PepQuery

PepQuery is a universal targeted peptide search engine for identifying or validating known and novel peptides of interest in any local or publicly available mass spectrometry-based proteomics datasets. It's different from the spectrum-centric search engines, such as Mascot, MaxQuant and MS-GF+.
You can read the following papers to know more about how PepQuery version 1 works:

Wen, Bo, Xiaojing Wang, and Bing Zhang. "PepQuery enables fast, accurate, and convenient proteomic validation of novel genomic alterations." Genome research 29.3 (2019): 485-493.
Wen, Bo, Kai Li, Yun Zhang, and Bing Zhang. "Cancer neoantigen prioritization through sensitive and reliable proteogenomics analysis." Nature communications 11. 1 (2020): 1-14.

We recently developed PepQuery2 which leverages a new MS/MS indexing approach and cloud storage to enable ultrafast, targeted identification of both novel and known peptides. The standalone version of PepQuery2 allows users to search more than one billion MS/MS data indexed in the PepQueryDB from any computers with internet access. It also supports direct analysis of user provided MS/MS data, any public datasets in PRIDE, MassIVE, jPOSTrepo and iProX, or Universal Spectrum Identifiers (USIs) from ProteomeXchange. Meanwhile, we have extended the web version to include public proteomics datasets from all flagship CPTAC studies, leading to a total of 48 datasets.

Installation

PepQuery is available as a standalone application as well as a PepQuery server (PepQuery Web). In order to run the standalone version of PepQuery, you must have Java 1.8 or newer installed. To check your java version please open your terminal application and run the following command:

java -version

Please go to the Download page to download the standalone version of PepQuery. It's written by Java and is platform independent. This package is a tar.gz file. After you download it, please uncompress it using the following command line and you will find a jar file in the package folder. The whole installation process typically only takes one or two minutes.

tar xvzf pepquery-2.0.2.tar.gz

If you want to take a Variant Call Format (VCF), Browser Extensible Data (BED) or Gene Transfer Format (GTF) file as input, you will need to install R and the R package PGA. You need to prepare annotation data according to the user's manual of PGA before you start to run PepQuery with taking VCF, BED or GTF file as input. PepQuery uses PGA to translate the events in the VCF, BED or GTF file to protein sequences. If you want to know more about PGA, please read the paper of PGA (doi: 10.1186/s12859-016-1133-3). If you only want to take a peptide, protein or DNA sequence as input, you don't need to install R and PGA.

System requirements

For PepQuery Web version, there is no specific need for the system. It only requires a web browser with internet connection.

For PepQuery standalone version, we recommend a computer with 8 GB of memory and 4 CPUs. Windows, Mac OS and Linux systems are supported.

The latest version of PepQuery is 2.0.2. We have tested this on different datasets.

Web application

You can use PepQuery through PepQuery Web. Using PepQuery Web, you don't need to prepare the MS/MS data and protein reference database. Of course, you also don't need to install the PepQuery software in your computer. All you need to provide is the target peptide/protein/DNA sequence or protein ID which you want to identify. Please note the searching using the web server could be reproduced using stand alone version of PepQuery. The parameter section of the result page contains the command line used for a searching. In addition, please note the web server only allows to submit one job at a time to make full use of the computer resource to speed up the search. When a job is running, users have to wait the job to be finished to view the main page or submit a new job.

Input data

Currently, the web interface accepts taking peptide, protein, DNA sequences or protein ID as input. For each search, only one sequence is supported. If you have multiple sequences, pelase do multiple searches or use the standalone version. The standalone version also supports to search all the indexed MS/MS datasets available in the web server in any local computer with internet connection.

Parameters

There are only a few parameters you need to set. Below is a screenshot of the web inferface parameters:

PepQuery

Please find the details about the parameters below:

  • MS/MS dataset: select one MS/MS dataset which you want to search. Detailed information about the datasets can be found at the homepage of the web server: web server
  • Task type: novel or known peptide/protein identification.
  • Target event: select the sequence class you provide. For the web interface, peptide, protein and DNA sequences are supported for novel peptide/protein identification. Peptide sequence and protein ID are supported for known peptide/protein identification.
  • Input peptide/protein/DNA sequence or a protein ID: a peptide/protein/DNA sequnce or a protein ID depends on your selection for target event. For one search, only one sequence is accepted.
  • Reference database: a protein reference database;
  • Scoring algorithm: peptide spectrum scoring algorithm: Hyperscore and MVH are available. Default is Hyperscore. We recommend to use Hyperscore.
  • Fast searching mode: choose to use the fast mode for searching or not. In fast mode, only one better match from reference peptide-based competitive filtering steps will be returned. For most applications, 'Yes' should work fine. A peptide identified or not is not affected by this setting.

