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: Circularized Chromosome Conformation Capture (4C) is a powerful technique for studying the spatial interactions of a specific genomic region called the 'viewpoint' with the rest of the genome, both in a single condition or comparing different experimental conditions or cell types. Observed ligation frequencies typically show a strong, regular dependence on genomic distance from the viewpoint, on top of which specific interaction peaks are superimposed. Here, we address the computational task to find these specific peaks and to detect changes between different biological conditions. RESULTS: We model the overall trend of decreasing interaction frequency with genomic distance by fitting a smooth monotonically decreasing function to suitably transformed count data. Based on the fit, z-scores are calculated from the residuals, and high z-scores are interpreted as peaks providing evidence for specific interactions. To compare different conditions, we normalize fragment counts between samples, and call for differential contact frequencies using the statistical method DESEQ2: adapted from RNA-Seq analysis. AVAILABILITY AND IMPLEMENTATION: A full end-to-end analysis pipeline is implemented in the R package FourCSeq available at www.bioconductor.org. CONTACT: [email protected] or [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: RNA sequencing is becoming a standard for expression profiling experiments and many tools have been developed in the past few years to analyze RNA-Seq data. Numerous 'Bioconductor' packages are available for next-generation sequencing data loading in R, e.g. ShortRead and Rsamtools as well as to perform differential gene expression analyses, e.g. DESeq and edgeR. However, the processing tasks lying in between these require the precise interplay of many Bioconductor packages, e.g. Biostrings, IRanges or external solutions are to be sought. RESULTS: We developed 'easyRNASeq', an R package that simplifies the processing of RNA sequencing data, hiding the complex interplay of the required packages behind a single functionality. AVAILABILITY: The package is implemented in R (as of version 2.15) and is available from Bioconductor (as of version 2.10) at the URL: http://bioconductor.org/packages/release/bioc/html/easyRNASeq.html, where installation and usage instructions can be found. CONTACT: [email protected].

Precise patterns of spatial and temporal gene expression are central to metazoan complexity and act as a driving force for embryonic development. While there has been substantial progress in dissecting and predicting cis-regulatory activity, our understanding of how information from multiple enhancer elements converge to regulate a gene's expression remains elusive. This is in large part due to the number of different biological processes involved in mediating regulation as well as limited availability of experimental measurements for many of them. Here, we used a Bayesian approach to model diverse experimental regulatory data, leading to accurate predictions of both spatial and temporal aspects of gene expression. We integrated whole-embryo information on transcription factor recruitment to multiple cis-regulatory modules, insulator binding and histone modification status in the vicinity of individual gene loci, at a genome-wide scale during Drosophila development. The model uses Bayesian networks to represent the relation between transcription factor occupancy and enhancer activity in specific tissues and stages. All parameters are optimized in an Expectation Maximization procedure providing a model capable of predicting tissue- and stage-specific activity of new, previously unassayed genes. Performing the optimization with subsets of input data demonstrated that neither enhancer occupancy nor chromatin state alone can explain all gene expression patterns, but taken together allow for accurate predictions of spatio-temporal activity. Model predictions were validated using the expression patterns of more than 600 genes recently made available by the BDGP consortium, demonstrating an average 15-fold enrichment of genes expressed in the predicted tissue over a naïve model. We further validated the model by experimentally testing the expression of 20 predicted target genes of unknown expression, resulting in an accuracy of 95% for temporal predictions and 50% for spatial. While this is, to our knowledge, the first genome-wide approach to predict tissue-specific gene expression in metazoan development, our results suggest that integrative models of this type will become more prevalent in the future.

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.