Prediction of plant lncRNA by ensemble machine learning classifiers

Abstract Background In plants, long non-protein coding RNAs are believed to have essential roles in development and stress responses. However, relative to advances on discerning biological roles for long non-protein coding RNAs in animal systems, this RNA class in plants is largely understudied. With comparatively few validated plant long non-coding RNAs, research on this potentially critical class of RNA is hindered by a lack of appropriate prediction tools and databases. Supervised learning models trained on data sets of mostly non-validated, non-coding transcripts have been previously used to identify this enigmatic RNA class with applications largely focused on animal systems. Our approach uses a training set comprised only of empirically validated long non-protein coding RNAs from plant, animal, and viral sources to predict and rank candidate long non-protein coding gene products for future functional validation. Results Individual stochastic gradient boosting and random forest classifiers trained on only empirically validated long non-protein coding RNAs were constructed. In order to use the strengths of multiple classifiers, we combined multiple models into a single stacking meta-learner. This ensemble approach benefits from the diversity of several learners to effectively identify putative plant long non-coding RNAs from transcript sequence features. When the predicted genes identified by the ensemble classifier were compared to those listed in GreeNC, an established plant long non-coding RNA database, overlap for predicted genes from Arabidopsis thaliana, Oryza sativa and Eutrema salsugineum ranged from 51 to 83% with the highest agreement in Eutrema salsugineum. Most of the highest ranking predictions from Arabidopsis thaliana were annotated as potential natural antisense genes, pseudogenes, transposable elements, or simply computationally predicted hypothetical protein. Due to the nature of this tool, the model can be updated as new long non-protein coding transcripts are identified and functionally verified. Conclusions This ensemble classifier is an accurate tool that can be used to rank long non-protein coding RNA predictions for use in conjunction with gene expression studies. Selection of plant transcripts with a high potential for regulatory roles as long non-protein coding RNAs will advance research in the elucidation of long non-protein coding RNA function

Investigation of intercellular salicylic acid accumulation during compatible and incompatible Arabidopsis-pseudomonas syringae interactions using a fast neutron-generated mutant allele of EDS5 identified by genetic mapping and whole-genome sequencing.

A whole-genome sequencing technique developed to identify fast neutron-induced deletion mutations revealed that iap1-1 is a new allele of EDS5 (eds5-5). RPS2-AvrRpt2-initiated effector-triggered immunity (ETI) was compromised in iap1-1/eds5-5 with respect to in planta bacterial levels and the hypersensitive response, while intra- and intercellular free salicylic acid (SA) accumulation was greatly reduced, suggesting that SA contributes as both an intracellular signaling molecule and an antimicrobial agent in the intercellular space during ETI. During the compatible interaction between wild-type Col-0 and virulent Pseudomonas syringae pv. tomato (Pst), little intercellular free SA accumulated, which led to the hypothesis that Pst suppresses intercellular SA accumulation. When Col-0 was inoculated with a coronatine-deficient strain of Pst, high levels of intercellular SA accumulation were observed, suggesting that Pst suppresses intercellular SA accumulation using its phytotoxin coronatine. This work suggests that accumulation of SA in the intercellular space is an important component of basal/PAMP-triggered immunity as well as ETI to pathogens that colonize the intercellular space

Data for salt cress

The filtered SNP data and database for deletious mutatio

RNA-Seq effectively monitors gene expression in Eutrema salsugineum plants growing in an extreme natural habitat and in controlled growth cabinet conditions

Abstract 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