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A key analysis task in mass spectrometry proteomics is matching the acquired tandem mass spectra to their originating peptides by sequence database searching or spectral library searching. Machine learning is an increasingly popular postprocessing approach to maximize the number of confident spectrum identifications that can be obtained at a given false discovery rate threshold. Here, we have integrated semisupervised machine learning in the ANN-SoLo tool, an efficient spectral library search engine that is optimized for open modification searching to identify peptides with any type of post-translational modification. We show that machine learning rescoring boosts the number of spectra that can be identified for both standard searching and open searching, and we provide insights into relevant spectrum characteristics harnessed by the machine learning model. The semisupervised machine learning functionality has now been fully integrated into ANN-SoLo, which is available as open source under the permissive Apache 2.0 license on GitHub at https://github.com/bittremieux/ANN-SoLo.

Motivation The T-cell receptor (TCR) determines the specificity of a T-cell towards an epitope. As of yet, the rules for antigen recognition remain largely undetermined. Current methods for grouping TCRs according to their epitope specificity remain limited in performance and scalability. Multiple methodologies have been developed, but all of them fail to efficiently cluster large datasets exceeding 1 million sequences. To account for this limitation, we developed ClusTCR, a rapid TCR clustering alternative that efficiently scales up to millions of CDR3 amino acid sequences, without knowledge about their antigen specificity. Results Benchmarking comparisons revealed similar accuracy of ClusTCR as compared to other TCR clustering methods, as measured by cluster retention, purity and consistency. ClusTCR offers a drastic improvement in clustering speed, which allows the clustering of millions of TCR sequences in just a few minutes through ultraefficient similarity searching and sequence hashing. Availability and implementation ClusTCR was written in Python 3. It is available as an anaconda package (https://anaconda.org/svalkiers/clustcr) and on github (https://github.com/svalkiers/clusTCR). Supplementary information Supplementary data are available at Bioinformatics online.

The outbreak of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of the coronavirus 2019 disease, has led to an ongoing global pandemic since 2019. Mass spectrometry can be used to understand the molecular mechanisms of viral infection by SARS-CoV-2, for example, by determining virus-host protein-protein interactions through which SARS-CoV-2 hijacks its human hosts during infection, and to study the role of post-translational modifications. We have reanalyzed public affinity purification-mass spectrometry data using open modification searching to investigate the presence of post-translational modifications in the context of the SARS-CoV-2 virus-host protein-protein interaction network. Based on an over twofold increase in identified spectra, our detected protein interactions show a high overlap with independent mass spectrometry-based SARS-CoV-2 studies and virus-host interactions for alternative viruses, as well as previously unknown protein interactions. In addition, we identified several novel modification sites on SARS-CoV-2 proteins that we investigated in relation to their interactions with host proteins. A detailed analysis of relevant modifications, including phosphorylation, ubiquitination, and S-nitro-sylation, provides important hypotheses about the func-tional role of these modifications during viral infection by SARS-CoV-2.

For differential expression studies in all omics disciplines, data normalization is a crucial step that is often subject to a balance between speed and effectiveness. To keep up with the data produced by high-throughput instruments, researchers require fast and easy-to-use yet effective methods that fit into automated analysis pipelines. The CONSTANd normalization method meets these criteria, so we have made its source code available for R/BioConductor and Python. We briefly review the method and demonstrate how it can be used in different omics contexts for experiments of any scale. Widespread adoption across omics disciplines would ease data integration in multiomics experiments.

The SARS-CoV-2 virus is the causative agent of the 2020 pandemic leading to the COVID-19 respiratory disease. With many scientific and humanitarian efforts ongoing to develop diagnostic tests, vaccines, and treatments for COVID-19, and to prevent the spread of SARS-CoV-2, mass spectrometry research, including proteomics, is playing a role in determining the biology of this viral infection. Proteomics studies are starting to lead to an understanding of the roles of viral and host proteins during SARS-CoV-2 infection, their protein–protein interactions, and post-translational modifications. This is beginning to provide insights into potential therapeutic targets or diagnostic strategies that can be used to reduce the long-term burden of the pandemic. However, the extraordinary situation caused by the global pandemic is also highlighting the need to improve mass spectrometry data and workflow sharing. We therefore describe freely available data and computational resources that can facilitate and assist the mass spectrometry-based analysis of SARS-CoV-2. We exemplify this by reanalyzing a virus–host interactome data set to detect protein–protein interactions and identify host proteins that could potentially be used as targets for drug repurposing.

Auranofin (AF) is an FDA-approved antirheumatic drug with anticancer properties that acts as a thioredoxin reductase 1 (TrxR) inhibitor. The exact mechanisms through which AF targets cancer cells remain elusive. To shed light on the mode of action, this study provides an in-depth analysis on the molecular mechanisms and immunogenicity of AF-mediated cytotoxicity in the non-small cell lung cancer (NSCLC) cell line NCI–H1299 (p53 Null) and its two isogenic derivates with mutant p53 R175H or R273H accumulation. TrxR is highly expressed in a panel of 72 NSCLC patients, making it a valid druggable target in NSCLC for AF. The presence of mutant p53 overexpression was identified as an important sensitizer for AF in (isogenic) NSCLC cells as it was correlated with reduced thioredoxin (Trx) levels in vitro. Transcriptome analysis revealed dysregulation of genes involved in oxidative stress response, DNA damage, granzyme A (GZMA) signaling and ferroptosis. Although functionally AF appeared a potent inhibitor of GPX4 in all NCI–H1299 cell lines, the induction of lipid peroxidation and consequently ferroptosis was limited to the p53 R273H expressing cells. In the p53 R175H cells, AF mainly induced large-scale DNA damage and replication stress, leading to the induction of apoptotic cell death rather than ferroptosis. Importantly, all cell death types were immunogenic since the release of danger signals (ecto-calreticulin, ATP and HMGB1) and dendritic cell maturation occurred irrespective of (mutant) p53 expression. Finally, we show that AF sensitized cancer cells to caspase-independent natural killer cell-mediated killing by downregulation of several key targets of GZMA. Our data provides novel insights on AF as a potent, clinically available, off-patent cancer drug by targeting mutant p53 cancer cells through distinct cell death mechanisms (apoptosis and ferroptosis). In addition, AF improves the innate immune response at both cytostatic (natural killer cell-mediated killing) and cytotoxic concentrations (dendritic cell maturation)., Graphical abstract Image 1

Herniaria hirsuta L. (Caryophyllaceae) is used for treatment of urinary stones and as a diuretic. Little is known about the active compounds and the mechanism of action. The phytochemical composition of H. hirsuta was comprehensively characterized using UHPLC-UV-HRMS (Ultrahigh-Performance Liquid Chromatography-Ultraviolet-High Resolution Mass Spectrometry) data. An in vitro gastrointestinal model was used to simulate biotransformation, which allowed the monitoring of the relative abundances of individual compounds over time. To analyze the longitudinal multiclass LC&ndash, MS data, XCMS, a platform that enables online metabolomics data processing and interpretation, and EDGE, a statistical method for time series data, were used to extract significant differential profiles from the raw data. An interactive Shiny app in R was used to rate the quality of the resulting features. These ratings were used to train a random forest model. The most abundant aglycone after gastrointestinal biotransformation was subjected to hepatic biotransformation using human S9 fractions. A diversity of compounds was detected, mainly saponins and flavonoids. Besides the known saponins, 15 new saponins were tentatively identified as glycosides of medicagenic acid, acetylated medicagenic acid and zanhic acid. It is suggested that metabolites of phytochemicals present in H. hirsuta, most likely saponins, are responsible for the pharmaceutical effects. It was observed that the relative abundance of saponin aglycones increased, indicating loss of sugar moieties during colonic biotransformation, with medicagenic acid as the most abundant aglycone. Hepatic biotransformation of this aglycone resulted in different metabolites formed by phase I and II reactions.