For each dataset in this web application, we have selected a set of optimized MS/MS searching parameters so that users don't need to set these parameters by theirself. In the result page, you can find the detailed MS/MS searching parameters for the selected MS/MS dataset. If you want to change any of the parameter settings for a dataset, you could use the standalone version.

Datasets

Detailed information about the datasets can be found at the homepage of the web server: web server

Result page

Through the result web page of a search, you can find the detailed identification result about your input sequence. The result page is divided into four panels:

Identification overview: the first panel contains the identification parameters for the selected dataset:
PepQuery
Identification result: this panel contains the detailed identification result for input sequence. If a row is green, it indicates this identification is confident (column confident == Yes). You can click a row then you can find the spectrum annotation figure in the "Spectrum annotation" panel. This figure can help you further evaluate the quality of the peptide spectrum matching. If you want ot download the identification table, you can click the "Download" button in the bottom of this panel. Using the "Search" function, you can quickly filter the rows you want. PepQuery
Spectrum annotation: this panel displays the spectrum annotation figure of the identification you selected in the Identification result panel. The matched ions are displayed with different colors. The figure can be zoomed in and zoomed out. You can download the figure and the MS/MS data refered to this identification through the functions in this panel. PepQuery
The best matched peptide from the reference database for the selected spectrum is also listed in this panel as shown below: PepQuery
If unrestricted modification searching is selected, the best matched peptide from the reference database for the selected spectrum in this searching is also listed in this panel as shown below: PepQuery
Sample information: you can find the sample information for the selected row in the "Identification result" panel. PepQuery

Standalone version

Input data

As for the standalone version of PepQuery, usually you need to provide MS/MS data in MGF format, a reference protein database in FASTA format and a peptide, protein, DNA sequence or a pair of peptide and spectrum ID. However, if you want to take a VCF, BED or GTF as input, you will need to provide a folder which includes annotation data. This annotation data will be used to translate the input events into protein sequences.

Parameters

Please find the command line parameters of PepQuery below:

  • -i: The value for this parameter could be:
    (1) a single peptide sequence;
    (2) multiple peptide sequences separated by ',';
    (3) a file contains peptide sequence(s). If it is a file contains peptide sequence(s), each row is a single peptide sequence and there is no header in the file;
    (4) a file contains peptide sequence and spectrum ID and they are separated by tab '\t'. There is no header in the file. This is used when perform PSM level validation;
    (5) a protein sequence. This is used when perform novel protein identification;
    (6) a protein ID. This is used for targeted known protein identification;
    (7) a DNA sequence;
    (8) a VCF file;
    (9) a BED file;
    (10) a GTF file.
  • -t: Input type for parameter -i: peptide (or pep) when the setting for -i belongs to (1)-(4), protein (or pro) when the setting for -i belongs to (5)-(6), DNA (or dna), VCF (or vcf), BED (or bed), GTF or (gtf). The default is peptide.
  • -s: Task type: 1 => novel peptide/protein validation (default), 2 => known peptide/protein validation. Default is 1.
  • -ms: MS/MS data used for identification. MGF, mzML, mzXML, mgf.gz, mzML.gz, mzXML.gz and Thermo raw formats are supported.
  • MGF/mzML/mzXML format files can be converted from raw files using msconvert before running PepQuery:
    For example, from raw MS/MS data to MGF data:

    msconvert --filter "peakPicking true 1-2" --mgf *.raw

    From mzML format data to MGF data:

    msconvert --filter "peakPicking true 1-2" --mgf *.mzML
  • -indexType: When the input MS/MS data is in MGF format, what type of index will be used: 1 => index (1-based), 2 => spectrum title in MGF file. Default is 1
  • -b: MS/MS dataset ID(s) to search. Two types of dataset ID are supported: the dataset ID from indexed PepQueryDB and dataset ID from public proteomics data repositories including PRIDE, MassIVE, jPOSTrepo and iProX. Multiple datasets from PepQueryDB must be separated by comma. A pattern to match datasets in PepQueryDB is also supported, for example, use '-b CPTAC' to search all datasets contain 'CPTAC'. In addition, dataset selection from PepQueryDB based on data type (w:global proteome, p:phosphorylation, a:acetylation, u:ubiquitination, g:glycosylation) is also supported. For example, use '-b p' to search all phosphoproteomics datasets in PepQueryDB. Use '-b show' to present all MS/MS datasets available in PepQueryDB. For dataset ID from public proteomics data repositories, one dataset is supported for each analysis. For example, use '-b PXD000529' to use all MS/MS data from dataset PXD000529 or use '-b PXD000529:LM3' to use data files containing LM3 from dataset PXD000529.
  • To show all the indexed MS/MS datasets in PepQueryDB:

