Quality Control Analysis of MS/MS Proteomics Data
Introduction

Data quality is a concern in proteomics experiments. In this report, we assess the intrinsic features of the data set at multiple levels.

Methods and Data
Summary of Data Set

Table 1.  The Design of Experiment

sample bioRep techRep No. of fraction
55 1 1 1
Data Analysis

X!Tandem was used for analyzing the data. Parameters used in the X!Tandem search are shown in Table 2. Protein identifications were inferred from peptide identifications, and each identified protein had at least one associated unique peptide sequence identified at q-value equal or less than 0.01 (equivalent to a 1% FDR). The Occam's razor approach (Nesvizhskii, et al., 2003) was applied to deal with degenerate peptides by finding a minimum subset of proteins that covered all of the identified peptides.

Table 2.  The database search parameters

Item Value
Search engine X!Tandem
Enzyme NA
Fixed modifications Carbamidomethyl (C)
Variable modifications Oxidation (M)
Database target_decoy.fasta
Missed cleavages 1
Precursor mass error 10 ppm
Fragment mass error 0.6 Daltons
Results
Summary of protein identification

This part contains the basic statistics of MS/MS data.

Table 3.  Protein identification results for each fraction

sample bioRep techRep fraction spectrum_total spectrum peptide protein
55 1 1 5 87707 208(0.237%) 105 22
Summary charts

Summary plot for each technical replicate experiment.

Summary charts for sample: 55, biological: 1, technical: 1.

Figure 1.  Missed cleavages chart for sample: 55, biological replicate: 1, technical replicate: 1.

Figure 2.  Precursor ion charge chart for sample: 55, biological replicate: 1, technical replicate: 1.

Figure 3.  Peptide length chart for sample: 55, biological replicate: 1, technical replicate: 1.

Figure 4.  Precursor mass delta (Da) chart for sample: 55, biological replicate: 1, technical replicate: 1.

Figure 5.  Precursor mass delta (ppm) chart for sample: 55, biological replicate: 1, technical replicate: 1.

Figure 6.  Fragment ion mass delta (Da) chart for sample: 55, biological replicate: 1, technical replicate: 1.

Figure 7.  Unique spectrum per protein chart for sample: 55, biological replicate: 1, technical replicate: 1.

Figure 8.  Unique peptide per protein chart for sample: 55, biological replicate: 1, technical replicate: 1.

Figure 9.  Protein mass chart for sample: 55, biological replicate: 1, technical replicate: 1.

Contaminants stat

The common Repository of Adventitious Proteins, cRAP (pronounced "cee-RAP"), is an attempt to create a list of proteins commonly found in proteomics experiments that are present either by accident or through unavoidable contamination of protein samples. The types of proteins included fall into three general classes:

  1. common laboratory proteins;

  2. proteins added by accident through dust or physical contact; and

  3. proteins used as molecular weight or mass spectrometry quantitation standards.

We added the cRAP database in database searching.

Table 4.  Identification of contaminant proteins

Accession Peptides Spectrum Sample ID Reason Class Description
ALBU_BOVIN 14 22 55_1_1 1 Reagent and lab contaminant Laboratory proteins (P02769) Serum albumin precursor (Allergen Bos d 6) (BSA)
Reproducibility
Reproducibility of result for each fraction

Reproducibility of total spectra for each fraction

Figure 10.  The distribution of the total spectra number

Figure 11.  Error bar plot of the total spectra number for each fraction

Reproducibility of identified spectra for each fraction

Figure 12.  The distribution of the identified spectra number

Figure 13.  Error bar plot of the identified spectra for each fraction

Reproducibility of identified peptides for each fraction

Figure 14.  The distribution of identified peptides number

Figure 15.  Error bar plot of the identified peptides for each fraction

Reproducibility of identified proteins for each fraction

Figure 16.  The distribution of the identified proteins number

Figure 17.  Error bar plot of the identified proteins for each fraction

Mass accuracy

Figure 18.  The mass error (Da) of the fragment ions

Figure 19.  The mass error (ppm) of the precusor

Figure 20.  The mass error (Da) of the precusor

Separation efficiency
Identification-independent quality metrics

Figure 21.  TIC of MS1 distribution

Figure 22.  MS1 peak count distribution

Figure 23.  MS1 ion count distribution

Figure 24.  MS2 peak density distribution

Figure 25.  MS2 peak boxplot

Figure 26.  MS1 peak count distribution

Figure 27.  MS1 peak count distribution

Figure 28.  MS1 peak boxplot