A novel approach to phylogenetic tree construction using stochastic optimization and clustering

BACKGROUND: The problem of inferring the evolutionary history and constructing the phylogenetic tree with high performance has become one of the major problems in computational biology. RESULTS: A new phylogenetic tree construction method from a given set of objects (proteins, species, etc.) is presented. As an extension of ant colony optimization, this method proposes an adaptive phylogenetic clustering algorithm based on a digraph to find a tree structure that defines the ancestral relationships among the given objects. CONCLUSION: Our phylogenetic tree construction method is tested to compare its results with that of the genetic algorithm (GA). Experimental results show that our algorithm converges much faster and also achieves higher quality than GA

Elemental Composition determination based on MS

Motivation: Identification of metabolites is essential for its use as biomarkers, for research in systems biology, and for drug discovery. The first step before a structure can be elucidated is to determine its elemental composition. High resolution mass spectrometry, which provides the exact mass, together with common constraint-rules, for rejecting false proposed elemental compositions, can not always provide one unique elemental composition solution. Results: The Multi-stage Elemental Formula (MEF) tool is presented in this paper to enable the correct assignment of elemental composition to compounds, their fragment ions, and neutral losses that originate from the molecular ion by using multi-stage mass spectrometry (MSn). The method provided by MEF reduces the list of predicted elemental compositions for each ion by analyzing the elemental compositions of its parent (precursor ion) and descendants (fragments). MSn data of several metabolites were processed using the MEF tool to assign the correct elemental composition and validate the efficacy of the method. Especially the link between the mass accuracy needed to generate one unique elemental composition and the topology of the MSn tree (the width and the depth of the tree) was addressed. This method makes an important step towards semi-automatic de novo identification of metabolites using MSn data

Hierarchical clustering analysis of blood plasma lipidomics profiles from mono- and dizygotic twin families

Twin and family studies are typically used to elucidate the relative contribution of genetic and environmental variation to phenotypic variation. Here, we apply a quantitative genetic method based on hierarchical clustering, to blood plasma lipidomics data obtained in a healthy cohort consisting of 37 monozygotic and 28 dizygotic twin pairs, and 52 of their biological nontwin siblings. Such data are informative of the concentrations of a wide range of lipids in the studied blood samples. An important advantage of hierarchical clustering is that it can be applied to a high-dimensional 'omics' type data, whereas the use of many other quantitative genetic methods for analysis of such data is hampered by the large number of correlated variables. For this study we combined two lipidomics data sets, originating from two different measurement blocks, which we corrected for block effects by 'quantile equating'. In the analysis of the combined data, average similarities of lipidomics profiles were highest between monozygotic (MZ) cotwins, and became progressively lower between dizygotic (DZ) cotwins, among sex-matched nontwin siblings and among sex-matched unrelated participants, respectively. Our results suggest that (1) shared genetic background, shared environment, and similar age contribute to similarities in blood plasma lipidomics profiles among individuals; and (2) that the power of quantitative genetic analyses is enhanced by quantile equating and combination of data sets obtained in different measurement blocks. © 2013 Macmillan Publishers Limited. All rights reserved