PhysBinder : improving the prediction of transcription factor binding sites by flexible inclusion of biophysical properties

The most important mechanism in the regulation of transcription is the binding of a transcription factor (TF) to a DNA sequence called the TF binding site (TFBS). Most binding sites are short and degenerate, which makes predictions based on their primary sequence alone somewhat unreliable. We present a new web tool that implements a flexible and extensible algorithm for predicting TFBS. The algorithm makes use of both direct (the sequence) and several indirect readout features of protein-DNA complexes (biophysical properties such as bendability or the solvent-excluded surface of the DNA). This algorithm significantly outperforms state-of-the-art approaches for in silico identification of TFBS. Users can submit FASTA sequences for analysis in the PhysBinder integrative algorithm and choose from >60 different TF-binding models. The results of this analysis can be used to plan and steer wet-lab experiments. The PhysBinder web tool is freely available at http://bioit.dmbr.ugent.be/physbinder/index.php

ConTra v3 : a tool to identify transcription factor binding sites across species, update 2017

Transcription factors are important gene regulators with distinctive roles in development, cell signaling and cell cycling, and they have been associated with many diseases. The ConTra v3 web server allows easy visualization and exploration of predicted transcription factor binding sites (TFBSs) in any genomic region surrounding coding or non-coding genes. In this updated version, with a completely re-implemented user interface using latest web technologies, users can choose from nine reference organisms ranging from human to yeast. ConTra v3 can analyze promoter regions, 5'-UTRs, 3'-UTRs and introns or any other genomic region of interest. Thousands of position weight matrices are available to choose from for detecting specific binding sites. Besides this visualization option, additional new exploration functionality is added to the tool that will automatically detect TFBSs having at the same time the highest regulatory potential, the highest conservation scores of the genomic regions covered by the predicted TFBSs and strongest co-localizations with genomic regions exhibiting regulatory activity. The ConTra v3 web server is freely available at http://bioit2.irc.ugent.be/contra/v3

Genome dynamics of the human embryonic kidney 293 lineage in response to cell biology manipulations

The HEK293 human cell lineage is widely used in cell biology and biotechnology. Here we use whole-genome resequencing of six 293 cell lines to study the dynamics of this aneuploid genome in response to the manipulations used to generate common 293 cell derivatives, such as transformation and stable clone generation (293T); suspension growth adaptation (293S); and cytotoxic lectin selection (293SG). Remarkably, we observe that copy number alteration detection could identify the genomic region that enabled cell survival under selective conditions (i.c. ricin selection). Furthermore, we present methods to detect human/vector genome breakpoints and a user-friendly visualization tool for the 293 genome data. We also establish that the genome structure composition is in steady state for most of these cell lines when standard cell culturing conditions are used. This resource enables novel and more informed studies with 293 cells, and we will distribute the sequenced cell lines to this effect

TISRover : ConvNets learn biologically relevant features for effective translation initiation site prediction

Being a key component in gene regulation, translation initiation is a well-studied topic. However, recent findings have shown translation initiation to be more complex than initially thought, urging for more effective prediction methods. In this paper, we present TISRover, a multi-layered convolutional neural network architecture for translation initiation site prediction. We achieve state-of-the-art results, outperforming a previous deep learning approach by 4% to 23% in terms of auPRC, and other approaches by at least 68% in terms of error rate. Furthermore, we present a methodology to analyse the decision-making process of our network models, revealing various biologically relevant features for translation initiation site prediction that are automatically learnt from scratch, without any prior knowledge. The most notable features found are the Kozak consensus sequence, the reading frame characteristics, the influence of stop and start codons in the sequence, and the presence of donor splice site patterns

SpliceRover : interpretable convolutional neural networks for improved splice site prediction

