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BACKGROUND Apples are rich in bioactive polyphenols and fiber. Evidence suggests that consumption of apples or their bioactive components is associated with beneficial effects on lipid metabolism and other markers of cardiovascular disease (CVD). However, adequately powered randomized controlled trials are necessary to confirm these data and explore the mechanisms. OBJECTIVE We aimed to determine the effects of apple consumption on circulating lipids, vascular function, and other CVD risk markers. METHODS The trial was a randomized, controlled, crossover, intervention study. Healthy mildly hypercholesterolemic volunteers (23 women, 17 men), with a mean ± SD BMI 25.3 ± 3.7 kg/m2 and age 51 ± 11 y, consumed 2 apples/d [Renetta Canada, rich in proanthocyanidins (PAs)] or a sugar- and energy-matched apple control beverage (CB) for 8 wk each, separated by a 4-wk washout period. Fasted blood was collected before and after each treatment. Serum lipids, glucose, insulin, bile acids, and endothelial and inflammation biomarkers were measured, in addition to microvascular reactivity, using laser Doppler imaging with iontophoresis, and arterial stiffness, using pulse wave analysis. RESULTS Whole apple (WA) consumption decreased serum total (WA: 5.89 mmol/L; CB: 6.11 mmol/L; P = 0.006) and LDL cholesterol (WA: 3.72 mmol/L; CB: 3.86 mmol/L; P = 0.031), triacylglycerol (WA: 1.17 mmol/L; CB: 1.30 mmol/L; P = 0.021), and intercellular cell adhesion molecule-1 (WA: 153.9 ng/mL; CB: 159.4 ng/mL; P = 0.028), and increased serum uric acid (WA: 341.4 μmol/L; CB: 330 μmol/L; P = 0.020) compared with the CB. The response to endothelium-dependent microvascular vasodilation was greater after the apples [WA: 853 perfusion units (PU), CB: 760 PU; P = 0.037] than after the CB. Apples had no effect on blood pressure or other CVD markers. Conclusions These data support beneficial hypocholesterolemic and vascular effects of the daily consumption of PA-rich apples by mildly hypercholesterolemic individuals. This trial was registered at clinicaltrials.gov as NCT01988389.

The life sciences are currently being transformed by an unprecedented wave of developments in molecular analysis, which include important advances in instrumental analysis as well as biocomputing. In light of the central role played by metabolism in nutrition, metabolomics is rapidly being established as a key analytical tool in human nutritional studies. Consequently, an increasing number of nutritionists integrate metabolomics into their study designs. Within this dynamic landscape, the potential of nutritional metabolomics (nutrimetabolomics) to be translated into a science, which can impact on health policies, still needs to be realized. A key element to reach this goal is the ability of the research community to join, to collectively make the best use of the potential offered by nutritional metabolomics. This article, therefore, provides a methodological description of nutritional metabolomics that reflects on the state‐of‐the‐art techniques used in the laboratories of the Food Biomarker Alliance (funded by the European Joint Programming Initiative "A Healthy Diet for a Healthy Life" (JPI HDHL)) as well as points of reflections to harmonize this field. It is not intended to be exhaustive but rather to present a pragmatic guidance on metabolomic methodologies, providing readers with useful "tips and tricks" along the analytical workflow.

Plasmopara viticola is the causal agent of grapevine downy mildew (DM). DM resistant varieties deploy effector-triggered immunity (ETI) to inhibit pathogen growth, which is activated by major resistance loci, the most common of which are Rpv3 and Rpv12. We previously showed that a quick metabolome response lies behind the ETI conferred by Rpv3 TIR-NB-LRR genes. Here we used a grape variety operating Rpv12-mediated ETI, which is conferred by an independent locus containing CC-NB-LRR genes, to investigate the defence response using GC/MS, UPLC, UHPLC and RNA-Seq analyses. Eighty-eight metabolites showed significantly different concentration and 432 genes showed differential expression between inoculated resistant leaves and controls. Most metabolite changes in sugars, fatty acids and phenols were similar in timing and direction to those observed in Rpv3-mediated ETI but some of them were stronger or more persistent. Activators, elicitors and signal transducers for the formation of reactive oxygen species were early observed in samples undergoing Rpv12-mediated ETI and were paralleled and followed by the upregulation of genes belonging to ontology categories associated with salicylic acid signalling, signal transduction, WRKY transcription factors and synthesis of PR-1, PR-2, PR-5 pathogenesis-related proteins.