Despite the increasing importance of non-targeted metabolomics to answer various life science questions, extracting biochemically relevant information from metabolomics spectral data is still an incompletely solved problem. Most computational tools to identify tandem mass spectra focus on a limited set of molecules of interest. However, such tools are typically constrained by the availability of reference spectra or molecular databases, limiting their applicability to identify unknown metabolites. In contrast, recent advances in the field illustrate the possibility to expose the underlying biochemistry without relying on metabolite identification, in particular via substructure prediction. We describe an automated method for substructure recommendation motivated by association rule mining. Our framework captures potential relationships between spectral features and substructures learned from public spectral libraries. These associations are used to recommend substructures for any unknown mass spectrum. Our method does not require any predefined metabolite candidates, and therefore it can be used for the partial identification of unknown unknowns. The method is called MESSAR (MEtabolite SubStructure Auto-Recommender) and is implemented in a free online web service available at messar.biodatamining.be.Author SummaryMass spectrometry is one of most used techniques to detect and identify metabolites. However, learning metabolite structures directly from mass spectrometry data has always been a challenging task. Thousands of mass spectra from various biological systems still remain unanalyzed simply because no current bioinformatic tools are able to generate structural hypotheses. By manually studying mass spectra of standard compounds, chemists discovered that metabolites that share common substructures can also share spectral features. As data scientists, we believe that such relationships can be unraveled from massive structure and spectra data by machine learning. In this study, we adapted “association rule mining”, traditionally used in market basket analysis, to structural and spectral data, allowing us to investigate all spectral features - metabolite substructures relationships. We further collected all statistically sound relationships into a database and used them to assign substructral hypotheses to unexplored spectra. We named our approach MESSAR, MEtabolite SubStructure Auto-Recommender, available to the metabolomics and mass spectrometry community as a free and open web service.

Tris (2-butoxyethyl) phosphate (TBOEP) is a compound produced at high volume that is used as both a flame retardant and a plasticizer. It is persistent and bioaccumulative, yet little is known of its toxicological modes of action. Such insight may aid risk assessment in a weight-of-evidence approach supplementing current testing strategies. We used an RNA sequencing approach as an unbiased and sensitive tool to explore potential negative health effects of sub-cytotoxic concentrations of TBOEP on the transcriptome of the human liver hepatocellular carcinoma cell line, HepG2, with the lowest concentration used potentially holding relevance to human physiological levels. Over-representation and gene set enrichment analysis corresponded well and revealed that TBOEP treatments resulted in an upregulation of genes involved in protein and energy metabolism, along with DNA replication. Such increases in cell and macromolecule metabolism could explain the increase in mitochondrial activity at lower TBOEP concentrations. In addition, TBOEP affected a wide variety of biological processes, the most notable one being the general stress response, wound healing. Finally, TBOEP showed effects on steroid hormone biosynthesis and activation, regulation, and potentiation of immune responses, in agreement with other studies. As such, this study is the first study investigating genome-wide changes in gene transcription in response to TBOEP in human cells.

Metabolites represent the most downstream information of the cellular organisation. Hence, metabolomics experiments are extremely valuable to unravel the endogenous pathways involved in a toxicological mode of action. However, every external stimulus can introduce alterations in the cell homeostasis, thereby obscuring the involved endogenous pathways, biasing the interpretation of the results. Here we report on sodium saccharin, which is considered to be not hepatotoxic and therefore can serve as a reference compound to detect metabolic alterations that are not related to liver toxicity. Exposure of HepaRG cells to high levels of sodium saccharin (&gt, 10 mM) induced cell death, probably due to an increase in the osmotic pressure. Yet, a low number (n = 15) of significantly altered metabolites were also observed in the lipidome, including a slight decrease in phospholipids and an increase in triacylglycerols, upon daily exposure to 5 mM sodium saccharin for 72 h. The observation that a non-hepatotoxic compound can affect the metabolome underpins the importance of correct experimental design and data interpretation when investigating toxicological modes of action via metabolomics.

Open modification searching (OMS) is a powerful search strategy to identify peptides with any type of modification. OMS works by using a very wide precursor mass window to allow modified spectra to match against their unmodified variants, after which the modification types can be inferred from the corresponding precursor mass differences. A disadvantage of this strategy, however, is the large computational cost, because each query spectrum has to be compared against a multitude of candidate peptides. We have previously introduced the ANN-SoLo tool for fast and accurate open spectral library searching. ANN-SoLo uses approximate nearest neighbor indexing to speed up OMS by selecting only a limited number of the most relevant library spectra to compare to an unknown query spectrum. Here we demonstrate how this candidate selection procedure can be further optimized using graphics processing units. Additionally, we introduce a feature hashing scheme to convert high-resolution spectra to low-dimensional vectors. On the basis of these algorithmic advances, along with low-level code optimizations, the new version of ANN-SoLo is up to an order of magnitude faster than its initial version. This makes it possible to efficiently perform open searches on a large scale to gain a deeper understanding about the protein modification landscape. We demonstrate the computational efficiency and identification performance of ANN-SoLo based on a large data set of the draft human proteome. ANN-SoLo is implemented in Python and C++. It is freely available under the Apache 2.0 license at https://github.com/bittremieux/ANN-SoLo. This document is the unedited Author's version of a Submitted Work that was subsequently accepted for publication in the Journal of Proteome Research, copyright © American Chemical Society after peer review. To access the final edited and published work see https://pubs.acs.org/articlesonrequest/AOR-TCA25r6IZJVZDTDN337H.

In the context of omics disciplines and especially proteomics and biomarker discovery, the analysis of a clinical sample using label-based tandem mass spectrometry (MS) can be affected by sample preparation effects or by the measurement process itself, resulting in an incorrect outcome. Detection and correction of these mistakes using state-of-the-art methods based on, f.i., mixed models can take a lot of (computing) time. MS-based proteomics labs are high-throughput and need to avoid a bottleneck in their quantitative pipeline by quickly discriminating between high- and low-quality data. To this end we developed an easy-to-use web-tool called QCQuan (available at https://qcquan.net/) which is built around the CONSTANd normalization algorithm. It automatically provides the user with exploratory and quality control information as well as a differential expression analysis based on conservative, simple statistics. In this document we describe in detail the scientifically relevant steps that constitute the workflow, and assess its qualitative and quantitative performance on three reference data sets. We find that QCQuan provides clear and accurate indications about the scientific value of both a high- and a low-quality data set. Moreover, it performed quantitatively better on a third data set than a comparable workflow assembled using established, reliable software.

