and diversity contrasts starkly with our limited understanding of its fundamental structure. Despite recent advances in DNA sequencing, a lack of standardized protocols and common analytical frameworks impedes comparisons among studies, hindering the development of global inferences about microbial life on Earth. Here we present a meta-analysis of microbial community samples collected by hundreds of r...

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Section: Methodsmentioning

confidence: 99%

A communal catalogue reveals Earth’s multiscale microbial diversity

Thompson¹,

Sanders²,

McDonald³

et al. 2017

Nature

2,014

1,758

View full text Add to dashboard Cite

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“…Other resources outside of GO have modeled their annotation capture systems on the GAF format. For example, the Ontology of Microbial Phenotypes [ 17 ] uses a modifi ed version of the GO GAF, but employs ECO terms instead of GO evidence codes. The full use of ECO terms by the GO would enhance the integration of data derived from such diverse sources.…”

Section: Eco Terms Versus Go Codesmentioning

confidence: 99%

The Evidence and Conclusion Ontology (ECO): Supporting GO Annotations

Chibucos¹,

Siegele²,

Hu³

et al. 2016

Methods in Molecular Biology

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The Evidence and Conclusion Ontology (ECO) is a community resource for describing the various types of evidence that are generated during the course of a scientifi c study and which are typically used to support assertions made by researchers. ECO describes multiple evidence types, including evidence resulting from experimental (i.e., wet lab) techniques, evidence arising from computational methods, statements made by authors (whether or not supported by evidence), and inferences drawn by researchers curating the literature. In addition to summarizing the evidence that supports a particular assertion, ECO also offers a means to document whether a computer or a human performed the process of making the annotation. Incorporating ECO into an annotation system makes it possible to leverage the structure of the ontology such that associated data can be grouped hierarchically, users can select data associated with particular evidence types, and quality control pipelines can be optimized. Today, over 30 resources, including the Gene Ontology, use the Evidence and Conclusion Ontology to represent both evidence and how annotations are made.

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“…More recently it has been shown that at least one CRISPR-associated 38 (Cas) protein can suppress non-homologous end-joining (NHEJ) DNA repair, 39 which may lead to selection against having CRISPR in some taxa [21]. In or- 40 der to determine the relative importances of these different mechanisms, we 41 must first identify the habitats and microbial lifestyles associated with CRISPR 42 immunity. 43 Here we aim to expand on previous analyses of CRISPR incidence in three 44 ways: (1) by drastically expanding the number of environmental and lifestyle 45 traits considered as predictors using the combination of a large prokaryotic trait 46 database and machine learning approaches, (2) by incorporating appropriate 47 statistical corrections for non-independence among taxa due to shared evolu- 48 tionary history, and (3) by simultaneously looking for patterns in RM systems, 49 which will help us untangle the difference between environments that specifically 50 favor CRISPR adaptive immunity versus DNA-degrading intracellular immune 51 systems in general (RM and CRISPR).…”

Section: Introductionmentioning

confidence: 99%

“…With the number of microbial genomes in public databases con-338 stantly expanding, so too should efforts to provide metadata about each of the 339 organisms represented by those genomes. At least part of the problem lies in the 340 lack of a universally accepted controlled vocabulary for microbial traits (sim-341 ilar to that provided by the Gene Ontology Consortium [39]), although some 342 admirable attempts have been made [40,41]. This would both facilitate the 343 construction of more expansive trait databases, and would help deal with the 344 issue of comparing traits that span many different scales.…”

mentioning

confidence: 99%

Ecology Shapes Microbial Immune Strategy: Temperature and Oxygen as Determinants of the Incidence of CRISPR Adaptive Immunity

Weissman

Rmr

et al. 2018

Preprint

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Bacteria and archaea are locked in a near-constant battle with their viral pathogens. Despite previous mechanistic characterization of numerous prokaryotic defense strategies, the underlying ecological drivers of different strategies remain largely unknown and predicting which species will take which strategies remains a challenge. Here, we focus on the CRISPR immune strategy and develop a phylogenetically-corrected machine learning approach to build a predictive model of CRISPR incidence using data on over 100 traits across over 2600 species. We discover a strong but hithertounknown negative interaction between CRISPR and aerobicity, which we hypothesize may result from interference between CRISPR associated proteins and non-homologous end-joining DNA repair due to oxidative stress. Our predictive model also quantitatively confirms previous observations of an association between CRISPR and temperature. Finally, we contrast the environmental associations of different CRISPR system types (I, II, III) and restriction modification systems, all of which act as intracellular immune systems.

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An ontology for microbial phenotypes

Cited by 37 publications

References 33 publications

A communal catalogue reveals Earth’s multiscale microbial diversity

A communal catalogue reveals Earth’s multiscale microbial diversity

The Evidence and Conclusion Ontology (ECO): Supporting GO Annotations

Ecology Shapes Microbial Immune Strategy: Temperature and Oxygen as Determinants of the Incidence of CRISPR Adaptive Immunity

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