Vitessce: a framework for integrative visualization of multi-modal and spatially-resolved single-cell data

Vitessce is an open-source interactive visualization framework for exploration of multi-modal and spatially-resolved single-cell data, with a modular architecture compatible with transcriptomic, proteomic, genome-mapped, and imaging data types. Its modular, coordinated multiple view implementation facilitates a wide range of visualization tasks to support all common single-cell assays. Vitessce is a client-side web application designed to be integrated with computational analysis tools and data resources and does not require specialized server infrastructure. The software is available at http://vitessce.io.

Delta integrates 3D physical structure with topology and genomic data of chromosomes

MotivationThe regulation of gene transcription and DNA replication are tightly associated with the 3D chromosomal structures and genomic features, e.g. epigenetic marks, transcription factor bindings and non-coding RNAs. The interaction between the features and the chromosomal structures forming a multilayer 3D regulatory network. Therefore, it is necessary to integrate the physical 3D architecture of genome and features to comprehensive depict their connection to gene regulation.ResultsHere, we present an integrative visualization and analysis platform, Delta, to facilitate visually annotating and exploring the 3D physical architecture of genomes. Delta takes Hi-C or ChIA-PET contact matrix as input and predicts the topology associated domains and chromatin loops in the genome, and generates a physical 3D model which represents the plausible consensus 3D structure of the genome. Delta features a highly interactive visualization tool, which enhanced the integration of genome topology/physical structure and extensive genome annotation, by juxtaposition of the 3D model with diverse genomic assay outputs. Finally, we showcased that Delta could be helpful to reveal potentially interesting findings by a case study on the β-globin gene region.Availability and implementationhttp://delta.big.ac.cn/[email protected] informationSupplementary data are available at Bioinformatics online.

RISE: a database of RNA interactome from sequencing experiments

We present RISE (http://rise.zhanglab.net), a database of RNA Interactome from Sequencing Experiments. RNA-RNA interactions (RRIs) are essential for RNA regulation and function. RISE provides a comprehensive collection of RRIs that mainly come from recent transcriptome-wide sequencing-based experiments like PARIS, SPLASH, LIGR-seq, and MARIO, as well as targeted studies like RIA-seq, RAP-RNA and CLASH. It also includes interactions aggregated from other primary databases and publications. The RISE database currently contains 328,811 RNA-RNA interactions mainly in human, mouse and yeast. While most existing RNA databases mainly contain interactions of miRNA targeting, notably, more than half of the RRIs in RISE are among mRNA and long non-coding RNAs. We compared different RRI datasets in RISE and found limited overlaps in interactions resolved by different techniques and in different cell lines. It may suggest technology preference and also dynamic natures of RRIs. We also analyzed the basic features of the human and mouse RRI networks and found that they tend to be scale-free, small-world, hierarchical and modular. The analysis may nominate important RNAs or RRIs for further investigation. Finally, RISE provides a Circos plot and several table views for integrative visualization, with extensive molecular and functional annotations to facilitate exploration of biological functions for any RRI of interest.