Motivation The T-cell receptor (TCR) is responsible for recognizing epitopes presented on cell surfaces. Linking TCR sequences to their ability to target specific epitopes is currently an unsolved problem, yet one of great interest. Indeed, it is currently unknown how dissimilar TCR sequences can be before they no longer bind the same epitope. This question is confounded by the fact that there are many ways to define the similarity between two TCR sequences. Here we investigate both issues in the context of TCR sequence unsupervised clustering. Results We provide an overview of the performance of various distance metrics on two large independent datasets with 412 and 2835 TCR sequences respectively. Our results confirm the presence of structural distinct TCR groups that target identical epitopes. In addition, we put forward several recommendations to perform unsupervised T-cell receptor sequence clustering. Availability and implementation Source code implemented in Python 3 available at https://github.com/pmeysman/TCRclusteringPaper. Supplementary information Supplementary data are available at Bioinformatics online.

Although some herbal remedies have been used for decades, little is known about the active compounds and the mechanism of action. Many natural products, such as glycosides, can be considered as prodrugs, which become active after biotransformation. To optimize the workflow of in vitro biotransformation followed by automated data analysis, hederacoside C was used as a model compound for saponins. Hederacoside C was subjected to gastrointestinal enzymes and fecal microflora. Samples were analyzed with UHPLC-PDA-HRMS before, during and after in vitro biotransformation, which allowed the monitoring of the relative abundances of the compound and its metabolites. The data-analysis workflow was optimized to render as much information as possible from the longitudinal LCMS data. XCMS was used to convert the raw data into features via peak-picking, followed by grouping, and EDGE was used for the extraction of significant differential profiles. To evaluate if the workflow was suitable for dynamic multiclass metabolomics data, an interactive Shiny web app was developed in R to rate the quality of the resulting features. These ratings were used to train a random forest model for predicting experts response. A performance analysis revealed that the random forest model was capable of correctly predicting the reviewers response in most cases (AUC 0.926 with 10 fold cross validation). The automated data analysis workflow was used for unbiased screening for metabolites and revealed the biotransformation of hederacoside C. As expected, a decrease in relative abundance of hederacoside C was observed over time. Additionally, the relative abundance of metabolites increased, illustrating the biotransformation of hederacoside C, especially in the colon phase, where microbial fermentation takes place. Stepwise progressive elimination of sugar moieties was the major metabolic pathway.

Data analysis for metabolomics is undergoing rapid progress thanks to the proliferation of novel tools and the standardization of existing workflows. However, as datasets and experiments continue to increase in size and complexity, standardized workflows are often not sufficient. In addition, as the ground truth for metabolomics experiments is intrinsically unknown, there is no way to critically evaluate the performance of tools. Here, we investigate the problem of dynamic multi-class metabolomics experiments using a simulated dataset with a known ground truth and evaluate the performance of tinderesting, a new and intuitive tool based on gathering expert knowledge to be used in machine learning, and compare it to EDGE, a statistical method for sequence data. This paper presents three novel outcomes. First we present a way to simulate dynamic metabolomics data with a known ground truth based on ordinary differential equations. This method is made available through the MetaboLouise R package. Second, we show that the EDGE tool, originally developed for genomics data analysis, is highly performant in analyzing dynamic case vs control metabolomics data. Last, we introduce the tinderesting method to analyse more complex dynamic metabolomics experiments that performs on par with statistical methods. This tool consists of a Shiny app for collecting expert knowledge, which in turn is used to train a machine learning model to emulate the decision process of the expert. This approach does not replace traditional data analysis workflows for metabolomics, but can provide additional information, improved performance or easier interpretation of results. The advantage is that the tool is agnostic to the complexity of the experiment, and thus is easier to use in advanced setups. All code for the presented analysis, MetaboLouise and tinderesting are freely available.

Searching for interesting common subgraphs in graph data is a well-studied problem in data mining. Subgraph mining techniques focus on the discovery of patterns in graphs that exhibit a specific network structure that is deemed interesting within these data sets. The definition of which subgraphs are interesting and which are not is highly dependent on the application. These techniques have seen numerous applications and are able to tackle a range of biological research questions, spanning from the detection of common substructures in sets of biomolecular compounds, to the discovery of network motifs in large-scale molecular interaction networks. Thus far, information about the bioinformatics application of subgraph mining remains scattered over heterogeneous literature. In this review, we provide an introduction to subgraph mining for life scientists. We give an overview of various subgraph mining algorithms from a bioinformatics perspective and present several of their potential biomedical applications. Electronic supplementary material The online version of this article (10.1186/s13040-018-0181-9) contains supplementary material, which is available to authorized users.

The inaugural European Bioinformatics Community (EuBIC) developer's meeting was held from January 9th to January 12th 2018 in Ghent, Belgium. While the meeting kicked off with an interactive keynote session featuring four internationally renowned experts in the field of computational proteomics, its primary focus were the hands-on hackathon sessions which featured six community-proposed projects revolving around three major topics: (i) quality control; (ii) workflows, protocols, and guidelines; and (iii) quantification. Here, we present an overview of the scientific program of the EuBIC developer's meeting and provide a starting point for follow-up on the covered projects.

The flame retardant, 9,10-dihydro-9-oxa-10-phosphaphenanthrene 10-oxide (DOPO), has been receiving great interest given its superior fire protection properties, and its predicted low level of persistence, bioaccumulation, and toxicity. However, empirical toxicological data that are essential for a complete hazard assessment are severely lacking. In this study, we attempted to identify the potential toxicological modes of action by transcriptome (RNA-seq) profiling of the human liver hepatocellular carcinoma cell line, HepG2. Such insight may help in identifying compounds of concern and potential toxicological phenotypes. DOPO was found to have little cytotoxic potential, with lower effective concentrations compared to other flame retardants studied in the same cell line. Differentially expressed genes revealed a wide range of molecular effects including changes in protein, energy, DNA, and lipid metabolism, along with changes in cellular stress response pathways. In response to 250 μM DOPO, the most perturbed biological processes were fatty acid metabolism, androgen metabolism, glucose transport, and renal function and development, which is in agreement with other studies that observed similar effects of other flame retardants in other species. However, treatment with 2.5 μM DOPO resulted in very few differentially expressed genes and failed to indicate any potential effects on biology, despite such concentrations likely being orders of magnitude greater than would be encountered in the environment. This, together with the low levels of cytotoxicity, supports the potential replacement of the current flame retardants by DOPO, although further studies are needed to establish the nephrotoxicity and endocrine disruption of DOPO.

The main result of a great deal of the published proteomics studies is a list of identified proteins, which then needs to be interpreted in relation to the research question and existing knowledge. In the early days of proteomics this interpretation was only based on expert insights, acquired by digesting a large amount of relevant literature. With the growing size and complexity of the experimental datasets, many computational techniques, databases and tools have claimed a central role in this task. In this review we discuss commonly and less commonly used methods to functionally interpret experimental proteome lists and compare them with available knowledge. We first address several functional analysis and enrichment techniques based on ontologies and literature. Then we outline how various types of network and pathway information can be used. While the problem of functional interpretation of proteome data is to an extent equivalent to the interpretation of transcriptome or other omics data, this paper addresses some of the specific challenges and solutions of the proteomics field.

