SUMMARY: Here we introduce a Fiji plugin utilizing the HPC-as-a-Service concept, significantly mitigating the challenges life scientists face when delegating complex data-intensive processing workflows to HPC clusters. We demonstrate on a common Selective Plane Illumination Microscopy image processing task that execution of a Fiji workflow on a remote supercomputer leads to improved turnaround time despite the data transfer overhead. The plugin allows the end users to conveniently transfer image data to remote HPC resources, manage pipeline jobs and visualize processed results directly from the Fiji graphical user interface. AVAILABILITY AND IMPLEMENTATION: The code is distributed free and open source under the MIT license. Source code: https://github.com/fiji-hpc/hpc-workflow-manager/, documentation: https://imagej.net/SPIM_Workflow_Manager_For_HPC. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Scientific Visualization is generally based on very large and often high-dimensional data sets. In many cases (e.g., in the biomedical domain), high-resolution 3D data that changes over time and multiple data sets are involved. One important task in making sense of this data is finding interesting 2D views in the 3D space and additionally finding ways to handle changes over time. We explore how the combination of spatially aware mobiles and large displays can be used in this context. Specifically, we contribute both a precise selection technique for 2D cross sections and a concept for time-dependent 4D bookmarks that follow a point of interest over time. We implemented these techniques in a prototype application that visualizes biological Light Sheet Fluorescence Microscopy data and report on initial user feedback.

UNLABELLED: Selective Plane Illumination Microscopy (SPIM) allows to image developing organisms in 3D at unprecedented temporal resolution over long periods of time. The resulting massive amounts of raw image data requires extensive processing interactively via dedicated graphical user interface (GUI) applications. The consecutive processing steps can be easily automated and the individual time points can be processed independently, which lends itself to trivial parallelization on a high performance computing (HPC) cluster. Here, we introduce an automated workflow for processing large multiview, multichannel, multiillumination time-lapse SPIM data on a single workstation or in parallel on a HPC cluster. The pipeline relies on snakemake to resolve dependencies among consecutive processing steps and can be easily adapted to any cluster environment for processing SPIM data in a fraction of the time required to collect it. AVAILABILITY AND IMPLEMENTATION: The code is distributed free and open source under the MIT license http://opensource.org/licenses/MIT The source code can be downloaded from github: https://github.com/mpicbg-scicomp/snakemake-workflows Documentation can be found here: http://fiji.sc/Automated_workflow_for_parallel_Multiview_Reconstruction CONTACT: : [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Vol. 28 no. 22 2012, pages 3009–3011 doi:10.1093/bioinformatics/bts543 We regret that, due to a production error, the below references were incorrect in the original paper and should appear as below: Yoo,T.S. et al. (2002) Engineering and algorithm design for an image processing API: A technical report on ITK - the insight toolkit. In: Westwood, J. et al. (eds.) MMVR, pp. 586–592. Preibisch,S. et al. (2010) Into ImgLib—generic image processing in Java. In: Jahnen,A. and Moll,C. (eds.) ImageJ User and Developer Conference. Mondorf-les-Bains, Luxembourg. Rueden,C. et al. (2010) ImageJDev: Next generation ImageJ. In: Jahnen,A. and Moll,C. (eds.) ImageJ User and Developer Conference. Mondorf-les-Bains, Luxembourg. Matas,J. et al. (2002) Robust wide baseline stereo from maximally stable extremal regions. In: Marshall,D. and Rosin,P.L. (eds.) BMVC, Vol. 1, pp. 384–393. Nistér,D. and Stewénius,H. (2008) Linear time maximally stable extremal regions. In: Forsyth,D. et al. (eds.) ECCV, pp. 183–196.

SUMMARY: ImgLib2 is an open-source Java library for n-dimensional data representation and manipulation with focus on image processing. It aims at minimizing code duplication by cleanly separating pixel-algebra, data access and data representation in memory. Algorithms can be implemented for classes of pixel types and generic access patterns by which they become independent of the specific dimensionality, pixel type and data representation. ImgLib2 illustrates that an elegant high-level programming interface can be achieved without sacrificing performance. It provides efficient implementations of common data types, storage layouts and algorithms. It is the data model underlying ImageJ2, the KNIME Image Processing toolbox and an increasing number of Fiji-Plugins. AVAILABILITY: ImgLib2 is licensed under BSD. Documentation and source code are available at http://imglib2.net and in a public repository at https://github.com/imagej/imglib. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics Online. CONTACT: [email protected]

SUMMARY: An essential element when analysing the structure, function, and dynamics of biological networks is the identification of communities of related nodes. An algorithm proposed recently enhances this process by clustering the links between nodes, rather than the nodes themselves, thereby allowing each node to belong to multiple overlapping or nested communities. The R package 'linkcomm' implements this algorithm and extends it in several aspects: (i) the clustering algorithm handles networks that are weighted, directed, or both weighted and directed; (ii) several visualization methods are implemented that facilitate the representation of the link communities and their relationships; (iii) a suite of functions are included for the downstream analysis of the link communities including novel community-based measures of node centrality; (iv) the main algorithm is written in C++ and designed to handle networks of any size; and (v) several clustering methods are available for networks that can be handled in memory, and the number of communities can be adjusted by the user. AVAILABILITY: The program is freely available from the Comprehensive R Archive Network (http://cran.r-project.org/) under the terms of the GNU General Public License (version 2 or later).

