Our publications


  1. Batut,B. et al. (2018) ASaiM: a Galaxy-based framework to analyze microbiota data. GigaScience, 7.
  2. Batut,B. et al. (2018) Community-driven data analysis training for biology. BioRxiv, 225680.
  3. Boers,S.A. et al. (2018) Development and evaluation of a culture-free microbiota profiling platform (MYcrobiota) for clinical diagnostics. European Journal of Clinical Microbiology & Infectious Diseases, 37, 1081–1089.
  4. Afgan,E. et al. (2018) The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2018 update. Nucleic acids research, gky379.


  1. IJspeert,H. et al. (2017) Antigen receptor galaxy: a user-friendly, web-based tool for analysis and visualization of T and B cell receptor repertoire data. The Journal of Immunology, 198, 4156–4165.
  2. Theunissen,P.M.J. et al. (2017) Antigen receptor sequencing of paired bone marrow samples shows homogeneous distribution of acute lymphoblastic leukemia subclones. haematologica, 102, 1869–1877.
  3. Zhang,C. et al. (2017) Systematically linking tranSMART, Galaxy and EGA for reusing human translational research data. F1000Research, 6.
  4. Grüning,B.A. et al. (2017) The RNA workbench: best practices for RNA and high-throughput sequencing bioinformatics in Galaxy. Nucleic acids research, 45, W560–W566.


  1. Olvedy,M. et al. (2016) A comprehensive repertoire of tRNA-derived fragments in prostate cancer. Oncotarget, 7, 24766.
  2. IJspeert,H. et al. (2016) Evaluation of the antigen-experienced B-cell receptor repertoire in healthy children and adults. Frontiers in immunology, 7, 410.
  3. Erdem-Eraslan,L. et al. (2016) Identification of patients with recurrent glioblastoma who may benefit from combined bevacizumab and CCNU therapy: a report from the BELOB trial. Cancer research, 76, 525–534.
  4. Hoogstrate,Y. et al. (2016) Integration of EGA secure data access into Galaxy. F1000Research, 5.


  1. Hiltemann,S. et al. (2015) Discriminating somatic and germline mutations in tumor DNA samples without matching normals. Genome research, 25, 1382–1390.
  2. Hoogstrate,Y. et al. (2015) FuMa: reporting overlap in RNA-seq detected fusion genes. Bioinformatics, 32, 1226–1228.
  3. Alves,I.T. et al. (2015) Next-generation sequencing reveals novel rare fusion events with functional implication in prostate cancer. Oncogene, 34, 568.


  1. Hiltemann,S. et al. (2014) CGtag: complete genomics toolkit and annotation in a cloud-based Galaxy. GigaScience, 3, 1.
  2. Moorhouse,M.J. et al. (2014) ImmunoGlobulin galaxy (IGGalaxy) for simple determination and quantitation of immunoglobulin heavy chain rearrangements from NGS. BMC immunology, 15, 59.
  3. Hiltemann,S. et al. (2014) iReport: a generalised Galaxy solution for integrated experimental reporting. GigaScience, 3, 19.
  4. Hoogstrate,Y. et al. (2014) FlaiMapper: computational annotation of small ncRNA-derived fragments using RNA-seq high-throughput data. Bioinformatics, 31, 665–673.
  5. Swagemakers,S.M.A. et al. (2014) Pollitt syndrome patients carry mutation in TTDN1. Meta gene, 2, 616–618.
  6. IJspeert,H. et al. (2014) Similar recombination-activating gene (RAG) mutations result in similar immunobiological effects but in different clinical phenotypes. Journal of Allergy and Clinical Immunology, 133, 1124–1133.


  1. Alves,I.T. et al. (2013) Gene fusions by chromothripsis of chromosome 5q in the VCaP prostate cancer cell line. Human genetics, 132, 709–713.
  2. Hiltemann,S. et al. (2013) iFUSE: integrated fusion gene explorer. Bioinformatics, 29, 1700–1701.


  1. Stubbs,A. et al. (2012) Huvariome: a web server resource of whole genome next-generation sequencing allelic frequencies to aid in pathological candidate gene selection. Journal of clinical bioinformatics, 2, 19.