Motivation: During the last decade, improvements in high-throughput sequencing have generated a wealth of genomic data. Functionally interpreting these sequences and finding the biological signals that are hallmarks of gene function and regulation is currently mostly done using automated genome annotation platforms, which mainly rely on integrated machine learning frameworks to identify different functional sites of interest, including splice sites. Splicing is an essential step in the gene regulation process, and the correct identification of splice sites is a major cornerstone in a genome annotation system. Results: In this paper, we present SpliceRover, a predictive deep learning approach that outperforms the state-of-the-art in splice site prediction. SpliceRover uses convolutional neural networks (CNNs), which have been shown to obtain cutting edge performance on a wide variety of prediction tasks. We adapted this approach to deal with genomic sequence inputs, and show it consistently outperforms already existing approaches, with relative improvements in prediction effectiveness of up to 80.9% when measured in terms of false discovery rate. However, a major criticism of CNNs concerns their 'black box' nature, as mechanisms to obtain insight into their reasoning processes are limited. To facilitate interpretability of the SpliceRover models, we introduce an approach to visualize the biologically relevant information learnt. We show that our visualization approach is able to recover features known to be important for splice site prediction (binding motifs around the splice site, presence of polypyrimidine tracts and branch points), as well as reveal new features (e.g. several types of exclusion patterns near splice sites)

Internal and interfacial microstructure characterization of ice droplets on surfaces by X-ray computed tomography

Hypothesis: Characterizing the microstructure of an ice/surface interface and its effect on the icephobic behavior of surfaces remains a significant challenge. Introducing X-ray Computed Tomography (XCT) can provide unprecedented insights into the internal (porosity) and interfacial structures, i.e. wetting regime, between (super)hydrophobic surfaces and ice by visualizing these optically inaccessible regions. Experiments: Frozen droplets with controlled volume were deposited on top of metallic and polymeric substrates with different levels of wettability. Different modes of XCT (3D and 4D) were utilized to obtain information on the internal and interfacial structure of the ice/surface system. The results were supplemented by conventional surface analysis techniques, including optical profilometry and contact angle measurements. Findings: Using XCT on ice/surface systems, the 3D and 4D (imaging with temporal resolution) structural information can be visualized. From these datasets, qualitative and quantitative results were obtained, not only for characterizing the interface but also for analyzing the entire droplet/surface system, e.g., measurement of porosity size, shape, and location. These results highlight the potential of XCT in the characterization of both droplets and substrates and proves that the technique can aid to develop hydrophobic surfaces for use as icephobic materials

Structural basis of activation and antagonism of receptor signaling mediated by interleukin-27

Interleukin-27 (IL-27) uniquely assembles p28 and EBI3 subunits to a heterodimeric cytokine that signals via IL-27Ra and gp130. To provide the structural framework for receptor activation by IL-27 and its emerging therapeutic targeting, we report here crystal structures of mouse IL-27 in complex with IL-27Ra and of human IL-27 in complex with SRF388, a monoclonal antibody undergoing clinical trials with oncology indications. One face of the helical p28 subunit interacts with EBI3, while the opposite face nestles into the interdomain elbow of IL-27Ra to juxtapose IL-27Ra to EBI3. This orients IL-27Ra for paired signaling with gp130, which only uses its immunoglobulin domain to bind to IL-27. Such a signaling complex is distinct from those mediated by IL-12 and IL-23. The SRF388 binding epitope on IL-27 overlaps with the IL-27Ra interaction site explaining its potent antagonistic properties. Collectively, our findings will facilitate the mechanistic interrogation, engineering, and therapeutic targeting of IL-27

Structural basis of activation and antagonism of receptor signaling mediated by interleukin-27

Interleukin-27 (IL-27) uniquely assembles p28 and EBI3 subunits to a heterodimeric cytokine that signals via IL-27Rα and gp130. To provide the structural framework for receptor activation by IL-27 and its emerging therapeutic targeting, we report here crystal structures of mouse IL-27 in complex with IL-27Rα and of human IL-27 in complex with SRF388, a monoclonal antibody undergoing clinical trials with oncology indications. One face of the helical p28 subunit interacts with EBI3, while the opposite face nestles into the interdomain elbow of IL-27Rα to juxtapose IL-27Rα to EBI3. This orients IL-27Rα for paired signaling with gp130, which only uses its immunoglobulin domain to bind to IL-27. Such a signaling complex is distinct from those mediated by IL-12 and IL-23. The SRF388 binding epitope on IL-27 overlaps with the IL-27Rα interaction site explaining its potent antagonistic properties. Collectively, our findings will facilitate the mechanistic interrogation, engineering, and therapeutic targeting of IL-27