Dr. J.m. Ruijter

Biography

Cardiac growth and development / Analysis of quantitative PCR data   J.M. Ruijter is one of the AMC Principal Investigators

Research Interest

In previous years I have developed methods for 3D analysis and visualization of gene expression patterns and morphogenetic parameters during embryogenesis and for the analysis of quantitative PCR (LinRegPCR) and transcript count (SAGEstat and Gtest) data. The programs resulting from this work were made available and are frequently downloaded; papers related to LinRegPCR are cited  over 2500 times. In the field of cardiac development this has lead to new methods for analysis and visualization of morphogenetic parameters and gene expression patterns in 3D-space, which have helped the departmental research lines to become world leading. In the EU-funded CHeartED project these methods were used to genetically annotate and identify components of the developing heart. In the EU-funded CardioNeT project, the methods for 3D measurement of growth and differentiation of the heart are applied to determine the function of several regulators of development in mouse models of normal and abnormal growth. In the NHS funded  COBRA3 program, the regeneration capacity of the normal and congenetally malformed hearts will be compared. The research efforts in these projects will stay focussed on 3D quantification of gene expression and cardiac morphogenesis data in series of developmental stages of different transgenic mouse models. To determine the basis of congenital malformations, transgenic lineage markers will be included to trace the cellular and developmental origin of the different cardiac compartments. Furthermore, I will further develop and update the LinRegPCR program for analysis of qPCR data. The introduction of several massive parallel sequencing modalities, has renewed the interest in comparison and statistical analysis of transcript counts which has lead to the publicationof a peak calling program (OccuPeak) and a method for predicting regulatory regions inthe genome by combining the information in diverse genomic datasets. My group currently further develops these tools to improve the analysis of sequencing data generated in cardiac development and arrhythmia projects.