Our publications


  1. Rahman,N. et al. (2024) Mobilisation and analyses of publicly available SARS-CoV-2 data for pandemic responses. Microbial Genomics, 10.
  2. Larivière,D. et al. (2024) Scalable, accessible and reproducible reference genome assembly and evaluation in Galaxy. Nature Biotechnology.
  3. Finn,R.D. et al. (2024) Establishing the ELIXIR Microbiome Community. F1000Research, 13.


  1. Péguilhan,R. et al. (2023) Clouds, oases for airborne microbes–Differential metagenomics/metatranscriptomics analyses of cloudy and clear atmospheric situations. bioRxiv, 2023–12.
  2. Kumar,A. et al. (2023) Transformer-based tool recommendation system in Galaxy. BMC Bioinformatics, 24.
  3. David,R. et al. (2023) “Be sustainable”: EOSC‐Life recommendations for implementation of FAIR principles in life science data handling. The EMBO Journal.
  4. Williams,J.J. et al. (2023) An international consensus on effective, inclusive, and career-spanning short-format training in the life sciences and beyond. PLOS ONE, 18, 1–19.
  5. Weil,H.L. et al. (2023) \lessscp\greaterPLANTdataHUB\less/scp\greater: a collaborative platform for continuous \lessscp\greaterFAIR\less/scp\greater data sharing in plant research. The Plant Journal.
  6. Mehta,S. et al. (2023) A Galaxy of informatics resources for MS-based proteomics. Expert Review of Proteomics, 1–16.
  7. The European Reference Genome Atlas: piloting a decentralised approach to equitable biodiversity genomics (2023) bioRxiv.
  8. Härdtner,C. et al. (2023) A comparative gene expression matrix in Apoe-deficient mice identifies unique and atherosclerotic disease stage-specific gene regulation patterns in monocytes and macrophages. Atherosclerosis, 371, 1–13.
  9. Rahman,N. et al. (2023) Mobilisation and analyses of publicly available SARS-CoV-2 data for pandemic responses.
  10. Guerler,A. et al. (2023) Fast and accurate genome-wide predictions and structural modeling of protein–protein interactions using Galaxy. BMC Bioinformatics, 24.
  11. Riesle,A.J. et al. (2023) Activator-blocker model of transcriptional regulation by pioneer-like factors. Nature Communications, 14.
  12. Schiml,V.C. et al. (2023) Integrative meta-omics in Galaxy and beyond. Environmental Microbiome, 18.
  13. Hiltemann,S. et al. (2023) Galaxy Training: A powerful framework for teaching! PLOS Computational Biology, 19, e1010752.
  14. Bray,S. et al. (2023) The Planemo toolkit for developing, deploying, and executing scientific data analyses in Galaxy and beyond. Genome Research.


  1. Rasche,H. et al. (2022) Training Infrastructure as a Service. GigaScience, 12.
  2. Kumar,A. et al. (2022) An accessible infrastructure for artificial intelligence using a Docker-based JupyterLab in Galaxy. GigaScience, 12.
  3. Enis Afgan,and et al. (2022) The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2022 update. Nucleic Acids Research, 50, W345–W351.
  4. Mehta,S. et al. (2022) Catching the Wave: Detecting Strain-Specific SARS-CoV-2 Peptides in Clinical Samples Collected during Infection Waves from Diverse Geographical Locations. Viruses, 14, 2205.
  5. Meier,R. et al. (2022) The antileukemic activity of decitabine upon PML/RARA-negative AML blasts is supported by all-trans retinoic acid: in vitro and in vivo evidence for cooperation. Blood Cancer Journal, 12.
  6. Wolff,J. et al. (2022) Loop detection using Hi-C data with HiCExplorer. GigaScience, 11.
  7. VijayKrishna,N. et al. (2022) Expanding the Galaxy’s reference data. Bioinformatics Advances, 2.
  8. Mühlhaus,T. et al. (2022) DataPLANT – Tools and Services to structure the Data Jungle for fundamental plant researchers.
  9. Pinter,N. et al. (2022) MaxQuant and MSstats in Galaxy Enable Reproducible Cloud-Based Analysis of Quantitative Proteomics Experiments for Everyone. Journal of Proteome Research.
  10. Bray,S. et al. (2022) Galaxy workflows for fragment-based virtual screening: a case study on the SARS-CoV-2 main protease. Journal of Cheminformatics, 14.
  11. Martin,D.P. et al. (2022) Selection analysis identifies clusters of unusual mutational changes in Omicron lineage BA.1 that likely impact Spike function. Molecular Biology and Evolution.
  12. Brack,P. et al. (2022) Ten simple rules for making a software tool workflow-ready. PLOS Computational Biology, 18, e1009823.
  13. Mossad,O. et al. (2022) Gut microbiota drives age-related oxidative stress and mitochondrial damage in microglia via the metabolite N6-carboxymethyllysine. Nature Neuroscience.
  14. Gao,M. et al. (2022) Pluripotency factors determine gene expression repertoire at zygotic genome activation. Nature Communications, 13.
  15. Fahrner,M. et al. (2022) Democratizing data-independent acquisition proteomics analysis on public cloud infrastructures via the Galaxy framework. GigaScience, 11.