    java -jar pepquery-2.0.2.jar -b show
  • -db: A protein reference database in FASTA format. For example, a protein sequence database from RefSeq, GENCODE, Ensembl or UniProt. A string like swissprot:human, refseq:human or gencode:human is also accepted. If later is the case, PepQuery will automatically download protein database from online databases.
  • -frame: The frame to translate DNA sequence to protein. The right format is like this: '1,2,3,4,5,6', '1,2,3', '1' or '0' which means to keep the longest frame. In default, for each frame only the longest protein is used. It is only used when the input for '-i' is a DNA sequence.
  • -anno: Annotation files folder for VCF/BED/GTF. Please follow the instruction of PGA to prepare the annotation data;
  • -decoy: In known protein identification mode, try to identity the decoy version of the selected target protein. Default is false.
  • -e: Enzyme used for protein digestion. 0:Non enzyme, 1:Trypsin (default), 2:Trypsin (no P rule), 3:Arg-C, 4:Arg-C (no P rule), 5:Arg-N, 6:Glu-C, 7:Lys-C.
  • -c: The max missed cleavages, default is 2.
  • -minLength: The minimum length of peptide to consider, default is 7.
  • -maxLength: The maximum length of peptide to consider, default is 45.
  • -tol: Precursor ion m/z tolerance, default is 10.
  • -tolu: The unit of precursor ion m/z tolerance, default is ppm.
  • -itol: Fragment ion m/z tolerance in Da, default is 0.6.
  • -fixMod: Fixed modification, the format is like : 1,2,3. Use '-printPTM' to show all supported modifications. Default is 1 (Carbamidomethylation(C)[57.02]). If there is no fixed modification, set it as '-fixMod no' or '-fixMod 0'.
  • -varMod: Variable modification, the format is same with -fixMod. Default is 2 (Oxidation(M)[15.99]). If there is no variable modification, set it as '-varMod no' or '-varMod 0'.
  • To show all the supported modifications in PepQuery:

    java -jar pepquery-2.0.2.jar -printPTM
  • -maxVar: Max number of variable modifications, default is 3.
  • -aa: Whether or not to consider aa substitution modifications when perform modification filtering. In default, don't consider.
  • -printPTM: Print PTMs.
  • -ti: Range of allowed isotope peak errors, such as '0,1'; Default: 0.
  • -p: MS/MS searching parameter set name. Use '-p show' to show all predefined parameter sets.
  • To show all predefined parameter settings in PepQuery:

    java -jar pepquery-2.0.2.jar -p show
  • -m: Scoring method: 1=HyperScore (default), 2=MVH. Find details about the two scoring algorithm in the PepQuery Genome Research paper.
  • -hc: When perform validation with unrestricted modification searching (UMS), whether or not to use more stringent criterion. TRUE: score(UMS)>=score(targetPSM); FALSE: score(UMS)>score(targetPSM), default.
  • -x: Add extra score validation. It means use two scoring algorithms for peptide identification.
  • -fast: Choose to use the fast mode for searching or not. In fast mode, only one better match from reference peptide-based competitive filtering steps will be returned. A peptide identified or not is not affected by this setting. For most applications, add '-fast' will speed up the analysis.
  • -n: The number of random peptides generated for p-value calculation, default is 10000.
  • -minPeaks: Min peaks in spectrum, default is 10.
  • -minScore: Minimum score to consider for peptide searching, default is 12.
  • -o: Output directory.
  • -cpu: The number of cpus used, default is to use all available CPUs.
  • -minCharge: The minimum charge to consider if the charge state is not available, default is 2.
  • -maxCharge: The maximum charge to consider if the charge state is not available, default is 3.
  • -dc: Tool path for Raw MS/MS data conversion: for example, /home/test/ThermoRawFileParser/ThermoRawFileParser.exe.
  • -plot: Generate PSM annotation data for plot.
  • -h: print all the command line options.

Output data

PepQuery outputs results as several tab-delimited text files.

psm_rank.txt: this is the mainly result file of PepQuery. This file includes the detailed identification results for the input target peptide. We recommend users to filter the identification result based on the column "confident" in this file with confident equal to "Yes". If a PSM with "Yes" in column "confident", it means this identification passed all the validation steps in PepQuery.

Column Description
peptide A target peptide sequence.
modification Modification information of the target peptide. For example, "Deamidation of N@6[0.9840]". "-" means there is no modification.
n The number of candidate spectra for the target peptide.
spectrum_title Sepctrum title of the matched spectrum.
charge The charge state of the precursor ion.
exp_mass The experimental mass of the matched spectrum.
tol_ppm Mass error in ppm unit.
tol_da Mass error in Da unit.
isotope_error Isotope error.
pep_mass The theoretical mass of the peptide.
mz The mass-over-charge value of the precursor ion.
score The score of the identification, where larger is better.
n_db The number of better matched peptides from the reference protein database to the matched spectrum.
total_db The total number of matched peptides from the reference protein database without regarding the score.
n_random The number of better matched random peptides to the matched spectrum.
total_random The total number of random peptides.
pvalue The pvalue of the identification, where smaller is better. If the pvalue for a PSM is 100 in the file, it means the corresponding PSM is not confident and is not necessary to have p-value validation (step 4). This is because this PSM is found to be not confident based on the processing in step 2-3 showing in the Figure 2 of the PepQuery paper.
rank The rank of the identification.
n_ptm The number of better matched modification peptides when performing the unrestricted modification searching.
confident Yes: the PSM is confident, No: the PSM is not confident. Users can directly use this to filter the identification results.
ref_delta_score The delta score between the match to the target peptide and the "best match" from competitive filtering based on reference sequences (step 3). In fast model when '-fast' is set, the "best match" may not be real best match.
mod_delta_score The delta score between the match to the target peptide and the "best match" from competitive filtering based on unrestricted post-translational modification searching. (step 5). In fast model when '-fast' is set, the "best match" may not be real best match..

