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A Massive Proteogenomic Screen Identifies Thousands of Novel Peptides From the Human "Dark" Proteome.
Cao, Xiaolong; Sun, Siqi; Xing, Jinchuan.
Afiliação
  • Cao X; Department of Anesthesiology, Zhujiang Hospital, Southern Medical University, Guangzhou, Guangdong, China; Department of Genetics, Rutgers, The State University of New Jersey, Piscataway, New Jersey, USA; Human Genetic Institute of New Jersey, Rutgers, The State University of New Jersey, Piscataway, New Jersey, USA.
  • Sun S; Department of Genetics, Rutgers, The State University of New Jersey, Piscataway, New Jersey, USA; Human Genetic Institute of New Jersey, Rutgers, The State University of New Jersey, Piscataway, New Jersey, USA.
  • Xing J; Department of Genetics, Rutgers, The State University of New Jersey, Piscataway, New Jersey, USA; Human Genetic Institute of New Jersey, Rutgers, The State University of New Jersey, Piscataway, New Jersey, USA. Electronic address: jinchuan.xing@rutgers.edu.
Mol Cell Proteomics ; 23(2): 100719, 2024 Feb.
Article em En | MEDLINE | ID: mdl-38242438
ABSTRACT
Although the human gene annotation has been continuously improved over the past 2 decades, numerous studies demonstrated the existence of a "dark proteome", consisting of proteins that were critical for biological processes but not included in widely used gene catalogs. The Genotype-Tissue Expression project generated more than 15,000 RNA-seq datasets from multiple tissues, which modeled 30 million transcripts in the human genome. To provide a resource of high-confidence novel proteins from the dark proteome, we screened 50,000 mass spectrometry runs from over 900 projects to identify proteins translated from the Genotype-Tissue Expression transcript model with proteomic support. We also integrated 3.8 million common genetic variants from the gnomAD database to improve peptide identification. As a result, we identified 170,529 novel peptides with proteomic evidence, of which 6048 passed the strictest standard we defined and were supported by PepQuery. We provided a user-friendly website (https//ncorf.genes.fun/) for researchers to check the evidence of novel peptides from their studies. The findings will improve our understanding of coding genes and facilitate genomic data interpretation in biomedical research.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Proteogenômica Tipo de estudo: Prognostic_studies Limite: Humans Idioma: En Ano de publicação: 2024 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Proteogenômica Tipo de estudo: Prognostic_studies Limite: Humans Idioma: En Ano de publicação: 2024 Tipo de documento: Article