PEAKS Q: Label Free Quantification – Live Walkthrough


Video Dialogue

Create Project

I will now demonstrate how to use PEAKS label-free quantification tool with the real dataset. The first step is to build a PEAKS project for the experiments.
Give a name to the project and specify a location to save the project.
Click the button of “Add data” to add the data
Navigate to the LC-MS/MS data location, and select the data to be analyzed.
Select a file or files: use the add file button, and give a name for the file to show in the result
Set enzyme, instrument, fragmentation and continue until all files are added.
After this, the data selection part is finished.

Data Refinement

“correct precursor” is checked with “mass only” option for Orbitrap instruments.


Precursor mass error: 20 ppm, Fragment mass error: 0.5 Da
Modification: Fixed: Caramidomethylation
Var: Oxidation on M & Deamidation
Database: Swiss-Prot; Taxa: mouse
Complete identification (DB + PTM + SPIDER)


Label-free quantification parameters:
Estimated peptide precursor m/z shift between samples: 20 ppm
Estimated retention shift of a peptide precursor between samples: 5 minutes
The “Sample Groups”: each sample ran in triplets. Those replicates are grouped.
We have now finished setting the parameters and are ready to import data then perform the analysis. Click “Finish”.

Analysis & Results

After completing analysis, identification and quantification results were present.
Click node 11 and the details of quantification result will be present.
Protein Heat map shows protein abundance profiles among all samples.
These proteins are up-regulated in diseased samples. And their abundance is consistent among replicates.
These proteins are down-regulated in diseased samples. Also their abundance is consistent among replicates.
This is the volcano plot of quantified proteins, which plot significance versus fold-change for proteins.
Three groups: the Majority of proteins are 1. background proteins, 2. Up-regulated proteins which have sufficient up fold change and large significance, 3. And down-regulated proteins which have sufficient down fold change and large significance.
Here are the histograms of feature retention time shifts and precursor m/z shifts, respectively. The red one identifies the shift before retention time alignment and blue one is after alignment.
On the top, there are 2 sets of filters: peptide feature level and protein level.
Significance of peptide feature, fold-change of feature, quality of feature, intensity of feature, etc are used to filter out the peptide features. Protein significance, protein fold-change, and a number of unique peptides in a protein are used to filter out the proteins.

Protein Tab

In the protein tab, a list of quantified proteins were displayed, sorted by significance. For each protein, its abundance profile in all samples and groups, etc are displayed. The coverage map shows quantified peptides supporting to this protein.

Features Tab

The details of each peptide feature is listed in the “Feature” tab. The top three peptides with the highest intensities are used for protein ratio calculation. PEAKS also provides peptide feature quantification. The Feature tab shows the list of quantified features.
For each peptide feature, the XICs in all samples were shown, and the integration areas of its XICs are listed in the table.
The characterization of the feature in all samples are shown in the “Sample Feature” tab. The red line shows the boundary of the feature, the blue square is MS/MS spectrum providing peptide identification.
It also provides 3-D view. Press “Ctrl” button, using wheel of mouse to adjust.

No ID? No Problem!

PEAKS can quantify peptide features without the pre-requisite of peptide identification, by un-checking “With peptide ID”.
Here is one example: there is no database peptide hit for this feature. PEAKS DB provides more answers by integrating with de novo sequencing. Let’s look at the de novo sequencing result from PEAKS database search.
Open SPIDER node 10. Select “LC-MS” tab, go file of SMA 1.
LC-MS heat map: PEAKS associates all identification and quantification results with peptide feature in the heat map.
Blue squares indicates the MS/MS spectra.
Solid blue squares are the MS/MS spectrum with a confident database peptide hit.
Solid orange squares show the MS/MS spectrum without a confident database peptide hit, but with a confident de novo peptide sequence.
The red circles are the detected features. Solid ones are the confident features which satisfy the filters. Our purpose is to see if the feature (514.7 17.03) has a de novo sequence. Note that the heat map can zoom. In this case, we use the search function. PEAKS will automatically zoom to corresponding window in the heat map.

Greater Value

PEAKS DB provides more answers by integrating with de novo sequencing. Let’s look at the de novo sequencing result from PEAKS database search. Here is one example where there is no database peptide hit for a feature. That said, there is a de novo sequence: ALNAAGASEPK.
When we BLAST it, and we find that it is a peptide from Myosin-binding protein C, which is a myosin-associated protein found in the cross-bridge-bearing zone of A bands in striated muscle. Myosins comprises a family of ATP-dependent motor proteins and are best known for their role in muscle contraction. With this finding, we get a new biomarker candidate by using de novo only peptides.


You will also note that PEAKS supports the Html format for easy web viewing and text format for sharing and down stream analysis.

Closing Remarks

If you would like to learn more about how PEAKS can bring sensitivity and accuracy to your identifications, download a demo or check out more of our tutorial videos.