psm_rank.mgf: this is an MGF file which contains all the MS/MS spectra in psm_rank.txt file. The users can extract the MS/MS spectra from this file if they want to do some downstream analysis.

ptm_detail.txt: this file contains the identifications of better matched modification peptides from the unrestricted modification searching. It's possible to be empty when there is no better match. Except containing all the columns in psm_rank.txt, it also contains the following columns:

Column Description
ptm_spectrum_title Sepctrum title of the matched spectrum.
ptm_peptide Modification peptide sequence.
ptm_charge The charge state of the precursor ion.
ptm_exp_mass The experimental mass of the matched spectrum.
ptm_pep_mass The theoretical mass of the peptide.
ptm_tol_ppm Mass error in ppm unit.
ptm_tol_da Mass error in Da unit.
ptm_isotope_error Isotope error.
ptm_modification Modification information of the target peptide. For example, "Carbamidomethylation of C@2[57.0215];Methyl of C@2[14.0157]".
ptm_score The score of the identification, where larger is better.

ptm.txt: this file contains all the identifications from competitive filtering based on unrestricted post-translational modification searching. (step 5). All the modification peptides in ptm_detail.txt which are from the unrestricted modification searching are also included in this file.

Column Description
spectrum_title Sepctrum title of the matched spectrum.
peptide Modification peptide sequence.
charge The charge state of the precursor ion.
exp_mass The experimental mass of the matched spectrum.
pep_mass The theoretical mass of the peptide.
tol_ppm Mass error in ppm unit.
tol_da Mass error in Da unit.
isotope_error Isotope error.
modification Modification information of the target peptide. For example, "Carbamidomethylation of C@2[57.0215];Methyl of C@2[14.0157]".
score The score of the identification, where larger is better.

detail.txt: this file contains all the identifications from competitive filtering based on reference sequences (step 3).

Column Description
spectrum_title Sepctrum title of the matched spectrum.
peptide peptide sequence.
modification Modification information of the target peptide. For example, "Carbamidomethylation of C@2[57.0215]".
exp_mass The experimental mass of the matched spectrum.
pep_mass The theoretical mass of the peptide.
tol_ppm Mass error in ppm unit.
tol_da Mass error in Da unit.
isotope_error Isotope error.
score The score of the identification, where larger is better.

psm_type.txt: this file contains PSM level validation result. This is only available when performing PSM level validation.

Column Description
peptide peptide sequence.
spectrum_title Sepctrum title of the matched spectrum.
type The type of PSM validation result: no_spectrum_match, low_score, high_p_value, better_ref_pep, better_ref_pep_with_mod, confident and invalid_peptide.

parameter.txt: this file contains the parameters used for PepQuery analysis.

Result visualization

The result files of PepQuery can be imported to PDV which is developed by our lab to visualize. Currently, the file psm_rank.txt, ptm.txt or detail.txt along with the psm_rank.mgf can be imported into PDV for visualization. Through PDV, you can check the spectrum annotation figure for each identification one by one and you can also export high-quality annotation figure in different formats such as png and pdf. This function can help you further evaluate the quality of the identification manually.

Below is how to load the PepQuery identification result (psm_rank.txt and psm_rank.mgf) into PDV for visualization and how it looks like:

Load the data into PDV:

PepQuery

The main visualization panel:

PepQuery

Export a selected annotated spectrum in PNG or PDF format:

PepQuery

Searching multiple datasets in PepQueryDB

It's very easy to search multiple MS/MS datasets available in PepQueryDB in a single analysis. Below is an example:

java -jar pepquery-2.0.2/pepquery-2.0.2.jar -b CPTAC_LUAD_Discovery_Study_Proteome_PDC000153,CPTAC_PDA_Discovery_Study_Proteome_PDC000270,CPTAC_UCEC_Discovery_Study_Proteome_PDC000125 -db gencode:human -hc -o pepquery_kras_g12d/ -i LVVVGADGVGK

Demo

1. Search a novel peptide against a single MS/MS dataset from PepQueryDB which contains 48 indexed proteomics datasets. All the proteomics data in PepQueryDB can be directly accessed through PepQuery standalone version.

