PROSE: phenotype-specific network signatures from individual proteomic samples.
Brief Bioinform
; 24(2)2023 03 19.
Article
em En
| MEDLINE
| ID: mdl-36907650
ABSTRACT
Proteomic studies characterize the protein composition of complex biological samples. Despite recent advancements in mass spectrometry instrumentation and computational tools, low proteome coverage and interpretability remains a challenge. To address this, we developed Proteome Support Vector Enrichment (PROSE), a fast, scalable and lightweight pipeline for scoring proteins based on orthogonal gene co-expression network matrices. PROSE utilizes simple protein lists as input, generating a standard enrichment score for all proteins, including undetected ones. In our benchmark with 7 other candidate prioritization techniques, PROSE shows high accuracy in missing protein prediction, with scores correlating strongly to corresponding gene expression data. As a further proof-of-concept, we applied PROSE to a reanalysis of the Cancer Cell Line Encyclopedia proteomics dataset, where it captures key phenotypic features, including gene dependency. We lastly demonstrated its applicability on a breast cancer clinical dataset, showing clustering by annotated molecular subtype and identification of putative drivers of triple-negative breast cancer. PROSE is available as a user-friendly Python module from https//github.com/bwbio/PROSE.
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Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Proteoma
/
Proteômica
Tipo de estudo:
Prognostic_studies
Idioma:
En
Ano de publicação:
2023
Tipo de documento:
Article