Penalized Splines as Frequency Selective Filters - Reducing the Excess Variability at the Margins

Penalized splines have become a popular tool to model the trend component in economic time series. The outcome of the spline predominantly depends on the choice of a penalization parameter that controls the smoothness of the trend. This paper derives the penalization of splines by frequency domain aspects and points out their link to rational square wave filters. As a novel contribution this paper focuses on the so called excess variability at the margins that describes the undesired increasing variability of the trend estimation to the ends of the series. It will be shown that the too high volatility at the margins can be reduced considerably by a time varying penalization, which yields more reliable estimations for the most recent periods

Trend Estimation with Penalized Splines as Mixed Models for Series with Structural Breaks

On purpose to extract trend and cycle from a time series many competing techniques have been developed. The probably most prevalent is the Hodrick Prescott filter. However this filter suffers from diverse shortcomings, especially the subjective choice of its penalization parameter. To this point penalized splines within a mixed model framework offer the advantage of a data driven derivation of the penalization parameter. Nevertheless the Hodrick-Prescott filter as well as penalized splines fail to estimate trend and cycle when one deals with times series that contain structural breaks. This paper extends the technique of splines within a mixed model framework to account for break points in the data. It explains how penalized splines as mixed models can be used to avoid distortions caused by breaks and finally provides an empirical application to German data which exhibit structural breaks due to the reunification in 1990

An evolutionary and structural characterization of mammalian protein complex organization

Background: We have recently released a comprehensive, manually curated database of mammalian protein complexes called CORUM. Combining CORUM with other resources, we assembled a dataset of over 2700 mammalian complexes. The availability of a rich information resource allows us to search for organizational properties concerning these complexes. Results: As the complexity of a protein complex in terms of the number of unique subunits increases, we observed that the number of such complexes and the mean non-synonymous to synonymous substitution ratio of associated genes tend to decrease. Similarly, as the number of different complexes a given protein participates in increases, the number of such proteins and the substitution ratio of the associated gene also tend to decrease. These observations provide evidence relating natural selection and the organization of mammalian complexes. We also observed greater homogeneity in terms of predicted protein isoelectric points, secondary structure and substitution ratio in annotated versus randomly generated complexes. A large proportion of the protein content and interactions in the complexes could be predicted from known binary protein-protein and domain-domain interactions. In particular, we found that large proteins interact preferentially with much smaller proteins. Conclusions: We observed similar trends in yeast and other data. Our results support the existence of conserved relations associated with the mammalian protein complexes

Knowledge-based matrix factorization temporally resolves the cellular responses to IL-6 stimulation

<p>Abstract</p> <p>Background</p> <p>External stimulations of cells by hormones, cytokines or growth factors activate signal transduction pathways that subsequently induce a re-arrangement of cellular gene expression. The analysis of such changes is complicated, as they consist of multi-layered temporal responses. While classical analyses based on clustering or gene set enrichment only partly reveal this information, matrix factorization techniques are well suited for a detailed temporal analysis. In signal processing, factorization techniques incorporating data properties like spatial and temporal correlation structure have shown to be robust and computationally efficient. However, such correlation-based methods have so far not be applied in bioinformatics, because large scale biological data rarely imply a natural order that allows the definition of a delayed correlation function.</p> <p>Results</p> <p>We therefore develop the concept of graph-decorrelation. We encode prior knowledge like transcriptional regulation, protein interactions or metabolic pathways in a weighted directed graph. By linking features along this underlying graph, we introduce a partial ordering of the features (e.g. genes) and are thus able to define a graph-delayed correlation function. Using this framework as constraint to the matrix factorization task allows us to set up the fast and robust graph-decorrelation algorithm (GraDe). To analyze alterations in the gene response in <it>IL-6 </it>stimulated primary mouse hepatocytes, we performed a time-course microarray experiment and applied GraDe. In contrast to standard techniques, the extracted time-resolved gene expression profiles showed that <it>IL-6 </it>activates genes involved in cell cycle progression and cell division. Genes linked to metabolic and apoptotic processes are down-regulated indicating that <it>IL-6 </it>mediated priming renders hepatocytes more responsive towards cell proliferation and reduces expenditures for the energy metabolism.</p> <p>Conclusions</p> <p>GraDe provides a novel framework for the decomposition of large-scale 'omics' data. We were able to show that including prior knowledge into the separation task leads to a much more structured and detailed separation of the time-dependent responses upon <it>IL-6 </it>stimulation compared to standard methods. A Matlab implementation of the GraDe algorithm is freely available at <url>http://cmb.helmholtz-muenchen.de/grade</url>.</p

Diversity and activity of sugar transporters in nematode-induced root syncytia

The plant-parasitic nematode Heterodera schachtii stimulates plant root cells to form syncytial feeding structures which synthesize all nutrients required for successful nematode development. Cellular re-arrangements and modified metabolism of the syncytia are accompanied by massive intra- and intercellular solute allocations. In this study the expression of all genes annotated as sugar transporters in the Arabidopsis Membrane Protein Library was investigated by Affymetrix gene chip analysis in young and fully developed syncytia compared with non-infected Arabidopsis thaliana roots. The expression of three highly up-regulated (STP12, MEX1, and GTP2) and three highly down-regulated genes (SFP1, STP7, and STP4) was analysed by quantitative RT-PCR (qRT-PCR). The most up-regulated gene (STP12) was chosen for further in-depth studies using in situ RT-PCR and a nematode development assay with a T-DNA insertion line revealing a significant reduction of male nematode development. The specific role of STP12 expression in syncytia of male juveniles compared with those of female juveniles was further shown by qRT-PCR. In order to provide evidence for sugar transporter activity across the plasma membrane of syncytia, fluorescence-labelled glucose was used and membrane potential recordings following the application of several sugars were performed. Analyses of soluble sugar pools revealed a highly specific composition in syncytia. The presented work demonstrates that sugar transporters are specifically expressed and active in syncytia, indicating a profound role in inter- and intracelluar transport processes

Quantification and monosaccharide composition of hemicelluloses from different plant functional types

Hemicelluloses are the second most abundant polysaccharide in nature after cellulose. So far, thechemical heterogeneity of cell-wall hemicelluloses and the relatively large sample-volume required inexisting methods represent major obstacles for large-scale, cross-species analyses of this important plant compound. Here, we apply a new micro-extraction method to analyse hemicelluloses and the ratioof &lsquo;cellulose and lignin&rsquo; to hemicelluloses in different tissues of 28 plant species comprising fourplant functional types (broad-leaved trees, conifers, grasses and herbs). For this study, the fiber analysisafter Van Soest was modified to enable the simultaneous quantitative and qualitative measurements ofhemicelluloses in small sample volumes. Total hemicellulose concentrations differed markedly amongfunctional types and tissues with highest concentration in sapwood of broad-leaved trees (31% d.m.in Fraxinus excelsior) and lowest concentration between 10 and 15% d.m. in leaves and bark of woodyspecies as well as in roots of herbs. As for total hemicellulose concentrations, plant functional types and tissues exhibited characteristic ratios between the sum of cellulose plus lignin and hemicelluloses, with very high ratios (2) in all investigated leaves. Additional HPLC analyses of hydrolysed hemicelluloses showed xylose to be the dominant hemicellulose monosaccharide in tissues of broad-leaved trees, grasses and herbs while coniferous species showed higher amounts of arabinose, galactose and mannose. Overall, the micro-extraction method permitted for the simultaneous determination of hemicelluloses of various tissues and plant functional types which exhibited characteristic hemicellulose concentrations and monosaccharide patterns