MOTIVATION: Visualization of genomic data is fundamental for gaining insights into genome function. Yet, co-visualization of a large number of datasets remains a challenge in all popular genome browsers and the development of new visualization methods is needed to improve the usability and user experience of genome browsers. RESULTS: We present Dynamix, a JBrowse plugin that enables the parallel inspection of hundreds of genomic datasets. Dynamix takes advantage of a priori knowledge to automatically display data tracks with signal within a genomic region of interest. As the user navigates through the genome, Dynamix automatically updates data tracks and limits all manual operations otherwise needed to adjust the data visible on screen. Dynamix also introduces a new carousel view that optimizes screen utilization by enabling users to independently scroll through groups of tracks. AVAILABILITY AND IMPLEMENTATION: Dynamix is hosted at http://furlonglab.embl.de/Dynamix . CONTACT: [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BACKGROUND: The yield obtained from next generation sequencers has increased almost exponentially in recent years, making sample multiplexing common practice. While barcodes (known sequences of fixed length) primarily encode the sample identity of sequenced DNA fragments, barcodes made of random sequences (Unique Molecular Identifier or UMIs) are often used to distinguish between PCR duplicates and transcript abundance in, for example, single-cell RNA sequencing (scRNA-seq). In paired-end sequencing, different barcodes can be inserted at each fragment end to either increase the number of multiplexed samples in the library or to use one of the barcodes as UMI. Alternatively, UMIs can be combined with the sample barcodes into composite barcodes, or with standard Illumina® indexing. Subsequent analysis must take read duplicates and sample identity into account, by identifying UMIs. RESULTS: Existing tools do not support these complex barcoding configurations and custom code development is frequently required. Here, we present Je, a suite of tools that accommodates complex barcoding strategies, extracts UMIs and filters read duplicates taking UMIs into account. Using Je on publicly available scRNA-seq and iCLIP data containing UMIs, the number of unique reads increased by up to 36 %, compared to when UMIs are ignored. CONCLUSIONS: Je is implemented in JAVA and uses the Picard API. Code, executables and documentation are freely available at http://gbcs.embl.de/Je . Je can also be easily installed in Galaxy through the Galaxy toolshed.

Given the complexity of developmental networks, it is often difficult to predict the effect of genetic perturbations, even within coding genes. Regulatory factors generally have pleiotropic effects, exhibit partially redundant roles, and regulate highly interconnected pathways with ample cross-talk. Here, we delineate a logical model encompassing 48 components and 82 regulatory interactions involved in mesoderm specification during Drosophila development, thereby providing a formal integration of all available genetic information from the literature. The four main tissues derived from mesoderm correspond to alternative stable states. We demonstrate that the model can predict known mutant phenotypes and use it to systematically predict the effects of over 300 new, often non-intuitive, loss- and gain-of-function mutations, and combinations thereof. We further validated several novel predictions experimentally, thereby demonstrating the robustness of model. Logical modelling can thus contribute to formally explain and predict regulatory outcomes underlying cell fate decisions.

MOTIVATION: CoCo, ChIP-on-Chip online, is an open-source web application that supports the annotation and curation of regulatory regions and associated target genes discovered in ChIP-on-chip experiments. CoCo integrates ChIP-on-chip results with diverse types of gene expression data (expression profiling, in situ hybridization) and displays them within a genomic context. Regulatory relationships between the transcription factor-bound regions and putative target genes can be stored and expanded throughout different sessions. AVAILABILITY: http://furlonglab.embl.de/methods/tools/coco.