taxize: taxonomic search and retrieval in R

Chamberlain, Scott; Szöcs, Eduard

doi:10.12688/f1000research.2-191.v2

Cited by 296 publications

(256 citation statements)

References 18 publications

(16 reference statements)

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“…In general, these data coincided with recently published information on the phylogenetic relationships of ambrosia beetles [11]. The taxonomy of beetle species to X. glabratus was checked using information validated for GBIF and NCBI with the R package taxize [12].…”

Section: Beetle and Plant Host Incidence Matrixsupporting

confidence: 78%

Combining Phylogenetic and Occurrence Information for Risk Assessment of Pest and Pathogen Interactions with Host Plants

Robles-Fernández

Lira‐Noriega

2017

Front. Appl. Math. Stat.

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Phytosanitary agencies conduct plant biosecurity activities, including early detection of potential introduction pathways, to improve control and eradication of pest and pathogen incursions. For such actions, analytical tools based on solid scientific knowledge regarding plant-pest or pathogen relationships for pest risk assessment are needed. Recent evidence indicating that closely related species share a higher chance of becoming infected or attacked by pests has allowed the identification of taxa with different degrees of vulnerability. Here, we use information readily available online about pest-host interactions and their geographic distributions, in combination with host phylogenetic reconstructions, to estimate a pest-host interaction (in some cases infection) index in geographic space as a more comprehensive, spatially explicit tool for risk assessment. We demonstrate this protocol using phylogenetic relationships for 20 beetle species and 235 host plant genera: first, we estimate the probability of a host sharing pests, and second, we project the index in geographic space. Overall, the predictions allow identification of the pest-host interaction type (e.g., generalist or specialist), which is largely determined by both host range and phylogenetic constraints. Furthermore, the results can be valuable in terms of identifying hotspots where pests and vulnerable hosts interact. This knowledge is useful for anticipating biological invasions or spreading of disease. We suggest that our understanding of biotic interactions will improve after combining information from multiple dimensions of biodiversity at multiple scales (e.g., phylogenetic signal and host-vector-pathogen geographic distribution).

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Section: Beetle and Plant Host Incidence Matrixsupporting

confidence: 78%

Combining Phylogenetic and Occurrence Information for Risk Assessment of Pest and Pathogen Interactions with Host Plants

Robles-Fernández

Lira‐Noriega

2017

Front. Appl. Math. Stat.

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“…In addition packages to overcome significant challenges in compiling large datasets and maintaining these data through time are being improved. For example, the R package 'taxize' (Chamberlain and Szocs 2013), which relies on accessing freely available and accurate information on species taxonomy, including from WoRMS. This emphasises the benefits of scientists and institutes publishing monitoring data in order to advance our understanding of biodiversity change.…”

Section: Discussionmentioning

confidence: 99%

Methods for the Study of Marine Biodiversity

Costello

Basher

McLeod

et al. 2016

The GEO Handbook on Biodiversity Observation Networks

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Recognition of the threats to biodiversity and its importance to society has led to calls for globally coordinated sampling of trends in marine ecosystems. As a step to defining such efforts, we review current methods of collecting and managing marine biodiversity data. A fundamental component of marine biodiversity is knowing what, where, and when species are present. However, monitoring methods are invariably biased in what taxa, ecological guilds, and body sizes they collect.

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“…The R package taxize (Chamberlain and Szöcs, 2013) was used to recover taxonomic information using the species name provided in the BRENDA database. Activating/inhibiting interactions with the same ligand ID (regulating metabolite) and EC number (target enzyme) were aggregated, and the number of such unique entries for each taxonomic group was calculated.…”

Section: Methodsmentioning

confidence: 99%

Genome-Scale Architecture of Small Molecule Regulatory Networks and the Fundamental Trade-Off between Regulation and Enzymatic Activity

et al. 2017

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Summary Metabolic flux is in part regulated by endogenous small molecules that modulate the catalytic activity of an enzyme, e.g. allosteric inhibition. In contrast to transcriptional regulation of enzymes, technical difficulties have hindered the production of a genome-scale atlas of small molecule/enzyme regulatory interactions. Here, we develop a framework leveraging the vast, but fragmented, biochemical literature to reconstruct and analyze the small molecule regulatory network (SMRN) of the model organism Escherichia coli, including the primary metabolite regulators and enzyme targets. Using metabolic control analysis, we prove a fundamental trade-off between regulation and enzymatic activity, and combine it with metabolomic measurements and the SMRN to make inferences on the sensitivity of enzymes to their regulators. Generalizing the analysis to other organisms, we identify highly conserved regulatory interactions across evolutionarily divergent species, further emphasizing a critical role for small molecule interactions in the maintenance of metabolic homeostasis. 2

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taxize: taxonomic search and retrieval in R

Cited by 296 publications

References 18 publications

Combining Phylogenetic and Occurrence Information for Risk Assessment of Pest and Pathogen Interactions with Host Plants

Combining Phylogenetic and Occurrence Information for Risk Assessment of Pest and Pathogen Interactions with Host Plants

Methods for the Study of Marine Biodiversity

Genome-Scale Architecture of Small Molecule Regulatory Networks and the Fundamental Trade-Off between Regulation and Enzymatic Activity

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