  1. Dai,C. et al. (2021) A proteomics sample metadata representation for multiomics integration and big data analysis. Nature Communications, 12.
  2. Mehta,S. et al. (2021) ASaiM-MT: a validated and optimized ASaiM workflow for metatranscriptomics analysis within Galaxy framework. F1000Research, 10.
  3. Batut,B. et al. (2021) RNA-Seq Data Analysis in Galaxy. In, RNA Bioinformatics. Springer, pp. 367–392.
  4. Maier,W. et al. (2021) Ready-to-use public infrastructure for global SARS-CoV-2 monitoring. Nature Biotechnology, 1–2.
  5. Roncoroni,M. et al. (2021) A SARS-CoV-2 sequence submission tool for the European Nucleotide Archive. Bioinformatics.
  6. Gu,Q. et al. (2021) Galaxy-ML: An accessible, reproducible, and scalable machine learning toolkit for biomedicine. PLOS Computational Biology, 17, e1009014.
  7. Rajczewski,A.T. et al. (2021) A rigorous evaluation of optimal peptide targets for MS-based clinical diagnostics of Coronavirus Disease 2019 (COVID-19). Clinical Proteomics, 18, 15.
  8. Wolff,J. et al. (2021) Robust and efficient single-cell Hi-C clustering with approximate k-nearest neighbor graphs. Bioinformatics.
  9. Gallardo-Alba,C. et al. (2021) A constructivist-based proposal for bioinformatics teaching practices during lockdown. PLOS Computational Biology, 17, 1–11.
  10. Serrano-Solano,B. et al. (2021) Fostering accessible online education using Galaxy as an e-learning platform. PLOS Computational Biology, 17, 1–10.
  11. Moreno,P. et al. (2021) User-friendly, scalable tools and workflows for single-cell RNA-seq analysis. Nature Methods.
  12. Bai,J. et al. (2021) BioContainers Registry: Searching Bioinformatics and Proteomics Tools, Packages, and Containers. Journal of Proteome Research.
  13. Videm,P. et al. (2021) ChiRA: an integrated framework for chimeric read analysis from RNA-RNA interactome and RNA structurome data. GigaScience, 10.
  14. Kumar,A. et al. (2021) Tool recommender system in Galaxy using deep learning. GigaScience, 10.
  15. Wolff,J. et al. (2021) Scool: a new data storage format for single-cell Hi-C data. Bioinformatics.


