World's Best Scientists 2026 revealed!
Bernhard Y. Renard

Bernhard Y. Renard

D-Index & Metrics

Computer Science

D-Index
33
Citations
4110
World Ranking
12759
National Ranking
623

Bernhard Y. Renard publication distribution in Computer Science in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Computer Science in 2026. The highlighted bar marks where Bernhard Y. Renard sits on this spectrum.

32–41 publications: 7 scientists 42–51 publications: 22 scientists 52–61 publications: 82 scientists 62–71 publications: 134 scientists 72–81 publications: 249 scientists 82–91 publications: 324 scientists 92–101 publications: 421 scientists 102–111 publications: 420 scientists 112–121 publications: 497 scientists 122–131 publications: 544 scientists 132–141 publications: 555 scientists 142–151 publications: 609 scientists 152–161 publications: 559 scientists 162–171 publications: 534 scientists 172–181 publications: 556 scientists 182–191 publications: 583 scientists 192–201 publications: 519 scientists 202–211 publications: 508 scientists 212–221 publications: 490 scientists 222–231 publications: 437 scientists 232–241 publications: 423 scientists 242–251 publications: 408 scientists 252–261 publications: 377 scientists 262–271 publications: 301 scientists 272–281 publications: 335 scientists 282–291 publications: 320 scientists 292–301 publications: 293 scientists 302–311 publications: 250 scientists 312–321 publications: 238 scientists 322–331 publications: 206 scientists 332–341 publications: 209 scientists 342–351 publications: 208 scientists 352–361 publications: 162 scientists 362–371 publications: 176 scientists 372–381 publications: 127 scientists 382–391 publications: 158 scientists 392–401 publications: 128 scientists 402–411 publications: 104 scientists 412–421 publications: 94 scientists 422–431 publications: 99 scientists 432–441 publications: 83 scientists 442–451 publications: 108 scientists 452–461 publications: 73 scientists 462–471 publications: 77 scientists 472–481 publications: 69 scientists 482–491 publications: 84 scientists 492–501 publications: 62 scientists 502–511 publications: 54 scientists 512–521 publications: 57 scientists 522–531 publications: 51 scientists 532–541 publications: 51 scientists 542–551 publications: 32 scientists 552–561 publications: 38 scientists 562–571 publications: 28 scientists 572–581 publications: 43 scientists 582–591 publications: 33 scientists 592–601 publications: 41 scientists 602–611 publications: 32 scientists 612–621 publications: 28 scientists 622–631 publications: 25 scientists 632–641 publications: 27 scientists 642–651 publications: 17 scientists 652–661 publications: 20 scientists 662–671 publications: 17 scientists 672–681 publications: 15 scientists 682–691 publications: 14 scientists 692–701 publications: 21 scientists 702–711 publications: 13 scientists 712–721 publications: 12 scientists 722–731 publications: 19 scientists 732–741 publications: 14 scientists 742–751 publications: 12 scientists 752–761 publications: 10 scientists 762–771 publications: 10 scientists 772–781 publications: 11 scientists 782–791 publications: 10 scientists 792–801 publications: 11 scientists 802–811 publications: 8 scientists 812–821 publications: 8 scientists 822–831 publications: 7 scientists 832–841 publications: 11 scientists 842–851 publications: 10 scientists 852–861 publications: 5 scientists 862–871 publications: 9 scientists 872–881 publications: 4 scientists 882–891 publications: 6 scientists 892–901 publications: 3 scientists 902–911 publications: 6 scientists 912–921 publications: 3 scientists 922–931 publications: 2 scientists 932–941 publications: 2 scientists 942–951 publications: 2 scientists 952–961 publications: 3 scientists 962–971 publications: 3 scientists 972–981 publications: 3 scientists 982–990 publications: 5 scientists 991+ publications: 100 scientists
32 publications 991+

This scientist: 106 publications — 10th percentile

10% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 991 publications or more.

Bernhard Y. Renard D-index placement in Computer Science in 2026

The chart shows the D-index (discipline H-index) distribution of Computer Science scientists ranked by Research.com in 2026. The highlighted bar marks where Bernhard Y. Renard sits on this spectrum.

