A data-driven approach to integrative taxonomy
This paper integrates multi-omics data to improve species delimitation and reveal evolutionary drivers in liverworts.
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Justus-Liebig-Universität GießenORCID 0000-0002-4321-0257Unclaimed ORCID profile · follow to get notified of new preprints
8 papers
Recent·Most cited
This paper integrates multi-omics data to improve species delimitation and reveal evolutionary drivers in liverworts.
Kristian Peters, Nicole M. van Dam, Steffen Neumann
The paper identifies Essential Molecular Variables (EMVs) in liverworts, showing they are structured and functionally related to biotic interactions and bioclimatic properties, providing a mechanistic link to plant performance in ecosystems.
Mahnoor Zulfiqar, Anne-Susann Abel, Emanuel Barth…·Maria Sorokina
This study uses metabolomics and transcriptomics to analyze interactions between two marine microalgae in mono-cultures and co-cultures, revealing metabolic communication and cellular repair mechanisms.
Kristian Peters, Jörg Ziegler, Steffen Neumann
The paper introduces a computational framework to estimate essential molecular traits from biodiversity data using field samples and multiple omics approaches.
Mahnoor Zulfiqar, Luiz M. R. Gadelha Jr., Christoph Steinbeck…·Kristian Peters
MAW automates metabolome annotation using LC-MS2 data by integrating spectral databases and tools, improving candidate ranking and reproducibility.
Kristian Peters, Birgitta König‐Ries
The paper presents a high-quality reference dataset of macro- and microscopic images of Scapaniaceae bryophytes with FAIR metadata and documentation for biodiversity research.
Payam Emami Khoonsari, Pablo A Moreno, Sven Bergmann…·Ola Spjuth
The paper introduces a microservices-based framework for scalable data analysis, validated in metabolomics, enabling dynamic scaling and integration of software tools.
Kristian Peters, James Bradbury, Sven Bergmann…·Christoph Steinbeck
PhenoMeNal provides a cloud-based platform for metabolomics data analysis, integrating tools and workflows to enable reproducible and scalable research.