  1. Bray,S.A. et al. (2020) Intuitive, reproducible high-throughput molecular dynamics in Galaxy: a tutorial. Journal of Cheminformatics, 12, 1–13.
  2. Greve,G. et al. (2020) Decitabine induces gene derepression on monosomic chromosomes: in vitro and in vivo effects in adverse-risk cytogenetics AML. Cancer Research, canres.1430.2020.
  3. Marais,G.A.B. et al. (2020) Genome Evolution: Mutation Is the Main Driver of Genome Size in Prokaryotes. Current Biology, 30, R1083–R1085.
  4. Garcia,L. et al. (2020) Ten simple rules for making training materials FAIR. PLOS Computational Biology, 16, 1–9.
  5. Tekman,M. et al. (2020) A single-cell RNA-sequencing training and analysis suite using the Galaxy framework. GigaScience, 9.
  6. de Koning,W. et al. (2020) NanoGalaxy: Nanopore long-read sequencing data analysis in Galaxy. GigaScience, 9.
  7. Rasche,H. and Gruening,B.A. (2020) Training Infrastructure as a Service.
  8. Baker,D. et al. (2020) No more business as usual: Agile and effective responses to emerging pathogen threats require open data and open analytics. PLOS Pathogens, 16, e1008643.
  9. Lopez-Delisle,L. et al. (2020) pyGenomeTracks: reproducible plots for multivariate genomic data sets. Bioinformatics.
  10. Dass,G. et al. (2020) The omics discovery REST interface. Nucleic Acids Research, 48, W380–W384.
  11. Wolff,J. et al. (2020) Galaxy HiCExplorer 3: a web server for reproducible Hi-C, capture Hi-C and single-cell Hi-C data analysis, quality control and visualization. Nucleic Acids Research, 48, W177–W184.
  12. Schäfer,R.A. et al. (2020) GLASSGo in Galaxy: high-throughput, reproducible and easy-to-integrate prediction of sRNA homologs. Bioinformatics.
  13. Bray,S.A. et al. (2020) The ChemicalToolbox: reproducible, user-friendly cheminformatics analysis on the Galaxy platform. Journal of Cheminformatics, 12.
  14. Hille,L. et al. (2020) Ultrastructural, transcriptional and functional differences between human reticulated and non-reticulated platelets. Journal of Thrombosis and Haemostasis.
  15. Murat,K. et al. (2020) Ewastools: Infinium Human Methylation BeadChip pipeline for population epigenetics integrated into Galaxy. GigaScience, 9.
  16. Perez-Riverol,Y. et al. (2020) CHAPTER 19. Cross-platform Software Development and Distribution with Bioconda and BioContainers. In, Processing Metabolomics and Proteomics Data with Open Software. Royal Society of Chemistry, pp. 415–426.
  17. Wolff,J. et al. (2020) Loop detection using Hi-C data with HiCExplorer.
  18. Wolff,J. et al. (2020) Approximate k-nearest neighbors graph for single-cell Hi-C dimensional reduction with MinHash.
  19. Suchodoletz,D. von et al. (2020) Lessons learned from Virtualized Research Environments in today’s scientific compute infrastructures. E-Science-Tage 2019.
  20. Dass,G. et al. (2020) The omics discovery REST interface. Nucleic Acids Research.


  1. Senapathi,T. et al. (2019) Biomolecular Reaction and Interaction Dynamics Global Environment (BRIDGE). Bioinformatics, 35, 3508–3509.
  2. Gruening,B. et al. (2019) Recommendations for the packaging and containerizing of bioinformatics software. F1000Research, 7, 742.
  3. Wibberg,D. et al. (2019) The de. NBI/ELIXIR-DE training platform-Bioinformatics training in Germany and across Europe within ELIXIR. F1000Research, 8.
  4. Scholz,A. et al. (2019) uORF-Tools—Workflow for the determination of translation-regulatory upstream open reading frames. PLOS ONE, 14, e0222459.
  5. Veil,M. et al. (2019) Pou5f3, SoxB1, and Nanog remodel chromatin on High Nucleosome Affinity Regions at Zygotic Genome Activation. Genome Research, gr.240572.118.
  6. Walz,J.M. et al. (2019) Impact of angiogenic activation and inhibition on miRNA profiles of human retinal endothelial cells. Experimental Eye Research.
  7. Fries,A. et al. (2019) Alteration of the Route to Menaquinone towards Isochorismate-Derived Metabolites. ChemBioChem.
  8. Fallmann,J. et al. (2019) The RNA workbench 2.0: next generation RNA data analysis. Nucleic Acids Research.
  9. Grüning,B.A. et al. (2019) Software engineering for scientific big data analysis. GigaScience, 8.
  10. Ison,J. et al. (2019) The bio.tools registry of software tools and data resources for the life sciences. Genome Biology, 20.
  11. Miladi,M. et al. (2019) GraphClust2: Annotation and discovery of structured RNAs with scalable and accessible integrative clustering. GigaScience, 8.
  12. Föll,M.C. et al. (2019) Accessible and reproducible mass spectrometry imaging data analysis in Galaxy. GigaScience, 8.
  13. Wibberg,D. et al. (2019) The de.NBI / ELIXIR-DE training platform - Bioinformatics training in Germany and across Europe within ELIXIR. F1000Research, 8, 1877.