30–31 D-Index: 879 scientists 32–33 D-Index: 983 scientists 34–35 D-Index: 918 scientists 36–37 D-Index: 990 scientists 38–39 D-Index: 968 scientists 40–41 D-Index: 907 scientists 42–43 D-Index: 821 scientists 44–45 D-Index: 763 scientists 46–47 D-Index: 689 scientists 48–49 D-Index: 543 scientists 50–51 D-Index: 543 scientists 52–53 D-Index: 518 scientists 54–55 D-Index: 500 scientists 56–57 D-Index: 458 scientists 58–59 D-Index: 400 scientists 60–61 D-Index: 337 scientists 62–63 D-Index: 308 scientists 64–65 D-Index: 292 scientists 66–67 D-Index: 249 scientists 68–69 D-Index: 213 scientists 70–71 D-Index: 192 scientists 72–73 D-Index: 189 scientists 74–75 D-Index: 165 scientists 76–77 D-Index: 139 scientists 78–79 D-Index: 119 scientists 80–81 D-Index: 121 scientists 82–83 D-Index: 113 scientists 84–85 D-Index: 88 scientists 86–87 D-Index: 87 scientists 88–89 D-Index: 75 scientists 90–91 D-Index: 69 scientists 92–93 D-Index: 57 scientists 94–95 D-Index: 46 scientists 96–97 D-Index: 38 scientists 98–99 D-Index: 34 scientists 100–101 D-Index: 36 scientists 102–103 D-Index: 27 scientists 104–105 D-Index: 37 scientists 106–107 D-Index: 18 scientists 108–109 D-Index: 31 scientists 110–111 D-Index: 19 scientists 112–113 D-Index: 16 scientists 114–115 D-Index: 12 scientists 116–117 D-Index: 20 scientists 118–119 D-Index: 15 scientists 120–121 D-Index: 5 scientists 122–123 D-Index: 20 scientists 124–125 D-Index: 8 scientists 126–127 D-Index: 5 scientists 128–129 D-Index: 7 scientists 130 D-Index: 3 scientists 131+ D-Index: 98 scientists
30 D-Index 131+

This scientist: 33 D-Index — 13th percentile

13% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 131 D-Index or more.

Overview

Bernhard Y. Renard is affiliated with the Hasso Plattner Institute in Germany and has an extensive publication record primarily in the fields of Biochemistry, Genetics and Molecular Biology. Their scientific contributions encompass subfields such as Molecular Biology, Spectroscopy, Plant Science, Ecology, and Artificial Intelligence.

Their research topics cover a broad range of areas including:

  • Genomics and Phylogenetic Studies
  • Advanced Proteomics Techniques and Applications
  • Metabolomics and Mass Spectrometry Studies
  • Bacteriophages and microbial interactions
  • Plant Pathogens and Fungal Diseases
  • Mass Spectrometry Techniques and Applications
  • Bioinformatics and Genomic Networks

Among recent notable papers authored or co-authored by Bernhard Y. Renard are:

  • Critical Assessment of Metagenome Interpretation: the second round of challenges, 2022, published in Nature Methods
  • Critical Assessment of MetaProteome Investigation (CAMPI): a multi-laboratory comparison of established workflows, 2021, published in Nature Communications
  • A complete and flexible workflow for metaproteomics data analysis based on MetaProteomeAnalyzer and Prophane, 2020, published in Nature Protocols
  • ganon: precise metagenomics classification against large and up-to-date sets of reference sequences, 2020, published in Bioinformatics
  • Comprehensive evaluation of peptide de novo sequencing tools for monoclonal antibody assembly, 2022, published in Briefings in Bioinformatics

Bernhard Y. Renard collaborates frequently with a number of co-authors including:

  • Jakub M. Bartoszewicz
  • Thilo Muth
  • Melania Nowicka
  • Ferdous Nasri
  • Vitor C. Piro

Their work has been published in a variety of scientific venues, with repeated contributions to:

  • bioRxiv (Cold Spring Harbor Laboratory)
  • Zenodo (CERN European Organization for Nuclear Research)
  • Bioinformatics
  • arXiv (Cornell University)
  • Journal of Proteome Research

Best Publications

  • Critical Assessment of Metagenome Interpretation - A benchmark of metagenomics software

    Alexander Sczyrba;Peter Hofmann;Peter Hofmann;Peter Belmann;David Koslicki

  • Docking small peptides remains a great challenge: an assessment using AutoDock Vina

    Robert Rentzsch;Bernhard Y Renard

  • Concise Representation of Mass Spectrometry Images by Probabilistic Latent Semantic Analysis

    Michael Hanselmann;Marc Kirchner;Bernhard Y. Renard;Erika R. Amstalden

  • Toward digital staining using imaging mass spectrometry and random forests.

