Author

Sebastian J. Schultheiß

Observed names: Schultheiss, Sebastian J, Schultheiss, Sebastian J., Sebastian J Schultheiss, Sebastian J. Schultheiss, Sebastian Schultheiss

Observed institutions: Memorial Sloan Kettering Cancer Center, Max Planck Society, Heidelberg University, Max Planck Institute for Developmental Biology, European Molecular Biology Laboratory, Friedrich Miescher Laboratory, Max Planck Institute for Biology, Université de Nantes, Technische Universität Berlin, University of Tübingen, Nantes Université

ORCID: 0000-0001-8488-906X

Observed ORCID: 0000-0001-8488-906X

OpenAlex ID: A5000233990

De novo assembly of the cattle reference genome with single-molecule sequencing

2020 · Benjamin D Rosen, Derek M Bickhart, et al. · GigaScience · 803

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Multiple reference genomes and transcriptomes for Arabidopsis thaliana

2011 · Xiangchao Gan, Oliver Stegle, et al. · Nature · 644

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Hormonal control of the shoot stem-cell niche

2010 · Zhong Zhao, Stig U. Andersen, et al. · Nature · 529

Transcriptional Control of a Plant Stem Cell Niche

2010 · Wolfgang Busch, Andrej Miotk, et al. · Developmental Cell · 228

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Accurate machine learning-based germination detection, prediction and quality assessment of three grain crops

2020 · Nikita Genze, Richa Bharti, et al. · Plant Methods · 111

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MethylScore, a pipeline for accurate and context-aware identification of differentially methylated regions from population-scale plant whole-genome bisulfite sequencing data

2022 · Patrick Hüther, Jörg Hagmann, et al. · Quantitative Plant Biology · 44

A comparison of classical and machine learning-based phenotype prediction methods on simulated data and three plant species

2022 · Maura John, Florian Haselbeck, et al. · Frontiers in Plant Science · 41

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Modernizing the Bovine Reference Genome Assembly

2018 · Benjamin D. Rosen, Derek M. Bickhart, et al. · World Congress on Genetics Applied to Livestock Production (WCGALP) · 33

Interpretable machine learning decodes soil microbiome’s response to drought stress

2024 · Michelle Hagen, Rupashree Dass, et al. · Environmental Microbiome · 30

Persistence and Availability of Web Services in Computational Biology

2011 · Sebastian J. Schultheiss, Marc-Christian Münch, et al. · PLoS ONE · 29

KIRMES: kernel-based identification of regulatory modules in euchromatic sequences

2009 · Sebastian J. Schultheiss, Wolfgang Busch, et al. · Bioinformatics · 23

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Oqtans: the RNA-seq workbench in the cloud for complete and reproducible quantitative transcriptome analysis

2014 · Vipin T. Sreedharan, Sebastian J. Schultheiss, et al. · Bioinformatics · 14

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Ten Simple Rules for Providing a Scientific Web Resource

2011 · Sebastian J. Schultheiss · PLoS Computational Biology · 12

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KIRMES: kernel-based identification of regulatory modules in euchromatic sequences

2009 · Sebastian J Schultheiss, Wolfgang Busch, et al. · BMC Bioinformatics · 7

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MethylScore, a pipeline for accurate and context-aware identification of differentially methylated regions from population-scale plant WGBS data

2022 · Patrick Hüther, Jörg Hagmann, et al. · bioRxiv (Cold Spring Harbor Laboratory) · 6

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Oqtans: a multifunctional workbench for RNA-seq data analysis

2014 · Vipin T Sreedharan, Sebastian J Schultheiss, et al. · BMC Bioinformatics · 4

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Oqtans: a Galaxy-integrated workflow for quantitative transcriptome analysis from NGS Data

2011 · Sebastian J Schultheiss, Géraldine Jean, et al. · BMC Bioinformatics · 3

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Kernel-Based Identification of Regulatory Modules

2010 · Sebastian J. Schultheiss · Methods in molecular biology · 3

Harnessing crop breeding innovations for a sustainable agricultural future

2024 · Sebastian Schultheiss · Open Access Government · 1

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Approaches taken, progress made, and enhanced utility of long read-based goat, swine, cattle and sheep reference genomes

2016 · Timothy P. L. Smith, Sergey Koren, et al. · Plant and Animal Genome XXIV Conference · 1

Learn from the Best

2014 · Virginie Bernard, Sebastian J. Schultheiss, et al. · PLoS Computational Biology · 1

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KIRMES: Kernel-based Identification of Regulatory Modules in Euchromatic Sequences.

2008 · Sebastian J. Schultheiß, Wolfgang Busch, et al. · German Conference on Bioinformatics · 1

CropML: Neue Maschinelle Lernverfahren zur akkurateren Pflanzenzüchtung durch Integration heterogener externer Faktoren des BMBF Programm "Erforschung, Entwicklung und Nutzung von Methoden der Künstlichen Intelligenz in KMU"

2026 · Dass, Rupashree, Dreischer, Christian, et al. · RENATE

The power of next-generation sequencing and machine learning for causal gene finding and prediction of phenotypes.

2021 · Anna S. Sowa, Lisa Dussling, et al. · CABI eBooks

Supplementary Material for the Bioinformatics submission “KIRMES: Kernel-based Identification of Regulatory Modules in Euchromatic Sequences”

2015 · Sebastian J. Schultheiss, Wolfgang Busch, et al.

Works count: 28

Citation count: 2,568

H-index: 12

I10-index: 13