All 20.000 different fish species vary greatly in their ability to tolerate and survive fluctuating oxygen concentrations in the water. Especially fish of the genus Carassius, e.g. the crucian carp and the goldfish, exhibit a remarkable tolerance to limited/absent oxygen concentrations. The metabolic changes of anoxia-tolerant crucian carp were recently studied and published. Contrary to crucian carp, the hypoxia-tolerant common carp cannot survive a complete lack of oxygen (anoxia). Therefore, we studied the 1H-NMR-based metabolomics of brain, heart, liver and white muscle extracts of common carp, subjected to anoxia (0 mg O2 l−1) and hypoxia (0.9 mg O2 l−1) at 5 °C. Specifically, fish were exposed to normoxia (i.e. 9 mg O2 l−1; controls 24 h, 1 week and 2 weeks), acute hypoxia (24 h), chronic hypoxia (1 week) and chronic hypoxia (1 week) with normoxic reoxygenation (1 week). Additionally, we also investigated the metabolic responses of fish to anoxia for 2 h. Both anoxia and hypoxia significantly changed the tissue levels of standard energy metabolites as lactate, glycogen, ATP/ADP and phosphocreatine. Remarkably, anoxia induced increased lactate levels in all tissues except for the heart whereas hypoxia resulted in decreased lactate concentrations in all tissues except for brains. Furthermore, hypoxia and anoxia influenced amino acids (alanine, valine/(iso)leucine) and neurotransmitters levels (GABA, glutamate). Lastly, we also detected other i.e. previously not reported compounds to play a role in the present context. Scyllo-inositol levels changed significantly in heart, liver and muscle, providing novel insights into the anoxia/hypoxic responses of the common carp.

One of the most significant challenges in the comparative analysis of Nuclear Magnetic Resonance (NMR) metabolome profiles is the occurrence of shifts between peaks across different spectra, for example caused by fluctuations in pH, temperature, instrument factors and ion content. Proper alignment of spectral peaks is therefore often a crucial preprocessing step prior to downstream quantitative analysis. Various alignment methods have been developed specifically for this purpose. Other methods were originally developed to align other data types (GC, LC, SELDI-MS, etc.), but can also be applied to NMR data. This review discusses the available methods, as well as related problems such as reference determination or the evaluation of alignment quality. We present a generic alignment framework that allows for comparison and classification of different alignment approaches according to their algorithmic principles, and we discuss their performance.

Withaferin A (WA), a natural phytochemical derived from the plant Withania somnifera, is a well-studied bioactive compound exerting a broad spectrum of health promoting effects. To gain better insight in the potential therapeutic capacity of WA, we evaluated the transcriptional effects of WA on primary human umbilical vein endothelial cells (HUVECs) and an endothelial cell line (EA.hy926). RNA microarray analysis of WA treated HUVEC cells demonstrated increased expression of the antioxidant gene heme oxygenase (HO-1). Transcriptional regulation of this gene is strongly dependent on the transcription factor NF-E2-related factor 2 (Nrf2), which senses chemical changes in the cell and coordinates transcriptional responses to maintain chemical homeostasis via expression of antioxidant genes and cytoprotective Phase II detoxifying enzymes. Under normal conditions, Nrf2 is kept in the cytoplasm by Kelch-like ECH-associated protein 1 (Keap1), an adaptor protein controlling the half-life of Nrf2 via constant proteasomal degradation. In this study we demonstrate that WA time- and concentration-dependently induces HO-1 expression in endothelial cells via upregulation and increased nuclear translocation of Nrf2. According to the crucial negative regulatory role of Keap1 in Nrf2 expression levels, a direct interaction of WA with Keap1 could be demonstrated. In vitro and in silico evaluations suggest that specific cysteine residues in Keap1 might be involved in the interaction with WA.

A novel feature selection method to extract multiple adjacent solutions for viral genomic sequences classification Background Leveraging improvements of next generation technologies, genome sequencing of several samples in different conditions led to an exponential growth of biological sequences. However, these collections are not easily treatable by biologists to obtain a thorough data characterization and require a high cost-time investment. Therefore, computing strategies and specifically automatic knowledge extraction methods that optimize the analysis focusing on what data are meaningful and should be sequenced are essential [1]. Methods Here, we present a new feature-selection algorithm based on mixed integer programming methods [2] able to extract multiple and adjacent solutions for supervised learning problems applied to biological data. We focus on those problems where the relative position of a feature (i.e., nucleotide locus) is relevant. In particular, we aim to find sets of distinctive features, which are as close as possible to each other and which appear with the same required characteristics. Our algorithm adopts a fast and effective method to evaluate the quality of the extracted sets of features and it has been successfully integrated in a rule-based classification framework [3]. Results Our algorithm has been applied to three viral datasets (i.e., Rhino-, Influenza-, Polyomaviruses [4-6]) and enables to extract all the alternative solutions of virus specimen to species assignments, by identifying portions of sequence that are discriminant, compact, and as shorter as possible. To conclude, we succeeded in extracting a wide set of equivalent classification rules, focusing on short regions of sequences with high reliability and low computational time, in order to provide the biologists with short and highly informative genome parts to be sequenced, as well as a powerful instrument both scientifically and diagnostically, e.g., for automatic virus detection.

With the current expanded technical capabilities to perform mass spectrometry-based biomedical proteomics experiments, an improved focus on the design of experiments is crucial. As it is clear that ignoring the importance of a good design leads to an unprecedented rate of false discoveries which would poison our results, more and more tools are developed to help researchers designing proteomic experiments. In this review, we apply statistical thinking to go through the entire proteomics workflow for biomarker discovery and validation and relate the considerations that should be made at the level of hypothesis building, technology selection, experimental design and the optimization of the experimental parameters. peerreview_statement: The publishing and review policy for this title is described in its Aims & Scope. aims_and_scope_url: http://www.tandfonline.com/action/journalInformation?show=aimsScope&journalCode=ieru20 ispartof: Expert Review of Proteomics vol:13 issue:5 pages:495-511 ispartof: location:England status: published

Despite many technological and computational advances, the results of a mass spectrometry proteomics experiment are still subject to a large variability. For the understanding and evaluation of how technical variability affects the results of an experiment, several computationally derived quality control metrics have been introduced. However, despite the availability of these metrics, a systematic approach to quality control is often still lacking because the metrics are not fully understood and are hard to interpret. Here, we present a toolkit of powerful techniques to analyze and interpret multivariate quality control metrics to assess the quality of mass spectrometry proteomics experiments. We show how unsupervised techniques applied to these quality control metrics can provide an initial discrimination between low-quality experiments and high-quality experiments prior to manual investigation. Furthermore, we provide a technique to obtain detailed information on the quality control metrics that are related to the decreased performance, which can be used as actionable information to improve the experimental setup. Our toolkit is released as open-source and can be downloaded from https://bitbucket.org/proteinspector/qc_analysis/.

Affinity purificationmass spectrometry is one of the most common techniques for the analysis of proteinprotein interactions, but inferring bona fide interactions from the resulting data sets remains notoriously difficult. We introduce SFINX, a Straightforward Filtering INdeX that identifies true-positive protein interactions in a fast, user-friendly, and highly accurate way. SFINX outperforms alternative techniques on two benchmark data sets and is available via the Web interface at http://sfinx.ugent.be/.