The following command line searchs the KRAS G12D variant peptide "LVVVGADGVGK" in the CPTAC LUAD global proteome dataset (Use command line "java -jar pepquery-2.0.2/pepquery-2.0.2.jar -b show" to show all available datasets in PepQueryDB or go to PepQuery Web version to look at the available dataset information). The reference database used in the example is the latest human protein reference from GENCODE. The protein database file will be downloaded from GENCODE website on the fly. The output folder is "pepquery_kras_g12d".

To run this command line on a computer, an internet connection is needed since it requires to access the proteomics data in PepQueryDB hosted on Amazon S3 cloud storage.

java -jar pepquery-2.0.2/pepquery-2.0.2.jar -b CPTAC_LUAD_Discovery_Study_Proteome_PDC000153 -db gencode:human -hc -o pepquery_kras_g12d/ -i LVVVGADGVGK

The above command line takes about 1 minute on a Mac Pro computer (Memory: 16 GB, Processor: 3.1 GHz Quad-Core Intel Core i7). It takes about similar time on a Linux computer (Memory: 64 GB, CPUs: 12).

The following four spectra are expected to be confidently identified in this search:

25CPTAC_LUAD_W_BI_20180901_KR_f13:26228:2
22CPTAC_LUAD_W_BI_20180820_KR_f13:27848:2
19CPTAC_LUAD_W_BI_20180730_KL_f12:26558:2
03CPTAC_LUAD_W_BI_20180521_KR_f13:25749:2

2. Search two novel peptides against a local MS/MS dataset

This example shows how to search peptides in user's MS/MS data. In this example, two novel peptides (LLSYVDDEAFIRDVAK,VGDANPALQK) are searched against an MS/MS dataset in MGF format (iPRG2015/JD_06232014_sample1-A.mgf). Both the reference database (iPRG2015/iPRG2015_no6.fasta) and the MS/MS file (iPRG2015/JD_06232014_sample1-A.mgf) used in this example can be downloaded from this link. More information about the dataset could be found in a previous publication

In this example, we consider the following parameters for MS/MS matching:

-fixMod 1 -> Fixed modification: 1 = Carbamidomethylation of C
-varMod 2 -> Variable modification: 2 = Oxidation of M
-tol -> Precursor mass tolerance: 10.0 ppm
-itol -> Fragment ion mass tolerance: 0.05 Da

Default:
Enzyme: 1 = Trypsin
Max Missed cleavages: 2
java -jar pepquery-2.0.2/pepquery-2.0.2.jar -db iPRG2015/iPRG2015_no6.fasta -ms iPRG2015/JD_06232014_sample1-A.mgf -fixMod 1 -varMod 2 -tol 10 -itol 0.05 -i LLSYVDDEAFIRDVAK,VGDANPALQK -o out

The above command line takes about 30 seconds on a Mac Pro computer (Memory: 16 GB, Processor: 3.1 GHz Quad-Core Intel Core i7). It takes about similar time on a Linux computer (Memory: 64 GB, CPUs: 12).

Two spectra are identified for peptide LLSYVDDEAFIRDVAK and one spectrum is identified for peptide VGDANPALQK.

3. Search a novel peptide against multiple MS/MS datasets from PepQueryDB

java -jar pepquery-2.0.2/pepquery-2.0.2.jar -b CPTAC_LUAD_Discovery_Study_Proteome_PDC000153,CPTAC_PDA_Discovery_Study_Proteome_PDC000270,CPTAC_UCEC_Discovery_Study_Proteome_PDC000125 -db gencode:human -hc -o pepquery_kras_g12d/ -i LVVVGADGVGK

4. Search a novel peptide against all MS/MS datasets from PepQueryDB

java -jar pepquery-2.0.2/pepquery-2.0.2.jar -b all -db gencode:human -hc -o pepquery_kras_g12d/ -i LVVVGADGVGK

5. Search a novel peptide against all MS/MS datasets which contain CPTAC from PepQueryDB

java -jar pepquery-2.0.2/pepquery-2.0.2.jar -b CPTAC -db gencode:human -hc -o pepquery_kras_g12d/ -i LVVVGADGVGK

6. Search a novel peptide against all phosphoproteome datasets from PepQueryDB

java -jar pepquery-2.0.2/pepquery-2.0.2.jar -b p -db gencode:human -hc -o pepquery_kras_g12d/ -i LVVVGADGVGK

7. Search a novel peptide against all global proteome datasets from PepQueryDB

java -jar pepquery-2.0.2/pepquery-2.0.2.jar -b w -db gencode:human -hc -o pepquery_kras_g12d/ -i LVVVGADGVGK

8. Search a novel peptide against all glycosylation datasets from PepQueryDB

java -jar pepquery-2.0.2/pepquery-2.0.2.jar -b g -db gencode:human -hc -o pepquery_kras_g12d/ -i LVVVGADGVGK

