Title: Plant Metabolic Network 16: expansion of underrepresented plant groups and experimentally supported enzyme data
Abstract The Plant Metabolic Network (PMN) is a free online database of plant metabolism available at https://plantcyc.org. The latest release, PMN 16, provides metabolic databases representing >1200 metabolic pathways, 1.3 million enzymes, >8000 metabolites, >10 000 reactions and >15 000 citations for 155 plant and green algal genomes, as well as a pan-plant reference database called PlantCyc. This release contains 29 additional genomes compared with PMN 15, including species listed by the African Orphan Crop Consortium and nonflowering plant species. Furthermore, 52 new enzymes with experimentally supported function information have been included in this release. The single-species databases contain a combination of experimental information from the literature and computationally predicted information obtained through PMN’s database generation pipeline for a single species, while PlantCyc contains only experimental information but for any species within Viridiplantae. PMN is a comprehensive resource for querying, visualizing, analyzing and interpreting omics data with metabolic knowledge. It also serves as a useful and interactive tool for teaching plant metabolism. more »« less
Damashek, Julian; Sheik, Cody S; Petro, Caitlin; Reeder, Christian F; Chowdhury, Subhadeep; Kramer, Benjamin J; DeVilbiss, Stephen E; Pierella_Karlusich, Juan J; Marks, Jane C; Valdespino-Castillo, Patricia M; et al
(, Microbiology Resource Announcements)
Newton, Irene_L G
(Ed.)
ABSTRACT Microbial nitrogen fixation (diazotrophy) is a critical ecological process. We curated DiazoTIME (Diazotroph Taxonomic Identity and MEtabolism), a comprehensive database of diazotroph genomes including taxonomic annotation and metabolic prediction. DiazoTIME is unique among databases for classifying diazotrophs because it resolves both taxonomy and metabolic functionality.
McGivern, Bridget B; Woyda, Reed; Flynn, Rory M; Wrighton, Kelly C
(, bioRxiv)
Summary: Polyphenols are diverse and abundant carbon sources across ecosystems- having important roles in host-associated and terrestrial systems alike. However, the microbial genes encoding polyphenol metabolic enzymes are poorly represented in commonly used annotation databases, limiting widespread surveying of this metabolism. Here we present CAMPER, a tool that combines custom annotation searches with database-derived searches to both annotate and summarize polyphenol metabolism genes for a wide audience. With CAMPER, users will identify potential polyphenol-active genes and genomes to more broadly understand microbial carbon cycling in their datasets. Availability and Implementation: CAMPER is implemented in Python and is published under the GNU General Public License Version 3. It is available as both a standalone tool and as a database in DRAM v.1.5+. The source code and full documentation is available on GitHub at https://github.com/WrightonLabCSU/CAMPER.
Siriwardana, Chamindika L; Carlton, Ashleigh S; Moncayo, Thalia Lizeth; O'Bier, Elizabeth A; Bartley, Laura E
(, Plant Direct)
ABSTRACT This beginner's guide is intended for plant biologists new to network analysis. Here, we introduce key concepts and resources for researchers interested in incorporating network analysis into research, either as a stand‐alone component for generating hypotheses or as a framework for examining and visualizing experimental results. Network analysis provides a powerful tool to predict gene functions. Advances in and reduced costs for systems biology techniques, such as genomics, transcriptomics, and proteomics, have generated abundant omics data for plants; however, the functional annotation of plant genes lags. Therefore, predictions from network analysis can be a starting point to annotate genes and ultimately elucidate genotype–phenotype relationships. In this paper, we introduce networks and compare network‐building resources available for plant biologists, including databases and software for network analysis. We then compare four databases available for plant biologists in more detail: AraNet, GeneMANIA, ATTED‐II, and STRING. AraNet and GeneMANIA are functional association networks, ATTED‐II is a gene coexpression database, and STRING is a protein–protein interaction database. AraNet and ATTED‐II are plant‐specific databases that can analyze multiple plant species, whereas GeneMANIA builds networks forArabidopsis thalianaand nonplant species and STRING for multiple species. Finally, we compare the performance of the four databases in predicting known and probable gene functions of theA. thalianaNuclear Factor‐Y (NF‐Y) genes. We conclude that plant biologists have an invaluable resource in these databases and discuss how users can decide which type of database to use depending on their research question.
