We present Grid Analysis of Time series Expression (GATE), an integrated computational software platform for the analysis and visualization of high-dimensional biomolecular time series. GATE uses a correlation-based clustering algorithm to arrange molecular time series on a two-dimensional hexagonal array and dynamically colors individual hexagons according to the expression level of the molecular component to which they are assigned, to create animated movies of systems-level molecular regulatory dynamics. In order to infer potential regulatory control mechanisms from patterns of correlation, GATE also allows interactive interroga-tion of movies against a wide variety of prior knowledge datasets. GATE movies can be paused and are interactive, allowing users to reconstruct networks and perform functional enrichment analyses. Movies created with GATE can be saved in Flash format and can be inserted directly into PDF manuscript files as interactive figures. AVAILABILITY: GATE is available for download and is free for academic use from http://amp.pharm.mssm.edu/maayan-lab/gate.htm
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"These approaches have their own advantages and disadvantages but most of these alternative methods have not been widely adopted. In the past, we have developed the software tool grid-analysis of time-series expression (GATE), which is a tool to visualize time-series gene expression data on a hexagonal grid movie (MacArthur, et al., 2010). Neighboring genes on the GATE hexagonal grid can be considered connected in a network where links between genes are established based on gene-gene time-series correlation. "
"This tool gives a clustered view of all proteins or phosphopeptides that were discovered (each represented by a hexagon). The clustering was performed using the GATE software . The brightness of each hexagon indicates the significance of change determined for the protein or phosphopeptide and selected oncogene (red for up-regulation and green for down-regulation). "
[Show abstract][Hide abstract] ABSTRACT: Mutated tyrosine kinases are associated with a number of different haematological malignancies including myeloproliferative disorders, lymphoma and acute myeloid leukaemia. The potential commonalities in the action of six of these leukemogenic proteins on nuclear proteins were investigated using systematic proteomic analysis. The effects on over 3600 nuclear proteins and 1500 phosphopeptide sites were relatively quantified in seven isogenic cell lines. The effects of the kinases were diverse although some commonalities were found. Comparison of the nuclear proteomic data with transcriptome data and cytoplasmic proteomic data indicated that the major changes are due to post-translational mechanisms rather than changes in mRNA or protein distribution. Analysis of the promoter regions of genes whose protein levels changed in response to the kinases showed the most common binding site found was that for NFκB whilst other sites such as those for the glucocorticoid receptor were also found. Glucocorticoid receptor levels and phosphorylation were decreased by all 6 PTKs. Whilst Glucocorticoid receptor action can potentiate NFκB action those proteins where genes have NFκB binding sites were in often regulated post-translationally. However all 6 PTKs showed evidence of NFkB pathway modulation via activation via altered IkB and NFKB levels. Validation of a common change was also undertaken with PMS2, a DNA mismatch repair protein. PMS2 nuclear levels were decreased in response to the expression of all 6 kinases, with no concomitant change in mRNA level or cytosolic protein level. Response to thioguanine, that requires the mismatch repair pathway, was modulated by all 6 oncogenic kinases. In summary common targets for 6 oncogenic PTKs have been found that are regulated by post-translational mechanisms. They represent potential new avenues for therapies but also demonstrate the post-translational regulation is a key target of leukaemogenic kinases.
PLoS ONE 06/2012; 7(6):e38928. DOI:10.1371/journal.pone.0038928 · 3.23 Impact Factor
"In contrast, they are considered to be opposite in behavior if, during any observed period ½t i ,t i þ 1 in which S 1 increases, S 2 decreases and (vice-versa) with the same growth rate (in absolute value). Until recently, many applications in different domains (e.g., speech recognition, system design control, functional MRI, microarrays and gene expression analysis) have used the Pearson correlation coefficient as a behavior proximity measure between signals     . Let us consider an equivalent formula for the "
[Show abstract][Hide abstract] ABSTRACT: This paper proposes an extension of classification trees to time series input variables. A new split criterion based on time series proximities is introduced. First, the criterion relies on an adaptive (i.e., parameterized) time series metric to cover both behaviors and values proximities. The metrics parameters may change from one internal node to another to achieve the best bisection of the set of time series. Second, the criterion involves the automatic extraction of the most discriminating subsequences. The proposed time series classification tree is applied to a wide range of datasets: public and new, real and synthetic, univariate and multivariate data. We show, through the experiments performed in this study, that the proposed tree outperforms temporal trees using standard time series distances and performs well compared to other competitive time series classifiers.