Benchmarking ontologybased query rewriting systems

Query rewriting is a prominent reasoning technique in ontology-based data access applications. A wide variety of query rewriting algorithms have been proposed in recent years and implemented in highly optimised reasoning systems. Query rewriting systems are complex software programs; even if based on provably correct algorithms, sophisticated optimisations make the systems more complex and errors become more likely to happen. In this paper, we present an algorithm that, given an ontology as input, synthetically generates “relevant ” test queries. Intuitively, each of these queries can be used to verify whether the system correctly performs a certain set of “inferences”, each of which can be traced back to axioms in the input ontology. Furthermore, we present techniques that allow us to determine whether a system is unsound and/or incomplete for a given test query and ontology. Our evaluation shows that most publicly available query rewriting systems are unsound and/or incomplete, even on commonly used benchmark ontologies; more importantly, our techniques revealed the precise causes of their correctness issues and the systems were then corrected based on our feedback. Finally, since our evaluation is based on a larger set of test queries than existing benchmarks, which are based on hand-crafted queries, it also provides a better understanding of the scalability behaviour of each system

Genetic and genomic analysis of Arabidopsis thaliana with low-coverage next-generation sequencing data

Next-generation sequencing technologies have transformed our understanding of genetic variation segregating in populations and its relationship with phenotypic traits. Sequencing large populations at low coverage, thus sampling only a fraction of the genome of each individual, may increase statistical power in genetic mapping [Pasaniuc,2012] compared to genotyping arrays. This thesis explores several novel applications of low-coverage population-based sequencing, using data from 488 recombinant inbred lines from the MAGIC population of Arabidopsis thaliana, descended from 19 inbred founder accessions. Based on the full catalogue of genetic variation that is available in the 19 founders [Gan, 2011], I describe every MAGIC genome as a mosaic of founder haplotypes and analyse the accuracy of the mosaics by simulation. I then use the mosaics in three ways. First, I investigate structural variation using a novel method that treats anomalies in the alignment of sequencing reads, potentially representing signatures of structural variants (SVs), as quantitative traits. These can be mapped genetically to identify loci in which genetic variation correlates with signatures of SVs. The method can distinguish short- (e.g. indels) and long-range (e.g. translocations) SVs and has led to the discovery of a large number of SVs segregating in the MAGIC population, including thousands of long-range SVs. I show that SVs have a significant impact on silencing gene expression and that they explain a large fraction of the phenotypic variation in several physiological traits. Second, I use the mosaic structure of the MAGIC lines to map recombination events and analyse lineage-specific recombination in MAGIC. I infer recombination hotspots and compared recombination in the MAGIC lines to the Arabidopsis genetic map. Finally, I detect bacterial endosymbionts hosted in MAGIC genomes from unmapped reads that have high sequence similarity with bacterial DNA and examine whether variation in the presence of endosymbionts can be explained by host genetic variation.</p

Benchmarking Ontology-Based Query Rewriting Systems

Query rewriting is a prominent reasoning technique in ontology-based data access applications. A wide variety of query rewriting algorithms have been proposed in recent years and implemented in highly optimised reasoning systems. Query rewriting systems are complex software programs; even if based on provably correct algorithms, sophisticated optimisations make the systems more complex and errors become more likely to happen. In this paper, we present an algorithm that, given an ontology as input, synthetically generates ``relevant'' test queries. Intuitively, each of these queries can be used to verify whether the system correctly performs a certain set of ``inferences'', each of which can be traced back to axioms in the input ontology. Furthermore, we present techniques that allow us to determine whether a system is unsound and/or incomplete for a given test query and ontology. Our evaluation shows that most publicly available query rewriting systems are unsound and/or incomplete, even on commonly used benchmark ontologies; more importantly, our techniques revealed the precise causes of their correctness issues and the systems were then corrected based on our feedback. Finally, since our evaluation is based on a larger set of test queries than existing benchmarks, which are based on hand-crafted queries, it also provides a better understanding of the scalability behaviour of each system

Systems genetics identifies a macrophage cholesterol network associated with physiological wound healing.

Among other cells, macrophages regulate the inflammatory and reparative phases during wound healing but genetic determinants and detailed molecular pathways that modulate these processes are not fully elucidated. Here, we took advantage of normal variation in wound healing in 1,378 genetically outbred mice, and carried out macrophage RNA-sequencing profiling of mice with extreme wound healing phenotypes (i.e., slow and fast healers, n = 146 in total). The resulting macrophage coexpression networks were genetically mapped and led to the identification of a unique module under strong trans-acting genetic control by the Runx2 locus. This macrophage-mediated healing network was specifically enriched for cholesterol and fatty acid biosynthetic processes. Pharmacological blockage of fatty acid synthesis with cerulenin resulted in delayed wound healing in vivo, and increased macrophage infiltration in the wounded skin, suggesting the persistence of an unresolved inflammation. We show how naturally occurring sequence variation controls transcriptional networks in macrophages, which in turn regulate specific metabolic pathways that could be targeted in wound healing