Nuclear Magnetic Resonance (NMR) spectroscopy is, together with liquid chromatography-mass spectrometry (LC-MS), the most established platform to perform metabolomics. In contrast to LC-MS however, NMR data is predominantly being processed with commercial software. This has the effect that its data processing remains tedious and dependent on user interventions. As a follow-up to speaq, a previously released workflow for NMR spectral alignment and quantitation, we present speaq 2.0. This completely revised framework to automatically analyze 1D NMR spectra uses wavelets to efficiently summarize the raw spectra with minimal information loss or user interaction. The tool offers a fast and easy workflow that starts with the common approach of peak-picking, followed by grouping. This yields a matrix consisting of features, samples and peak values that can be conveniently processed either by using included multivariate statistical functions or by using many other recently developed methods for NMR data analysis. speaq 2.0 facilitates robust and high-throughput metabolomics based on 1D NMR but is also compatible with other NMR frameworks or complementary LC-MS workflows. The methods are benchmarked using two publicly available datasets. speaq 2.0 is distributed through the existing speaq R package to provide a complete solution for NMR data processing. The package and the code for the presented case studies are freely available on CRAN (https://cran.r-project.org/package=speaq) and GitHub (https://github.com/beirnaert/speaq).Author summaryWe present speaq 2.0: a user friendly workflow for processing NMR spectra quickly and easily. By limiting the need for user interaction and allowing the construction of workflows by combining R functions, metabolomics data analysis becomes fully reproducible and shareable. Such advances are critical for the future of the metabolomics field as it needs to move towards a fully open-science approach. This is no trivial goal as many researchers are still using black-box commercial software that often requires manually doing several steps, thus hampering reproducibility. To encourage the shift towards open source, we deliberately made our method usable for anyone with the most basic of R experience, something that is easily acquired. speaq 2.0 allows a stand-alone analysis from spectra to statistical analysis. In addition, the package can be combined with existing tools to improve performance, as it provides a superior peak picking method compared to the standard binning approach.

Plants constantly monitor for pathogen challenge and utilize a diverse array of adaptive defense mechanisms, including differential protein regulation, during pathogen attack. A proteomic analysis of Nicotiana tabacum BY-2 cells was performed in order to investigate the dynamic changes following perception of bacterial lipopolysaccharides. A multiplexed proteome analysis, employing two-dimensional difference-in-gel-electrophoresis with CyDye™ DIGE fluors, as well as Ruthenium II tris (bathophenanthroline disulfonate) fluorescence staining and Pro-Q ® Diamond phosphoprotein-specific gel staining, monitored over 1500 proteins and resulted in the identification of 88 differentially regulated proteins and phosphoproteins responsive to LPS B.cep. -elicitation. Functional clustering of the proteins both at the level of their abundance and phosphorylation status, revealed 9 proteins involved in transport, ion homeostasis and signal transduction. A large number of responsive proteins were found to be involved in metabolism- and energy-related processes (36), representing various metabolic pathways. Another abundant category corresponded to proteins classified as molecular chaperones and involved in protein destination/targeting (12). Other categories of proteins found to be LPS B.cep. -responsive and differentially regulated include cell structure- and cytoskeletal rearrangement proteins (8) and proteins involved in transcription and translation as well as degradation (11). The results indicate that LPS B.cep. induces metabolic reprogramming and changes in cellular activities supporting protein synthesis, -folding, vesicle trafficking and secretion; accompanied by changes to the cytoskeleton and proteosome function. Many of the identified proteins are known to be interconnected at various levels through a complex web of activation/deactivation, complex formation, protein–protein interactions, and chaperoning reactions. The presented data offers novel insights and further evidence for the biochemical action of LPS B.cep. as a resistance elicitor, a pathogen-associated molecular pattern molecule and triggering agent of defense responses associated with innate immunity.

Motivation: Phosphorylation is a crucial post-translational protein modification mechanism with important regulatory functions in biological systems. It is catalyzed by a group of enzymes called kinases, each of which recognizes certain target sites in its substrate proteins. Several authors have built computational models trained from sets of experimentally validated phosphorylation sites to predict these target sites for each given kinase. All of these models suffer from certain limitations, such as the fact that they do not take into account the dependencies between amino acid motifs within protein sequences in a global fashion. Results: We propose a novel approach to predict phosphorylation sites from the protein sequence. The method uses a positive dataset to train a conditional random field (CRF) model. The negative training dataset is used to specify the decision threshold corresponding to a desired false positive rate. Application of the method on experimentally verified benchmark phosphorylation data (Phospho.ELM) shows that it performs well compared to existing methods for most kinases. This is to our knowledge that the first report of the use of CRFs to predict post-translational modification sites in protein sequences. Availability: The source code of the implementation, called CRPhos, is available from http://www.ptools.ua.ac.be/CRPhos/ Contact: kris.laukens@ua.ac.be Suplementary Information: Supplementary data are available at http://www.ptools.ua.ac.be/CRPhos/

Since the publication of REACH guidelines, the need for in vitro tools for toxicity testing has increased. We present here the development of a hepatotoxicity testing tool using human HepaRG cell cultures and metabolomics. HepaRG cells were exposed to either 4 mM acetaminophen (APAP) as reference toxicant for oxidative stress or 50 μM triphenyl phosphate (TPHP) as toxicant with unknown toxicity pathways (TPs). After 72 h exposure, cells were subjected to quenching and liquidliquid extraction which resulted in a polar and an apolar fraction. Analysis of fractions was performed by ultrahigh performance liquid chromatography-high resolution tandem mass spectrometry (UHPLC-QTOF-MS). Significantly up or down regulated metabolites were selected by univariate statistics prior to identification. In order to obtain robust and specific TP biomarkers, the experiment was also repeated using a different culture medium composition to assess which metabolites show consistent changes. Potential biomarkers belonging to different TPs were found for APAP and TPHP. For APAP, the biomarkers were related to a decrease in unsaturated phospholipids, and for TPHP to an accumulation of phosphoglycerolipids and increase of palmitoyl lysophosphatidylcholine. This first proof-of-concept opens new perspectives for the analysis of other (reference) toxicants with different TPs and it can be used to expand the in vitro tool for hepatotoxicity screening of various compounds.

Background The three-dimensional structure of a protein is an essential aspect of its functionality. Despite the large diversity in protein structures and functionality, it is known that there are common patterns and preferences in the contacts between amino acid residues, or between residues and other biomolecules, such as DNA. The discovery and characterization of these patterns is an important research topic within structural biology as it can give fundamental insight into protein structures and can aid in the prediction of unknown structures. Results Here we apply an efficient spatial pattern miner to search for sets of amino acids that occur frequently in close spatial proximity in the protein structures of the Protein DataBank. This allowed us to mine for a new class of amino acid patterns, that we term FreSCOs (Frequent Spatially Cohesive Component sets), which feature synergetic combinations. To demonstrate the relevance of these FreSCOs, they were compared in relation to the thermostability of the protein structure and the interaction preferences of DNA-protein complexes. In both cases, the results matched well with prior investigations using more complex methods on smaller data sets. Conclusions The currently characterized protein structures feature a diverse set of frequent amino acid patterns that can be related to the stability of the protein molecular structure and that are independent from protein function or specific conserved domains. Electronic supplementary material The online version of this article (doi:10.1186/s13040-015-0038-4) contains supplementary material, which is available to authorized users.

Mass spectrometers typically output data in proprietary binary formats. While converter suites and standardized XML formats have been developed in response, these conversion steps come with non-negligible computational time and storage space overhead. As a result, simple, everyday data inspection tasks are often beyond the skills of the mass spectrometrist, who is unable to freely access the acquired data. We therefore here describe the unthermo library for convenient, platform-independent access to Thermo Scientific RAW files and the associated online playground to transform small and easily understandable scriptlets into executable programs for end-users. By fostering the provision of code examples and snippet exchange, the interested mass spectrometrist or researcher can use this playground to quickly assemble custom scripts for their particular purpose. In this way, the data in these RAW files can be mined much more readily and directly by the user, and fast, automated raw data extraction or analysis can finally become part of the daily routine of the mass spectrometrist.