MOTIVATION: Evolutionarily conserved non-coding genomic sequences represent a potentially rich source for the discovery of gene regulatory region such as transcriptional enhancers. However, detecting orthologous enhancers using alignment-based methods in higher eukaryotic genomes is particularly challenging, as regulatory regions can undergo considerable sequence changes while maintaining their functionality. RESULTS: We have developed an alignment-free method which identifies conserved enhancers in multiple diverged species. Our method is based on similarity metrics between two sequences based on the co-occurrence of sequence patterns regardless of their order and orientation, thus tolerating sequence changes observed in non-coding evolution. We show that our method is highly successful in detecting orthologous enhancers in distantly related species without requiring additional information such as knowledge about transcription factors involved, or predicted binding sites. By estimating the significance of similarity scores, we are able to discriminate experimentally validated functional enhancers from seemingly equally conserved candidates without function. We demonstrate the effectiveness of this approach on a wide range of enhancers in Drosophila, and also present encouraging results to detect conserved functional regions across large evolutionary distances. Our work provides encouraging steps on the way to ab initio unbiased enhancer prediction to complement ongoing experimental efforts. AVAILABILITY: The software, data and the results used in this article are available at http://www.genome.duke.edu/labs/ohler/research/transcription/fly_enhancer/.

MOTIVATION: Tiled serial section Transmission Electron Microscopy (ssTEM) is increasingly used to describe high-resolution anatomy of large biological specimens. In particular in neurobiology, TEM is indispensable for analysis of synaptic connectivity in the brain. Registration of ssTEM image mosaics has to recover the 3D continuity and geometrical properties of the specimen in presence of various distortions that are applied to the tissue during sectioning, staining and imaging. These include staining artifacts, mechanical deformation, missing sections and the fact that structures may appear dissimilar in consecutive sections. RESULTS: We developed a fully automatic, non-rigid but as-rigid-as-possible registration method for large tiled serial section microscopy stacks. We use the Scale Invariant Feature Transform (SIFT) to identify corresponding landmarks within and across sections and globally optimize the pose of all tiles in terms of least square displacement of these landmark correspondences. We evaluate the precision of the approach using an artificially generated dataset designed to mimic the properties of TEM data. We demonstrate the performance of our method by registering an ssTEM dataset of the first instar larval brain of Drosophila melanogaster consisting of 6885 images. AVAILABILITY: This method is implemented as part of the open source software TrakEM2 (http://www.ini.uzh.ch/~acardona/trakem2.html) and distributed through the Fiji project (http://pacific.mpi-cbg.de).

MOTIVATION: Modern anatomical and developmental studies often require high-resolution imaging of large specimens in three dimensions (3D). Confocal microscopy produces high-resolution 3D images, but is limited by a relatively small field of view compared with the size of large biological specimens. Therefore, motorized stages that move the sample are used to create a tiled scan of the whole specimen. The physical coordinates provided by the microscope stage are not precise enough to allow direct reconstruction (Stitching) of the whole image from individual image stacks. RESULTS: To optimally stitch a large collection of 3D confocal images, we developed a method that, based on the Fourier Shift Theorem, computes all possible translations between pairs of 3D images, yielding the best overlap in terms of the cross-correlation measure and subsequently finds the globally optimal configuration of the whole group of 3D images. This method avoids the propagation of errors by consecutive registration steps. Additionally, to compensate the brightness differences between tiles, we apply a smooth, non-linear intensity transition between the overlapping images. Our stitching approach is fast, works on 2D and 3D images, and for small image sets does not require prior knowledge about the tile configuration. AVAILABILITY: The implementation of this method is available as an ImageJ plugin distributed as a part of the Fiji project (Fiji is just ImageJ: http://pacific.mpi-cbg.de/).

SUMMARY: High-resolution, three-dimensional (3D) imaging of large biological specimens generates massive image datasets that are difficult to navigate, annotate and share effectively. Inspired by online mapping applications like GoogleMaps, we developed a decentralized web interface that allows seamless navigation of arbitrarily large image stacks. Our interface provides means for online, collaborative annotation of the biological image data and seamless sharing of regions of interest by bookmarking. The CATMAID interface enables synchronized navigation through multiple registered datasets even at vastly different scales such as in comparisons between optical and electron microscopy. AVAILABILITY: http://fly.mpi-cbg.de/catmaid.