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Anthropogenic activities, such as industrial processes, mining, and agriculture, lead to an increase in heavy metal concentrations in soil, water, and air. Given their stability in the environment, heavy metals are difficult to eliminate and can constitute a human health risk by entering the food chain through uptake by crop plants. An excess of heavy metals is toxic for plants, which have various mechanisms to prevent their accumulation. However, once metals enter the plant, oxidative damage sometimes occurs, which can lead to plant death. Initial production of nitric oxide (NO), which may play a role in plant perception, signalling, and stress acclimation, has been shown to protect against heavy metals. Very little is known about NO-dependent mechanisms downstream from signalling pathways in plant responses to heavy metal stress. In this review, using bioinformatic techniques, we analyse studies of the involvement of NO in plant responses to heavy metal stress, its possible role as a cytoprotective molecule, and its relationship with reactive oxygen species. Some conclusions are drawn and future research perspectives are outlined to further elucidate the signalling mechanisms underlying the role of NO in plant responses to heavy metal stress. [ABSTRACT FROM AUTHOR]

Motivation: Small non-coding RNAs (sRNAs) have major roles in the post-transcriptional regulation in prokaryotes. The experimental validation of a relatively small number of sRNAs in few species requires developing computational algorithms capable of robustly encoding the available knowledge and using this knowledge to predict sRNAs within and across species.Results: We present a novel methodology designed to identify bacterial sRNAs by incorporating the knowledge encoded by different sRNA prediction methods and optimally aggregating them as potential predictors. Because some of these methods emphasize specificity, whereas others emphasize sensitivity while detecting sRNAs, their optimal aggregation constitutes trade-off solutions between these two contradictory objectives that enhance their individual merits. Many non-redundant optimal aggregations uncovered by using multiobjective optimization techniques are then combined into a multiclassifier, which ensures robustness during detection and prediction even in genomes with distinct nucleotide composition. By training with sRNAs in Salmonella enterica Typhimurium, we were able to successfully predict sRNAs in Sinorhizobium meliloti, as well as in multiple and poorly annotated species. The proposed methodology, like a meta-analysis approach, may begin to lay a possible foundation for developing robust predictive methods across a wide spectrum of genomic variability.Availability and implementation: Scripts created for the experimentation are available at http://m4m.ugr.es/SupInfo/sRNAOS/sRNAOSscripts.zip.Contact: delval@decsai.ugr.esSupplementary information: Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]