  1. Batut,B. et al. (2018) ASaiM: a Galaxy-based framework to analyze microbiota data. GigaScience, 7, giy057.
  2. Anatskiy,E. et al. (2018) Parkour LIMS: high-quality sample preparation in next generation sequencing. Bioinformatics, 35, 1422–1424.
  3. Nührenberg,T.G. et al. (2018) Uncontrolled Diabetes Mellitus Has No Major Influence on the Platelet Transcriptome. BioMed Research International, 2018, 1–9.
  4. Afgan,E. et al. (2018) The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2018 update. Nucleic Acids Research, 46, W537–W544.
  5. Ramı́rez Fidel et al. (2018) High-resolution TADs reveal DNA sequences underlying genome organization in flies. Nature communications, 9, 189.
  6. Gilsbach,R. et al. (2018) Distinct epigenetic programs regulate cardiac myocyte development and disease in the human heart in vivo. Nature communications, 9, 391.
  7. Thriene,K. et al. (2018) Combinatorial omics analysis reveals perturbed lysosomal homeostasis in collagen VII-deficient keratinocytes. Molecular & Cellular Proteomics, mcp–RA117.
  8. Blank,C. et al. (2018) Disseminating Metaproteomic Informatics Capabilities and Knowledge Using the Galaxy-P Framework. Proteomes, 6, 7.
  9. Wolff,J. et al. (2018) Galaxy HiCExplorer: a web server for reproducible Hi-C data analysis, quality control and visualization. Nucleic Acids Research, 46, W11–W16.
  10. Grüning,B. et al. (2018) Bioconda: sustainable and comprehensive software distribution for the life sciences. Nature Methods, 15, 475–476.
  11. Grüning,B. et al. (2018) Practical Computational Reproducibility in the Life Sciences. Cell Systems, 6, 631–635.
  12. Batut,B. et al. (2018) Community-Driven Data Analysis Training for Biology. Cell Systems, 6, 752–758.e1.
  13. Argentini,A. et al. (2018) Update on the moFF Algorithm for Label-Free Quantitative Proteomics. Journal of Proteome Research.


  1. Gruning,B.A. et al. (2017) Jupyter and Galaxy: Easing entry barriers into complex data analyses for biomedical researchers. PLoS Comput Biol, 13, e1005425.
  2. Backofen,R. et al. (2017) RNA-bioinformatics: Tools, services and databases for the analysis of RNA-based regulation. J Biotechnol.
  3. Gruning,B.A. et al. (2017) The RNA workbench: best practices for RNA and high-throughput sequencing bioinformatics in Galaxy. nar.
  4. Hornig,T. et al. (2017) GRIN3B missense mutation as an inherited risk factor for schizophrenia: whole-exome sequencing in a family with a familiar history of psychotic disorders. Genetics research, 99.
  5. Wreczycka,K. et al. (2017) Strategies for analyzing bisulfite sequencing data. Journal of biotechnology, 261, 105–115.
  6. Veiga Leprevost,F. da et al. (2017) BioContainers: an open-source and community-driven framework for software standardization. Bioinformatics, 33, 2580–2582.
  7. Backofen,R. et al. (2017) RNA-bioinformatics: tools, services and databases for the analysis of RNA-based regulation. Journal of biotechnology, 261, 76–84.
  8. 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.
  9. Jiménez,R.C. et al. (2017) Four simple recommendations to encourage best practices in research software. F1000Research, 6.
  10. Walz,J.M. et al. (2017) miRNA profile of human retinal endothelial cells in starvation or angiogenic stimulation with and without VEGF inhibitors. Investigative Ophthalmology & Visual Science, 58, 3567–3567.
  11. Fallmann,J. et al. (2017) Recent advances in RNA folding. Journal of biotechnology, 261, 97–104.
  12. Batut,B. et al. (2017) Building an open, collaborative, online infrastructure for bioinformatics training. F1000Research, 6.
  13. Doppelt-Azeroual,O. et al. (2017) ReGaTE, Registration of Galaxy Tools in Elixir. Gigascience.
  14. Glaser,L.V. et al. (2017) EBF1 binds to EBNA2 and promotes the assembly of EBNA2 chromatin complexes in B cells. PLoS Pathogens, 13, e1006664.
  15. Meier,K. et al. (2017) Virtualisierte wissenschaftliche Forschungsumgebungen und die zukünftige Rolle der Rechenzentren. 10. DFN-Forum Kommunikationstechnologien.
  16. Chambers,M.C. et al. (2017) An Accessible Proteogenomics Informatics Resource for Cancer Researchers. Cancer research, 77, e43–e46.
  17. Batut,B. and Grüning,B. (2017) ENASearch: A Python library for interacting with ENA’s API. The Journal of Open Source Software, 2, 418.
  18. Nothjunge,S. et al. (2017) DNA methylation signatures follow preformed chromatin compartments in cardiac myocytes. Nature communications, 8, 1667.


