As a PhD student, Alejandro Sifrim developed a keen interest in the development and application of computational methods to understand large-scale biological datasets produced by a wide array of cutting- edge technologies, using a diverse set of statistical and machine learning approaches.
He developed computational tools for the annotation, interpretation and interactive visualization of genomic variants (Sifrim et al., Genome Medicine 2012), the first genomic variant prioritization method by using machine learning approaches for data fusion of phenotypic and genomic data (Sifrim et al., Nature Methods, 2013) and algorithms for the detection of mosaic (King et al., Genome Research, 2017) and complex genomic variants using an adaptive learning approach. He was as a member of the analysis team of the Deciphering Developmental Disorders study (Wright et al., Lancet 2015; The DDD Study, Nature 2015; Akawi et al., Nature Genetics 2015; The DDD Study, Nature 2017; Short et al., Nature 2018), the largest study to date to identify the causes of rare Mendelian disorders, and took the lead in a multi-centre effort to understand the genetic architecture of congenital heart disease (Sifrim et al., Nature Genetics 2016).
Recently, he’s been focused on the development of integrative analysis methods for single- cell multi-omics technologies and applying these methodologies in fields such as mammary gland development (Wuidart and Sifrim et al., Nature Cell Biology, 2018; Centonze et al. Nature 2020), endothelial-to-mesenchymal transitioning of breast cancer tumour cells (Pastushenko et al., Nature 2018), mechanical and post-natal skin expansion stem cell dynamics (Aragona et al., Nature, 2020; Dekoninck et al., Cell, 2020) and preimplantation embryo development. He’s currently part of the international Gut Cell Atlas Consortium, studying Crohn’s disease and is a core member of the Leuven Single-Cell Institute.