This study focuses on the specific problems of protein extraction from recalcitrant plant tissues and evaluates several methods to bypass them. Sample preparation is a critical step in a two-dimensional gel electrophoresis proteome approach and is absolutely essential for good results. We evaluated four methods: the classical trichloroacetic acid (TCA)/acetone precipitation, TCA/acetone precipitation and fractionation, an alternative based on fractionation and without precipitation, and phenol extraction methanol/ammonium acetate precipitation. We optimized the phenol extraction protocol for small amounts of tissue, which is essential when the study material is limited. The protocol was optimized for banana (Musa spp.) and was subsequently applied to two other plant species: apple (Malus domestica L.) and potato (Solanum tuberosum L.). Banana (Musa spp.) is a good representative of a "difficult" plant species since it contains many interfering metabolites. Only classical TCA/acetone precipitation and phenol extraction methods proved useful as standard methods. Both methods are associated with a minor but reproducible loss of proteins. Every extraction method and the subsequent analytical procedure have their physicochemical limitations; both methods should be investigated before selecting an appropriate protocol. The study, which is presented in this paper, is useful for guiding the experimental setup of many other nonmodel species, containing various interfering elements.

Using two-dimensional gel electrophoresis (2-DE) and electrospray-tandem mass spectrometry (ESI-MS/MS), we have started the proteome analysis of the cell line Nicotiana tabacum cv. Bright Yellow-2 (tobacco BY-2). The BY-2 cell suspension culture is widely used as a model system to study the growth and development of plant cells. We present a protocol describing the sample preparation and 2-DE, enabling us to separate and display more than 1000 proteins from this cell culture. A reference gel was generated, using immobilized pH gradient isoelectric focusing in a linear gradient from pH 3 to 10 and 12% Sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE). Although the tobacco genome is not sequenced yet, a range of protein spots from this reference map was identified by means of a semi-automated liquid chromatography-ESI-quadrupole time of flight-tandem MS (LC-ESI-QTOF-MS-MS) setup and cross-species matching. These data were integrated in a database, which can be accessed at http://tby2-www.uia.ac.be/tby2/. On the on-line reference map, the identified protein spots are hyperlinked to individual protein entries. Each protein entry contains all identification information, as well as links to relevant entries in other on-line databases. Comprehensive search functions are implemented. Especially for an unsequenced but widespread model organism like tobacco BY-2, such a reference database is a convenient source for protein information that brings protein identification within reach without the need for extensive MS. This publicly accessible database provides a solid basis for tobacco BY-2 proteomics in the future.

Background Mass spectrometry-based proteomics experiments generate spectra that are rich in information. Often only a fraction of this information is used for peptide/protein identification, whereas a significant proportion of the peaks in a spectrum remain unexplained. In this paper we explore how a specific class of data mining techniques termed “frequent itemset mining” can be employed to discover patterns in the unassigned data, and how such patterns can help us interpret the origin of the unexpected/unexplained peaks. Results First a model is proposed that describes the origin of the observed peaks in a mass spectrum. For this purpose we use the classical correlative database search algorithm. Peaks that support a positive identification of the spectrum are termed explained peaks. Next, frequent itemset mining techniques are introduced to infer which unexplained peaks are associated in a spectrum. The method is validated on two types of experimental proteomic data. First, peptide mass fingerprint data is analyzed to explain the unassigned peaks in a full scan mass spectrum. Interestingly, a large numbers of experimental spectra reveals several highly frequent unexplained masses, and pattern mining on these frequent masses demonstrates that subsets of these peaks frequently co-occur. Further evaluation shows that several of these co-occurring peaks indeed have a known common origin, and other patterns are promising hypothesis generators for further analysis. Second, the proposed methodology is validated on tandem mass spectrometral data using a public spectral library, where associations within the mass differences of unassigned peaks and peptide modifications are explored. The investigation of the found patterns illustrates that meaningful patterns can be discovered that can be explained by features of the employed technology and found modifications. Conclusions This simple approach offers opportunities to monitor accumulating unexplained mass spectrometry data for emerging new patterns, with possible applications for the development of mass exclusion lists, for the refinement of quality control strategies and for a further interpretation of unexplained spectral peaks in mass spectrometry and tandem mass spectrometry. Electronic supplementary material The online version of this article (doi:10.1186/s12953-014-0054-1) contains supplementary material, which is available to authorized users.

Cell suspension cultures from different plant species act as important model systems for studying cellular processes in plant biology and are often used as green factories for the production of valuable secondary metabolites and recombinant proteins. While mass spectrometry based proteome analysis techniques are ideally suited to study plant cell metabolism and other fundamental cellular processes from a birds eye perspective, they remain underused in plant studies. We describe a comprehensive sample preparation and multidimensional shotgun proteomics strategy that can be generically applied to plant cell suspension cultures. This strategy was optimized and tested on an Arabidopsis thaliana ecotype Landsberg erecta culture. Furthermore, the implementation of strong cation exchange chromatography as a peptide fractionation step is elaborately tested. Its utility in mass spectrometry based proteome analysis is discussed. Using the presented analytical platform, over 13,000 unique peptides and 2640 proteins could be identified from a single plant cell suspension sample. Finally, the experimental setup is validated using Nicotiana tabacum cv. Bright Yellow-2 (BY-2) plant cell suspension cultures, thereby demonstrating that the presented analytical platform can also be valuable tool in proteome analysis of non-genomic model systems.

jqcML: A Java API for quality control for mass spectrometry experiments qcML is a new approach towards a standardized format for quality control metrics for mass spectrometry experiments. We here present jqcML, an open-source Java API for working with qcML data. Introduction In order to provide a pervasive and standardized means to report quality control information for mass spectrometry experiments, the qcML standard1 has recently been developed. The qcML standard aims to support an automated quality control pipeline by providing a set of useful metrics that can be calculated on the acquired data. Additionally, it aims to provide a standard format for the exchange of these metrics. To exchange qcML data an XML-based file format has been developed. This is a universal format that captures metrics and metadata about all kinds of mass spectrometry experiments. As such, the qcML file format can be used as a container to separate quality control information from the actual data analysis. Here we present jqcML, a fully operative Java API for working with qcML data. Firstly, jqcML provides a complete object model to interpret and manipulate qcML data, while retaining a small memory footprint without sacrificing the overall speed of data access. Furthermore, jqcML is able to interact with both XML-based qcML files and a qcDB relational database. This interaction is abstracted, so the user is able to work with both sources of qcML data in a consistent way. Methods The main approach to exchange qcML data will be through the XML-based qcML files. To handle this approach, jqcML is able to operate on qcML files by reading a full qcML file or only a specific part of a qcML file, and by creating and writing qcML files. In order to perform input and output on qcML files, the Java Architecture for XML Binding (JAXB) is used. By annotating specific elements of the object model, a mapping between the object model and the XML structure defined by the XML schema is constructed. This allows a translation from qcML files to the object model, and vice-versa. Using JAXB we are able to both read and write qcML files, thus enabling the interpretation of existing qcML files, as well as the creation of new qcML files. When interpreting data from a qcML file, special care is taken to be able to manipulate files that are arbitrarily large. It is possible to only read a specific part of a file by using an XML indexer component. This prevents having to read the full, possibly (very) large, qcML file into memory, while still being able to retrieve the required content. Besides the XML-based file format, qcML data can also be stored in a relational database, called qcDB. Equivalent to the XML-based file format, jqcML provides an application layer to be able to read and write qcML data from a qcDB. This interaction is abstracted, enabling the user to interface with an XML-based file or a qcDB in a consistent way. Consequently, jqcML can also be used as a converter between the XML-based file format and a qcDB, and vice-versa. Results & Discussion OpenMS2, an open-source library for LC/MS data management and analyses, provides a (modular) tool to calculate qcML data. By using this pipeline raw files detailing mass spectrometry experiments can easily be processed to output a qcML file. Using this data it is possible to perform advanced analyses between different runs. By performing a large-scale analysis on specific quality metrics, it is possible to obtain a classification between experiments. Subsequently, based on this classification quality boundaries can be determined. Finally, using these boundaries, thresholds can be defined in order to flag bad experiments. The qcML standard will be finalized by the end of the year. Using libraries such as jqcML, the capabilities of the qcML standard can easily be harnessed. This will enable the users to provide easier and better quality control for their mass spectrometry experiments, resulting in more high-quality results. References 1. qcml – A XML format for quality related data of mass spectrometry instruments. https://code.google.com/p/qcml/ (2013). 2. Kohlbacher, O. et al. TOPP--The OpenMS Proteomics Pipeline. Bioinformatics 23, e191–e197 (2007).

