2D SIFt: a matrix of ligand-receptor interactions

Depicting a ligand-receptor complex via Interaction Fingerprints has been shown to be both a viable data visualization and an analysis tool. The spectrum of its applications ranges from simple visualization of the binding site through analysis of molecular dynamics runs, to the evaluation of the homology models and virtual screening. Here we present a novel tool derived from the Structural Interaction Fingerprints providing a detailed and unique insight into the interactions between receptor and specific regions of the ligand (grouped into pharmacophore features) in the form of a matrix, a 2D-SIFt descriptor. The provided implementation is easy to use and extends the python library, allowing the generation of interaction matrices and their manipulation (reading and writing as well as producing the average 2D-SIFt). The library for handling the interaction matrices is available via repository http://bitbucket.org/zchl/sift2d.

3D reconstruction of genomic regions from sparse interaction data

Chromosome conformation capture (3C) technologies measure the interaction frequency between pairs of chromatin regions within the nucleus in a cell or a population of cells. Some of these 3C technologies retrieve interactions involving non-contiguous sets of loci, resulting in sparse interaction matrices. One of such 3C technologies is Promoter Capture Hi-C (pcHi-C) that is tailored to probe only interactions involving gene promoters. As such, pcHi-C provides sparse interaction matrices that are suitable to characterize short- and long-range enhancer–promoter interactions. Here, we introduce a new method to reconstruct the chromatin structural (3D) organization from sparse 3C-based datasets such as pcHi-C. Our method allows for data normalization, detection of significant interactions and reconstruction of the full 3D organization of the genomic region despite of the data sparseness. Specifically, it builds, with as low as the 2–3% of the data from the matrix, reliable 3D models of similar accuracy of those based on dense interaction matrices. Furthermore, the method is sensitive enough to detect cell-type-specific 3D organizational features such as the formation of different networks of active gene communities.

DO-CRIME: Dynamic On-sky Covariance Random Interaction Matrix Evaluation, a novel method for calibrating adaptive optics systems

Adaptive optics systems require a calibration procedure to operate, whether in closed loop or even more importantly in forward control. This calibration usually takes the form of an interaction matrix and is a measure of the response on the wavefront sensor to wavefront corrector stimulus. If this matrix is sufficiently well conditioned, it can be inverted to produce a control matrix, which allows to compute the optimal commands to apply to the wavefront corrector for a given wavefront sensor measurement vector. Interaction matrices are usually measured by means of an artificial source at the entrance focus of the adaptive optics system; however, adaptive secondary mirrors on Cassegrain telescopes offer no such focus and the measurement of their interaction matrices becomes more challenging and needs to be done on-sky using a natural star. The most common method is to generate a theoretical or simulated interaction matrix and adjust it parametrically (for example, decenter, magnification, rotation) using on-sky measurements. We propose a novel method of measuring on-sky interaction matrices ab initio from the telemetry stream of the AO system using random patterns on the deformable mirror with diagonal commands covariance matrices. The approach, being developed for the adaptive secondary mirror upgrade for the imaka wide-field AO system on the UH2.2m telescope project, is shown to work on-sky using the current imaka testbed.

Patent citations, knowledge flows, and catching-up: Evidences of different national experiences for the period 1982–2006

This paper uses information about patent citations to track the evolution of knowledge flows in selected countries engaged in catching-up processes. The analysis comprises patent citation data extracted from the USPTO database for the period 1982-2006. The data are presented through technological interaction matrices displaying the interaction between the technological fields of cited and citing patents. Each matrix cell matches the technological field(s) of one cited patent to the technological field(s) of its citing patent(s). The hypothesis is that the intensification and diversification of knowledge flows to a greater number of fields broadens the possibilities of identifying attractive opportunities for innovation, thereby multiplying the opportunities of development and catching-up. The analysis seeks to identify which technological fields concentrate the absorption and diffusion of knowledge in a given country over different periods, a consideration which tends to be related to the possibilities of catching-up processes.

Scool: a new data storage format for single-cell Hi-C data

Motivation Single-cell Hi-C research currently lacks an efficient, easy to use and shareable data storage format. Recent studies have used a variety of sub-optimal solutions: publishing raw data only, text-based interaction matrices, or reusing established Hi-C storage formats for single interaction matrices. These approaches are storage and pre-processing intensive, require long labour time and are often error-prone. Results The single-cell cooler file format (scool) provides an efficient, user-friendly and storage-saving approach for single-cell Hi-C data. It is a flavour of the established cooler format and guarantees stable API support. Availability and implementation The single-cell cooler format is part of the cooler file format as of API version 0.8.9. It is available via pip, conda and github: https://github.com/mirnylab/cooler. Contact [email protected] Supplementary information Supplementary data are available at Bioinformatics online.

3D reconstruction of genomic regions from sparse interaction data

Chromosome Conformation Capture (3C) technologies measure the interaction frequency between pairs of chromatin regions within the nucleus in a cell or a population of cells. Some of these 3C technologies retrieve interactions involving non-contiguous sets of loci, resulting in sparse interaction matrices. One of such 3C technologies is Promoter Capture Hi-C (pcHi-C) that is tailored to probe only interactions involving gene promoters. As such, pcHi-C provides sparse interaction matrices that are suitable to characterise short- and long-range enhancer-promoter interactions. Here, we introduce a new method to reconstruct the chromatin structural (3D) organisation from sparse 3C-based datasets such as pcHi-C. Our method allows for data normalisation, detection of significant interactions, and reconstruction of the full 3D organisation of the genomic region despite of the data sparseness. Specifically, it produces reliable reconstructions, in line with the ones obtained from dense interaction matrices, with as low as the 2-3% of the data from the matrix. Furthermore, the method is sensitive enough to detect cell-type-specific 3D organisational features such as the formation of different networks of active gene communities.

Identification of chromatin loops from Hi-C interaction matrices by CTCF-CTCF topology classification

ABSTRACTGenome-wide profiling of long-range interactions has revealed that the CCCTC-Binding factor (CTCF) often anchors chromatin loops and is enriched at boundaries of the so-called Topologically Associating Domains or TADs, which suggests that CTCF is essential in the 3D organization of chromatin. However, the systematic topological classification of pairwise CTCF-CTCF interactions has not been yet explored.Here, we developed a computational pipeline able to classify all CTCF-CTCF pairs according to their chromatin interactions from Hi-C experiments. The interaction profiles of all CTCF-CTCF pairs were further structurally clustered using Self-Organizing Feature Maps (SOFM) and their functionality characterized by their epigenetic states. The resulting cluster were then input to a convolutional neural network aiming at the de novo detecting chromatin loops from Hi-C interaction matrices.Our new method, called LOOPbit, is able to automatically detect higher number of pairwise interactions with functional significance compared to other loop callers highlighting the link between chromatin structure and function.

Loop detection using Hi-C data with HiCExplorer

Chromatin loops are an important factor in the structural organization of the genome. The detection of chromatin loops in Hi-C interaction matrices is a challenging and compute intensive task. The presented approach shows a chromatin loop detection algorithm which applies a strict candidate selection based on continuous negative binomial distributions and performs a Wilcoxon rank-sum test to detect enriched Hi-C interactions.