    Michael Hanselmann;Ullrich Köthe;Marc Kirchner;Bernhard Y. Renard

  • Recursive feature elimination in random forest classification supports nanomaterial grouping

    Aileen Bahl;Aileen Bahl;Bryan Hellack;Mihaela Balas;Anca Dinischiotu

  • Evaluating de novo sequencing in proteomics: already an accurate alternative to database-driven peptide identification?

    Thilo Muth;Bernhard Y Renard

  • NITPICK: peak identification for mass spectrometry data

    Bernhard Y. Renard;Bernhard Y. Renard;Marc Kirchner;Marc Kirchner;Hanno Steen;Judith A. J. Steen

  • Challenges and promise at the interface of metaproteomics and genomics: an overview of recent progress in metaproteogenomic data analysis

    Henning Schiebenhoefer;Tim Van Den Bossche;Stephan Fuchs;Bernhard Y Renard

  • Metaproteomic data analysis at a glance: advances in computational microbial community proteomics

    Thilo Muth;Bernhard Y. Renard;Lennart Martens

  • PaPrBaG: A machine learning approach for the detection of novel pathogens from NGS data

    Carlus Deneke;Robert Rentzsch;Bernhard Y. Renard

  • Metagenomic abundance estimation and diagnostic testing on species level

    Martin S. Lindner;Bernhard Y. Renard

  • When less can yield more - Computational preprocessing of MS/MS spectra for peptide identification.

    Bernhard Y. Renard;Bernhard Y. Renard;Marc Kirchner;Marc Kirchner;Flavio Monigatti;Alexander R. Ivanov

  • ganon: precise metagenomics classification against large and up-to-date sets of reference sequences.

    Vitor Piro;Vitor Piro;Vitor Piro;Temesgen Hailemariam Dadi;Enrico Seiler;Knut Reinert

  • ReadBouncer: precise and scalable adaptive sampling for nanopore sequencing

    Unknown

  • Interpretable detection of novel human viruses from genome sequencing data

    Jakub M Bartoszewicz;Anja Seidel;Bernhard Y Renard

  • DeePaC: predicting pathogenic potential of novel DNA with reverse-complement neural networks.

    Jakub M Bartoszewicz;Jakub M Bartoszewicz;Anja Seidel;Anja Seidel;Robert Rentzsch;Bernhard Y Renard

  • MPA Portable: A Stand-Alone Software Package for Analyzing Metaproteome Samples on the Go

    Thilo Muth;Fabian Kohrs;Robert Heyer;Dirk Benndorf;Dirk Benndorf

  • DUDes: a top-down taxonomic profiler for metagenomics.

    Vitor C. Piro;Vitor C. Piro;Martin S. Lindner;Bernhard Y. Renard

  • MSProGene: integrative proteogenomics beyond six-frames and single nucleotide polymorphisms.

    Franziska Zickmann;Bernhard Y. Renard

  • A Potential Golden Age to Come—Current Tools, Recent Use Cases, and Future Avenues for De Novo Sequencing in Proteomics

    Thilo Muth;Felix Hartkopf;Marc Vaudel;Marc Vaudel;Bernhard Y. Renard

  • Deuteration distribution estimation with improved sequence coverage for HX/MS experiments

    Xinghua Lou;Marc Kirchner;Bernhard Y. Renard;Ullrich Köthe

Frequent Co-Authors

Fred A. Hamprecht
Fred A. Hamprecht Heidelberg University
Andreas Nitsche
Andreas Nitsche Robert Koch Institute
Hanno Steen
Hanno Steen Boston Children's Hospital
Knut Reinert
Knut Reinert Freie Universität Berlin
Tobias Marschall
Tobias Marschall Heinrich Heine University Düsseldorf
Lennart Martens
Lennart Martens Ghent University
Ugur Sahin
Ugur Sahin Johannes Gutenberg University of Mainz
John C. Castle
John C. Castle Monte Rosa Therapeutics
Ron M. A. Heeren
Ron M. A. Heeren Maastricht University
Steven W. Singer
Steven W. Singer Lawrence Berkeley National Laboratory

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