BackgroundMolecule identification is a crucial step in metabolomics and environmental sciences. Besides in silico fragmentation, as performed by MetFrag, also machine learning and statistical methods evolved, showing an improvement in molecule annotation based on MS/MS data. In this work we present a new statistical scoring method where annotations of m/z fragment peaks to fragment-structures are learned in a training step. Based on a Bayesian model, two additional scoring terms are integrated into the new MetFrag2.4.5 and evaluated on the test data set of the CASMI 2016 contest.ResultsThe results on the 87 MS/MS spectra from positive and negative mode show a substantial improvement of the results compared to submissions made by the former MetFrag approach. Top1 rankings increased from 5 to 21 and Top10 rankings from 39 to 55 both showing higher values than for CSI:IOKR, the winner of the CASMI 2016 contest. For the negative mode spectra, MetFrag’s statistical scoring outperforms all other participants which submitted results for this type of spectra.ConclusionsThis study shows how statistical learning can improve molecular structure identification based on MS/MS data compared on the same method using combinatorial in silico fragmentation only. MetFrag2.4.5 shows especially in negative mode a better performance compared to the other participating approaches.

BackgroundTranscriptional gene regulation is a fundamental process in nature, and the experimental and computational investigation of DNA binding motifs and their binding sites is a prerequisite for elucidating this process. Approaches for de-novo motif discovery can be subdivided in phylogenetic footprinting that takes into account phylogenetic dependencies in aligned sequences of more than one species and non-phylogenetic approaches based on sequences from only one species that typically take into account intra-motif dependencies. It has been shown that modeling (i) phylogenetic dependencies as well as (ii) intra-motif dependencies separately improves de-novo motif discovery, but there is no approach capable of modeling both (i) and (ii) simultaneously.ResultsHere, we present an approach for de-novo motif discovery that combines phylogenetic footprinting with motif models capable of taking into account intra-motif dependencies. We study the degree of intra-motif dependencies inferred by this approach from ChIP-seq data of 35 transcription factors. We find that significant intra-motif dependencies of orders 1 and 2 are present in all 35 datasets and that intra-motif dependencies of order 2 are typically stronger than those of order 1. We also find that the presented approach improves the classification performance of phylogenetic footprinting in all 35 datasets and that incorporating intra-motif dependencies of order 2 yields a higher classification performance than incorporating such dependencies of only order 1.ConclusionCombining phylogenetic footprinting with motif models incorporating intra-motif dependencies leads to an improved performance in the classification of transcription factor binding sites. This may advance our understanding of transcriptional gene regulation and its evolution.

The plant pathogen Candidatus Phytoplasma mali (P. mali) is the causative agent of apple proliferation, a disease of increasing importance in apple‐growing areas within Europe. Despite its economic importance, little is known about the molecular mechanisms of disease manifestation within apple trees. In this study, we identified two TCP (TEOSINTE BRANCHED/CYCLOIDEA/PROLIFERATING CELL FACTOR) transcription factors of Malus x domestica as binding partners of the P. mali SAP11‐like effector ATP_00189. Phytohormone analyses revealed an effect of P. mali infection on jasmonates, salicylic acid and abscisic acid levels, showing that P. mali affects phytohormonal levels in apple trees, which is in line with the functions of the effector assumed from its binding to TCP transcription factors. To our knowledge, this is the first characterization of the molecular targets of a P. mali effector and thus provides the basis to better understand symptom development and disease progress during apple proliferation. As SAP11 homologues are found in several Phytoplasma species infecting a broad range of different plants, SAP11‐like proteins seem to be key players in phytoplasmal infection.

Immunity against pathogen infection depends on a host's ability to sense invading pathogens and to rapidly trigger defence reactions that block pathogen proliferation. Both plants and animals detect conserved structural motifs of microbe‐specific compounds, so‐called microbe‐associated molecular patterns (MAMPs), through germline‐encoded immune sensors, which are accordingly termed pattern recognition receptors (PRRs) (Akira et al., 2006; Boller and Felix, 2009). Activated PRRs initiate signal transduction and trigger innate immune responses. MAMPs are generally derived from elements essential for microbial fitness and are conserved across species, thus enabling the host to detect a range of potential pathogens. In mammals, innate immune sensing of MAMPs is not only crucial for basal immune responses but is also tightly connected with and required for a subsequent adaptive, antibody‐mediated immunity (Akira et al., 2006; Janeway and Medzhitov, 2002). Plants, lacking an adaptive immune system, have apparently evolved a greater capacity to detect a broader repertoire of MAMPs. Different plant species possess distinct sets of highly specific PRRs, but the downstream signalling pathways are rather conserved and converge on common signalling steps. This allows the transfer of PRRs, even to different plant families, whilst maintaining their functionality and specificity (Zipfel, 2014). This also enables researchers to use well‐studied, genetically amenable model systems for the identification of MAMPs and their respective PRRs. Several examples of interfamily PRR transfer have demonstrated that the introduction of novel PRRs into plant species can confer relevant levels of resistance to otherwise susceptible plants (e.g. Afroz et al., 2011; Hao et al., 2015; Lacombe et al., 2010; Mendes et al., 2010; Schoonbeek et al., 2015; Tripathi et al., 2014). Hence, MAMP sensing by PRRs has great potential for the engineering of disease resistance in crop plants. In recent years, it has therefore become a major task to identify and isolate MAMPs from a range of microorganisms, and their respective PRRs, to study their role in innate immunity and their application potential.