Shotgun proteomics is a powerful technology to study the protein population of a biological system. This approach employs tandem mass spectrometry for amino acid sequencing. Fragmented ion masses can be used in correlative database-searching, like SEQUEST or Mascot, to identify peptides. The database-search method depends upon a score function that evaluates matches between the predicted ions and the ions observed in the tandem mass spectrum. Principally, peptide identification based on tandem MS and database-search algorithms does not take into account information about isotope distributions of the precursor ions. To determine the effectiveness of these search algorithms in terms of their ability to distinguish between correct and incorrect peptide assignments, we propose an additional metric that quantifies the similarity between the theoretical isotopic distribution for the precursor ions selected for tandem MS and the experimental mass spectra by using Pearson's χ2 statistic. The observed association between Pearson's χ2 statistic and the score function indicates that good scores can be obtained for molecules which exhibit atypical isotope profiles, while low scores can be obtained for fragment spectra which have a clear peptide-like isotope pattern. These results demonstrate that Pearson's χ2 statistic can be used in conjunction with the score of database-search algorithms to increase the sensitivity and specificity of peptide identification. Biological significance In this manuscript, we present a workflow that provides a new perspective on the quality of peptide-to-spectrum matches (PSM) employed in database-searching strategies for peptide identification. Additional views on a dataset can facilitate a more hypothesis-driven interpretation of the mass spectrometry signals. The similarity metric on the PSM scores contemplates the isotopic profile and results in a measure that conveys a degree of biomolecular similarity observed from the precursor of the selected tandem MS spectra. A close agreement between the PSM score and the similarity metric will result in a higher confidence for the identification of the selected precursor ion.

Spectral library searching is a popular approach for MS/MS-based peptide identification. Because the size of spectral libraries continues to grow, the performance of searching algorithms is an important issue. This technical note introduces a strategy based on a minimum shared peak count between two spectra to reduce the set of admissible candidate spectra when issuing a query. A theoretical validation through time complexity analysis and an experimental validation based on an implementation of the candidate reduction strategy show that the approach can achieve a reduction of the set of candidate spectra by (at least) an order of magnitude, resulting in a significant improvement in the speed of the search. Meanwhile, more than 99% of the positive search results is retained. This efficient strategy to drastically improve the speed of spectral library searching with a negligible loss of sensitivity can be applied to any current spectral library search tool, irrespective of the employed similarity metric.

The aquatic environment is an important site for perfluorooctanoic acid (PFOA) deposit. Nevertheless, the exact mode of action and its resulting toxicological effects in aquatic organisms remain largely unknown. To gain a better understanding of the mode of action of teleost PFOA toxicity, transcriptomics, proteomics, biochemical parameters and reproduction were integrated in this study. Male and female zebrafish were exposed to nominal concentrations of 0.1, 0.5 and 1 mg L−1 PFOA for 4 and 28 d resulting in PFOA accumulation which was higher in males than in females. These gender-related differences were likely caused by different elimination rates due to distinct hormone levels and differences in transport activity by solute carriers. The general mode of action of PFOA was described as an increase of the mitochondrial membrane permeability followed by an impairment of aerobic ATP production. Depletion of liver glycogen stores together with altered expression levels of transcripts involved in carbohydrate metabolism, with emphasis on anaerobic metabolism, was probably a means of compensating for this decreased aerobic efficiency. The mitochondrial dysfunction further resulted in effects on oxidative stress and apoptosis at the gene transcript and protein level. As a consequence, evidence for the replacement of the affected cells and organelles to sustain tissue homeostasis was found at the transcript level, resulting in an even greater glycogen depletion. Despite this increase in metabolic expenditure, no effects on reproduction were found indicating that the fish seemed to cope with exposure to the tested concentrations of PFOA during the exposure period of 1 month.

As mass spectrometry-based proteomics has matured during the past decade a growing emphasis has been placed on quality control. For this purpose multiple computational quality control tools have been introduced. These tools generate a set of metrics that can be used to assess the quality of a mass spectrometry experiment. Here we review which different types of quality control metrics can be generated, and how they can be used to monitor both intra- and inter-experiment performance. We discuss the principal computational tools for quality control and list their main characteristics and applicability. As most of these tools have specific use cases it is not straightforward to compare their performance. For this survey we used different sets of quality control metrics derived from information at various stages in a mass spectrometry process and evaluated their effectiveness at capturing qualitative information about an experiment using a supervised learning approach. Furthermore, we discuss currently available algorithmic solutions that enable the usage of these quality control metrics for decision-making. This is the peer reviewed version of the following article: "Bittremieux, W., Valkenborg, D., Martens, L. & Laukens, K. Computational quality control tools for mass spectrometry proteomics. PROTEOMICS 17, 1600159 (2017)", which has been published in final form at https://doi.org/10.1002/pmic.201600159. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.

