ArticleiScience2023
Integrative processing of untargeted metabolomic and lipidomic data using MultiABLER.
Article in iScience, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Cross-domain transfer learning from peptides to metabolites using a multi-property fine-tuned LLM.Bioinformatics (Oxford, England) · 2026Article
- Research on Lipidomic Profiling and Biomarker Identification for Osteonecrosis of the Femoral Head.Biomedicines · 2024Article
- Integrative analysis of proteomics and lipidomic profiles reveal the fat deposition and meat quality in Duroc × Guangdong small spotted pig.Frontiers in veterinary science · 2024Article
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Authors and funding
5 authors.
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Abstract
Mass spectrometry (MS)-based untargeted metabolomic and lipidomic approaches are being used increasingly in biomedical research. The adoption and integration of these data are critical to the overall multi-omic toolkit. Recently, a sample extraction method called Multi-ABLE has been developed, which enables concurrent generation of proteomic and untargeted metabolomic and lipidomic data from a small amount of tissue. The proteomics field has a well-established set of software for processing of acquired data; however, there is a lack of a unified, off-the-shelf, ready-to-use bioinformatics pipeline that can take advantage of and prepare concurrently generated metabolomic and lipidomic data for joint downstream analyses. Here we present an R pipeline called MultiABLER as a unified and simple upstream processing and analysis pipeline for both metabolomics and lipidomics datasets acquired using liquid chromatography-tandem mass spectrometry. The code is available via an open-source license at https://github.com/holab-hku/MultiABLER.
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