Articles producció científica> Enginyeria Electrònica, Elèctrica i Automàtica

rMSIannotation: A peak annotation tool for mass spectrometry imaging based on the analysis of isotopic intensity ratios

  • Datos identificativos

    Identificador: imarina:9216934
    Autores:
    Sementé LBaquer GGarcía-Altares MCorreig-Blanchar XRàfols P
    Resumen:
    Mass spectrometry imaging (MSI) consist of spatially located spectra with thousands of peaks. Only a fraction of these peaks corresponds to unique monoisotopic peaks, as mass spectra include isotopes, adducts and fragments of compounds. Current peak annotation solutions depend on matching MS features to compounds libraries. We present rMSIannotation, a peak annotation algorithm to annotate carbon isotopes and adducts in metabolomics and lipidomics imaging mass spectrometry datasets without using supporting libraries. rMSIannotation measures and evaluates the intensity ratio between carbon isotopic peaks and models their distribution across the m/z axis of the compounds in the Human Metabolome Database. Monoisotopic peak selection is based on the isotopic likelihood score (ILS) made of three components: image morphology correlation, validation of isotopic intensity ratios, and peak centroid mass deviation. rMSIannotation proposes pairs of peaks that can be adducts based on three scores: isotopic pattern coherence, image correlation and mass error. We validated rMSIannotation with three MALDI-MSI datasets which were manually annotated by experts, and compared the annotations obtained with rMSIannotation and with the METASPACE annotation platform. rMSIannotation replicated more than 90% of the manual annotation reported in FT-ICR datasets and expanded the list of annotated compounds with additional monoisotopic peaks and neutral masses. Finally, we evaluated isotopic peak annotation as a data reduction method for MSI by comparing the results of PCA and k-means segmentation before and after removing non-monoisotopic peaks. The results show that monoisotopic peaks retain most of the biologic variance in the dataset.
  • Otros:

    Autor según el artículo: Sementé L; Baquer G; García-Altares M; Correig-Blanchar X; Ràfols P
    Departamento: Enginyeria Electrònica, Elèctrica i Automàtica
    Autor/es de la URV: Baquer Gómez, Gerard Sergi / Correig Blanchar, Francesc Xavier / Garcia-Altares Pérez, Maria / Ràfols Soler, Pere / Sementé Fernández, Lluc
    Palabras clave: Virus-infection spectra r package
    Resumen: Mass spectrometry imaging (MSI) consist of spatially located spectra with thousands of peaks. Only a fraction of these peaks corresponds to unique monoisotopic peaks, as mass spectra include isotopes, adducts and fragments of compounds. Current peak annotation solutions depend on matching MS features to compounds libraries. We present rMSIannotation, a peak annotation algorithm to annotate carbon isotopes and adducts in metabolomics and lipidomics imaging mass spectrometry datasets without using supporting libraries. rMSIannotation measures and evaluates the intensity ratio between carbon isotopic peaks and models their distribution across the m/z axis of the compounds in the Human Metabolome Database. Monoisotopic peak selection is based on the isotopic likelihood score (ILS) made of three components: image morphology correlation, validation of isotopic intensity ratios, and peak centroid mass deviation. rMSIannotation proposes pairs of peaks that can be adducts based on three scores: isotopic pattern coherence, image correlation and mass error. We validated rMSIannotation with three MALDI-MSI datasets which were manually annotated by experts, and compared the annotations obtained with rMSIannotation and with the METASPACE annotation platform. rMSIannotation replicated more than 90% of the manual annotation reported in FT-ICR datasets and expanded the list of annotated compounds with additional monoisotopic peaks and neutral masses. Finally, we evaluated isotopic peak annotation as a data reduction method for MSI by comparing the results of PCA and k-means segmentation before and after removing non-monoisotopic peaks. The results show that monoisotopic peaks retain most of the biologic variance in the dataset.
    Áreas temáticas: Spectroscopy Química Odontología Medicina ii Medicina i Materiais Matemática / probabilidade e estatística Interdisciplinar Geociências General medicine Farmacia Environmental chemistry Engenharias iv Engenharias iii Engenharias ii Enfermagem Ciências biológicas iii Ciências biológicas ii Ciências biológicas i Ciências agrárias i Ciência de alimentos Ciência da computação Chemistry, analytical Biotecnología Biodiversidade Biochemistry Astronomia / física Analytical chemistry
    Acceso a la licencia de uso: https://creativecommons.org/licenses/by/3.0/es/
    Direcció de correo del autor: maria.garcia-altares@urv.cat pere.rafols@urv.cat lluc.semente@estudiants.urv.cat lluc.semente@estudiants.urv.cat gerard.baquer@estudiants.urv.cat gerard.baquer@estudiants.urv.cat xavier.correig@urv.cat
    Identificador del autor: 0000-0002-9240-4058 0000-0002-4433-4972 0000-0002-4433-4972 0000-0002-6902-3054
    Fecha de alta del registro: 2024-07-27
    Volumen de revista: 1171
    Versión del articulo depositado: info:eu-repo/semantics/publishedVersion
    Enlace a la fuente original: https://www.sciencedirect.com/science/article/pii/S0003267021004955?via%3Dihub
    URL Documento de licencia: https://repositori.urv.cat/ca/proteccio-de-dades/
    Referencia al articulo segun fuente origial: Analytica Chimica Acta. 1171
    Referencia de l'ítem segons les normes APA: Sementé L; Baquer G; García-Altares M; Correig-Blanchar X; Ràfols P (2021). rMSIannotation: A peak annotation tool for mass spectrometry imaging based on the analysis of isotopic intensity ratios. Analytica Chimica Acta, 1171(), -. DOI: 10.1016/j.aca.2021.338669
    DOI del artículo: 10.1016/j.aca.2021.338669
    Entidad: Universitat Rovira i Virgili
    Año de publicación de la revista: 2021
    Tipo de publicación: Journal Publications
  • Palabras clave:

    Analytical Chemistry,Biochemistry,Chemistry, Analytical,Environmental Chemistry,Spectroscopy
    Virus-infection
    spectra
    r package
    Spectroscopy
    Química
    Odontología
    Medicina ii
    Medicina i
    Materiais
    Matemática / probabilidade e estatística
    Interdisciplinar
    Geociências
    General medicine
    Farmacia
    Environmental chemistry
    Engenharias iv
    Engenharias iii
    Engenharias ii
    Enfermagem
    Ciências biológicas iii
    Ciências biológicas ii
    Ciências biológicas i
    Ciências agrárias i
    Ciência de alimentos
    Ciência da computação
    Chemistry, analytical
    Biotecnología
    Biodiversidade
    Biochemistry
    Astronomia / física
    Analytical chemistry
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