21 research outputs found
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Integrating Data Clustering and Visualization for the Analysis of 3D Gene Expression Data
The recent development of methods for extracting precise measurements of spatial gene expression patterns from three-dimensional (3D) image data opens the way for new analyses of the complex gene regulatory networks controlling animal development. We present an integrated visualization and analysis framework that supports user-guided data clustering to aid exploration of these new complex datasets. The interplay of data visualization and clustering-based data classification leads to improved visualization and enables a more detailed analysis than previously possible. We discuss (i) integration of data clustering and visualization into one framework; (ii) application of data clustering to 3D gene expression data; (iii) evaluation of the number of clusters k in the context of 3D gene expression clustering; and (iv) improvement of overall analysis quality via dedicated post-processing of clustering results based on visualization. We discuss the use of this framework to objectively define spatial pattern boundaries and temporal profiles of genes and to analyze how mRNA patterns are controlled by their regulatory transcription factors
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Electron cloud effects on an intense ion beam in a four solenoid lattice
The Solenoid Transport Experiment (STX) at LBNL successfully demonstrated the transport of a space-charge dominated ion beam in a two-solenoid lattice. Initial experiments showed a strong dependence of electron cloud effects on solenoid field strength. A current-reducing aperture, two solenoids and in-bore diagnostics were added to the two-solenoid lattice in order to study electron cloud effects more closely. Experiments were conducted with a 10 {micro}s, singly charged potassium ion bunch at an ion energy of 0.3 MeV and currents of 26 mA and 45 mA. A qualitative comparison of experimental and calculated results are presented, including a comparison of the effects of manipulating electrons on the beam dynamics, quantifying beam-induced gas desorption, ionization, and electron effects
Iterative model building, structure refinement and density modification with the PHENIX AutoBuild wizard.
The PHENIX AutoBuild wizard is a highly automated tool for iterative model building, structure refinement and density modification using RESOLVE model building, RESOLVE statistical density modification and phenix.refine structure refinement. Recent advances in the AutoBuild wizard and phenix.refine include automated detection and application of NCS from models as they are built, extensive model-completion algorithms and automated solvent-molecule picking. Model-completion algorithms in the AutoBuild wizard include loop building, crossovers between chains in different models of a structure and side-chain optimization. The AutoBuild wizard has been applied to a set of 48 structures at resolutions ranging from 1.1 to 3.2 A, resulting in a mean R factor of 0.24 and a mean free R factor of 0.29. The R factor of the final model is dependent on the quality of the starting electron density and is relatively independent of resolution
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Iterative build OMIT maps: Map improvement by iterative model-building and refinement without model bias
A procedure for carrying out iterative model-building, density modification and refinement is presented in which the density in an OMIT region is essentially unbiased by an atomic model. Density from a set of overlapping OMIT regions can be combined to create a composite 'Iterative-Build' OMIT map that is everywhere unbiased by an atomic model but also everywhere benefiting from the model-based information present elsewhere in the unit cell. The procedure may have applications in the validation of specific features in atomic models as well as in overall model validation. The procedure is demonstrated with a molecular replacement structure and with an experimentally-phased structure, and a variation on the method is demonstrated by removing model bias from a structure from the Protein Data Bank