Deployment and dockerisation of the MAIT metabolomics tool API: empowering accessibility and scalability
Current advances in liquid chromatography coupled with mass spectrometry (LC-MS) in the field of metabolomics, the study of metabolites in biological samples, have allowed scientists to acquire hundreds and thousands of samples or data sets in a very short amount of time. Therefore, this high-throug...
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| Tipo de recurso: | tesis de maestría |
| Fecha de publicación: | 2025 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/445601 |
| Acceso en línea: | https://hdl.handle.net/2117/445601 |
| Access Level: | acceso abierto |
| Palabra clave: | Bioinformatics Cloud computing Bioinformàtica Computació en núvol Àrees temàtiques de la UPC::Informàtica Àrees temàtiques de la UPC::Informàtica::Sistemes operatius Àrees temàtiques de la UPC::Informàtica::Aplicacions de la informàtica::Bioinformàtica |
| Sumario: | Current advances in liquid chromatography coupled with mass spectrometry (LC-MS) in the field of metabolomics, the study of metabolites in biological samples, have allowed scientists to acquire hundreds and thousands of samples or data sets in a very short amount of time. Therefore, this high-throughput technique has also forced computer science to advance so that the data obtained can be analysed as fast and as reliably as possible. Many algorithms have been developed to characterise different metabolites from a single sample so that a proper clinical diagnosis is made. These algorithms are sometimes very resource consuming, computationally speaking, and cannot be run on regular computers to obtain a result in a reasonable time. Here, containerisation will prove useful. Being able to encapsulate these algorithms on a Docker image and then deploy that image to a cloud server to compute remotely is a big leap for data science today. So, in this project, an approach to containerise MAIT (Metabolite Automatic Identification Toolkit), and also create an API for it using Docker and Plumber will be portrayed. An easily deployable and portable Docker image that contains MAIT’s algorithms coded in R to be run individually as an API on some cloud computing service, ensuring accessibility to any metabolomics experiment stage from anywhere. |
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