BackgroundFor three decades, sequence logos are the de facto standard for the visualization of sequence motifs in biology and bioinformatics. Reasons for this success story are their simplicity and clarity. The number of inferred and published motifs grows with the number of data sets and motif extraction algorithms. Hence, it becomes more and more important to perceive differences between motifs. However, motif differences are hard to detect from individual sequence logos in case of multiple motifs for one transcription factor, highly similar binding motifs of different transcription factors, or multiple motifs for one protein domain.ResultsHere, we present DiffLogo, a freely available, extensible, and user-friendly R package for visualizing motif differences. DiffLogo is capable of showing differences between DNA motifs as well as protein motifs in a pair-wise manner resulting in publication-ready figures. In case of more than two motifs, DiffLogo is capable of visualizing pair-wise differences in a tabular form. Here, the motifs are ordered by similarity, and the difference logos are colored for clarity. We demonstrate the benefit of DiffLogo on CTCF motifs from different human cell lines, on E-box motifs of three basic helix-loop-helix transcription factors as examples for comparison of DNA motifs, and on F-box domains from three different families as example for comparison of protein motifs.ConclusionsDiffLogo provides an intuitive visualization of motif differences. It enables the illustration and investigation of differences between highly similar motifs such as binding patterns of transcription factors for different cell types, treatments, and algorithmic approaches.

BackgroundOntology-based enrichment analysis aids in the interpretation and understanding of large-scale biological data. Ontologies are hierarchies of biologically relevant groupings. Using ontology annotations, which link ontology classes to biological entities, enrichment analysis methods assess whether there is a significant over or under representation of entities for ontology classes. While many tools exist that run enrichment analysis for protein sets annotated with the Gene Ontology, there are only a few that can be used for small molecules enrichment analysis.ResultsWe describe BiNChE, an enrichment analysis tool for small molecules based on the ChEBI Ontology. BiNChE displays an interactive graph that can be exported as a high-resolution image or in network formats. The tool provides plain, weighted and fragment analysis based on either the ChEBI Role Ontology or the ChEBI Structural Ontology.ConclusionsBiNChE aids in the exploration of large sets of small molecules produced within Metabolomics or other Systems Biology research contexts. The open-source tool provides easy and highly interactive web access to enrichment analysis with the ChEBI ontology tool and is additionally available as a standalone library.

BackgroundUntargeted metabolomics generates a huge amount of data. Software packages for automated data processing are crucial to successfully process these data. A variety of such software packages exist, but the outcome of data processing strongly depends on algorithm parameter settings. If they are not carefully chosen, suboptimal parameter settings can easily lead to biased results. Therefore, parameter settings also require optimization. Several parameter optimization approaches have already been proposed, but a software package for parameter optimization which is free of intricate experimental labeling steps, fast and widely applicable is still missing.ResultsWe implemented the software package IPO (‘Isotopologue Parameter Optimization’) which is fast and free of labeling steps, and applicable to data from different kinds of samples and data from different methods of liquid chromatography - high resolution mass spectrometry and data from different instruments.IPO optimizes XCMS peak picking parameters by using natural, stable 13C isotopic peaks to calculate a peak picking score. Retention time correction is optimized by minimizing relative retention time differences within peak groups. Grouping parameters are optimized by maximizing the number of peak groups that show one peak from each injection of a pooled sample. The different parameter settings are achieved by design of experiments, and the resulting scores are evaluated using response surface models. IPO was tested on three different data sets, each consisting of a training set and test set. IPO resulted in an increase of reliable groups (146% - 361%), a decrease of non-reliable groups (3% - 8%) and a decrease of the retention time deviation to one third.ConclusionsIPO was successfully applied to data derived from liquid chromatography coupled to high resolution mass spectrometry from three studies with different sample types and different chromatographic methods and devices. We were also able to show the potential of IPO to increase the reliability of metabolomics data.The source code is implemented in R, tested on Linux and Windows and it is freely available for download at https://github.com/glibiseller/IPO. The training sets and test sets can be downloaded from https://health.joanneum.at/IPO.