In order to differentiate the extra virgin olive oils (EVOO) of different origin of purchase, such as monovarietal Italian EVOO with protected denomination of origin (PDO) and commercial-blended EVOO purchased in supermarkets, a number of samples was subjected to the analysis of volatile aroma compounds by both targeted gas chromatography/mass spectrometry (GC-MS) and untargeted profiling by comprehensive two-dimensional gas chromatography/mass spectrometry (GC&times, GC-TOF-MS), analysis of phenols by targeted high-performance liquid chromatography/mass spectrometry (HPLC-DAD-ESI/MS), and quantitative descriptive sensory analysis. Monovarietal PDO EVOOs were characterized by notably higher amounts of positive LOX-derived C6 and C5 volatile compounds, which corresponded to the higher intensities of all the assessed positive fruity and green odor sensory attributes. Commercial-blended EVOOs had larger quantities of generally undesirable esters, alcohols, acids, and aldehydes, which coincided with the occurrence of sensory defects in many samples. Many minor volatile compounds that were identified by GC&times, GC-TOF-MS were found to differentiate each of the two investigated groups. The differences between the groups with respect to phenols and taste characteristics were evident, but less pronounced. The results that were obtained in this study have undoubtedly confirmed the existence of the large heterogeneity of oils that are sold declared as EVOO. It was shown that GC-MS, GC&times, GC-TOF-MS, and HPLC-DAD-ESI/MS analyses have complementary outputs, and that their use in combination has advantages in supporting the results of sensory analysis and objectively differentiating these groups of EVOO.

Reconstructing a gene regulatory network from one or more sets of omics measurements has been a major task of computational biology in the last 20 years. Despite an overwhelming number of algorithms proposed to solve the network inference problem either in the general scenario or in an ad-hoc tailored situation, assessing the stability of reconstruction is still an uncharted territory and exploratory studies mainly tackled theoretical aspects. We introduce here empirical stability, which is induced by variability of reconstruction as a function of data subsampling. By evaluating differences between networks that are inferred using different subsets of the same data we obtain quantitative indicators of the robustness of the algorithm, of the noise level affecting the data, and, overall, of the reliability of the reconstructed graph. We show that empirical stability can be used whenever no ground truth is available to compute a direct measure of the similarity between the inferred structure and the true network. The main ingredient here is a suite of indicators, called NetSI, providing statistics of distances between graphs generated by a given algorithm fed with different data subsets, where the chosen metric is the Hamming-Ipsen-Mikhailov (HIM) distance evaluating dissimilarity of graph topologies with shared nodes. Operatively, the NetSI family is demonstrated here on synthetic and high-throughput datasets, inferring graphs at different resolution levels (topology, direction, weight), showing how the stability indicators can be effectively used for the quantitative comparison of the stability of different reconstruction algorithms.

Reconstructing a gene regulatory network from one or more sets of omics measurements has been a major task of computational biology in the last 20 years. Despite an overwhelming number of algorithms proposed to solve the network inference problem either in the general scenario or in an ad-hoc tailored situation, assessing the stability of reconstruction is still an uncharted territory and exploratory studies mainly tackled theoretical aspects. We introduce here empirical stability, which is induced by variability of reconstruction as a function of data subsampling. By evaluating differences between networks that are inferred using different subsets of the same data we obtain quantitative indicators of the robustness of the algorithm, of the noise level affecting the data, and, overall, of the reliability of the reconstructed graph. We show that empirical stability can be used whenever no ground truth is available to compute a direct measure of the similarity between the inferred structure and the true network. The main ingredient here is a suite of indicators, called NetSI, providing statistics of distances between graphs generated by a given algorithm fed with different data subsets, where the chosen metric is the Hamming-Ipsen-Mikhailov (HIM) distance evaluating dissimilarity of graph topologies with shared nodes. Operatively, the NetSI family is demonstrated here on synthetic and high-throughput datasets, inferring graphs at different resolution levels (topology, direction, weight), showing how the stability indicators can be effectively used for the quantitative comparison of the stability of different reconstruction algorithms.

BackgroundThe ability of finding complex associations in large omics datasets, assessing their significance, and prioritizing them according to their strength can be of great help in the data exploration phase. Mutual Information based measures of association are particularly promising, in particular after the recent introduction of the TICeand MICeestimators, which combine computational efficiency with good bias/variance properties. Despite that, a complete software implementation of these two measures and of a statistical procedure to test the significance of each association is still missing.FindingsIn this paper we present MICtools, a comprehensive and effective pipeline which combines TICeand MICeinto a multi-step procedure that allows the identification of relationships of various degrees of complexity. MICtools calculates their strength assessing statistical significance using a permutation-based strategy. The performances of the proposed approach are assessed by an extensive investigation in synthetic datasets and an example of a potential application on a metagenomic dataset is also illustrated.ConclusionsWe show that MICtools, combining TICeand MICe, is able to highlight associations that would not be captured by conventional strategies. MICtools is implemented in Python, and is available for download athttps://github.com/minepy/mictools.

