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.

Calcium (Ca2+) ions play pivotal roles as second messengers in intracellular signal transduction, and coordinate many biological processes. Changes in intracellular Ca2+ levels are perceived by Ca2+ sensors such as calmodulin (CaM) and CaM-like (CML) proteins, which transduce Ca2+ signals into cellular responses by regulation of diverse target proteins. Insights into molecular functions of CaM targets are thus essential to understand the molecular and cellular basis of Ca2+ signaling. During the last decade, IQ67-domain (IQD) proteins emerged as the largest class of CaM targets in plants with mostly unknown functions. In the March issue of Plant Physiology, we presented the first comprehensive characterization of the 33-membered IQD family in Arabidopsis thaliana. We showed, by analysis of the subcellular localization of translational green fluorescent protein (GFP) fusion proteins, that most IQD members label microtubules (MTs), and additionally often localize to the cell nucleus or to membranes, where they recruit CaM Ca2+ sensors. Important functions at MTs are supported by altered MT organization and plant growth in IQD gain-of-function lines. Because IQD proteins share structural hallmarks of scaffold proteins, we propose roles of IQDs in the assembly of macromolecular complexes to orchestrate Ca2+ CaM signaling from membranes to the nucleus. Interestingly, expression of several IQDs is regulated by auxin, which suggests functions of IQDs as hubs in cellular auxin and calcium signaling to regulate plant growth and development.

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.

Expression takes place for most of the jasmonic acid (JA)-induced genes in a COI1-dependent manner via perception of its conjugate JA-Ile in the SCFCOI1-JAZ co-receptor complex. There are, however, numerous genes and processes, which are preferentially induced COI1-independently by the precursor of JA, 12-oxo-phytodienoic acid (OPDA). After recent identification of the Ile-conjugate of OPDA, OPDA-Ile, biological activity of this compound could be unequivocally proven in terms of gene expression. Any interference of OPDA, JA, or JA-Ile in OPDA-Ile-induced gene expression could be excluded by using different genetic background. The data suggest individual signaling properties of OPDA-Ile. Future studies for analysis of an SCFCOI1-JAZ co-receptor-independent route of signaling are proposed.

AvrRpt2 is one of the first Pseudomonas syringae effector proteins demonstrated to be delivered into host cells. It suppresses plant immunity by modulating auxin signaling and cleavage of the membrane-localized defense regulator RIN4. We recently uncovered a novel potential virulence function of AvrRpt2, where it specifically blocked activation of mitogen-activated protein kinases, MPK4 and MPK11, but not of MPK3 and MPK6. Putative AvrRpt2 homologs from different phytopathogens and plant-associated bacteria showed distinct activities with respect to MPK4/11 activation suppression and RIN4 cleavage. Apart from differences in sequence similarity, 3 of the analyzed homologs were apparently “truncated.” To examine the role of the AvrRpt2 N-terminus, we modeled the structures of these AvrRpt2 homologs and performed deletion and domain swap experiments. Our results strengthen the finding that RIN4 cleavage is irrelevant for the ability to suppress defense-related MPK4/11 activation and indicate that full protease activity or cleavage specificity is affected by the N-terminus.

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.

Out of the 34 members of the VQ-motif-containing protein (VQP) family, 10 are phosphorylated by the mitogen-activated protein kinases (MAPKs), MPK3 and MPK6. Most of these MPK3/6-targeted VQPs (MVQs) interacted with specific sub-groups of WRKY transcription factors in a VQ-motif-dependent manner. In some cases, the MAPK appears to phosphorylate either the MVQ or the WRKY, while in other cases, both proteins have been reported to act as MAPK substrates. We propose a network of dynamic interactions between members from the MAPK, MVQ and WRKY families – either as binary or as tripartite interactions. The compositions of the WRKY-MVQ transcriptional protein complexes may change – for instance, through MPK3/6-mediated modulation of protein stability – and therefore control defense gene transcription.

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.