2010
DOI: 10.1038/nbt0710-647
|View full text |Cite
|
Sign up to set email alerts
|

PeptideClassifier for protein inference and targeted quantitative proteomics

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1
1

Citation Types

1
86
0

Year Published

2012
2012
2021
2021

Publication Types

Select...
10

Relationship

0
10

Authors

Journals

citations
Cited by 82 publications
(87 citation statements)
references
References 15 publications
1
86
0
Order By: Relevance
“…Oxidation (M), deamidation (NQ), and methylation (DE) were used as variable modifications and carbamidomethylation (C) as a fixed modification. Using the decoy option of MS-GFϩ, we filtered the list of peptide spectrum matches (PSMs) to an estimated overall false discovery rate (FDR) of 0.2%, classified the PSMs with PeptideClassifier (27), and further considered only peptides (tryptic or semitryptic) that unambiguously imply one bacterial protein sequence. The FDR at the peptide level amounted to about 1.5%, and that at the protein level amounted to about 2.5% when requiring two peptides or three spectra for protein identification.…”
Section: Methodsmentioning
confidence: 99%
“…Oxidation (M), deamidation (NQ), and methylation (DE) were used as variable modifications and carbamidomethylation (C) as a fixed modification. Using the decoy option of MS-GFϩ, we filtered the list of peptide spectrum matches (PSMs) to an estimated overall false discovery rate (FDR) of 0.2%, classified the PSMs with PeptideClassifier (27), and further considered only peptides (tryptic or semitryptic) that unambiguously imply one bacterial protein sequence. The FDR at the peptide level amounted to about 1.5%, and that at the protein level amounted to about 2.5% when requiring two peptides or three spectra for protein identification.…”
Section: Methodsmentioning
confidence: 99%
“…Ratios were corrected for unequal mixing of labeled and unlabeled proteins in each sample by the geometric median of light-to-heavy ratios of all peptides in that sample. Peptides were classified according to their information content according to Qeli and Ahrens (2010). Class 1a peptides unambiguously identify a single unique protein sequence, class 1b is only ambiguous regarding protein isoforms, class 2a and 2b identify a distinct gene model, while class 2a peptides additionally identifies a proper subset.…”
Section: Nano-lc-ms/ms Protein Identification Quantification and Anmentioning
confidence: 99%
“…The FDR distributions of all search algorithms were aligned to allow for overall peptide identifications with greater than 95% probability. For protein inference, peptides were classified according to their information content according to Qeli and Ahrens (2010). The Occam's Razor approach (Nesvizhskii et al, 2003) was applied to each peptide class from high to low information content to report a minimal set of proteins explaining the identified peptides by proteins and protein groups.…”
Section: N Quantitative Shotgun Proteomicsmentioning
confidence: 99%