Pattern mining of mass spectrometry quality control data

Pattern mining of mass spectrometry quality control data Mass spectrometry is widely used to identify proteins based on the mass distribution of their peptides. Unfortunately, because of its inherent complexity, the results of a mass spectrometry experiment can be subject to a large variability. As a means of quality control, recently several qualitative metrics have been defined. [1] Initially these quality control metrics were evaluated independently in order to separately assess particular stages of a mass spectrometry experiment. However, this method is insufficient because the different stages of an experiment do not function in isolation, instead they will influence each other. As a result, subsequent work employed a multivariate statistics approach to assess the correlation structure of the different quality control metrics. [2] However, by making use of some more advanced data mining techniques, additional useful information can be extracted from these quality control metrics. Various pattern mining techniques can be employed to discover hidden patterns in this quality control data. Subspace clustering tries to detect clusters of items based on a restricted set of dimensions. [3] This can be leveraged to for example detect aberrant experiments where only a few of the quality control metrics are outliers, but the experiment still behaved correctly in general. In addition, specialized frequent itemset mining and association rule learning techniques can be used to discover relationships between the various stages of a mass spectrometry experiment, as they are exhibited by the different quality control metrics. Finally, a major source of untapped information lies in the temporal aspect. Most often, problems in a mass spectrometry setup appear gradually, but are only observed after a critical juncture. As previous analyses have not used this temporal information directly, there remains a large potential to detect these problems as soon as they start to manifest by taking this additional dimension of information into account. Based on the previously discovered patterns, these can be evaluated over time by making use of sequential pattern mining techniques. The awareness has risen that suitable quality control information is mandatory to assess the validity of a mass spectrometry experiment. Current efforts aim to standardize this quality control information [4], which will facilitate the dissemination of the data. This results in a large amount of as of yet untapped information, which can be leveraged by making use of specific data mining techniques in order to harness the full power of this new information. [1] Rudnick, P. A. et al. Performance metrics for liquid chromatography-tandem mass spectrometry systems in proteomics analyses. Molecular & Cellular Proteomics 9, 225–241 (2010). [2] Wang, X. et al. QC metrics from CPTAC raw LC-MS/MS data interpreted through multivariate statistics. Analytical Chemistry 86, 2497–2509 (2014). [3] Aksehirli, E., Goethals, B., Müller, E. & Vreeken, J. Cartification: A neighborhood preserving transformation for mining high dimensional data. in Thirteenth IEEE International Conference on Data Mining - ICDM ’13 937–942 (IEEE, 2013). doi:10.1109/ICDM.2013.146 [4] Walzer, M. et al. qcML: An exchange format for quality control metrics from mass spectrometry experiments. Molecular & Cellular Proteomics (2014). doi:10.1074/mcp.M113.03590

Grasping frequent subgraph mining for bioinformatics applications

Abstract 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

MESSAR: Automated recommendation of metabolite substructures from tandem mass spectra.

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 of generating structural hypotheses for 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 hypothesis generation or 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

Unravelling associations between unassigned mass spectrometry peaks with frequent itemset mining techniques

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

MESSAR : automated recommendation of metabolite substructures from tandem mass spectra

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 of generating structural hypotheses for 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 hypothesis generation or 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

MESSAR: Automated recommendation of metabolite substructures from tandem mass spectra

<p>Data set associated with:</p> <p>Mrzic, A. et al. MESSAR: Automated recommendation of metabolite substructures from tandem mass spectra. bioRxiv (2017). doi:10.1101/134189</p> <p>This data set is provided by Janssen Pharmaceutica. It consists of known standard pharmaceutical compounds for which high quality Q-Exactive MS/MS data is provided. The data set contain drugs ranging from antifungal and antipsychotic agents to inhibitors of the hepatitis C virus and a compound that slows down progression of Alzheimer's disease.</p