9. Search a novel peptide against all acetylation datasets from PepQueryDB

java -jar pepquery-2.0.2/pepquery-2.0.2.jar -b a -db gencode:human -hc -o pepquery_kras_g12d/ -i LVVVGADGVGK

10. Search a novel peptide against all ubiquitination datasets from PepQueryDB

java -jar pepquery-2.0.2/pepquery-2.0.2.jar -b u -db gencode:human -hc -o pepquery_kras_g12d/ -i LVVVGADGVGK

11. Search multiple novel peptides against a single MS/MS dataset from PepQueryDB

java -jar pepquery-2.0.2/pepquery-2.0.2.jar -b CPTAC_TCGA_Colon_Cancer_Proteome_PDC000111 -db gencode:human -hc -o pepquery_out/ -i LVVVGADGVGK,NPLLDLAAYDQEGR

12. Search multiple novel peptides in a file against a single MS/MS dataset from PepQueryDB

java -jar pepquery-2.0.2/pepquery-2.0.2.jar -b CPTAC_TCGA_Colon_Cancer_Proteome_PDC000111 -db gencode:human -hc -o pepquery_out/ -i pep.txt

The file pep.txt looks like below:

LVVVGADGVGK
NPLLDLAAYDQEGR

13. Search a known peptide against a single MS/MS dataset from PepQueryDB

java -jar pepquery-2.0.2/pepquery-2.0.2.jar -b CPTAC_LUAD_Discovery_Study_Proteome_PDC000153 -db gencode:human -hc -o pepquery_out/ -i AHSSMVGVNLPQK -s 2

14. Validate PSMs from known peptide against a single MS/MS dataset from PepQueryDB

java -jar pepquery-2.0.2/pepquery-2.0.2.jar -b CPTAC_LUAD_Discovery_Study_Proteome_PDC000153 -db gencode:human -hc -o pepquery_out/ -i psm.txt -s 2

The file psm.txt looks like below:

AHSSMVGVNLPQK 01CPTAC_LUAD_W_BI_20180515_KR_f01:15128:4
AHSSMVGVNLPQK 01CPTAC_LUAD_W_BI_20180515_KR_f01:15349:5

15. Search a novel protein against a single MS/MS dataset from PepQueryDB

java -jar pepquery-2.0.2/pepquery-2.0.2.jar -b CPTAC_TCGA_Colon_Cancer_Proteome_PDC000111 -db gencode:human -hc -o pepquery_out/ -i MKKFFDSRREQGGSGLGSGSSGGGGSTSGLGSGYIGRVFGIGRQQVTVDEVLAEGGFAIVFLVRTSNGMKCALKRMFVNNEHDLQVCKREIQIMRDLSGHKNIVGYIDSSINNVSSGDVWEVLILMDFCRGGQVVNLMNQRLQTGFTENEVLQIFCDTCEAVARLHQCKTPIIHRDLKVENILLHDRKVFHELTQTDKMGAQELLR -t protein

16. Search a novel DNA sequence against a single MS/MS dataset from PepQueryDB

java -jar pepquery-2.0.2/pepquery-2.0.2.jar -b CPTAC_TCGA_Colon_Cancer_Proteome_PDC000111 -db gencode:human -hc -o pepquery_out/ -i GAGCCCAGGAGTTTGAGGCTGCAGTGAGTTGTGAGTCCAGCCTGGGTGACAGAGTGAGACACTGTCTCCAAAAATATAATAATAAAAATAAAGCCTGTATCTCAATGGGAAGATTTCTGCTAAGGCAGTTCAACTCACTGGAAAGAACTGCTGCATTAGGTTTCAACTTTAATGCTTTCTGTTACTTCTAGTAAAGGTTAAGTGATTTTTCCATTAGCTGTAGAAGTTTGGAGAGCTATTTACCAAGCCAGATCAATGATTTAAAAATTATTGGAAATTCATCTAAGAATCAAGTCTGAATGCCCAATTCTATTGCGGTTGATTAGGTGTGATATTCTTTAAAGTTCAGGAATATTGGCAGTAAAAAATGAGCAGCTACTTTTCAATACTTTGTCCTTTTTTGGTGGTCTCTGCCTATTTCAAATGTCCTGATCAAAAGATAAATAATTGGCACTGTGCCAAGGTTTGGTTTTCCAACTAAGGTTTCAACTGTGCCAGAAACCTATGTCCTTCACTTTGGTGGATGCTAAATGTTATTCTAAGAATATGCTTTTTCCCAATTCTCCTTTCTGATTTTTATGTATTAGTGGATGCAAAATTGTCTTTCTAGTTGAATGAATAATTTCGGCTAATGCACGTGGAACTTTGCACCCCAGATTCTTCCCATGGTCATTATCAAGTGAAGCCCTCAAAAACATGAGCGAAGAGCCTAGAAATACTCAGGGAGATTTCTCACCCCAACTCAGAAATTTTTTTTTTTTTTTTCAAGACGGCGTCTTGCTCTGTCGCTCAGGCGGGAGTGCAGTGGTGTGATCTCGGCTCACTGCAACCTCCATTTTCCGAGTTCAAGCGATTCTGCCTCAGCCTCCCGAGTAGCTGGGATTATAGGCACACACCACCGCGCCTGGCTAATTTTTGTATTCTTAGTAGAGATGGGGTTTCACCATGTTGGCCAG -t DNA