Salinity tolerance has been extensively investigated in recent years due to its agricultural importance. Several features, such as the regulation of ionic transporters and metabolic adjustments, have been identified as salt tolerance hallmarks. Nevertheless, due to the complexity of the trait, the results achieved to date have met with limited success in improving the salt tolerance of rice plants when tested in the field, thus suggesting that a better understanding of the tolerance mechanisms is still required. In this work, differences between two varieties of rice with contrasting salt sensitivities were revealed by the imaging of photosynthetic parameters, ion content analysis and a transcriptomic approach. The transcriptomic analysis conducted on tolerant plants supported the setting up of an adaptive program consisting of sodium distribution preferentially limited to the roots and older leaves, and in the activation of regulatory mechanisms of photosynthesis in the new leaves. As a result, plants resumed grow even under prolonged saline stress. In contrast, in the sensitive variety, RNA-seq analysis revealed a misleading response, ending in senescence and cell death. The physiological response at the cellular level was investigated by measuring the intracellular profile of H2O2 in the roots, using a fluorescent probe. In the roots of tolerant plants, a quick response was observed with an increase in H2O2 production within 5 min after salt treatment. The expression analysis of some of the genes involved in perception, signal transduction and salt stress response confirmed their early induction in the roots of tolerant plants compared to sensitive ones. By inhibiting the synthesis of apoplastic H2O2, a reduction in the expression of these genes was detected. Our results indicate that quick H2O2 signaling in the roots is part of a coordinated response that leads to adaptation instead of senescence in salt-treated rice plants.

Summary Pathway Inspector is an easy-to-use web application helping researchers to find patterns of expression in complex RNAseq experiments. The tool combines two standard approaches for RNAseq analysis: the identification of differentially expressed genes and a topology-based analysis of enriched pathways. Pathway Inspector is equipped with ad hoc interactive graphical interfaces simplifying the discovery of modulated pathways and the integration of the differentially expressed genes in the corresponding pathway topology. Availability and Implementation Pathway Inspector is available at the website http://admiral.fmach.it/PI and has been developed in Python, making use of the Django Web Framework.

The concordance of RNA-sequencing (RNA-seq) with microarrays for genome-wide analysis of differential gene expression has not been rigorously assessed using a range of chemical treatment conditions. Here we use a comprehensive study design to generate Illumina RNA-seq and Affymetrix microarray data from the same liver samples of rats exposed in triplicate to varying degrees of perturbation by 27 chemicals representing multiple modes of action (MOAs). The cross-platform concordance in terms of differentially expressed genes (DEGs) or enriched pathways is linearly correlated with treatment effect size (R-2 approximate to 0.8). Furthermore, the concordance is also affected by transcript abundance and biological complexity of the MOA. RNA-seq outperforms microarray (93% versus 75%) in DEG verification as assessed by quantitative PCR, with the gain mainly due to its improved accuracy for low-abundance transcripts. Nonetheless, classifiers to predict MOAs perform similarly when developed using data from either platform. Therefore, the endpoint studied and its biological complexity, transcript abundance and the genomic application are important factors in transcriptomic research and for clinical and regulatory decision making.

Based on the glocal HIM metric and its induced graph kernel, we propose a novel solution in differential network analysis that integrates network comparison and classification tasks. The HIM distance is defined as the one-parameter family of product metrics linearly combining the normalised Hamming distance H and the normalised Ipsen–Mikhailov spectral distance IM. The combination of the two components within a single metric allows overcoming their drawbacks and obtaining a measure that is simultaneously global and local. Furthermore, plugging the HIM kernel into a Support Vector Machine gives us a classification algorithm based on the HIM distance. First, we outline the theory underlying the metric construction. We introduce two diverse applications of the HIM distance and the HIM kernel to biological datasets. This versatility supports the adoption of the HIM family as a general tool for information extraction, quantifying difference among diverse instances of a complex system. An Open Source implementation of the HIM metrics is provided by the R package nettools and in its web interface ReNette.

