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TopFD: A Proteoform Feature Detection Tool for Top-Down Proteomics.
Basharat, Abdul Rehman; Zang, Yong; Sun, Liangliang; Liu, Xiaowen.
Afiliação
  • Basharat AR; Department of BioHealth Informatics, School of Informatics and Computing, Indiana University-Purdue University Indianapolis, Indianapolis, Indiana 46202, United States.
  • Zang Y; Department of Biostatistics and Health Data Sciences, Indiana University School of Medicine, Indianapolis, Indiana 46202, United States.
  • Sun L; Department of Chemistry, Michigan State University, East Lansing, Michigan 48824, United States.
  • Liu X; Deming Department of Medicine, Tulane University School of Medicine, New Orleans, Louisiana 70112, United States.
Anal Chem ; 95(21): 8189-8196, 2023 05 30.
Article em En | MEDLINE | ID: mdl-37196155
ABSTRACT
Top-down liquid chromatography-mass spectrometry (LC-MS) analyzes intact proteoforms and generates mass spectra containing peaks of proteoforms with various isotopic compositions, charge states, and retention times. An essential step in top-down MS data analysis is proteoform feature detection, which aims to group these peaks into peak sets (features), each containing all peaks of a proteoform. Accurate protein feature detection enhances the accuracy in MS-based proteoform identification and quantification. Here, we present TopFD, a software tool for top-down MS feature detection that integrates algorithms for proteoform feature detection, feature boundary refinement, and machine learning models for proteoform feature evaluation. We performed extensive benchmarking of TopFD, ProMex, FlashDeconv, and Xtract using seven top-down MS data sets and demonstrated that TopFD outperforms other tools in feature accuracy, reproducibility, and feature abundance reproducibility.
Assuntos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Proteoma / Proteômica Tipo de estudo: Diagnostic_studies / Prognostic_studies Idioma: En Revista: Anal Chem Ano de publicação: 2023 Tipo de documento: Article País de afiliação: Estados Unidos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Proteoma / Proteômica Tipo de estudo: Diagnostic_studies / Prognostic_studies Idioma: En Revista: Anal Chem Ano de publicação: 2023 Tipo de documento: Article País de afiliação: Estados Unidos