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Table 1 Comparison of the performance of conventional criteria, PeptideProphet and SFOER in peptide identifications for the analysis of human liver tissue lysatea

From: Optimization of filtering criterion for SEQUEST database searching to improve proteome coverage in shotgun proteomics

 

Conventional criteriab

PeptideProphetc

SFOERd

# 1+

99

26

162

# 2+

17950

17451

18606

# 3+

7388

12587

11313

# total

25428

30064

30081

%incr

/

18.2%

18.3%

# false pep

126

113

147

FDR

0.99%

0.75%

0.98%

#unique pep

4591

5175

5285

%incr unique pep

/

12.7%

15.12%

# proteins

1467

1596

1665

  1. a. Summary of each category returned by different strategies: #1+, #2+ and #3+ indicates the number of peptide identifications for charge states of 1+, 2+ and 3+ respectively. #total = (#1+) + (#2+) + (#3+). #false pep indicates the number of peptides from reversed database, while #unique pep is the number of total unique peptides. Increase of peptide identifications (%incr) and unique peptide identifications (%incr unique pep) by SFOER and PeptideProphet are shown. #proteins are the number of positive proteins identified by the strategies. FDR of identifications are also shown.
  2. b. Conventional criteria. Xcorr > 2.0, 2.5 and 3.8 for singly, doubly and triply charged peptides, respectively and ΔCn > 0.164 for all charge states [25, 33].
  3. c. Cutoff is set as PeptideProphet probability > 0.9 [13, 36-38].
  4. d. Optimized criteria determined by SFOER are: according to the charge states of 1+, 2+ and 3+, Xcorr scores > 1.76, 2.31 and 2.41, ΔCn > 0.061, 0.199 and 0.265, Sp > 44.42, 104 and 276.9 and Rsp < 3, 4 and 2.