Barkman, Todd J
(, Philosophical Transactions of the Royal Society B: Biological Sciences)
Studies of enzymes in modern-day plants have documented the diversity of metabolic activities retained by species today but only provide limited insight into how those properties evolved. Ancestral sequence reconstruction (ASR) is an approach that provides statistical estimates of ancient plant enzyme sequences which can then be resurrected to test hypotheses about the evolution of catalytic activities and pathway assembly. Here, I review the insights that have been obtained using ASR to study plant metabolism and highlight important methodological aspects. Overall, studies of resurrected plant enzymes show that (i) exaptation is widespread such that even low or undetectable levels of ancestral activity with a substrate can later become the apparent primary activity of descendant enzymes, (ii) intramolecular epistasis may or may not limit evolutionary paths towards catalytic or substrate preference switches, and (iii) ancient pathway flux often differs from modern-day metabolic networks. These and other insights gained from ASR would not have been possible using only modern-day sequences. Future ASR studies characterizing entire ancestral metabolic networks as well as those that link ancient structures with enzymatic properties should continue to provide novel insights into how the chemical diversity of plants evolved. This article is part of the theme issue ‘The evolution of plant metabolism’.
Siriwardanam, Chamindika L; Carlton, Ashleigh S; Moncayo, Thalia Lizeth; O'Bier, Elizabeth A; Bartley, Laura E
(, Authorea)
This beginner’s guide is intended for plant biologists new to network analysis. Here, we introduce key concepts and resources for researchers interested in incorporating network analysis into research, either as a stand-alone component for generating hypotheses or as a framework for examining and visualizing experimental results. Network analysis provides a powerful tool to predict gene functions. Advances in and reduced costs for systems biology techniques, such as genomics, transcriptomics, and proteomics, have generated abundant -omics data for plants; however, the functional annotation of plant genes lags. Therefore, predictions from network analysis can be a starting point to annotate genes and ultimately elucidate genotype-phenotype relationships. In this paper, we introduce networks and compare network-building resources available for plant biologists, including databases and software for network analysis. We then compare four databases available for plant biologists in more detail: AraNet, GeneMANIA, ATTED-II, and STRING. AraNet, and GeneMANIA are functional association networks, ATTED-II is a gene coexpression database, and STRING is a protein-protein interaction database. AraNet, and ATTED-II are plant-specific databases that can analyze multiple plant species, whereas GeneMANIA builds networks for Arabidopsis thaliana and non-plant species, and STRING for multiple species. Finally, we compare the performance of the four databases in predicting known and probable gene functions of the A. thaliana Nuclear Factor-Y (NF-Y) genes. We conclude that plant biologists have an invaluable resource in these databases and discuss how users can decide which type of database to use depending on their research question.
Hawkins, Charles, Xue, Bo, Yasmin, Farida, Wyatt, Gabrielle, Zerbe, Philipp, and Rhee, Seung Y.
"Plant Metabolic Network 16: expansion of underrepresented plant groups and experimentally supported enzyme data". Nucleic Acids Research 53 (D1). Country unknown/Code not available: Oxford University Press. https://doi.org/10.1093/nar/gkae991.https://par.nsf.gov/biblio/10555811.
@article{osti_10555811,
place = {Country unknown/Code not available},
title = {Plant Metabolic Network 16: expansion of underrepresented plant groups and experimentally supported enzyme data},
url = {https://par.nsf.gov/biblio/10555811},
DOI = {10.1093/nar/gkae991},
abstractNote = {Abstract The Plant Metabolic Network (PMN) is a free online database of plant metabolism available at https://plantcyc.org. The latest release, PMN 16, provides metabolic databases representing >1200 metabolic pathways, 1.3 million enzymes, >8000 metabolites, >10 000 reactions and >15 000 citations for 155 plant and green algal genomes, as well as a pan-plant reference database called PlantCyc. This release contains 29 additional genomes compared with PMN 15, including species listed by the African Orphan Crop Consortium and nonflowering plant species. Furthermore, 52 new enzymes with experimentally supported function information have been included in this release. The single-species databases contain a combination of experimental information from the literature and computationally predicted information obtained through PMN’s database generation pipeline for a single species, while PlantCyc contains only experimental information but for any species within Viridiplantae. PMN is a comprehensive resource for querying, visualizing, analyzing and interpreting omics data with metabolic knowledge. It also serves as a useful and interactive tool for teaching plant metabolism.},
journal = {Nucleic Acids Research},
volume = {53},
number = {D1},
publisher = {Oxford University Press},
author = {Hawkins, Charles and Xue, Bo and Yasmin, Farida and Wyatt, Gabrielle and Zerbe, Philipp and Rhee, Seung Y.},
}
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