Mario Stanke

30.2k total citations · 10 hit papers
63 papers, 12.9k citations indexed

About

Mario Stanke is a scholar working on Molecular Biology, Plant Science and Genetics. According to data from OpenAlex, Mario Stanke has authored 63 papers receiving a total of 12.9k indexed citations (citations by other indexed papers that have themselves been cited), including 48 papers in Molecular Biology, 12 papers in Plant Science and 9 papers in Genetics. Recurrent topics in Mario Stanke's work include Genomics and Phylogenetic Studies (41 papers), Machine Learning in Bioinformatics (24 papers) and RNA and protein synthesis mechanisms (24 papers). Mario Stanke is often cited by papers focused on Genomics and Phylogenetic Studies (41 papers), Machine Learning in Bioinformatics (24 papers) and RNA and protein synthesis mechanisms (24 papers). Mario Stanke collaborates with scholars based in Germany, United States and Switzerland. Mario Stanke's co-authors include Burkhard Morgenstern, Stephan Waack, Katharina J. Hoff, Mark Borodovsky, Alexandre Lomsadze, Mark Diekhans, David Haussler, Robert Baertsch, Irfan Gunduz and O. Keller and has published in prestigious journals such as Science, Proceedings of the National Academy of Sciences and Nucleic Acids Research.

In The Last Decade

Mario Stanke

62 papers receiving 12.8k citations

Hit Papers

AUGUSTUS: ab initio prediction of alternative transcripts 2003 2026 2010 2018 2006 2008 2003 2005 2004 500 1000 1.5k
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. AUGUSTUS: ab initio prediction of alternative transcripts
    2006 1.6k citations Mario Stanke, Stephan Waack et al. profile →
  2. Using native and syntenically mapped cDNA alignments to improve de novo gene finding
    2008 1.5k citations Mario Stanke, Mark Diekhans et al. Bioinformatics profile →
  3. Gene prediction with a hidden Markov model and a new intron submodel
    2003 1.1k citations Mario Stanke, Stephan Waack Bioinformatics profile →
  4. AUGUSTUS: a web server for gene prediction in eukaryotes that allows user-defined constraints
    2005 1.0k citations Mario Stanke, Burkhard Morgenstern profile →
  5. AUGUSTUS: a web server for gene finding in eukaryotes
    2004 903 citations Mario Stanke, Stephan Waack et al. profile →
  6. Gene prediction in eukaryotes with a generalized hidden Markov model that uses hints from external sources
    2006 837 citations Mario Stanke, Burkhard Morgenstern et al. BMC Bioinformatics profile →
  7. BRAKER2: automatic eukaryotic genome annotation with GeneMark-EP+ and AUGUSTUS supported by a protein database
    2021 803 citations Tomáš Brůna, Katharina J. Hoff et al. NAR Genomics and Bioinformatics profile →
  8. BRAKER1: Unsupervised RNA-Seq-Based Genome Annotation with GeneMark-ET and AUGUSTUS
    2015 717 citations Katharina J. Hoff, Simone Lange et al. Bioinformatics profile →
  9. Whole-Genome Annotation with BRAKER
    2019 443 citations Katharina J. Hoff, Alexandre Lomsadze et al. Methods in molecular biology profile →
  10. BRAKER3: Fully automated genome annotation using RNA-seq and protein evidence with GeneMark-ETP, AUGUSTUS, and TSEBRA
    2024 172 citations Lars Gabriel, Tomáš Brůna et al. Genome Research profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Mario Stanke Germany 36 7.4k 5.2k 2.3k 1.6k 1.5k 63 12.9k
Evgenia V. Kriventseva Switzerland 22 6.9k 0.9× 4.1k 0.8× 2.7k 1.2× 2.1k 1.3× 1.0k 0.7× 28 12.1k
Panagiotis Ioannidis Greece 23 5.7k 0.8× 4.1k 0.8× 2.3k 1.0× 2.0k 1.2× 1.1k 0.8× 41 10.9k
Felipe A. Simão Switzerland 8 5.7k 0.8× 3.8k 0.7× 2.3k 1.0× 1.9k 1.2× 984 0.7× 10 10.5k
Robert M. Waterhouse Switzerland 28 6.4k 0.9× 4.0k 0.8× 2.8k 1.2× 2.0k 1.2× 976 0.7× 62 12.1k
Steven Kelly United Kingdom 44 6.9k 0.9× 4.3k 0.8× 1.6k 0.7× 1.6k 1.0× 725 0.5× 147 11.8k
Alejandro P. Rooney United States 49 5.1k 0.7× 3.6k 0.7× 1.7k 0.7× 2.5k 1.5× 1.9k 1.3× 117 10.4k
Mihaela Pertea United States 24 12.0k 1.6× 6.6k 1.3× 3.0k 1.3× 1.3k 0.8× 943 0.6× 44 19.8k
Ian Korf United States 38 7.3k 1.0× 3.9k 0.7× 2.3k 1.0× 1.1k 0.7× 977 0.7× 70 11.1k
Christina A. Cuomo United States 53 6.7k 0.9× 4.0k 0.8× 1.3k 0.6× 1.5k 0.9× 2.0k 1.4× 145 15.1k
Evgeny M. Zdobnov Switzerland 45 11.0k 1.5× 6.0k 1.2× 4.0k 1.7× 3.1k 1.9× 1.4k 0.9× 94 19.8k

Countries citing papers authored by Mario Stanke

Since Specialization
Citations

This map shows the geographic impact of Mario Stanke's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Mario Stanke with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mario Stanke more than expected).

