Drought has serious effects on the physiology of cereal crops. At the cellular and specifically the metabolite level, many individual compounds are increased to provide osmoprotective functions, prevent the dissociation of enzymes, and to decrease the number of reactive oxygen species present in the cell. We have used a targeted GC-MS approach to identify compounds that differ in three different cultivars of bread wheat characterized by different levels of tolerance to drought under drought stress (Kukri, intolerant; Excalibur and RAC875, tolerant). Levels of amino acids, most notably proline, tryptophan, and the branched chain amino acids leucine, isoleucine, and valine were increased under drought stress in all cultivars. In the two tolerant cultivars, a small decrease in a large number of organic acids was also evident. Excalibur, a cultivar genotypically related to Kukri, showed a pattern of response that was more similar to Kukri under well-watered conditions. Under drought stress, Excalibur and RAC875 had a similar response; however, Excalibur did not have the same magnitude of response as RAC875. Here, the results are discussed in the context of previous work in physiological and proteomic analyses of these cultivars under drought stress.

Changes in wind due to global warming may have large geophysical and societal impacts. The 10 m winds from the Coupled Model Intercomparison Project Phase 3 multi-model ensemble are assessed against reanalysis winds and found to exhibit lowest skill over land areas. Maps of future change in mean wind speed, direction and 99th percentile wind speed are presented to convey spatial information as well as the multi-model agreement on sign and magnitude of the change. The utility of these maps in providing context for the design of more detailed impact studies, for which wind is a required input, is discussed.

Model evaluation is an important tool to help rate confidence in climate model simulations. This can add to the overall confidence assessment for future projections of the Australian climate. Additionally it can highlight significant model deficiencies that may affect the selection of a subset of models for use in impact assessment.Here we present results from an extensive model evaluation undertaken as part of the Natural Resource Management (NRM) Project in order to inform the newest set of climate change projections for Australia.The assessment covers mean climate skill over Australia as well as variability measures and teleconnections from up to 47 CMIP5 models and 23 CMIP3 models (for comparison where appropriate). Additionally, the skill in representing important climate features such as MJO, SAM, blocking and cut-off lows are also reviewed. Selected extremes are evaluated as well as simulations of two different types of downscaling simulations used within the NRM project. Finally, an attempt is made to synthesise this information in order to highlight a small group of CMIP5 models which show consistent deficiencies in representing the Australian climate and its features.

Simple sequence repeat (SSR) molecular genetic markers have become important tools for a broad range of applications such as genome mapping and genetic diversity studies. SSRs are readily identified within DNA sequence data and PCR primers can be designed for their amplification. These PCR primers frequently cross amplify within related species. We report a web-based tool, SSR Primer, that integrates SPUTNIK, an SSR repeat finder, with Primer3, a primer design program, within one pipeline. On submission of multiple FASTA formatted sequences, the script screens each sequence for SSRs using SPUTNIK. Results are then parsed to Primer3 for locus specific primer design. We have applied this tool for the discovery of SSRs within the complete GenBank database, and have designed PCR amplification primers for over 13 million SSRs. The SSR Taxonomy Tree server provides web-based searching and browsing of species and taxa for the visualisation and download of these SSR amplification primers. These tools are available at .

BackgroundGas chromatography–mass spectrometry (GC-MS) is a technique frequently used in targeted and non-targeted measurements of metabolites. Most existing software tools for processing of raw instrument GC-MS data tightly integrate data processing methods with graphical user interface facilitating interactive data processing. While interactive processing remains critically important in GC-MS applications, high-throughput studies increasingly dictate the need for command line tools, suitable for scripting of high-throughput, customized processing pipelines.ResultsPyMS comprises a library of functions for processing of instrument GC-MS data developed in Python. PyMS currently provides a complete set of GC-MS processing functions, including reading of standard data formats (ANDI- MS/NetCDF and JCAMP-DX), noise smoothing, baseline correction, peak detection, peak deconvolution, peak integration, and peak alignment by dynamic programming. A novel common ion single quantitation algorithm allows automated, accurate quantitation of GC-MS electron impact (EI) fragmentation spectra when a large number of experiments are being analyzed. PyMS implements parallel processing for by-row and by-column data processing tasks based on Message Passing Interface (MPI), allowing processing to scale on multiple CPUs in distributed computing environments. A set of specifically designed experiments was performed in-house and used to comparatively evaluate the performance of PyMS and three widely used software packages for GC-MS data processing (AMDIS, AnalyzerPro, and XCMS).ConclusionsPyMS is a novel software package for the processing of raw GC-MS data, particularly suitable for scripting of customized processing pipelines and for data processing in batch mode. PyMS provides limited graphical capabilities and can be used both for routine data processing and interactive/exploratory data analysis. In real-life GC-MS data processing scenarios PyMS performs as well or better than leading software packages. We demonstrate data processing scenarios simple to implement in PyMS, yet difficult to achieve with many conventional GC-MS data processing software. Automated sample processing and quantitation with PyMS can provide substantial time savings compared to more traditional interactive software systems that tightly integrate data processing with the graphical user interface.

SNPServer is a real-time flexible tool for the discovery of SNPs (single nucleotide polymorphisms) within DNA sequence data. The program uses BLAST, to identify related sequences, and CAP3, to cluster and align these sequences. The alignments are parsed to the SNP discovery software autoSNP, a program that detects SNPs and insertion/deletion polymorphisms (indels). Alternatively, lists of related sequences or pre-assembled sequences may be entered for SNP discovery. SNPServer and autoSNP use redundancy to differentiate between candidate SNPs and sequence errors. For each candidate SNP, two measures of confidence are calculated, the redundancy of the polymorphism at a SNP locus and the co-segregation of the candidate SNP with other SNPs in the alignment. SNPServer is available at .

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Atmospheric circulation change is likely to be the dominant driver of multidecadal rainfall trends in the midlatitudes with climate change this century. This study examines circulation features relevant to southern Australian rainfall in January and July and explores emergent constraints suggested by the intermodel spread and their impact on the resulting rainfall projection in the CMIP5 ensemble. The authors find relationships between models’ bias and projected change for four features in July, each with suggestions for constraining forced change. The features are the strength of the subtropical jet over Australia, the frequency of blocked days in eastern Australia, the longitude of the peak blocking frequency east of Australia, and the latitude of the storm track within the polar front branch of the split jet. Rejecting models where the bias suggests either the direction or magnitude of change in the features is implausible produces a constraint on the projected rainfall reduction for southern Australia. For RCP8.5 by the end of the century the constrained projections are for a reduction of at least 5% in July (with models showing increase or little change being rejected). Rejecting these models in the January projections, with the assumption the bias affects the entire simulation, leads to a rejection of wet and dry outliers.