| Multiple reference genomes and transcriptomes for Arabidopsis thaliana X Gan, O Stegle, J Behr, JG Steffen, P Drewe, KL Hildebrand, R Lyngsoe, ... Nature 477 (7365), 419-423, 2011 | 764 | 2011 |
| Hormonal control of the shoot stem-cell niche Z Zhao, SU Andersen, K Ljung, K Dolezal, A Miotk, SJ Schultheiss, ... Nature 465 (7301), 1089-1092, 2010 | 565 | 2010 |
| De novo assembly of the cattle reference genome with single-molecule sequencing BD Rosen, DM Bickhart, RD Schnabel, S Koren, CG Elsik, E Tseng, ... Gigascience 9 (3), giaa021, 2020 | 514 | 2020 |
| Transcriptional control of a plant stem cell niche W Busch, A Miotk, FD Ariel, Z Zhao, J Forner, G Daum, T Suzaki, ... Developmental cell 18 (5), 841-853, 2010 | 245 | 2010 |
| Accurate machine learning-based germination detection, prediction and quality assessment of three grain crops N Genze, R Bharti, M Grieb, SJ Schultheiss, DG Grimm Plant methods 16, 1-11, 2020 | 73 | 2020 |
| Modernizing the bovine reference genome assembly BD Rosen, DM Bickhart, RD Schnabel, S Koren, CG Elsik, A Zimin, ... Proceedings of the 11th world congress on genetics applied to livestock …, 2018 | 57 | 2018 |
| De novo assembly of the cattle reference genome with singlemolecule sequencing. Gigascience 9: giaa021 BD Rosen, DM Bickhart, RD Schnabel, S Koren, CG Elsik, E Tseng, ... | 40 | 2020 |
| KIRMES: kernel-based identification of regulatory modules in euchromatic sequences SJ Schultheiss, W Busch, JU Lohmann, O Kohlbacher, G Rätsch Bioinformatics 25 (16), 2126-2133, 2009 | 40 | 2009 |
| KIRMES: kernel-based identification of regulatory modules in euchromatic sequences S Schultheiss, W Busch, J Lohmann, O Kohlbacher, G Rätsch BMC Bioinformatics 10 (Suppl 13), O1, 2009 | 40 | 2009 |
| Persistence and availability of Web services in computational biology SJ Schultheiss, MC Münch, GD Andreeva, G Rätsch PloS one 6 (9), e24914, 2011 | 38 | 2011 |
| A comparison of classical and machine learning-based phenotype prediction methods on simulated data and three plant species M John, F Haselbeck, R Dass, C Malisi, P Ricca, C Dreischer, ... Frontiers in Plant Science 13, 932512, 2022 | 24 | 2022 |
| Oqtans: the RNA-seq workbench in the cloud for complete and reproducible quantitative transcriptome analysis VT Sreedharan, SJ Schultheiss, G Jean, A Kahles, R Bohnert, P Drewe, ... Bioinformatics 30 (9), 1300-1301, 2014 | 19 | 2014 |
| Ten simple rules for providing a scientific web resource SJ Schultheiss PLoS Computational Biology 7 (5), e1001126, 2011 | 18 | 2011 |
| MethylScore, a pipeline for accurate and context-aware identification of differentially methylated regions from population-scale plant whole-genome bisulfite sequencing data P Hüther, J Hagmann, A Nunn, I Kakoulidou, R Pisupati, D Langenberger, ... Quantitative Plant Biology 3, e19, 2022 | 17 | 2022 |
| Oqtans: a Galaxy-integrated workflow for quantitative transcriptome analysis from NGS Data SJ Schultheiss, G Jean, J Behr, R Bohnert, P Drewe, N Görnitz, A Kahles, ... BMC Bioinformatics 12, 1-2, 2011 | 6 | 2011 |
| Kernel-based identification of regulatory modules SJ Schultheiss Computational Biology of Transcription Factor Binding, 213-223, 2010 | 5 | 2010 |
| Approaches taken, progress made, and enhanced utility of long read-based goat, swine, cattle and sheep reference genomes TPL Smith, S Koren, A Phillippy, DM Bickhart, BD Rosen, KC Worley, ... Plant and Animal Genome XXIV, 2016 | 3 | 2016 |
| Learn from the Best V Bernard, SJ Schultheiss, M Michaut PLoS computational biology 10 (5), e1003645, 2014 | 3 | 2014 |
| Interpretable machine learning decodes soil microbiome’s response to drought stress M Hagen, R Dass, C Westhues, J Blom, SJ Schultheiss, S Patz Environmental Microbiome 19 (1), 35, 2024 | 1 | 2024 |
| Post secret: geheime Träume, Wünsche und Ängste auf Postkarten SJ Schultheiß, F Warren Mosaik, 2014 | 1 | 2014 |
Gunnar RätschProfessor, ETH ZürichVerified email at inf.ethz.ch
