A hierarchical statistical model to assess the confidence of peptides and proteins inferred from tandem mass spectrometry | Zendy

Changyu Shen | Zendy; Zhiping Wang | Zendy; Ganesh M. Shankar | Zendy; Xiang Zhang | Zendy; Lang Li | Zendy

Open Access

A hierarchical statistical model to assess the confidence of peptides and proteins inferred from tandem mass spectrometry

Author(s) -

Changyu Shen,

Zhiping Wang,

Ganesh M. Shankar,

Xiang Zhang,

Lang Li

Publication year - 2007

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/btm555

Subject(s) - computer science , proteome , statistical model , tandem mass spectrometry , measure (data warehouse) , confidence interval , identification (biology) , false discovery rate , data mining , computational biology , statistical power , mass spectrometry , bioinformatics , statistics , artificial intelligence , chemistry , biology , mathematics , chromatography , biochemistry , botany , gene

Statistical evaluation of the confidence of peptide and protein identifications made by tandem mass spectrometry is a critical component for appropriately interpreting the experimental data and conducting downstream analysis. Although many approaches have been developed to assign confidence measure from different perspectives, a unified statistical framework that integrates the uncertainty of peptides and proteins is still missing.

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