| Snakemake—a scalable bioinformatics workflow engine J Köster, S Rahmann Bioinformatics 28 (19), 2520-2522, 2012 | 2822 | 2012 |
| Bioconda: sustainable and comprehensive software distribution for the life sciences B Grüning, R Dale, A Sjödin, BA Chapman, J Rowe, CH Tomkins-Tinch, ... Nature methods 15 (7), 475-476, 2018 | 1284 | 2018 |
| Eleven grand challenges in single-cell data science D Lähnemann, J Köster, E Szczurek, DJ McCarthy, SC Hicks, ... Genome biology 21, 1-35, 2020 | 1180 | 2020 |
| Sustainable data analysis with Snakemake F Mölder, KP Jablonski, B Letcher, MB Hall, CH Tomkins-Tinch, V Sochat, ... F1000Research 10, 33, 2021 | 1170 | 2021 |
| Quality control, modeling, and visualization of CRISPR screens with MAGeCK-VISPR W Li, J Köster, H Xu, CH Chen, T Xiao, JS Liu, M Brown, XS Liu Genome biology 16, 1-13, 2015 | 469 | 2015 |
| Mutational dynamics between primary and relapse neuroblastomas A Schramm, J Köster, Y Assenov, K Althoff, M Peifer, E Mahlow, ... Nature genetics 47 (8), 872-877, 2015 | 392 | 2015 |
| The public repository of xenografts enables discovery and randomized phase II-like trials in mice EC Townsend, MA Murakami, A Christodoulou, AL Christie, J Köster, ... Cancer cell 29 (4), 574-586, 2016 | 359 | 2016 |
| VIPER: Visualization Pipeline for RNA-seq, a Snakemake workflow for efficient and complete RNA-seq analysis MI Cornwell, M Vangala, L Taing, Z Herbert, J Köster, B Li, H Sun, T Li, ... BMC bioinformatics 19, 1-14, 2018 | 188 | 2018 |
| Practical computational reproducibility in the life sciences B Grüning, J Chilton, J Köster, R Dale, N Soranzo, M Van Den Beek, ... Cell systems 6 (6), 631-635, 2018 | 162 | 2018 |
| MiR‐137 functions as a tumor suppressor in neuroblastoma by downregulating KDM1A K Althoff, A Beckers, A Odersky, P Mestdagh, J Köster, IM Bray, K Bryan, ... International journal of cancer 133 (5), 1064-1073, 2013 | 133 | 2013 |
| SimLoRD: simulation of long read data BK Stöcker, J Köster, S Rahmann Bioinformatics 32 (17), 2704-2706, 2016 | 123 | 2016 |
| Sustainable data analysis with Snakemake. F1000Res 10: 33 F Mölder, KP Jablonski, B Letcher, MB Hall, CH Tomkins-Tinch, V Sochat, ... | 82 | 2021 |
| CRISPR-DO for genome-wide CRISPR design and optimization J Ma, J Köster, Q Qin, S Hu, W Li, C Chen, Q Cao, J Wang, S Mei, Q Liu, ... Bioinformatics 32 (21), 3336-3338, 2016 | 66 | 2016 |
| Targeting the innate immunoreceptor RIG-I overcomes melanoma-intrinsic resistance to T cell immunotherapy L Such, F Zhao, D Liu, B Thier, VTK Le-Trilling, A Sucker, C Coch, ... The Journal of clinical investigation 130 (8), 4266-4281, 2020 | 59 | 2020 |
| Rust-Bio: a fast and safe bioinformatics library J Köster Bioinformatics 32 (3), 444-446, 2016 | 59 | 2016 |
| Full-length de novo viral quasispecies assembly through variation graph construction JA Baaijens, B Van der Roest, J Köster, L Stougie, A Schönhuth Bioinformatics 35 (24), 5086-5094, 2019 | 55 | 2019 |
| Next‐generation RNA sequencing reveals differential expression of MYCN target genes and suggests the mTOR pathway as a promising therapy target in MYCN‐amplified neuroblastoma A Schramm, J Köster, T Marschall, M Martin, M Schwermer, K Fielitz, ... International journal of cancer 132 (3), E106-E115, 2013 | 51 | 2013 |
| Sustainable data analysis with Snakemake. F1000Res. 2021; 10: 33 F Mölder, KP Jablonski, B Letcher, MB Hall, CH Tomkins-Tinch, V Sochat, ... PubMed Abstract| Publisher Full Text, 0 | 50 | |
| Machine learning reveals a PD-L1–independent prediction of response to immunotherapy of non-small cell lung cancer by gene expression context M Wiesweg, F Mairinger, H Reis, M Goetz, J Kollmeier, D Misch, ... European Journal of Cancer 140, 76-85, 2020 | 44 | 2020 |
| Cancer evolution, mutations, and clonal selection in relapse neuroblastoma M Schulte, J Köster, S Rahmann, A Schramm Cell and tissue research 372 (2), 263-268, 2018 | 39 | 2018 |
Sven RahmannSaarland UniversityZweryfikowany adres z cs.uni-saarland.de