A statistical framework for protein quantitation in bottom-up MS-based proteomics | Zendy

Yuliya V. Karpievitch | Zendy; Jeffrey A. Stanley | Zendy; Thomas Taverner | Zendy; Jianhua Z. Huang | Zendy; Joshua Adkins | Zendy; Charles Ansong | Zendy; Fred Heffron | Zendy; Thomas Metz | Zendy; Weijun Qian | Zendy; Hyunjin Yoon | Zendy; Richard Smith | Zendy; Alan R. Dabney | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Open Access

A statistical framework for protein quantitation in bottom-up MS-based proteomics

Author(s) -

Yuliya V. Karpievitch,

Jeffrey A. Stanley,

Thomas Taverner,

Jianhua Z. Huang,

Joshua Adkins,

Charles Ansong,

Fred Heffron,

Thomas Metz,

Weijun Qian,

Hyunjin Yoon,

Richard Smith,

Alan R. Dabney

Publication year - 2009

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btp362

Subject(s) - proteomics , computer science , computational biology , quantitative proteomics , chromatography , chemistry , biology , biochemistry , gene

Quantitative mass spectrometry-based proteomics requires protein-level estimates and associated confidence measures. Challenges include the presence of low quality or incorrectly identified peptides and informative missingness. Furthermore, models are required for rolling peptide-level information up to the protein level.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.

Having issues? You can contact us here

Accelerating Research