Biology subjects

Freeman, T. C.

Publications and source records attributed to Freeman, T. C..

5 recordsLinked to original sources

bioRxiv2018

Network analysis of canine brain morphometry links tumour risk to oestrogen deficiency and accelerated brain ageing

BackgroundStructural brain age is a valuable but complex biomarker for several brain disorders. The dog is an unrivalled comparator for neurological disease modeling, however brain phenotypic diversity among pedigrees creates computational and statistical challenges.\n\nMethodsWe applied unbiased network correlation analysis in dogs to explore complex interactions between brain morphometrics, patient metadata, and neurological disease. Twenty-four parameters measured from each of 286 brain magnetic resonance imaging scans generated 9,438 data points that were used to cluster canine patients according to their brain morphometry profiles. The network was then explored for statistically significant enrichments within breed, sex, age, and diagnostic categories.\n\nFindingsMorphometric comparisons revealed an advanced aged-brain profile in the Boxer breed, consisting of a small brain length, width, and volume, combined with ventriculomegaly. Key features of this profile were paralleled in neutered female dogs which, relative to un-neutered females, had an 11-fold greater risk of developing primary brain tumours. Enrichment analysis confirmed that Boxers and geriatric individuals were enriched for brain tumour diagnoses, despite a lack of geriatric Boxers within the cohort.\n\nInterpretationThese findings suggest that accelerated brain ageing might contribute to tumour risk in Boxers and may be influenced by oestrogen deficiency -- a risk factor for dementia and brain tumours in humans. We propose that morphometric features of brain ageing in dogs, like humans, might better predict neurological disease risk than a patients chronological age.\n\nFundingWellcome Trust Integrated Training Fellowship for Veterinarians (096409/Z/11/Z to N.M.R) and an MSD Animal Health Connect Bursary (to O.M.S.).

neuroscience↗

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↗

bioRxiv2018

Characterisation of the UK honey bee (Apis mellifera) metagenome.

The European honey bee (Apis mellifera) plays a major role in pollination and food production, but is under threat from emerging pathogens and agro-environmental insults. As with other organisms, honey bee health is a complex product of environment, host genetics and associated microbes (commensal, opportunistic and pathogenic). Improved understanding of bee genetics and their molecular ecology can help manage modern challenges to bee health and production. Sampling bee and cobiont genomes, we characterised the metagenome of 19 honey bee colonies across Britain. Low heterozygosity was observed in bees from many Scottish colonies, sharing high similarity to the native dark bee, A. mellifera mellifera. Apiaries exhibited high diversity in the composition and relative abundance of individual microbiome taxa. Most non-bee sequences derived from known honey bee commensal bacteria or known pathogens, e.g. Lotmaria passim (Trypanosomatidae), and Nosema spp. (Microsporidia). However, DNA was also detected from numerous additional bacterial, plant (food source), protozoan and metazoan organisms. To classify sequences from cobionts lacking genomic information, we developed a novel network analysis approach clustering orphan contigs, allowing the identification of a pathogenic gregarine. Our analyses demonstrate the power of high-throughput, directed metagenomics in agroecosystems identifying potential threats to honey bees present in their microbiota.

genomics↗

bioRxiv2018

Assembly of a Parts List of the Human Mitotic Cell Cycle Machinery

The set of proteins required for mitotic division remains poorly characterised. Here, an extensive series of correlation analyses of human and mouse transcriptomics data was performed to identify genes strongly and reproducibly associated with cells undergoing S/G2-M phases of the cell cycle. In so doing, a list of 701 cell cycle-associated genes was defined and shown that whilst many are only expressed during these phases, the expression of others is also driven by alternative promoters. Of this list, 496 genes have known cell cycle functions, whereas 205 were assigned as putative cell cycle genes, 53 of which are functionally uncharacterised. Among these, 27 were screened for subcellular localisation revealing many to be nuclear localised and at least four to be novel centrosomal proteins. Furthermore, 10 others inhibited cell proliferation upon siRNA knockdown. This study presents the first comprehensive list of human cell cycle proteins, identifying many new candidate proteins.

genomics↗

bioRxiv2017

Continuous Biomarker Assessment by Exhaustive Survival Analysis

Publicly available high-throughput molecular data can enable biomarker identification and evaluation in a meta-analysis. However, a continuous biomarkers underlying distribution and/or potential confounding factors associated with outcome will inevitably vary between cohorts and is often ignored. The survivALL R package (https://CRAN.R-project.org/package=survivALL) allows researchers to generate visual and numerical comparisons of all possible points-of-separation, enabling quantitative biomarkers to be reliably evaluated within and across datasets, independent of compositional variation. Here, we demonstrate survivALLs ability to robustly and reproducibly determine an applicable level of gene expression for patient prognostic classification, in datasets of similar and dissimilar compositions. We believe survivALL represents a significant improvement over existing methodologies in stratifying patients and determining quantitative biomarker(s) cut-points for public and novel datasets.

bioinformatics↗