17. Search a known protein against a single MS/MS dataset from PepQueryDB

java -jar pepquery-2.0.2/pepquery-2.0.2.jar -b CPTAC_LUAD_Discovery_Study_Proteome_PDC000153 -db swissprot:human -hc -o pepquery_out/ -i "sp|P07205|PGK2_HUMAN" -t protein -s 2

sp|P07205|PGK2_HUMAN is a protein ID in the SwissProt human protein database:

>sp|P07205|PGK2_HUMAN Phosphoglycerate kinase 2 OS=Homo sapiens OX=9606 GN=PGK2 PE=1 SV=3
MSLSKKLTLDKLDVRGKRVIMRVDFNVPMKKNQITNNQRIKASIPSIKYCLDNGAKAVVL
MSHLGRPDGVPMPDKYSLAPVAVELKSLLGKDVLFLKDCVGAEVEKACANPAPGSVILLE
NLRFHVEEEGKGQDPSGKKIKAEPDKIEAFRASLSKLGDVYVNDAFGTAHRAHSSMVGVN
LPHKASGFLMKKELDYFAKALENPVRPFLAILGGAKVADKIQLIKNMLDKVNEMIIGGGM
AYTFLKVLNNMEIGASLFDEEGAKIVKDIMAKAQKNGVRITFPVDFVTGDKFDENAQVGK
ATVASGISPGWMGLDCGPESNKNHAQVVAQARLIVWNGPLGVFEWDAFAKGTKALMDEIV
KATSKGCITVIGGGDTATCCAKWNTEDKVSHVSTGGGASLELLEGKILPGVEALSNM

18. Search a known gene against a proteomics dataset from a public proteomics data repository

java -jar pepquery-2.0.2/pepquery-2.0.2.jar -varMod 2 -fixMod 0 -t protein -s 2 -ti 0,1 -tol 50 -itol 0.05 -hc -db uniprot-proteome_UP000005640_format.fasta -i F2Z2R5,F6T542,K7EMC0,K7EMY3,K7ESA5,Q8TDI0 -b "MSV000088555:j11456.mzML|b10335.mzML" -o pepquery_out/HDAC1/CHD5

F2Z2R5,F6T542,K7EMC0,K7EMY3,K7ESA5,Q8TDI0 are the protein isoforms of gene CHD5 in the protein reference database uniprot-proteome_UP000005640_format.fasta:

