European Bioinformatics Institute

The European Bioinformatics Institute (EMBL-EBI), a unit of the European Molecular Biology Laboratory (EMBL), is Europe’s flagship institute for bioinformatics. Legally, the EBI is part of EMBL, a non-profit multinational research institution with its main site in Heidelberg, Germany.

Research at the EBI is currently carried out by 16 independent research groups, covering key a broad range of critical areas, including genomics, transcriptomics, evolution, biomedicine, and text mining. The research initiatives at the EBI are tied in with Bioinformatics services for the community. Examples of leading services hosted at the EBI include Ensembl and the European Nucleotide Archive, providing genome annotation and data archival services for the Biomedical research community in Europe.

The scientific excellence is underlined by routine participation of EBI groups in leading consortia and studies, including the 1000genomes project, ENCODE, Blueprint and a larger number of other genome initiatives. Approximately 50% of the EBI staff is funded through external grants, further documenting the excellence of the institution.

The Statistical Genomics and Systems Genetics group lead by Oliver Stegle develops statistical approaches to model omics datasets, in particular in the context of genetic variation. Stegle has devised key statistical approaches to analyse high-dimensional data from genomics studies. In particular, he has developed association-mapping tools that account for unknown and hidden confounding variation in gene expression profiles (Stegle et al. PLoS Comput. Biol 2010, Fusi, et al. PLoS Comput Biol, 2012), thereby increasing power to detect biologically meaningful effects. Through open source software and protocols (Stegle et al. Nat. Protoc., 2012) these methods have been adopted in high-profile applied studies, including the 1,000 Genomes Project, HapMap expression projects and GEUVADIS (Lappalainen et al. Nature, 2012). The recently established research group at the EBI is engaged in the development of statistical approaches to fully exploit large-scale genomics data that are being generated in major initiatives.

WP1: P4 will contribute to the development of statistical methods for the analysis of genome- scale datasets. In particular, P4 will devise methods to integrate heterogeneous datasets from multiple studies and different technological platforms.

WP3: P4 will lead the statistical development of eQTL association-based annotation of regulatory variants. In addition, the group will devise approaches for integrating predictions from different approaches, building on Bayesian data integration techniques.

W6: P4 will contribute to the statistical aspects of the WP, in particular the dimension-reduction methods to reduce antigen signatures to an informative minimal set of biomarkers. P4 will also collaborate on the integration of sCD-based biomarkers into the analysis platform (WP1).

Oliver Stegle has extensive experience in the development of statistical approaches to analyse high- dimensional genome-scale datasets. He is involved in several consortia where association analysis and causal modelling are being used to extract information from omics data, including the PanCancer working group.

Oliver Stegle (male, PhD) received his doctorate degree in theoretical Physics from the University of Cambridge in the UK. He specialized in statistical machine learning methods to extract information from high-dimensional datasets, in particular in the context of computational biology and genomics. He is a Research Group Leader at the EMBL European Bioinformatics Institute (EMBL-EBI), heading the Statistical Genomics and Systems Genetics group. Oliver Stegle has been consistently supported by fellowships and grants, including the Cambridge Gates Trust, the Volkswagen foundation and the FP7 Marie Curie fellowship scheme.

Helena Kilpinen (female, PhD). Post-doctoral research fellow, published > 10 papers. Molecular biology background with extensive expertise in association genetic analyses of molecular traits.

Barbara Rakitsch (female). Post-doctoral research fellow, published > 3 papers. Statistical background with strong expertise in linear mixed models and related methods to model large-scale genomic data.

Danilo Horta (male, PhD). Post-doctoral research fellow, published > 6 papers. Theoretical computer scientist with extensive expertise in dealing with large data challenges and modeling.

The aims in WP1 require a dedicated software engineer to deliver high-quality implementations of the proposed algorithms. Consequently, one scientific programmer will be recruited solely from EC funding. For WP3 and WP6, we will recruit one additional postdoctoral fellow to perform research on integrating genetic variation data with molecular traits. The fellow will be solely paid from EC funding.

• Nicolo Fusi et Warped Linear Mixed Models for the Genetic Analysis of Transformed Phenotypes. Nature Communications 2014.
• Julien Gagneur et M. Interactions Reveal Causal Pathways That Mediate Genetic Effects on Phenotype. PLoS Genetics 2013.
• Lappalainen, T, et , Transcriptome and genome sequencing uncovers functional variation in humans. Nature 2013.
• Nicolo Fusi et al Detecting regulatory gene-environment interactions with unmeasured environmental Oxford Bioinformatics 2013.
• Oliver Stegle et al, Using Probabilistic Estimation of Expression Residuals (PEER) to obtain increased power and interpretability of gene expression Nature Protocols 2012.

HIPSCI. Integrated genetic analyses, modeling RNASeq data in combination with genetic and proteomics variation data. 2013-2017. P.I. F. Watt, R. Durbin (Coordinators); C.I. O. Stegle.

PCAWG. Pan Cancer Analysis of Whole-Genomes consortium of ICGC and 2013- 2015. P.I. P. Campbell, L. Stein, j. Korbel, G. Getz, J. Stuart (Coordinators); C.I. O. Stegle.

As one of the world leading institutes for bioinformatics, the EBI offers excellent computing facilities, which facilitate the implementation of this computationally intense research programme. In particular, the Stegle-group and collaborating researchers will have access to a compute cluster with more than 20,000 cores, which will be critical to carry out the planned genome-wide association analyses. Together with the neighbouring Wellcome Trust Sanger Institute, the EBI is also operating a single-cell genomics centre, providing access to state-of-the-art single cell transcriptomics such as the Fluidigm C1 system. In addition to primary research equipment at the EBI, the institute is embedded within the EMBL and researchers have access to extensive experimental facilities, including the core EMBL genomics facility, the Advanced Light Microscopy Facility and workshops. Legal teams and the EMBL technology transfer company EMBLEM will assist in handling IP- related matters and help to interface research aims with commercial objectives of SMEs who are partners on the project.