  1. Roidl,D. et al. (2016) DOT1L Activity Promotes Proliferation and Protects Cortical Neural Stem Cells from Activation of ATF4-DDIT3-Mediated ER Stress In Vitro. Stem Cells, 34, 233–245.
  2. Wecker,T. et al. (2016) MicroRNA profiling in aqueous humor of individual human eyes by next-generation sequencing. Investigative ophthalmology & visual science, 57, 1706–1713.
  3. Ramı́rez Fidel et al. (2016) deepTools2: a next generation web server for deep-sequencing data analysis. Nucleic acids research, 44, W160–W165.
  4. Afgan,E. et al. (2016) The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2016 update. Nucleic acids research, 44, W3–W10.
  5. Hüttel,W. et al. (2016) Echinocandin B biosynthesis: a biosynthetic cluster from Aspergillus nidulans NRRL 8112 and reassembly of the subclusters Ecd and Hty from Aspergillus pachycristatus NRRL 11440 reveals a single coherent gene cluster. BMC genomics, 17, 570.
  6. Döring,K. et al. (2016) PubMedPortable: a framework for supporting the development of text mining applications. PloS one, 11, e0163794.
  7. Kranzhöfer,D.K. et al. (2016) 5’-hydroxymethylcytosine precedes loss of CpG methylation in enhancers and genes undergoing activation in cardiomyocyte maturation. PloS one, 11, e0166575.


  1. Cock,P.J.A. et al. (2015) NCBI BLAST+ integrated into Galaxy. Gigascience, 4, 39.
  2. Lucas,X. et al. (2015) The purchasable chemical space: a detailed picture. Journal of chemical information and modeling, 55, 915–924.
  3. Preissl,S. et al. (2015) Deciphering the Epigenetic Code of Cardiac Myocyte TranscriptionNovelty and Significance. Circulation research, 117, 413–423.
  4. Yachdav,G. et al. (2015) Cutting edge: anatomy of BioJS, an open source community for the life sciences. Elife, 4, e07009.
  5. Gilsbach,R. et al. (2015) Genome wide epigenetic profiling of purified cardiomyocytes enables deep insights into gene expression control. NAUNYN-SCHMIEDEBERGS ARCHIVES OF PHARMACOLOGY, 388, S55–S55.
  6. Preissl,S. et al. (2015) Dynamics of epigenetic modifications in cardiomyocyte-specific cis-regulatory regions. NAUNYN-SCHMIEDEBERGS ARCHIVES OF PHARMACOLOGY, 388, S55–S56.
  7. Ison,J. et al. (2015) Tools and data services registry: a community effort to document bioinformatics resources. Nucleic acids research, 44, D38–D47.
  8. Li,J. et al. (2015) An NGS workflow blueprint for DNA sequencing data and its application in individualized molecular oncology. Cancer informatics, 14, CIN–S30793.
  9. Grüning,B. (2015) Integrierte bioinformatische Methoden zur reproduzierbaren und transparenten Hochdurchsatz-Analyse von Life Science Big Data.


