Plant Cell 2010 Aug;22(8):2530-6
The future bioinformatics needs of the Arabidopsis community as well as those of other scientific communities that depend on Arabidopsis resources were discussed at a pair of recent meetings held by the Multinational Arabidopsis Steering Committee and the North American Arabidopsis Steering Committee. There are extensive tools and resources for information storage, curation, and retrieval of Arabidopsis data that have been developed over recent years primarily through the activities of The Arabidopsis Information Resource, the Nottingham Arabidopsis Stock Centre, and the Arabidopsis Biological Resource Center, among others. However, the rapid expansion in many data types, the international basis of the Arabidopsis community, and changing priorities of the funding agencies all suggest the need for changes in the way informatics infrastructure is developed and maintained. We propose that there is a need for a single core resource that is integrated into a larger international consortium of investigators. We envision this to consist of a distributed system of data, tools, and resources, accessed via a single information portal and funded by a variety of sources, under shared international management of an International Arabidopsis Informatics Consortium (IAIC). This article outlines the proposal for the development, management, operations, and continued funding for the IAIC.
Ho CL, Wu Y, Shen HB, Provart NJ, Geisler M
Rice (N Y) 2012;5(1):15
BACKGROUND: Protein-protein interactions (PPIs) create the steps in signaling and regulatory networks central to most fundamental biological processes. It is possible to predict these interactions by making use of experimentally determined orthologous interactions in other species.
RESULTS: In this study, prediction of PPIs in rice was carried out by the interolog method of mapping deduced orthologous genes to protein interactions supported by experimental evidence from reference organisms. We predicted 37112 interactions for 4567 rice proteins, including 1671 predicted self interactions (homo-interactions) and 35441 predicted interactions between different proteins (hetero-interactions). These matched 168 of 675 experimentally-determined interactions in rice. Interacting proteins were significantly more co-expressed than expected by chance, which is typical of experimentally-determined interactomes. The rice interacting proteins were divided topologically into 981 free ends (proteins with single interactions), 499 pipes (proteins with two interactions) and 3087 hubs of different sizes ranging from three to more than 100 interactions.
CONCLUSIONS: This predicted rice interactome extends known pathways and improves functional annotation of unknown rice proteins and networks in rice, and is easily explored with software tools presented here.
Champigny MJ, Sung WW, Catana V, Salwan R, Summers PS, Dudley SA, Provart NJ, Cameron RK, Golding GB, Weretilnyk EA
BMC Genomics 2013;14:578
BACKGROUND: The investigation of extremophile plant species growing in their natural environment offers certain advantages, chiefly that plants adapted to severe habitats have a repertoire of stress tolerance genes that are regulated to maximize plant performance under physiologically challenging conditions. Accordingly, transcriptome sequencing offers a powerful approach to address questions concerning the influence of natural habitat on the physiology of an organism. We used RNA sequencing of Eutrema salsugineum, an extremophile relative of Arabidopsis thaliana, to investigate the extent to which genetic variation and controlled versus natural environments contribute to differences between transcript profiles.
RESULTS: Using 10 million cDNA reads, we compared transcriptomes from two natural Eutrema accessions (originating from Yukon Territory, Canada and Shandong Province, China) grown under controlled conditions in cabinets and those from Yukon plants collected at a Yukon field site. We assessed the genetic heterogeneity between individuals using single-nucleotide polymorphisms (SNPs) and the expression patterns of 27,016 genes. Over 39,000 SNPs distinguish the Yukon from the Shandong accessions but only 4,475 SNPs differentiated transcriptomes of Yukon field plants from an inbred Yukon line. We found 2,989 genes that were differentially expressed between the three sample groups and multivariate statistical analyses showed that transcriptomes of individual plants from a Yukon field site were as reproducible as those from inbred plants grown under controlled conditions. Predicted functions based upon gene ontology classifications show that the transcriptomes of field plants were enriched by the differential expression of light- and stress-related genes, an observation consistent with the habitat where the plants were found.
CONCLUSION: Our expectation that comparative RNA-Seq analysis of transcriptomes from plants originating in natural habitats would be confounded by uncontrolled genetic and environmental factors was not borne out. Moreover, the transcriptome data shows little genetic variation between laboratory Yukon Eutrema plants and those found at a field site. Transcriptomes were reproducible and biological associations meaningful whether plants were grown in cabinets or found in the field. Thus RNA-Seq is a valuable approach to study native plants in natural environments and this technology can be exploited to discover new gene targets for improved crop performance under adverse conditions.