Fields of papers citing papers by Mario Stanke

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Mario Stanke. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Mario Stanke. The network helps show where Mario Stanke may publish in the future.

Co-authorship network of co-authors of Mario Stanke

This figure shows the co-authorship network connecting the top 25 collaborators of Mario Stanke. A scholar is included among the top collaborators of Mario Stanke based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Mario Stanke. Mario Stanke is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Korody, Marisa L., Björn Brändl, Andy Wing Chun Pang, et al.. (2025). Genomic map of the functionally extinct northern white rhinoceros ( Ceratotherium simum cottoni ). Proceedings of the National Academy of Sciences. 122(20). e2401207122–e2401207122.
2.
Gabriel, Lars, et al.. (2024). Tiberius: end-to-end deep learning with an HMM for gene prediction. Bioinformatics. 40(12). 9 indexed citations
3.
Stanke, Mario, et al.. (2024). learnMSA2: deep protein multiple alignments with large language and hidden Markov models. Bioinformatics. 40(Supplement_2). ii79–ii86. 3 indexed citations
4.
Stanke, Mario, et al.. (2022). End-to-end learning of evolutionary models to find coding regions in genome alignments. Bioinformatics. 38(7). 1857–1862. 2 indexed citations
5.
Krause, E. Tobias, Mareike Fischer, Thomas Müller, et al.. (2021). Application of YOLOv4 for Detection and Motion Monitoring of Red Foxes. Animals. 11(6). 1723–1723. 25 indexed citations
6.
Brůna, Tomáš, Katharina J. Hoff, Alexandre Lomsadze, Mario Stanke, & Mark Borodovsky. (2021). BRAKER2: automatic eukaryotic genome annotation with GeneMark-EP+ and AUGUSTUS supported by a protein database. NAR Genomics and Bioinformatics. 3(1). lqaa108–lqaa108. 803 indexed citations breakdown →
7.
Franke, Kristin, Isabell Karl, Tonatiuh Peña Centeno, et al.. (2019). Effects of adult temperature on gene expression in a butterfly: identifying pathways associated with thermal acclimation. BMC Evolutionary Biology. 19(1). 32–32. 7 indexed citations
8.
Stanke, Mario, et al.. (2019). VARUS: sampling complementary RNA reads from the sequence read archive. BMC Bioinformatics. 20(1). 558–558. 9 indexed citations
9.
Fiddes, Ian T., Joel Armstrong, Mark Diekhans, et al.. (2018). Comparative Annotation Toolkit (CAT)—simultaneous clade and personal genome annotation. Genome Research. 28(7). 1029–1038. 60 indexed citations
10.
Xu, Shuqing, Aura Navarro‐Quezada, Heiner Kuhl, et al.. (2017). Wild tobacco genomes reveal the evolution of nicotine biosynthesis. Proceedings of the National Academy of Sciences. 114(23). 6133–6138. 135 indexed citations
11.
Haney, Robert A., Evelyn E. Schwager, Torsten Wierschin, et al.. (2017). House spider genome uncovers evolutionary shifts in the diversity and expression of black widow venom proteins associated with extreme toxicity. BMC Genomics. 18(1). 178–178. 38 indexed citations
12.
König, Stefanie, et al.. (2017). Comparative Genome Annotation. Methods in molecular biology. 1704. 189–212. 8 indexed citations
13.
Hoff, Katharina J., Simone Lange, Alexandre Lomsadze, Mark Borodovsky, & Mario Stanke. (2015). BRAKER1: Unsupervised RNA-Seq-Based Genome Annotation with GeneMark-ET and AUGUSTUS. Bioinformatics. 32(5). 767–769. 717 indexed citations breakdown →
14.
15.
Auwera, Sandra Van der, Ingo Bulla, Mario Ziller, et al.. (2014). ClassyFlu: Classification of Influenza A Viruses with Discriminatively Trained Profile-HMMs. PLoS ONE. 9(1). e84558–e84558. 8 indexed citations
16.
Schönknecht, Gerald, Wei‐Hua Chen, Guillaume G. Barbier, et al.. (2013). Gene Transfer from Bacteria and Archaea Facilitated Evolution of an Extremophilic Eukaryote. Science. 339(6124). 1207–1210. 338 indexed citations
17.
Chan, Agnes P., Jonathan Crabtree, Qi Zhao, et al.. (2010). Draft genome sequence of the oilseed species Ricinus communis. Nature Biotechnology. 28(9). 951–956. 366 indexed citations
18.
Castellana, Natalie, Samuel Payne, Zhouxin Shen, et al.. (2008). Discovery and revision of Arabidopsis genes by proteogenomics. Proceedings of the National Academy of Sciences. 105(52). 21034–21038. 213 indexed citations
19.
20.
Stanke, Mario, et al.. (2006). Gene prediction in eukaryotes with a generalized hidden Markov model that uses hints from external sources. BMC Bioinformatics. 7(1). 62–62. 837 indexed citations breakdown →

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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