This thesis focusses on computational approaches to study the genome of non-Hodgkin lymphomas. According to the World Health Organization, there are more than 30 types of non-Hodgkin’s lymphoma. These types are distinguished from each other on the basis of morphology (cell and tissue structure), immunophenotype (proteins in the cell and on the cell surface) and genomics (DNA alterations). The disease course of different types of lymphoma also varies in aggressiveness. This difference in clinical outcome is reflected in the two most common types: diffuse large B-cell lymphoma (DLBCL) and follicular lymphoma (FL). These two types of lymphomas are studied in this thesis through two main aims. The first aim is to develop a comprehensive assay for simultaneous screening of all genomic alterations using a limited amount of input DNA derived from standard diagnostic biopsy material without the need of patient-matched control DNA as reference, optimized to be implemented in clinical practice for lymphoma diagnostics. The second aim is to improve our understanding of the biological basis of the clinical heterogeneity of DLBCL and FL and thereby enable improved risk stratification for these patients. We intend to achieve this by applying the assays developed under aim 1, to large, selected patient cohorts of DLBCL and FL. This thesis describes how we successfully developed an ā€œall-in-oneā€ next-generation sequencing assay for diagnostic biopsy material, with a bioinformatics pipeline to detect all DNA alterations relevant for lymphoma. Despite the challenges in the clinical setting, including the frequent lack of matched-normal reference samples and the suboptimal DNA quality of standard diagnostic biopsy material, somatic mutations, copy number aberrations and translocations were identified. Therefore, adaptations were customized to the specific needs of DNA derived from diagnostic biopsy material for wet- and drylab procedures. In addition, a new algorithm was introduced, ACE, that allows for an accurate measure of tumor cell percentage, which in turn had been applied to quality select samples on basis of tumor cell percentage. The various drylab implementations have been converted to pipelines and were publicly made available for reuse. The application of the NGS assay, as described in the first aim, has led to improved insights into the molecular basis of these diseases with improved risk stratification, and clues for molecular-informed clinical trial designs and tailored treatment approaches as consequences. An in-depth molecular characterization of HHV8-negative effusion-based lymphoma has contributed to a more refined definition of the disease and has found its way into the recent 5th edition of the WHO Classification. Our studies of larger patient populations in DLBCL and in selected patients with uncommon presentations of FL show that the a priori recognition of a heterogeneous disease course of patients can be improved by molecular profiling. This allows us to better distinguish between DLBCL and FL patients with a good and poor prognosis. Moreover, these DNA profiles offer possibilities for personalized therapy. In the future, new medicines or treatment methods can be tested in patients based on these new insights. This offers hope for patients, especially those who now fall into a high-risk group. There is however still room for improvement to disentangle the underlying complex oncogenesis of malignant lymphoma that may go beyond DNA alterations and to eventually tailor personalized treatment options accordingly.