Amino acids (AAs), important constituents of natural moisturizing factors (NMFs) of the skin are decreased in diseased conditions such as psoriasis and atopic dermatitis. No study so far investigated the uptake of AAs into isolated corneocytes (COR). The present study was performed using 19 AAs, including taurine (TAU), to measure their amount diffused into the COR and binding of these AAs to keratin. Incubation of alanine, aspartic acid, asparagine, glutamine, glutamic acid, histidine, proline, serine and TAU with the isolated COR showed uptake after 24 h of 51.6, 95.4, 98.6, 94.1, 95.6, 90.1, 94.6, 72.9 and 57.8 %, respectively, into the COR but no binding with keratin. Uptake of TAU was validated by time dependent in-vitro diffusion models 'without COR and 'with COR'. The time dependent curve fitting showed that in in-vitro diffusion model 'without COR' there was no change in the total concentration of TAU until 72 hours, while in diffusion model 'with COR' the total conc. decreased to 37.8 % after 72 hours. The Pearson's correlation coefficient 'r' between the conc. curves of both in-vitro diffusion models was -0.54 that was an evidence of significant amount of TAU uptake by the COR. AAs as part of the NMFs have a great potential to be diffused into the COR. This property of the AAs can be employed in further dermatological research on diseased or aged skin conditions with NMFs deficiency.

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.

Hydnora abyssinica A.Br. (Hydnoraceae), a holoparasitic herb, is for the first time recorded for Abyan governorate of South Yemen. Flowers of this species were studied for their ethnobotanical, biological and chemical properties for the first time. In South Yemen, they are traditionally used as wild food and to cure stomach diseases, gastric ulcer and cancer. Phytochemical analysis of the extracts showed the presence of terpenes, tannins, phenols, and flavonoids. The volatile components of the air-dried powdered flowers were identified using a static headspace GC/MS analysis as acetic acid, ethyl acetate, sabinene, α-terpinene, (+)-D-limonene and γ-terpinene. These volatile compounds that characterize the odor and taste of the flowers were detected for the first time in a species of the family Hydnoraceae. The flowers were extracted by n-hexane, dichlormethane, ethyl acetate, ethanol, methanol and water. With exception of the water extract all extracts demonstrated activities against Gram-positive bacteria as well as remarkable radical scavenging activities in DPPH assay. Ethyl acetate, methanol and water extracts exhibited good antifungal activities. The cytotoxic activity of the extracts against FL cells, measured in neutral red assay, was only weak (IC50 > 500 μg /mL). The results justify the traditional use of the flowers of Hydnora abyssinica in South Yemen.

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.

BackgroundMass spectrometry has become the analytical method of choice in metabolomics research. The identification of unknown compounds is the main bottleneck. In addition to the precursor mass, tandem MS spectra carry informative fragment peaks, but the coverage of spectral libraries of measured reference compounds are far from covering the complete chemical space. Compound libraries such as PubChem or KEGG describe a larger number of compounds, which can be used to compare their in silico fragmentation with spectra of unknown metabolites.ResultsWe created the MetFrag suite to obtain a candidate list from compound libraries based on the precursor mass, subsequently ranked by the agreement between measured and in silico fragments. In the evaluation MetFrag was able to rank most of the correct compounds within the top 3 candidates returned by an exact mass query in KEGG. Compared to a previously published study, MetFrag obtained better results than the commercial MassFrontier software. Especially for large compound libraries, the candidates with a good score show a high structural similarity or just different stereochemistry, a subsequent clustering based on chemical distances reduces this redundancy. The in silico fragmentation requires less than a second to process a molecule, and MetFrag performs a search in KEGG or PubChem on average within 30 to 300 seconds, respectively, on an average desktop PC.ConclusionsWe presented a method that is able to identify small molecules from tandem MS measurements, even without spectral reference data or a large set of fragmentation rules. With today's massive general purpose compound libraries we obtain dozens of very similar candidates, which still allows a confident estimate of the correct compound class. Our tool MetFrag improves the identification of unknown substances from tandem MS spectra and delivers better results than comparable commercial software. MetFrag is available through a web application, web services and as java library. The web frontend allows the end-user to analyse single spectra and browse the results, whereas the web service and console application are aimed to perform batch searches and evaluation.

BackgroundLiquid chromatography coupled to mass spectrometry (LC-MS) has become a prominent tool for the analysis of complex proteomics and metabolomics samples. In many applications multiple LC-MS measurements need to be compared, e. g. to improve reliability or to combine results from different samples in a statistical comparative analysis. As in all physical experiments, LC-MS data are affected by uncertainties, and variability of retention time is encountered in all data sets. It is therefore necessary to estimate and correct the underlying distortions of the retention time axis to search for corresponding compounds in different samples. To this end, a variety of so-called LC-MS map alignment algorithms have been developed during the last four years. Most of these approaches are well documented, but they are usually evaluated on very specific samples only. So far, no publication has been assessing different alignment algorithms using a standard LC-MS sample along with commonly used quality criteria.ResultsWe propose two LC-MS proteomics as well as two LC-MS metabolomics data sets that represent typical alignment scenarios. Furthermore, we introduce a new quality measure for the evaluation of LC-MS alignment algorithms. Using the four data sets to compare six freely available alignment algorithms proposed for the alignment of metabolomics and proteomics LC-MS measurements, we found significant differences with respect to alignment quality, running time, and usability in general.ConclusionThe multitude of available alignment methods necessitates the generation of standard data sets and quality measures that allow users as well as developers to benchmark and compare their map alignment tools on a fair basis. Our study represents a first step in this direction. Currently, the installation and evaluation of the "correct" parameter settings can be quite a time-consuming task, and the success of a particular method is still highly dependent on the experience of the user. Therefore, we propose to continue and extend this type of study to a community-wide competition. All data as well as our evaluation scripts are available at http://msbi.ipb-halle.de/msbi/caap.