Biology subjects

Shih, B. B.

Publications and source records attributed to Shih, B. B..

3 recordsLinked to original sources

bioRxiv2020

Graphia: A platform for the graph-based visualisation and analysis of complex data

Quantitative and qualitative data derived from the analysis of genomes, genes, proteins or metabolites from tissue or cells are currently generated in huge volumes during biomedical research. Graphia is an open-source platform created for the graph-based analysis of such complex data, e.g. transcriptomics, proteomics, genomics data. The software imports data already defined as a network or a similarity matrix and is designed to rapidly visualise very large graphs in 2D or 3D space, providing a wide range of functionality for graph exploration. An extensive range of analysis algorithms, routines for graph transformation, and options for the visualisation of node and edge attributes are also available. Graphias core is extensible through the deployment of plugins, supporting rapid development of additional computational analyses and features necessary for a given analysis task or data source. A plugin for correlation network analysis is distributed with the core application, to support the generation of correlation graphs from any tabular matrix of continuous or discrete values. This provides a powerful analysis solution for the interpretation of high-dimensional data from many sources. Several use cases of Graphia are described, to showcase its wide range of applications. Graphia runs on all major desktop operating systems and is freely available to download from https://graphia.app/.

bioinformatics↗

bioRxiv2020

Monocyte-derived Prostaglandin E2 inhibits antigen-specific cutaneous immunity during ageing

Ageing results in a decline in immune function. We showed previously that healthy older humans (>65 years old) have reduced antigen-specific cutaneous immunity to varicella zoster virus (VZV) antigen challenge. This was associated with p38 MAP kinase driven inflammation that was induced by mild tissue injury caused by the injection of the antigen itself. Here we show that non-specific injury induced by injection of air or saline into the skin of older adults recruits CCR2+CD14+ monocytes by CCL2 produced by senescent fibroblasts. These monocytes reduced TRM proliferation via secretion of prostaglandin E2 (PGE2). Pre-treatment with a p38-MAPK inhibitor (Losmapimod) in older adults in vivo significantly decreased CCL2 expression, recruitment of monocyte into the skin, COX2 expression and PGE2 production. This enhanced the VZV response in the skin. Therefore, local inflammation arising from interaction between senescent cells and monocytes leads to immune decline in the skin during ageing, a process that can be reversed. SummaryInflammation resulting from tissue injury blocks antigen-specific cutaneous immunity during ageing. Monocytes recruited to the skin inhibit TRM function through COX2-derived prostaglandin E2 production. Blocking inflammation and resulting prostaglandin E2 production with a p38-MAP kinase inhibitor significantly enhances cutaneous antigen-specific responses.

immunology↗

bioRxiv2018

Visualisation and analysis of RNA-Seq assembly graphs

RNA-sequencing (RNA-Seq) is a powerful transcriptome profiling technology enabling transcript discovery and quantification. RNA-Seq data are large, and most commonly used as a source of genelevel quantification measurements, whilst the underlying assemblies of reads, if inspected, are usually viewed as sequence reads mapped on to a reference genome. Whilst sufficient for many needs, when the underlying transcript assemblies are complex, this visualisation approach can be limiting; errors in assembly can be difficult to spot and interpretation of splicing events is challenging.\n\nHere we report on the development of a graph-based visualisation method as a complementary approach to understanding transcript diversity and read assembly from short-read RNA-Seq data. Following the mapping of reads to the reference genome, read-to-read comparison is performed on all reads mapping to a given gene, producing a matrix of weighted similarity scores between reads. This is used to produce an RNA assembly graph where nodes represent reads derived from a cDNA and edges similarity scores between reads, above a defined threshold. Visualisation of resulting graphs is performed using Graphia Professional. This tool can render the often large and complex graph topologies that result from DNA/RNA sequence assembly in 3D space and supports info rmatio no verlay on to nodes, e.g. transcript models. We have also implemented an analysis pipeline for the creation of RNA assembly graphs with both a command-line and web-based interface that allows users to create and visualise these data. Here we demonstrate the utility of this approach on RNA-Seq data, including the unusual structure of these graphs and how they can be used to identify issues in assembly, repetitive sequences within transcripts and splice variants. We believe this approach has the potential to significantly improve our understanding of transcript complexity.

bioinformatics↗