Antonio RAUSELL

Dr Antonio Rausell holds an engineering degree specialising in biotechnology and a PhD in bioinformatics. He completed his PhD under the supervision of Professor Alfonso Valencia at the Spanish National Cancer Research Centre (CNIO). From 2012 to February 2016, he worked as a postdoctoral researcher with dual affiliation at the Swiss Institute of Bioinformatics (SIB) with Professor Ioannis Xenarios, and at the Lausanne University Hospital with Professor Amalio Telenti. During this period, he specialised in studying the genetic basis of susceptibility to infectious diseases and the heterogeneity of the innate immune response at the single-cell level. His findings have helped to better characterise two major paradigms emerging from large-scale genome and transcriptome sequencing projects: A) the significant contribution of rare loss-of-function variants to rare diseases via haploinsufficiency or negative dominance; and B) the transcriptional basis of inter-cellular heterogeneity in susceptibility to infection among individuals.

In March 2016, Dr Rausell joined Institut Imagine as director of the clinical bioinformatics laboratory. His group develops bioinformatics tools for clinical use in two main areas of research into genetic diseases:

•  The functional significance of human genetic variants, using artificial intelligence methods to identify the variants responsible for diseases; the integration of computational tools into personalised medicine protocols; and their application to exome/genome sequencing projects currently underway at the Imagine Institute and Necker Hospital.
• Analysis of data from single-cell analyses as part of functional genomics studies focusing on intra-individual cellular heterogeneity and its links to rare paediatric diseases, including developmental disorders, immunodeficiencies and ciliopathies. These analyses are carried out in collaboration with a wide range of clinical departments, disease reference centres and experimental research groups.

Dr Rausell coordinates the Integrated Care and Research Programme (iCARP) on medical decision-support information systems at the Imagine Institute, whose objectives are: (a) the identification of clinically valuable biomarkers for diagnosis, prognosis and treatment, and (b) the development of artificial intelligence tools to aid clinical decision-making.

Dr Rausell has a strong track record of publications in high-impact journals, including *Nature Biotechnology*, *Genome Biology*, *PNAS* and *Science Immunology*. His work has been regularly presented at international conferences such as those organised by the American Society of Human Genetics (ASHG) and the International Society for Computational Biology (ISCB). Since 2018, he has been co-chair of the Special Interest Group on Variant Interpretation (VarI-COSI) of the International Society for Computational Biology. Furthermore, his laboratory is part of the Milieu Intérieur Consortium for the study of immune response variability.

Dr Rausell's laboratory has recently developed a series of machine learning methods for the clinical assessment of genetic variants based on clinical, genomic and multi-omic profiles of patient samples.

Please visit the Google Scholar page to view the full list of publications.
The laboratory's GitHub page can be found at https://github.com/RausellLab/.  Bioinformatics methods and software

• Cell-ID (Cortal et al. Nature Biotechnology 2021), a multivariate statistical method for extracting molecular signatures and identifying cell identity at the single-cell level from single-cell omics data: https://github.com/RausellLab/CelliD
• CNVxplorer (Requena et al. Nucleic Acids Research 2021), a web server for the computational assessment of structural variants in the context of the clinical diagnosis of patients with rare diseases.
• NCBoost (Caron et al., Genome Biology 2019), a method based on gradient tree boosting that utilises a diverse set of sequence conservation features to predict the pathogenic potential of non-coding nucleotide variants from exome/genome sequencing.
• Tiresias, software for supervised learning on multiplex biological networks for disease gene prediction and patient stratification. The methodological approaches covered in Tiresias include diffusion algorithms based on random walks, graph integrals and graph neural networks, including convolutional neural networks (CNNs).
• Sincell: R/Bioconductor software for the statistical evaluation of cellular state hierarchies from single-cell RNA sequencing data. http://bioconductor.org/packages/sincell
• NUTVAR: analysis of loss-of-function variants. Sequence-based functional annotation of truncated variants from genome and exome data.
• S3det - MCdet: C++ software for predicting the functional specificity of protein domains and subfamilies from multiple sequence alignments using multiple correspondence analysis. Software integrated into the TreeDet server and distributed as part of the JDet package
• JDet: interactive calculation and visualisation of functional conservation patterns in multiple sequence alignments and structures.