Gene expression data from microarrays are being applied to predict preclinical and clinical endpoints, but the reliability of these predictions has not been established. In the MAQC-II project, 36 independent teams analyzed six microarray data sets to generate predictive models for classifying a sample with respect to one of 13 endpoints indicative of lung or liver toxicity in rodents, or of breast cancer, multiple myeloma or neuroblastoma in humans. In total, >30,000 models were built using many combinations of analytical methods. The teams generated predictive models without knowing the biological meaning of some of the endpoints and, to mimic clinical reality, tested the models on data that had not been used for training. We found that model performance depended largely on the endpoint and team proficiency and that different approaches generated models of similar performance. The conclusions and recommendations from MAQC-II should be useful for regulatory agencies, study committees and independent investigators that evaluate methods for global gene expression analysis.

Summary: Here we introduce a novel web-infrastructure for differential network analysis. The aim of the web-site is to provide a comprehensive collection of tools for network inference, network comparison and network reproducibility analysis. Four main processes are available through the web service: the network inference process which include 11 reconstruction algorithms, the network distance process with 3 available metrics, the network stability process which includes all the network reconstruction methods and network distances and the netwok statistic process which computes the most common measure for network characterization. We introduce here a novel infrastructure which allows the user- interface logic to be separated from computing services and the asynchronous task management. Task submission is implemented mimicking the high performance computing queue submission system which allows to run multiple jobs without affecting the front-end server. Availability and Implementation: The web-site is available at https://renette.fbk.eu, the implementation is based on the django framework and Apache, with all major browsers supported. Furthermore, the whole project is Open Source under GPLv2 and the code is available on GitHub at https://github.com/MPBA/ renette for local installation.

Identifying the molecular pathways more prone to disruption during a pathological process is a key task in network medicine and, more generally, in systems biology. This chapter describes a pipeline that couples a machine learning solution for molecular profiling with a recent network comparison method. The pipeline can identify changes occurring between specific sub-modules of networks built in a case-control biomarker study, discriminating key groups of genes whose interactions are modified by an underlying condition. Different algorithms can be chosen to implement the workflow steps. Three applications on genome-wide data are presented regarding the susceptibility of children to air pollution, and early and late onset of Parkinsonʼs and Alzheimerʼs diseases.

Modelling the networks sustaining the fruitful coexistence between fungi and their mammalian hosts is becoming increasingly important to control emerging fungal pathogens. The C-type lectins Dectin-1 and Dectin-2 are involved in host defense mechanisms against fungal infection driving inflammatory and adaptive immune responses and complement in containing fungal burdens. Recognizing carbohydrate structures in pathogens, their engagement induces maturation of dendritic cells (DCs) into potent immuno-stimulatory cells endowed with the capacity to efficiently prime T cells. Owing to these properties, Dectin-1 and Dectin-2 agonists are currently under investigation as promising adjuvants in vaccination procedures for the treatment of fungal infection. Thus, a detailed understanding of events' cascade specifically triggered in DCs upon engagement is of great interest in translational research. Here, we summarize the current knowledge on Dectin-1 and Dectin-2 signalling in DCs highlighting similarities and differences. Detailed maps are annotated, using the Biological Connection Markup Language (BCML) data model, and stored in DC-ATLAS, a versatile resource for the interpretation of high-throughput data generated perturbing the signalling network of DCs.

Due to the ever rising importance of the network paradigm across several areas of science, comparing and classifying graphs represent essential steps in the networks analysis of complex systems. Both tasks have been recently tackled via quite different strategies, even tailored ad-hoc for the investigated problem. Here we deal with both operations by introducing the Hamming-Ipsen-Mikhailov (HIM) distance, a novel metric to quantitatively measure the difference between two graphs sharing the same vertices. The new measure combines the local Hamming distance and the global spectral Ipsen-Mikhailov distance so to overcome the drawbacks affecting the two components separately. Building then the HIM kernel function derived from the HIM distance it is possible to move from network comparison to network classification via the Support Vector Machine (SVM) algorithm. Applications of HIM distance and HIM kernel in computational biology and social networks science demonstrate the effectiveness of the proposed functions as a general purpose solution., Comment: Frontiers of Network Analysis: Methods, Models, and Applications - NIPS 2013 Workshop

When modeling coexpression networks from high-throughput time course data, Pearson Correlation Coefficient (PCC) is one of the most effective and popular similarity functions. However, its reliability is limited since it cannot capture non-linear interactions and time shifts. Here we propose to overcome these two issues by employing a novel similarity function, Dynamic Time Warping Maximal Information Coefficient (DTW-MIC), combining a measure taking care of functional interactions of signals (MIC) and a measure identifying time lag (DTW). By using the Hamming-Ipsen-Mikhailov (HIM) metric to quantify network differences, the effectiveness of the DTW-MIC approach is demonstrated on a set of four synthetic and one transcriptomic datasets, also in comparison to TimeDelay ARACNE and Transfer Entropy.

