ArticleFrontiers in genetics2021
GeenaR: A Web Tool for Reproducible MALDI-TOF Analysis.
Article in Frontiers in genetics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Hempseed Water-Soluble Protein Fraction and Its Hydrolysate Display Different Biological Features.Life (Basel, Switzerland) · 2025Article
- Impact of holder pasteurization on protein and eNAMPT/Visfatin content in human breast milk.Scientific reports · 2024Article
- Recent advances in N-glycan biomarker discovery among human diseases.Acta biochimica et biophysica Sinica · 2024Review
- Applications of Mass Spectrometry in the Characterization, Screening, Diagnosis, and Prognosis of COVID-19.Advances in experimental medicine and biology · 2024Article
- Protein Mass Fingerprinting and Antioxidant Power of Hemp Seeds in Relation to Plant Cultivar and Environment.Plants (Basel, Switzerland) · 2023Article
- High-Throughput Glycomic Methods.Chemical reviews · 2022Review
- Evaluation of the diagnostic value of serum-based proteomics for colorectal cancer.World journal of gastrointestinal oncology · 2022Article
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Authors and funding
5 authors.
Funding
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Abstract
Mass spectrometry is a widely applied technology with a strong impact in the proteomics field. MALDI-TOF is a combined technology in mass spectrometry with many applications in characterizing biological samples from different sources, such as the identification of cancer biomarkers, the detection of food frauds, the identification of doping substances in athletes' fluids, and so on. The massive quantity of data, in the form of mass spectra, are often biased and altered by different sources of noise. Therefore, extracting the most relevant features that characterize the samples is often challenging and requires combining several computational methods. Here, we present GeenaR, a novel web tool that provides a complete workflow for pre-processing, analyzing, visualizing, and comparing MALDI-TOF mass spectra. GeenaR is user-friendly, provides many different functionalities for the analysis of the mass spectra, and supports reproducible research since it produces a human-readable report that contains function parameters, results, and the code used for processing the mass spectra. First, we illustrate the features available in GeenaR. Then, we describe its internal structure. Finally, we prove its capabilities in analyzing oncological datasets by presenting two case studies related to ovarian cancer and colorectal cancer. GeenaR is available at
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Registered trials
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