Research Research Poster Gallery Pangenomes Sequence graphs are a intuitive way to represent the variation in a collection of DNA sequence. Sets of sequences scale from homologous copies of multi-copy chromosomes, to sets of related bacterial strains, to collections of plant cultivars. We develop techniques to create and analyze such graph representations. Genome and transcriptome assembly Two of our most powerful tools to measure biological processes and the organisms involved are DNA- and RNA-sequencing. In many settings, assembly of reads is needed to identify and quantify the molecules they originated from. We are particularly interested in the algorithmically challenging cases where classical approaches break down. Computational Neurobiology Brain transcriptome atlases allow researchers to probe the molecular mechanisms which define neuronal identities, neuroanatomy, and patterns of connectivity. Despite the immense effort put into generating such atlases, to answer fundamental questions in neuroscience, an even greater effort is needed to develop methods to probe the resulting high-dimensional multivariate data. DBL contributes by developing various computational methods to analyze brain transcriptome atlases. Single-cell omics The most recent breakthrough in genomics is the ability to measure within single cells. Data from hundred thousands, even millions of cells, is becoming available, measuring full genomes, transcriptomes and proteomic measurements for every single cell. This gives a wealth of data to capture heterogeneity in samples. For example, which cells do appear in blood, and how does their transcriptional profile look like. Or, which cells make up the hypocampus of the brain, and what tasks do they perform. Aging research Why do some people reach the age of 100 in a healthy condition with no signs of neurodegeneration while others do not. DBL investigates healthy aging in the context of age related diseases with partners at the Vrije University Medical Center (a.o., the 100-plus research) and the Leiden University Medical Center (a.o, the Leiden longevity study). Our research within this theme deals with novel ways in detecting genetic variants, modelling biases within the samples, models to represent multivariate phenotypes, and new ways to associate genotype-phenotype. Microbial genomics Microbes are pervasive in all aspects of life, society and industry. We are particularly interested in the effects of genotypes on phenotypes of medical or industrial importance. In the medical area we focus on antibiotic resistance in pathogenic bacteria, while in the industrial arena we focus on mutations that affect yield and conversion efficiency. The lab is specialized in creating novel algorithms to accurately reconstruct the complex heterogeneous genome and community architectures found in these organisms from large sequencing experiments and to identify relevant mutations. Variant prediction and annotation Genetic difference between and within species drive how we evolve, but also what causes malfunctioning of genes and phenotypic differences. Genetic differences include SNPs as well as larger structural variations. But how to detect variants reliably? And, what if a genome is polyploid, can you detect variants for each of the alleles? Furthermore, not all genetic variants do have a functional impact. DBL develops tools to predict and annotate genetic variations in different contexts. Functional genomics Over the last years, it has become clear that genes do not encode for one protein, and that different isoforms do have different functions. Moreover, we have seen that genes on different alleles are not necessarily active simultaneously, and that this difference occurs at different stages of development. DBL sets out to develop methods to better functionally describe genes. Molecular diagnostics Many diseases are caused by genetic variations. Predisposition for a disease can thus be derived from studying one's genome. Hence, molecular diagnostics is a rapidly evolving field to support medical decisions in early stages (precision medicine). Basically, one searches for a biological marker that is indicative for a particular endpoint, a stage of a disease, or even success of a therapy. In collaboration with clinical geneticists, DBL develops methodology to find new markers for complex diseases and ways to detect biological markers reliably. Cancer research Cancer is a devastating disease affecting more than 100 million people, globally. It is the uncontrolled growth of cells that eventually can spread throughout the body. The transformation of normal cells into cancer cells is still not well understood, although several hallmarks of changes need to be acquired. A better understanding of the molecular mechanisms that lead to cancer is essential to improve the diagnosis, prevention, and curation. But, this is hampered by the complexity of several steps involved. Large international efforts like The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC) are acquiring genomic data that give a broad spectrum on the changes that cancer cells undergo, like copy number alterations, methylation profiles or over-active genes. The combined analysis of such data is, however, not trivial, and DBL investigates novel ways to do so, based on advances in machine learning. Alternatively, recent advances in cancer therapies have shown that combination of drugs (drug cocktails) seem to be particular effective. Combined analysis methodologies can also be used to predict effects of combination drugs to accelerate the search for effective therapies. In its cancer research, DBL closely collaborates with the Dutch Cancer Institute (NKI). Erik van den Akker - April 2016 Thies Gehrmann - April 2016 Ahmed Mafouz - December 2015 Ahmed Mafouz - November 2015 Sjoerd Huisman - Oktober 2015 Arlin Keo - 2015 Ahmed Mafouz - July 2015 Ahmed Mafouz - February 2014 Bastiaan van den Berg - Apr 2013 Thies Gehrmann - 2013 Sepideh Babaei - Sep 2012 Bastiaan van den Berg - Apr 2012 Jeroen de Ridder - Mar 2012 Bastiaan van den Berg - Jan 2012 Bastiaan van den Berg - Sep 2011 Bastiaan van den Berg - Apr 2011 Erdogan Taskesen - May 2011 Bastiaan van den Berg - Mar 2010 Jeroen de Ridder Bastiaan van den Berg - Dec 2009 Jeroen de Ridder Jeroen de Ridder Alexey Gritsenko- Apr 2013 Ahmed Mahfouz Marc Hulsman Alexey Gritsenko Jeroen de Ridder Marc Hulsman Jeroen de Ridder Jeroen de Ridder Share this page: Facebook Linkedin Twitter Email WhatsApp Share this page