We introduce a novel implementation in ANSI C of the MINE family of algorithms for computing maximal information-based measures of dependence between two variables in large datasets, with the aim of a low memory footprint and ease of integration within bioinformatics pipelines. We provide the libraries minerva (with the R interface) and minepy for Python, MATLAB, Octave and C++. The C solution reduces the large memory requirement of the original Java implementation, has good upscaling properties, and offers a native parallelization for the R interface. Low memory requirements are demonstrated on the MINE benchmarks as well as on large (n=1340) microarray and Illumina GAII RNA-seq transcriptomics datasets. Availability and Implementation: Source code and binaries are freely available for download under GPL3 licence at http://minepy.sourceforge.net for minepy and through the CRAN repository http://cran.r-project.org for the R package minerva. All software is multiplatform (MS Windows, Linux and OSX)., Bioinformatics 2012, in press

IntroductionThe traditional staging system is inadequate to identify those patients with stage II colorectal cancer (CRC) at high risk of recurrence or with stage III CRC at low risk. A number of gene expression signatures to predict CRC prognosis have been proposed, but none is routinely used in the clinic. The aim of this work was to assess the prediction ability and potential clinical usefulness of these signatures in a series of independent datasets.MethodsA literature review identified 31 gene expression signatures that used gene expression data to predict prognosis in CRC tissue. The search was based on the PubMed database and was restricted to papers published from January 2004 to December 2011. Eleven CRC gene expression datasets with outcome information were identified and downloaded from public repositories. Random Forest classifier was used to build predictors from the gene lists. Matthews correlation coefficient was chosen as a measure of classification accuracy and its associated p-value was used to assess association with prognosis. For clinical usefulness evaluation, positive and negative post-tests probabilities were computed in stage II and III samples.ResultsFive gene signatures showed significant association with prognosis and provided reasonable prediction accuracy in their own training datasets. Nevertheless, all signatures showed low reproducibility in independent data. Stratified analyses by stage or microsatellite instability status showed significant association but limited discrimination ability, especially in stage II tumors. From a clinical perspective, the most predictive signatures showed a minor but significant improvement over the classical staging system.ConclusionsThe published signatures show low prediction accuracy but moderate clinical usefulness. Although gene expression data may inform prognosis, better strategies for signature validation are needed to encourage their widespread use in the clinic.

Motivation: Identifying the molecular pathways more prone to disruption during a pathological process is a key task in network medicine and, more in general, in systems biology. Results: In this work we propose a pipeline that couples a machine learning solution for molecular profiling with a recent network comparison method. The pipeline can identify changes occurring between specific sub-modules of networks built in a case-control biomarker study, discriminating key groups of genes whose interactions are modified by an underlying condition. The proposal is independent from the classification algorithm used. Three applications on genomewide data are presented regarding children susceptibility to air pollution and two neurodegenerative diseases: Parkinson's and Alzheimer's. Availability: Details about the software used for the experiments discussed in this paper are provided in the Appendix.

The search for predictive biomarkers of disease from high-throughput mass spectrometry (MS) data requires a complex analysis path. Preprocessing and machine-learning modules are pipelined, starting from raw spectra, to set up a predictive classifier based on a shortlist of candidate features. As a machine-learning problem, proteomic profiling on MS data needs caution like the microarray case. The risk of overfitting and of selection bias effects is pervasive: not only potential features easily outnumber samples by 10(3) times, but it is easy to neglect information-leakage effects during preprocessing from spectra to peaks. The aim of this review is to explain how to build a general purpose design analysis protocol (DAP) for predictive proteomic profiling: we show how to limit leakage due to parameter tuning and how to organize classification and ranking on large numbers of replicate versions of the original data to avoid selection bias. The DAP can be used with alternative components, i.e. with different preprocessing methods (peak clustering or wavelet based), classifiers e.g. Support Vector Machine (SVM) or feature ranking methods (recursive feature elimination or I-Relief). A procedure for assessing stability and predictive value of the resulting biomarkers' list is also provided. The approach is exemplified with experiments on synthetic datasets (from the Cromwell MS simulator) and with publicly available datasets from cancer studies.