  1. Lucas,X. et al. (2014) ChemicalToolBoX and its application on the study of the drug like and purchasable space. Journal of Cheminformatics, 6, P51.
  2. Telukunta,K.K. et al. (2014) Dynamic information system for small molecules. Journal of cheminformatics, 6, P28.
  3. Ramı́rez Fidel et al. (2014) deepTools: a flexible platform for exploring deep-sequencing data. Nucleic acids research, 42, W187–W191.
  4. Patel,H. et al. (2014) PyWATER: a PyMOL plug-in to find conserved water molecules in proteins by clustering. Bioinformatics, 30, 2978–2980.
  5. Gilsbach,R. et al. (2014) Dynamic DNA methylation orchestrates cardiomyocyte development, maturation and disease. Nature communications, 5, 5288.
  6. Schubert,D. et al. (2014) Autosomal dominant immune dysregulation syndrome in humans with CTLA4 mutations. Nature medicine, 20, 1410.
  7. Volk,T. et al. (2014) Autosomal-Recessive Agammaglobulinemia Due to Homozygous Mutations in Artemis: Do We Need a Modifier? JOURNAL OF CLINICAL IMMUNOLOGY, 34, S146–S147.
  8. Schubert,D. et al. (2014) CTLA-4 Deficiency-A Novel Autosomal-Dominant Immune Dysregulation Syndrome. JOURNAL OF CLINICAL IMMUNOLOGY, 34, S144–S144.
  9. Bulashevska,A. et al. (2014) Bioinformatics Analysis of Exome Sequencing Data: Challenges and Solutions. JOURNAL OF CLINICAL IMMUNOLOGY, 34, S328–S328.
  10. Preissl,S. et al. (2014) CpG-methylation characterizes cardiomyocytes in development and disease. NAUNYN-SCHMIEDEBERGS ARCHIVES OF PHARMACOLOGY, 387, S76–S76.


  1. Grüning,B.A. et al. (2013) Draft genome sequence of Streptomyces viridochromogenes strain Tü57, producer of avilamycin. Genome announcements, 1, e00384–13.
  2. Cock,P.J.A. et al. (2013) Galaxy tools and workflows for sequence analysis with applications in molecular plant pathology. PeerJ, 1, e167.
  3. Youssar,L. et al. (2013) Characterization and phylogenetic analysis of the mitochondrial genome of Glarea lozoyensis indicates high diversity within the order Helotiales. PloS one, 8, e74792.


  1. Youssar,L. et al. (2012) Genome sequence of the fungus Glarea lozoyensis: the first genome sequence of a species from the Helotiaceae family. Eukaryotic cell, 11, 250–250.
  2. Senger,C. et al. (2012) Mining and evaluation of molecular relationships in literature. Bioinformatics, 28, 709–714.
  3. Lucas,X. et al. (2012) StreptomeDB: a resource for natural compounds isolated from Streptomyces species. Nucleic acids research, 41, D1130–D1136.
  4. Günther,S. et al. (2012) Genome Sequence of the Fungus Glarea. Eukaryotic Cell, 11, 250.


  1. Erxleben,A. et al. (2011) Genome sequence of Streptomyces sp. Tü6071. Journal of bacteriology, JB–00377.
  2. Grüning,B.A. et al. (2011) Compounds In Literature (CIL): screening for compounds and relatives in PubMed. Bioinformatics, 27, 1341–1342.
  3. Bieschke,J. et al. (2011) Small-molecule conversion of toxic oligomers to nontoxic β-sheet–rich amyloid fibrils. Nature Chemical Biology, 8, 93–101.


  1. Hildebrand,P.W. et al. (2009) SuperLooper—a prediction server for the modeling of loops in globular and membrane proteins. Nucleic acids research, 37, W571–W574.
  2. Rose,A. et al. (2009) RHYTHM—a server to predict the orientation of transmembrane helices in channels and membrane-coils. Nucleic acids research, 37, W575–W580.


  1. Rother,K. et al. (2008) Voronoia: analyzing packing in protein structures. Nucleic acids research, 37, D393–D395.
  2. Dunkel,M. et al. (2008) SuperScent—a database of flavors and scents. Nucleic acids research, 37, D291–D294.
  3. Schmidt,U. et al. (2008) SuperToxic: a comprehensive database of toxic compounds. Nucleic acids research, 37, D295–D299.
  4. Struck,S. et al. (2008) Toxicity versus potency: Elucidation of toxicity properties discriminating between toxins, drugs, and natural compounds. Genome Informatics 2008: Genome Informatics Series Vol. 20, 231–242.
  5. STRUCK,S.W.A.N.T.J.E. et al. (2008) PROPERTIES DISCRIMINATING BETWEEN TOXINS, DRUGS. Genome Informatics 2008: Genome Informatics Series Vol. 20, 231.