Background: Nuclear magnetic resonance spectroscopy (NMR) is a powerful technique to reveal and compare quantitative metabolic profiles of biological tissues. However, chemical and physical sample variations make the analysis of the data challenging, and typically require the application of a number of preprocessing steps prior to data interpretation. For example, noise reduction, normalization, baseline correction, peak picking, spectrum alignment and statistical analysis are indispensable components in any NMR analysis pipeline. Results: We introduce a novel suite of informatics tools for the quantitative analysis of NMR metabolomic profile data. The core of the processing cascade is a novel peak alignment algorithm, called hierarchical Cluster-based Peak Alignment (CluPA). The algorithm aligns a target spectrum to the reference spectrum in a top-down fashion by building a hierarchical cluster tree from peak lists of reference and target spectra and then dividing the spectra into smaller segments based on the most distant clusters of the tree. To reduce the computational time to estimate the spectral misalignment, the method makes use of Fast Fourier Transformation (FFT) cross-correlation. Since the method returns a high-quality alignment, we can propose a simple methodology to study the variability of the NMR spectra. For each aligned NMR data point the ratio of the between-group and within-group sum of squares (BW-ratio) is calculated to quantify the difference in variability between and within predefined groups of NMR spectra. This differential analysis is related to the calculation of the F-statistic or a one-way ANOVA, but without distributional assumptions. Statistical inference based on the BW-ratio is achieved by bootstrapping the null distribution from the experimental data. Conclusions: The workflow performance was evaluated using a previously published dataset. Correlation maps, spectral and grey scale plots show clear improvements in comparison to other methods, and the down-to-earth quantitative analysis works well for the CluPA-aligned spectra. The whole workflow is embedded into a modular and statistically sound framework that is implemented as an R package called "speaq" ("spectrum alignment and quantitation"), which is freely available from http://code.google.com/p/speaq/. TNV acknowledges support by a BOF interdisciplinary grant of the University of Antwerp. Koen Smets is supported by a Ph.D. Fellowship of the Research Foundation- Flanders (FWO). This work is further supported by an SBO grant (IWT-600450).

To understand physiological processes, insight into protein complexes is very important. Through a combination of blue native gel electrophoresis and LC-MS/MS, we were able to isolate protein complexes and identify their potential subunits from Nicotiana tabacum cv. Bright Yellow-2. For this purpose, a bioanalytical approach was used that works without a priori knowledge of the interacting proteins. Different clustering methods (e.g., k-means and hierarchical clustering) and a biclustering approach were evaluated according to their ability to group proteins by their migration profile and to correlate the proteins to a specific complex. The biclustering approach was identified as a very powerful tool for the exploration of protein complexes of whole cell lysates since it allows for the promiscuous nature of proteins. Furthermore, it searches for associations between proteins that co-occur frequently throughout the BN gel, which increases the confidence of the putative associations between co-migrating proteins. The statistical significance and biological relevance of the profile clusters were verified using functional gene ontology annotation. The proof of concept for identifying protein complexes by our BN PAGE/LC-MS/MS approach is provided through the analysis of known protein complexes. Both well characterized long-lived protein complexes as well as potential temporary sequential multi-enzyme complexes were characterized. ispartof: Journal of Proteomics vol:74 issue:8 pages:1201-1217 ispartof: location:Netherlands status: published

The congruent development of computational technology, bioinformatics and analytical instrumentation makes proteomics ready for the next leap. Present-day state of the art proteomics grew from a descriptive method towards a full stake holder in systems biology. High throughput and genome wide studies are now made at the functional level. These include quantitative aspects, functional aspects with respect to protein interactions as well as post translational modifications and advanced computational methods that aid in predicting protein function and mapping these functionalities across the species border. In this review an overview is given of the current status of these aspects in plant studies with special attention to non-genomic model plants.

Background: Molecular interaction networks can be efficiently studied using network visualization software such as Cytoscape. The relevant nodes, edges and their attributes can be imported in Cytoscape in various file formats, or directly from external databases through specialized third party plugins. However, molecular data are often stored in relational databases with their own specific structure, for which dedicated plugins do not exist. Therefore, a more generic solution is presented. Results: A new Cytoscape plugin 'CytoSQL' is developed to connect Cytoscape to any relational database. It allows to launch SQL ('Structured Query Language') queries from within Cytoscape, with the option to inject node or edge features of an existing network as SQL arguments, and to convert the retrieved data to Cytoscape network components. Supported by a set of case studies we demonstrate the flexibility and the power of the CytoSQL plugin in converting specific data subsets into meaningful network representations. Conclusions: CytoSQL offers a unified approach to let Cytoscape interact with relational databases. Thanks to the power of the SQL syntax, this tool can rapidly generate and enrich networks according to very complex criteria. The plugin is available at http://www.ptools.ua.ac.be/CytoSQL. © 2010 Laukens et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Wheat contains three different classes of proteinaceous xylanase inhibitors (XIs), i.e. Triticum aestivum xylanase inhibitors (TAXIs) xylanase-inhibiting proteins (XIPs), and thaumatin-like xylanase inhibitors (TLXIs) which are believed to act as a defensive barrier against phytopathogenic attack. In the absence of relevant data in wheat kernels, we here examined the response of the different members of the XI protein population to infection with a DeltaTri5 mutant of Fusarium graminearum, the wild type of which is one of the most important wheat ear pathogens, in early developing wheat grain. Wheat ears were inoculated at anthesis, analyzed using 2-D DIGE and multivariate analysis at 5, 15, and 25 days post anthesis (DPA), and compared with control samples. Distinct abundance patterns could be distinguished for different XI forms in response to infection with F. graminearum DeltaTri5. Some (iso)forms were up-regulated, whereas others were down-regulated. This pathogen-specific regulation of proteins was mostly visible at five DPA and levelled off in the samples situated further from the inoculation point. Furthermore, it was shown that most identified TAXI- and XIP-type XI (iso)forms significantly increased in abundance from the milky (15 DPA) to the soft dough stages (25 DPA) on a per kernel basis, although the extent of increase differed greatly. Non-glycosylated XIP forms increased more strongly than their glycosylated counterparts.

2D-DIGE experiments are a high-throughput technique for measuring protein abundances based on gel separation. Traditionally three samples are multiplexed per gel: two biological test samples and a third internal standard sample consisting of a pool of all test samples. We demonstrate that the use of an internal standard helps to account for technical variation caused by spatial intensity biases that exist in the gels and propose a novel data-preprocessing technique, a spatial intensity bias removal (SIBR), which can approximate these biases using only the data of biological replicates loaded on the gel. Using this technique we show that by replacing the internal standard with additional biological replicates, a significant increase in statistical power can be achieved compared to traditional 2D-DIGE designs. This boost in statistical power can be used to reduce the false positive rate for identifying differential protein abundances without compromising sensitivity, or to improve sensitivity without compromising false positive rate. A software implementation of SIBR can be downloaded at http://ibiza.biw.kuleuven.be/SIBR.

Wheat grains contain three classes of xylanase inhibitors (XIs), i.e. TAXI (Triticum aestivum xylanase inhibitor), XIP (xylanase inhibiting protein) and TLXI (thaumatin-like xylanase inhibitor). These proteins are involved in plant defence and strongly affect cereal based processes in which inhibitor-sensitive xylanases are used. This paper reports on the successful use of 2D-DIGE and tandem MS to discriminate XI (iso)forms and measure their qualitative and quantitative variation in six different wheat cultivars. In total, 18 TAXI-, 27 XIP- and 3 TLXI-type XI spots were identified. The multiple members of the large TAXI-gene family make a considerable contribution to the total TAXI population. For XIP-type XIs, XIP-I is expressed as the predominant form, albeit under variable degrees of PTMs. Only one TLXI genetic variant was identified, showing different degrees of glycosylation. Multiple comparison analysis revealed up to 5-fold intercultivar differences in protein level of XI (iso)forms. Evaluation of abundance patterns using multivariate statistical tools revealed highly distinctive as well as correlated levels of different XI forms among the six cultivars. As the constitutive (and induced) levels of the different XI (iso)forms, which are differentially regulated in response to various forms of stress in other wheat plant parts, considerably vary between the cultivars, it can be assumed that their degree of resistance against pathogenic attack differs. Similarities in abundance profiles between XI (iso)forms and pathogenesis-related chitinases are also in line with a role in plant defence.