Summary In this paper we apply a predictive profiling method to genome copy number aberrations (CNA) in combination with gene expression and clinical data to identify molecular patterns of cancer pathophysiology. Predictive models and optimal feature lists for the platforms are developed by a complete validation SVM-based machine learning system. Ranked list of genome CNA sites (assessed by comparative genomic hybridization arrays – aCGH) and of differentially expressed genes (assessed by microarray profiling with Affy HG-U133A chips) are computed and combined on a breast cancer dataset for the discrimination of Luminal/ ER+ (Lum/ER+) and Basal-like/ER- classes. Different encodings are developed and applied to the CNA data, and predictive variable selection is discussed. We analyze the combination of profiling information between the platforms, also considering the pathophysiological data. A specific subset of patients is identified that has a different response to classification by chromosomal gains and losses and by differentially expressed genes, corroborating the idea that genomic CNA can represent an independent source for tumor classification.

We present an interactive real-time visualization environment of time-ordered medical data aimed to support multidisciplinary disease management of patients with heart failure. Our prototype has been integrated into a working Electronic Patient Record implemented for the integrated management of heart failure patients. Since January 2005, the system has been evaluating in a clinical setting to assess its practical utility involving 30 General Practitioners, 7 Hospital Physicians and 50 Hospital and Community Nurses.

The number of available algorithms to infer a biological network from a dataset of high-throughput measurements is overwhelming and keeps growing. However, evaluating their performance is unfeasible unless a 'gold standard' is available to measure how close the reconstructed network is to the ground truth. One measure of this is the stability of these predictions to data resampling approaches. We introduce NetSI, a family of Network Stability Indicators, to assess quantitatively the stability of a reconstructed network in terms of inference variability due to data subsampling. In order to evaluate network stability, the main NetSI methods use a global/local network metric in combination with a resampling (bootstrap or cross-validation) procedure. In addition, we provide two normalized variability scores over data resampling to measure edge weight stability and node degree stability, and then introduce a stability ranking for edges and nodes. A complete implementation of the NetSI indicators, including the Hamming-Ipsen-Mikhailov (HIM) network distance adopted in this paper is available with the R package nettools. We demonstrate the use of the NetSI family by measuring network stability on four datasets against alternative network reconstruction methods. First, the effect of sample size on stability of inferred networks is studied in a gold standard framework on yeast-like data from the Gene Net Weaver simulator. We also consider the impact of varying modularity on a set of structurally different networks (50 nodes, from 2 to 10 modules), and then of complex feature covariance structure, showing the different behaviours of standard reconstruction methods based on Pearson correlation, Maximum Information Coefficient (MIC) and False Discovery Rate (FDR) strategy. Finally, we demonstrate a strong combined effect of different reconstruction methods and phenotype subgroups on a hepatocellular carcinoma miRNA microarray dataset (240 subjects), and we validate the analysis on a second dataset (166 subjects) with good reproducibility.

We show that the Confusion Entropy, a measure of performance in multiclass problems has a strong (monotone) relation with the multiclass generalization of a classical metric, the Matthews Correlation Coefficient. Analytical results are provided for the limit cases of general no-information (n-face dice rolling) of the binary classification. Computational evidence supports the claim in the general case.

Introduction Raspberries are becoming increasingly popular due to their reported health beneficial properties. Despite the presence of only trace amounts of anthocyanins, yellow varieties seems to show similar or better effects in comparison to conventional raspberries. Objectives The aim of this work is to characterize the metabolic differences between red and yellow berries, focussing on the compounds showing a higher concentration in yellow varieties. Methods The metabolomic profile of 13 red and 12 yellow raspberries (of different varieties, locations and collection dates) was determined by UPLC–TOF-MS. A novel approach based on Pearson correlation on the extracted ion chromatograms was implemented to extract the pseudospectra of the most relevant biomarkers from high energy LC–MS runs. The raw data will be made publicly available on MetaboLights (MTBLS333). Results Among the metabolites showing higher concentration in yellow raspberries it was possible to identify a series of compounds showing a pseudospectrum similar to that of A-type procyanidin polymers. The annotation of this group of compounds was confirmed by specific MS/MS experiments and performing standard injections. Conclusions In berries lacking anthocyanins the polyphenol metabolism might be shifted to the formation of a novel class of A-type procyanidin polymers. Electronic supplementary material The online version of this article (doi:10.1007/s11306-016-1090-x) contains supplementary material, which is available to authorized users.