>Q8TDI0 Chromodomain-helicase-DNA-binding protein 5 OS=Homo sapiens OX=9606 GN=CHD5 PE=1 SV=1
MRGPVGTEEELPRLFAEEMENEDEMSEEEDGGLEAFDDFFPVEPVSLPKKKKPKKLKENK
CKGKRKKKEGSNDELSENEEDLEEKSESEGSDYSPNKKKKKKLKDKKEKKAKRKKKDEDE
DDNDDGCLKEPKSSGQLMAEWGLDDVDYLFSEEDYHTLTNYKAFSQFLRPLIAKKNPKIP
MSKMMTVLGAKWREFSANNPFKGSSAAAAAAAVAAAVETVTISPPLAVSPPQVPQPVPIR
KAKTKEGKGPGVRKKIKGSKDGKKKGKGKKTAGLKFRFGGISNKRKKGSSSEEDEREESD
FDSASIHSASVRSECSAALGKKSKRRRKKKRIDDGDGYETDHQDYCEVCQQGGEIILCDT
CPRAYHLVCLDPELEKAPEGKWSCPHCEKEGIQWEPKDDDDEEEEGGCEEEEDDHMEFCR
VCKDGGELLCCDACPSSYHLHCLNPPLPEIPNGEWLCPRCTCPPLKGKVQRILHWRWTEP
PAPFMVGLPGPDVEPSLPPPKPLEGIPEREFFVKWAGLSYWHCSWVKELQLELYHTVMYR
NYQRKNDMDEPPPFDYGSGDEDGKSEKRKNKDPLYAKMEERFYRYGIKPEWMMIHRILNH
SFDKKGDVHYLIKWKDLPYDQCTWEIDDIDIPYYDNLKQAYWGHRELMLGEDTRLPKRLL
KKGKKLRDDKQEKPPDTPIVDPTVKFDKQPWYIDSTGGTLHPYQLEGLNWLRFSWAQGTD
TILADEMGLGKTVQTIVFLYSLYKEGHSKGPYLVSAPLSTIINWEREFEMWAPDFYVVTY
TGDKESRSVIRENEFSFEDNAIRSGKKVFRMKKEVQIKFHVLLTSYELITIDQAILGSIE
WACLVVDEAHRLKNNQSKFFRVLNSYKIDYKLLLTGTPLQNNLEELFHLLNFLTPERFNN
LEGFLEEFADISKEDQIKKLHDLLGPHMLRRLKADVFKNMPAKTELIVRVELSQMQKKYY
KFILTRNFEALNSKGGGNQVSLLNIMMDLKKCCNHPYLFPVAAVEAPVLPNGSYDGSSLV
KSSGKLMLLQKMLKKLRDEGHRVLIFSQMTKMLDLLEDFLEYEGYKYERIDGGITGGLRQ
EAIDRFNAPGAQQFCFLLSTRAGGLGINLATADTVIIYDSDWNPHNDIQAFSRAHRIGQN
KKVMIYRFVTRASVEERITQVAKRKMMLTHLVVRPGLGSKSGSMTKQELDDILKFGTEEL
FKDDVEGMMSQGQRPVTPIPDVQSSKGGNLAASAKKKHGSTPPGDNKDVEDSSVIHYDDA
AISKLLDRNQDATDDTELQNMNEYLSSFKVAQYVVREEDGVEEVEREIIKQEENVDPDYW
EKLLRHHYEQQQEDLARNLGKGKRIRKQVNYNDASQEDQEWQDELSDNQSEYSIGSEDED
EDFEERPEGQSGRRQSRRQLKSDRDKPLPPLLARVGGNIEVLGFNARQRKAFLNAIMRWG
MPPQDAFNSHWLVRDLRGKSEKEFRAYVSLFMRHLCEPGADGAETFADGVPREGLSRQHV
LTRIGVMSLVRKKVQEFEHVNGKYSTPDLIPEGPEGKKSGEVISSDPNTPVPASPAHLLP
APLGLPDKMEAQLGYMDEKDPGAQKPRQPLEVQALPAALDRVESEDKHESPASKERAREE
RPEETEKAPPSPEQLPREEVLPEKEKILDKLELSLIHSRGDSSELRPDDTKAEEKEPIET
QQNGDKEEDDEGKKEDKKGKFKFMFNIADGGFTELHTLWQNEERAAVSSGKIYDIWHRRH
DYWLLAGIVTHGYARWQDIQNDPRYMILNEPFKSEVHKGNYLEMKNKFLARRFKLLEQAL
VIEEQLRRAAYLNMTQDPNHPAMALNARLAEVECLAESHQHLSKESLAGNKPANAVLHKV
LNQLEELLSDMKADVTRLPSMLSRIPPVAARLQMSERSILSRLTNRAGDPTIQQGAFGSS
QMYSNNFGPNFRGPGPGGIVNYNQMPLGPYVTDI

19. Validate a USI

java -jar pepquery-2.0.2/pepquery-2.0.2.jar -i VLHPLEGAVVIIFK -ms "mzspec:PXD000561:Adult_Frontalcortex_bRP_Elite_85_f09:scan:17555" -db swissprot:human -hc -s 2 -o usi -itol 0.05

20. Validate a USI

java -jar pepquery-2.0.2/pepquery-2.0.2.jar -ms "mzspec:PXD010154:01284_E04_P013188_B00_N29_R1.mzML:scan:31291" -i DQNGTWEMESNENFEGYMK -itol 0.05 -hc -db swissprot:human -o usi -s 2 -varMod 2 -fixMod  1 -tol 10 -ti 0,1

Index MS/MS data for fast searching

You can index MS/MS data for fast searching by using the following command lines. This can significant speed up the search especially when the size of MS/MS data is very large.

java -cp pepquery-2.0.2/pepquery-2.0.2.jar index -h
usage: Options
 -c    The number of CPUs. Default is all available CPUs.
 -d    Dataset ID
 -f    File type (file suffix) to download.
 -h    Help
 -i    MS/MS data file or folder
 -m    Method for Raw MS/MS data conversion: ThermoRawFileParser
 -o    Output folder
 -p    Tool path for Raw MS/MS data conversion: for example, /home/test/ThermoRawFileParser/ThermoRawFileParser.exe
 -r    Delete raw file after data conversion
 -show Show all files without downloading any file.
 

Once we have the MS/MS data indexed, we can give the folder (this is the folder for parameter -o when we build MS/MS index) which containing the indexed MS/MS files to parameter -ms when we do PepQuery search. The orginal MS/MS data is not required any more for PepQuery search after we have the indexed MS/MS files.

Run PepQuery on immunopeptidomics data

PepQuery supports searching immunopeptidomics data but it may require large memory for the analysis.