Latest version of CIMAGE, an expanded tool for quantitative analysis of activity-based probe profiling (ABPP) data
Contact: chuwang@pku.edu.cn jinjungao@uchicago.edu or wendao@pku.edu.cn
R (tested on 3.4.1 and 3.4.4)
sudo apt install r-base r-cran-ncdf4
Python (tested on version 2.7)
Perl (tested on perl5 version 18 and 26)
Other libray
sudo apt install libxml2-dev libnetcdf-dev zlib1g-dev
XCMS (tested on version 1.51.1/1.52.0 and 3.21.0)
source("https://bioconductor.org/biocLite.R")
BiocInstaller::biocLite(c("xcms"))
A. Unpack the compressed CIMAGE file to a preferred location “your_folder”
B. Define an environment variable CIMAGE_PATH pointing to the “your_folder”
export CIMAGE_PATH=your_folder
#or setenv CIMAGE_PATH your_folderA. For LC-MS/MS data, CIMAGE takes .mzXML files as input, which can be easily converted from raw files by a variety of tools like ReAdW and RawConverter.
B. Before CIMAGE quantification, database search should be performed first and the output file is then used as the input for CIMAGE. CIMAGE can be connected with variety of database search engines (ProLuCID, Mascot, Andromeda, pFind and MSFragger).
The demo data has been deposited to the public iProX server (IPX0002900001), and the demo results have been uploaded to https://github.com/wangchulab/CIMAGE2/tree/master/tutorials.
The following analysis workflow mainly focuses on using ProLuCID dentifications as input. All the screenshots were generated by running CIMAGE for the isoTOP-ABPP demo data by ProLuCID input. Demo results for isoTOP-ABPP (peptide inputs by MASCOT, pFind and MSFragger), SILAC and rdTOP-ABPP have also been uploaded.
A. Make a folder such as "isoTOP-ABPP", upload LC-MS/MS data in mzXML format and create a folder (e.g. dta).
B. Before quantification, you need to edit your cimage.params file to point to the right light/heavy chemical composition files. Template cimage.params file and common light/heavy chemical composition files can be found in the params folder:
IA_tev – isoTOP-ABPP with IA-alkyne probe & Tev tag
SILAC – SILAC with Arg10 & Lys8
Light/heavy tables are strictly tab-delimited text files, so it is better to use EXCEL or notepad++ to import, edit and then export it.
C. Enter the “dta” folder and upload the database search results. In database search process, the LC-MS/MS data should be searched for light and heavy labelled peptides separately to generate two results containing either light or heavy peptides. By default, ProLuCID is recommended as it is flexible to fit different types of quantitative strategies, but you can also feel free to use your comfortable one.
- For ProLuCID, the DTASelect-filter.txt file should be renamed as DTASelect-filter_PREFIX_ISO.txt, in which PREFIX represents the prefix of your mzXML file and ISO represent isotope state. For example:
DTASelect-filter_20181026_1TO1_heavy.txt
DTASelect-filter_20181026_1TO1_light.txt
- For Andromeda (MaxQuant), the evidence.txt file should be renamed as evidence_light.txt or evidence_heavy.txt
- For Mascot, the database search results in csv format are uploaded directly
- For pFind, the spectra file should be rename as pFind_light.spectra or pFind_heavy.spectra
- For MSFragger light and heavy label need to be set as two varible modifications and search only once
D. go into the "dta" folder and run the "cimage" program by typing:
- For ProLuCID
cimage /path/of/cimage.params.IA_tev 20181026_1TO1
- For Mascot
SILAC: cimage SILAC your-cimage-params-file PREFIX
ABPP: cimage_mascot 1mod paramfile modify_AA light_number heavy_number PREFIX
cimage_mascot 2mod parameter_file modify_AA1 light_number1 heavy_number1 modify_AA2 light_number2 heavy_number2 PREFIX
#PS: modify_AA represents the labelled amino acid (e.g., the modify_AA should be “C” when labelling cysteine). light_number and heavy_number are the number allocated to light and heavy modifications.
- For Andromeda
cimage_andromeda your-cimage-params-file MOD PREFIX
#In which MOD is the modification symbol. For example, in PEPTI(ga)DE, the GlcNAc modification is represented by ga, so the MOD should be ‘ga’
- For pFind and MSFragger: see details in the tutorial folder
E. If it runs fine, it will generate an "output" folder in which there will be a "to_excel" text file for your editing in excel and a folder of "PNG" containing all individual graphic files. During CIMAGE processing, the screen will also print the progress
Total number of pages are 2663
working on pages 1--500
working on pages 501--1000
...
After this step, the dta folder should contain:
DTASelect-filter_20181026_1TO1_heavy.txt
DTASelect-filter_20181026_1TO1_heavy.txt.tagged
DTASelect-filter_20181026_1TO1_light.txt
DTASelect-filter_20181026_1TO1_light.txt.tagged
Rplots.pdf
all_scan.table
cross_scan.table
findMs1AcrossSetsFromDTASelect.Rout
ipi_name.table
output/
F. move to upper folder and combine the quantification by typing:
cimage_combine [by_protein] dta
It will generate a combine_dta.html and a raw text file combine_dta.txt. Skip the by_protein option if you would like to by default group identified peptides by their sequence instead of their parent proteins.
After this step, this folder should contain:
20181026_1TO1_1.mzXML
combined_dta.Rout
combined_dta.html
combined_dta.txt
combined_dta.vennDiagram.png
combined_dta_IR.png
combined_dta_LR.png
combined_dta_histogram_IR.png
combined_dta_histogram_LR.png
dta/
Open the combine_dta.html page and it will contain the list of peptides that have been quantified with information on protein ID, protein name, gene name, peptide sequence, mass, integrated ratio (column mr, ratio calculated by integrated intensity), slope of the least square fitting curve (column mlr), sd, charges, fractions (column segment) and image link as below. Columns for different sets and runs are used to compare different experiment sets and replicates when using cimage_compare module, respectively. Click on the hyperlink at the end of each line to visualize the raw chromatographic traces for calculate the quantitative ratios.
“combine_dta.txt” is a text version of the quantification results that can be further processed by users with their own customized scripts.
A. add_preview, to enhance cimage html output with preview script. For detailed usage, please refer to the “README.md” in cimage_preview folder (CIMAGE feature https://github.com/wangchulab/CIMAGE).
B. cimage_compare, to compare ratios obtained by multiple different cimage runs, you can run cimage_compare program by typing:
cimage_compare [by_protein] file1 column1 outname1 file2 column2 outname2 ...
in which "file1" and "file2" are full names (with paths) of the two combined_dta.txt files to be compared, "column1" and "column2" are names of ratio columns in each combined_dta.txt file, and "outname1" and "outname2" are names of ratios columns when they are output into a tab-delimited text file side by side that you can import to EXCEL for further analysis. for example, if you like to compare protein SILAC ratios (column "mr.set_1" in combined_dta.txt files) obtained from two experiments "exp1" and "exp2", the command to run would be:
cimage_compare [by_protein] /your-folder/exp1/combined_dta.txt set_1 my_exp1 /your-folder/exp2/combined_dta.txt set_1 my_exp2
C. pdf_generator, to generate high quality pdf plot for specific quantified peptide:
pdf_generator your-cimage-params-file PREFIX ID
the “ID” is entry No. showed in the plotted picture.
D. extract_LC_peaks.py, extract features from MS1 file for peaking paired peaks [obsolete]
extract_LC_peaks.py 20181026_1TO1_01.ms1 0.02
python check_pair_by_envelope.py env_[id].dat