Chromosome organization is crucial for genome function. Here, we present a method for visualizing chromosomal DNA at super-resolution and then integrating Hi-C data to produce three-dimensional models of chromosome organization. Using the super-resolution microscopy methods of OligoSTORM and OligoDNA-PAINT, we trace 8 megabases of human chromosome 19, visualizing structures ranging in size from a few kilobases to over a megabase. Focusing on chromosomal regions that contribute to compartments, we discover distinct structures that, in spite of considerable variability, can predict whether such regions correspond to active (A-type) or inactive (B-type) compartments. Imaging through the depths of entire nuclei, we capture pairs of homologous regions in diploid cells, obtaining evidence that maternal and paternal homologous regions can be differentially organized. Finally, using restraint-based modeling to integrate imaging and Hi-C data, we implement a method–integrative modeling of genomic regions (IMGR)–to increase the genomic resolution of our traces to 10 kb.

SUMMARYThe first Provenance Challenge was set up in order to provide a forum for the community to understand the capabilities of different provenance systems and the expressiveness of their provenance representations. To this end, a functional magnetic resonance imaging workflow was defined, which participants had to either simulate or run in order to produce some provenance representation, from which a set of identified queries had to be implemented and executed. Sixteen teams responded to the challenge, and submitted their inputs. In this paper, we present the challenge workflow and queries, and summarize the participants' contributions.

Abstract.We give an overview of VisTrails, a system that provides an infrastructure for systematically capturing detailed provenance and streamlining the data exploration process. A key feature that sets VisTrails apart from previous visualization and scientific workflow systems is a novel action-based mechanism that uniformly captures provenance for data products and workflows used to generate these products. This mechanism not only ensures reproducibility of results, but it also simplifies data exploration by allowing scientists to easily navigate through the space of workflows and parameter settings for an exploration task.

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The recent development of diffraction-unlimited far-field fluorescence microscopy has overcome the classical resolution limit of ~250 nm of conventional light microscopy by about a factor of ten. The improved resolution, however, reveals not only biological structures at an unprecedented resolution, but is also susceptible to sample drift on a much finer scale than previously relevant. Without correction, sample drift leads to smeared images with decreased resolution, and in the worst case to misinterpretation of the imaged structures. This poses a problem especially for techniques such as Fluorescence Photoactivation Localization Microscopy (FPALM/PALM) or Stochastic Optical Reconstruction Microscopy (STORM), which often require minutes recording time. Here we discuss an approach that corrects for three-dimensional (3D) drift in images of fixed samples without the requirement for fiduciary markers or instrument modifications. Drift is determined by calculating the spatial cross-correlation function between subsets of localized particles imaged at different times. Correction down to ~5 nm precision is achieved despite the fact that different molecules are imaged in each frame. We demonstrate the performance of our drift correction algorithm with different simulated structures and analyze its dependence on particle density and localization precision. By imaging mitochondria with Biplane FPALM we show our algorithm's feasibility in a practical application.