Python - OmicsDI

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Metabolite Name

L-Phenylalanine (44)
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Lab affiliation

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Cellzome, a GSK company (5)
Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA., Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, MA 02115, USA (5)
Institute of Integrative Biology of the Cell (I2BC), CEA, CNRS, Université Paris-Sud, Bâtiment 23A, 1 avenue de la Terrasse, F-91198 Gif-sur-Yvette cedex, France. (5)
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Medical University of Vienna, Max Perutz Labs, Dr. Bohr-Gasse 9, 1030 Vienna (3)
Medizinisches Proteom-Center, Ruhr-Universität Bochum, Bochum, Germany (3)
Professor. Department of Biology and Biological Engineering, Chalmers University of Technology, SE-41296 Gothenburg, Sweden; Wallenberg Center for Protein Research, Chalmers University of Technology, SE-41296 Gothenburg, Sweden; Novo Nordisk Foundation Center for Biosustainability, Chalmers University of Technology, SE-41296 Gothenburg, Sweden; Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, DK-2800 Lyngby, Denmark (3)
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Abiel Roche-Lima, MSc., PhD. Assistant Professor Director, Integrated Informatics Services core IIS-RCMI University of Puerto Rico Medical Science Campus RCMI Program PO Box 365067 San Juan, PR. 00936-5067 Phone: +1 787 758-2525 x 2194 (2)
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Charité - Universitätsmedizin Berlin Research group Biochemistry and Systems Biology of the Metabolism Institute of Biochemistry Germany (2)
Crick-GSK LinkLabs, UK (2)
Department of Biochemistry and Molecular Biology, Bio21 Molecular Science and Biotechnology Institute, The University of Melbourne (2)
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IsPrivate

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Model Flag

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Submitter Keywords

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A Python-based pipeline for preprocessing LC-MS data for untargeted metabolomics workflows

Preprocessing data in a reproducible and robust way is one of the current challenges in untargeted metabolomics workflows. Data curation in liquid chromatography-mass spectrometry (LC-MS) involves the removal of unwanted features (retention time; m/z pairs) to retain only high-quality data for subse...

2020-11-21 | MTBLS1919 | MetaboLights

ms-mint: Targeted metabolomics with Python

Metabolomics has emerged as a mainstream approach for investigating the complex metabolic underpinnings of living systems. Over recent years, metabolomics has increasingly been applied to large cohort studies that tax the limits of existing computational tools. Though effective when applied ...

2025-05-21 | MTBLS6696 | MetaboLights

A Proteomic Survey of Enzymes in the Plasma and Gastric Fluid of Burmese Python

Background: Exceptional and extreme feeding behaviour makes the Burmese python (Python bivittatus) an interesting model to study physiological remodelling and metabolic adaptation in response to refeeding after prolonged starvation. In this study, we used transcriptome sequencing of five visceral o...

ORGANISM(S): Python bivittatus

2018-10-24 | PXD006665 | Pride