Antonio Rausell

The Clinical Bioinformatics lab

Antonio Rausell
  • Akira Cortal
  • Loredana Martignetti
  • Stefani Dritsa
  • Barthélémy Caron
  • Francisco Requena
  • Antoine Favier

Meilleures publications

CARON B, LUO Y, RAUSELL A NCBoost classifies pathogenic non-coding variants in Mendelian diseases through supervised learning on purifying selection signals in humans. Genome Biology 2019 20:32 https://doi.org/10.1186/s13059-019-1634-2


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The Clinical Bioinformatics lab

Dr. Antonio Rausell has an Engineering degree with a focus in Biotechnology and a PhD in Bioinformatics. He did his PhD under the supervision of Prof. Alfonso Valencia at the Spanish National Cancer Research Center (CNIO). From 2012 until February 2016 he worked as a postdoctoral researcher with a double affiliation to the Swiss Institute of Bioinformatics (SIB) with Prof. Ioannis Xenarios, and the University Hospital of Lausanne with Prof. Amalio Telenti. During that time he specialized in the study of the genetic basis of susceptibility to infectious diseases and the heterogeneity in the innate immune response at single-cell level. His findings have contributed to better characterizing two main paradigms arising from large-scale genome and transcriptome sequencing projects: A) the widespread potential to cause disease of rare loss-of-function variants occurring in heterozygosis through haploinsufficiency or negative dominance; and B) the transcriptional basis of the heterogeneity in permissiveness to infection across single cells within individuals.
 
In March 2016, Dr. Rausell joined the Imagine Institute as Director of the new Clinical Bioinformatics lab. His group develops bioinformatics tools with a clinical focus in two main areas of current research in genetic diseases:
 
1) Functional assessment of human genetic variants, by contributing methods to predict disease-causing variants, their integration in Personalized Medicine pipelines and their application to exome/genome sequencing projects currently ongoing at the Imagine Institute and the Necker Hospital.
 
2) Analyses of high-dimensional single-cell data in functional genomics studies addressing intra-individual cell heterogeneity and how it relates to immune disorders and susceptibility to infectious diseases. These analyses are being performed in collaboration with Imagine’s experimental research groups and aim at the identification of markers with a clinical value for diagnosis and treatment.
 
Bioinformatics Methods & Software:
 

NCboost: python/R package of the pathogenicity assessment of non-coding variants from Whole Exome/Genome Sequencing (soon to be released as open-source software in github): https://www.biorxiv.org/content/early/2018/07/08/363903

Sincell: R/Bioconductor package for the statistical assessment of cell state hierarchies from single-cell RNA-seq data. http://bioconductor.org/packages/sincell

NUTVAR: Null and Truncating variant analysis. Sequence-based functional annotation of truncating variants from genome and exome data. https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1003757

S3det - MCdet: C++ software for the prediction of functional specificity residues and protein subfamilies from multiple sequence alignments using Multiple Correspondence Analysis. Software integrated in TreeDet server http://treedet.bioinfo.cnio.es and distributed within JDet package

JDet: interactive calculation and visualization of function-related conservation patterns in multiple sequence alignments and structures. http://csbg.cnb.csic.es/JDet