BackgroundThe ISA-Tab format and software suite have been developed to break the silo effect induced by technology-specific formats for a variety of data types and to better support experimental metadata tracking. Experimentalists seldom use a single technique to monitor biological signals. Providing a multi-purpose, pragmatic and accessible format that abstracts away common constructs for describing I nvestigations, S tudies and A ssays, ISA is increasingly popular. To attract further interest towards the format and extend support to ensure reproducible research and reusable data, we present the Risa package, which delivers a central component to support the ISA format by enabling effortless integration with R, the popular, open source data crunching environment.ResultsThe Risa package bridges the gap between the metadata collection and curation in an ISA-compliant way and the data analysis using the widely used statistical computing environment R. The package offers functionality for: i) parsing ISA-Tab datasets into R objects, ii) augmenting annotation with extra metadata not explicitly stated in the ISA syntax; iii) interfacing with domain specific R packages iv) suggesting potentially useful R packages available in Bioconductor for subsequent processing of the experimental data described in the ISA format; and finally v) saving back to ISA-Tab files augmented with analysis specific metadata from R. We demonstrate these features by presenting use cases for mass spectrometry data and DNA microarray data.ConclusionsThe Risa package is open source (with LGPL license) and freely available through Bioconductor. By making Risa available, we aim to facilitate the task of processing experimental data, encouraging a uniform representation of experimental information and results while delivering tools for ensuring traceability and provenance tracking.Software availabilityThe Risa package is available since Bioconductor 2.11 (version 1.0.0) and version 1.2.1 appeared in Bioconductor 2.12, both along with documentation and examples. The latest version of the code is at the development branch in Bioconductor and can also be accessed from GitHub https://github.com/ISA-tools/Risa, where the issue tracker allows users to report bugs or feature requests.

Rhynchosporium commune is a haploid fungus causing scald or leaf blotch on barley, other Hordeum spp. and Bromus diandrus.TaxonomyRhynchosporium commune is an anamorphic Ascomycete closely related to the teleomorph Helotiales genera Oculimacula and Pyrenopeziza.Disease symptomsRhynchosporium commune causes scald‐like lesions on leaves, leaf sheaths and ears. Early symptoms are generally pale grey oval lesions. With time, the lesions acquire a dark brown margin with the centre of the lesion remaining pale green or pale brown. Lesions often merge to form large areas around which leaf yellowing is common. Infection frequently occurs in the leaf axil, which can lead to chlorosis and eventual death of the leaf.Life cycleRhynchosporium commune is seed borne, but the importance of this phase of the disease is not fully understood. Debris from previous crops and volunteers, infected from the stubble from previous crops, are considered to be the most important sources of the disease. Autumn‐sown crops can become infected very soon after sowing. Secondary spread of disease occurs mainly through splash dispersal of conidia from infected leaves. Rainfall at the stem extension growth stage is the major environmental factor in epidemic development.Detection and quantificationRhynchosporium commune produces unique beak‐shaped, one‐septate spores both on leaves and in culture. The development of a specific polymerase chain reaction (PCR) and, more recently, quantitative PCR (qPCR) has allowed the identification of asymptomatic infection in seeds and during the growing season.Disease controlThe main measure for the control of R. commune is the use of fungicides with different modes of action, in combination with the use of resistant cultivars. However, this is constantly under review because of the ability of the pathogen to adapt to host plant resistance and to develop fungicide resistance.

Harpin HrpZ is one of the most abundant proteins secreted through the pathogenesis‐associated type III secretion system of the plant pathogen Pseudomonas syringae. HrpZ shows membrane‐binding and pore‐forming activities in vitro, suggesting that it could be targeted to the host cell plasma membrane. We studied the native molecular forms of HrpZ and found that it forms dimers and higher order oligomers. Lipid binding by HrpZ was tested with 15 different membrane lipids, with HrpZ interacting only with phosphatidic acid. Pore formation by HrpZ in artificial lipid vesicles was found to be dependent on the presence of phosphatidic acid. In addition, HrpZ was able to form pores in vesicles prepared from Arabidopsis thaliana plasma membrane, providing evidence for the suggested target of HrpZ in the host. To map the functions associated with HrpZ, we constructed a comprehensive series of deletions in the hrpZ gene derived from P. syringae pv. phaseolicola, and studied the mutant proteins. We found that oligomerization is mainly mediated by a region near the C‐terminus of the protein, and that the same region is also essential for membrane pore formation. Phosphatidic acid binding seems to be mediated by two regions separate in the primary structure. Tobacco, a nonhost plant, recognizes, as a defence elicitor, a 24‐amino‐acid HrpZ fragment which resides in the region indispensable for the oligomerization and pore formation functions of HrpZ.