Unearthing the genome of the earthworm Lumbricus rubellus

The earthworm has long been of interest to biologists, most notably Charles Darwin, who was the first to reveal their true role as eco-engineers of the soil. However, to fully understand an animal one needs to combine observational data with the fundamental building blocks of life, DNA. For many years, sequencing a genome was an incredibly costly and time-consuming process. Recent advances in sequencing technology have led to high quality, high throughput data being available at low cost. Although this provides large amounts of sequence data, the bioinformatics knowledge required to assemble and annotate these new data are still in their infancy. This bottleneck is slowly opening up, and with it come the first glimpses into the new and exciting biology of many new species. This thesis provides the first high quality draft genome assembly and annotation of an earthworm, Lumbricus rubellus. The assembly process and resulting data highlight the complexity of assembling a eukaryotic genome using short read data. To improve assembly, a novel approach was created utilising transcripts to scaffold the genome (https://github.com/elswob/SCUBAT). The annotation of the assembly provides the draft of the complete proteome, which is also supported by the first RNA-Seq generated transcriptome. These annotations have enabled detailed analysis of the protein coding genes including comparative analysis with two other annelids (a leech and a polychaete worm) and a symbiont (Verminephrobacter). This analysis identified four key areas which appear to be either highly enhanced or unique to L. rubellus. Three of these may be related to the unique environment from which the sequenced worms originated and add to the mounting evidence for the use of earthworms as bioindicators of soil quality. All data is stored in relational databases and available to search and browse via a website at www.earthworms.org. It is hoped that this genome will provide a springboard for many future investigations into the earthworm and continue research into this wonderful animal

EpiGraphDB: a database and data mining platform for health data science

Motivation: The wealth of data resources on human phenotypes, risk factors, molecular traits and therapeutic interventions presents new opportunities for population health sciences. These opportunities are paralleled by a growing need for data integration, curation and mining to increase research efficiency, reduce mis-inference and ensure reproducible research. Results: We developed EpiGraphDB (https://epigraphdb.org/), a graph database containing an array of different biomedical and epidemiological relationships and an analytical platform to support their use in human population health data science. In addition, we present three case studies that illustrate the value of this platform. The first uses EpiGraphDB to evaluate potential pleiotropic relationships, addressing mis-inference in systematic causal analysis. In the second case study, we illustrate how protein-protein interaction data offer opportunities to identify new drug targets. The final case study integrates causal inference using Mendelian randomization with relationships mined from the biomedical literature to 'triangulate' evidence from different sources