Learning Objectives:

Transcription

1 Proteomics Methodology for LC-MS/MS Data Analysis Methodology for LC-MS/MS Data Analysis Peptide mass spectrum data of individual protein obtained from LC-MS/MS has to be analyzed for identification of protein through database. Analysis involves processing the peaks and running algorithms for peak processing to optimize signal to noise ratio. Learning Objectives: After interacting with this learning object, the learner will be able to: Carry out peak processing. Handle the database search tools. Set up required parameters. Analyze the result from the database. Assess the troubleshooting steps involved in the experiments. Note: The current IDD exists in two modes- interactive and automatic. Students taking lab course should select interactive (set as default), while the automatic mode may be selected for general users.

2 Data Input Methodology for LC-MS/MS Data Analysis

3 Data Input The Mass to charge ratio peak obtained from LC- MS/MS is analysed using software such as MZmine. Click on the icon to open the software and proceed for further processing.

4 Data Input Now, the software is ready to get the raw data for processing in the format mentioned. Select the raw data files to load in the software.

5 Data Input In the beginning the data files for the analysis must be sorted out and saved accordingly.

6 Visualization Options in the visualization are for user observation, to check the pattern of the peaks, the spectrum, 2D/3D plot with time, peak list table, histogram of peaks, peak intensity info and peak intensity plot.click on visualization and spectra plot.

7 Visualization Select the checkbox, and confirm whether the required parameters are set before clicking OK. Select the project to visualize the peak. Select the peaks and zoom in for further analysis.

8 Visualization The pattern of the 2D spectrum plot can now be visualized. Select the peaks and zoom in for further analysis.

9 Visualization The pattern of the Histogram spectrum plot can be visualized by selecting the project. Now, click on Visualization option and Select the histogram option.

10 Peak Detection Two steps of peak detection are - First, Mass values are detected within each spectrum and a chromatogram is constructed for each of the mass values which span over certain time range. - Second, deconvolution algorithms are applied to each chromatogram to recognize the actual chromatographic peaks

11 Peak Detection This step helps to remove the unwanted peaks which fall below the specified range of noise intensity. The peaks which fall above the set noise range are taken for analysis. Click on noise tab and specify noise intensity.

12 Peak Detection Once peak detection is done; user can check individual peak point from the data file. This is carried out for each data file in the project. Once peak list is generated for each data file, peak processing is carried out.

13 Peak List Processing Peak processing helps user to compare the peaks and align them for the data analysis.

14 Peak List Processing Now, select project and click on Peak list methods option.the set parameters are used to align the peaks.

15 Peak List Processing All the peak lists should now be aligned and gaps for the missing values must be filled with the help of software. This can be done by first clicking on peak list method followed by gap filling option and lastly peak finder option.

16 Peak List Processing To fill in the data for the gaps within the data lists, a lot of trial and error should be done. Once the peak processing is over, a table is generated that can be used for the peak labelling.

17 Peak List Processing The values for the missing gap can be retrieved from the spectrum.

18 Peak List Table Click on peak list method and then gap filling option followed by peak list table option.

19 Peak List Table Nominal labelling of possible peaks can be done by user experience or by database search. For database search please follow IDD-MALDI-MS data analysis.

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