RA-MAP, molecular immunological landscapes in early rheumatoid arthritis and healthy vaccine recipients

Rheumatoid arthritis (RA) is a chronic inflammatory disorder with poorly defined aetiology characterised by synovial inflammation with variable disease severity and drug responsiveness. To investigate the peripheral blood immune cell landscape of early, drug naive RA, we performed comprehensive clinical and molecular profiling of 267 RA patients and 52 healthy vaccine recipients for up to 18 months to establish a high quality sample biobank including plasma, serum, peripheral blood cells, urine, genomic DNA, RNA from whole blood, lymphocyte and monocyte subsets. We have performed extensive multi-omic immune phenotyping, including genomic, metabolomic, proteomic, transcriptomic and autoantibody profiling. We anticipate that these detailed clinical and molecular data will serve as a fundamental resource offering insights into immune-mediated disease pathogenesis, progression and therapeutic response, ultimately contributing to the development and application of targeted therapies for RA.</p

Heterogeneous information sources for bioinformatics: integration methodology, search algorithms and case studies

Identifying the genetic basis associated with Mendelian disorders or complex phenotypes is essential in human genetics in order to design more effective and eventually to better understand the molecular mechanisms behind these genetic disorders.Usually, a list of candidates is obtained in a high-thoughput experiment, such as a genomewide association study. This set of genes (either a chromosomal region or a list of genes scattered in the genome) is usually not small enough to easily undertake a manually one-by-one validation and therefore a selection of the putative most interesting genes is needed. This problem has been named gene prioritization and in the last years, several computing based approaches have been proposed to cope with it. This thesis presents a work on gene prioritization.The first part of this text thoroughly reviews the web based gene prioritization tools that can be freely used by any user. We describe seventeen tools and we stress their similarities and differences with the aim to help the user to choose the most appropriate one for his type of data. We have also reviewed the bibliography associated with these tools in search of validations and tool performance comparisons and we have finally set up a website where this information and regular updates are stored. In the last two years, the number of tools described in the website has almost doubled.Furthermore, we have developed a performance review among gene prioritization tools, both using the whole genome as starting candidate set or a limited one. We have compared individual results with the combination of the tools and finally we have completed our review with the combination of the best performance gene prioritization tools in our benchmark in three real life experiments. All the expertise gathered in our complete review has been used to find new candidate genes involved in congenital heart disease, congenital diaphragmatic hernia and asthma.Finally, we propose the use of cluster analysis as a preprocessing step of gene prioritization approaches that use training genes to lead the prioritization. We claim that the automatic selection of a homogenous training set produces more accurate rankings than the expert selected ones. To this purpose, we have applied a transactional clustering algorithm, CLOPE, to two different gene prioritization tools: Endeavour and Genedistiller.Contents iv 1. Introduction 1 1.1. Human Genetics 1 1.2. Bioinformatics 3 1.3. Gene Prioritization 4 1.3.1. Candidate Set 5 1.3.2. Training Set 6 1.4. Cluster Analysis 6 1.4.1. Types of data 7 1.4.2. Traditional clustering approaches 8 1.4.3. Categorical clustering 9 1.4.4. Transactional clustering 11 1.4.5. Conclusion 12 1.5. Aims and objectives 13 1.6. Structure of the thesis and personal contribution 13 2. A guide to web tools to prioritize candidate genes 15 3. An unbiased evaluation of gene prioritization tools 35 4. Combination of gene prioritization tools gives an insight into disease gene discovery 67 5. A clustering based preprocessing method for gene prioritization 105 6. Conclusion 127 6.1. Overview 128 6.2. Clustering analysis and gene prioritization 129 6.3. Other lines of research 130 6.3.1. Haematlas 130 6.3.2. Daphnia and biclustering 133 7. Appendix A 135 8. Appendix B 139 9. Bibliography 147 10. List of publications 155 11. Curriculum vitae 159status: publishe

An unbiased evaluation of gene prioritization tools

MOTIVATION: Gene prioritization aims at identifying the most promising candidate genes among a large pool of candidates-so as to maximize the yield and biological relevance of further downstream validation experiments and functional studies. During the past few years, several gene prioritization tools have been defined and some of them have been implemented and made available through freely available web tools. In this study, we aim at comparing the predictive performance of eight publicly available prioritization tools on novel data. We have performed an analysis in which 42 recently reported disease gene associations from literature are used to benchmark these tools before the underlying databases are updated. RESULTS: Cross-validation on retrospective data provides performance estimate likely to be overoptimistic because some of the data sources are contaminated with knowledge from the disease-gene association. Our approach mimics a novel discovery more closely and thus provides more realistic performance estimates. There are however marked differences, and tools that rely on more advanced data integration schemes appear more powerful. CONTACT: [email protected]: publishe

Erratum: The RA-MAP Consortium: a working model for academia–industry collaboration

This corrects the article DOI: 10.1038/nrrheum.2017.200

The RA-MAP Consortium: A working model for academia-industry collaboration

Collaboration can be challenging; nevertheless, the emerging successes of large, multi-partner, multi-national cooperatives and research networks in the biomedical sector have sustained the appetite of academics and industry partners for developing and fostering new research consortia. This model has percolated down to national funding agencies across the globe, leading to funding for projects that aim to realise the true potential of genomic medicine in the 21st century and to reap the rewards of 'big data'. In this Perspectives article, the experiences of the RA-MAP consortium, a group of more than 140 individuals affiliated with 21 academic and industry organizations that are focused on making genomic medicine in rheumatoid arthritis a reality are described. The challenges of multi-partner collaboration in the UK are highlighted and wide-ranging solutions are offered that might benefit large research consortia around the world