DeepRescore

Overview

DeepRescore is an immunopeptidomics data analysis tool that leverages deep learning-derived peptide features to rescore peptide-spectrum matches (PSMs). DeepRescore takes as input MS/MS data in MGF format and identification results from a search engine. The current version supports four search engines, MS-GF+, Comet, X!Tandem, and MaxQuant.

Installation

1. Download DeepRescore: sh git clone https://github.com/bzhanglab/DeepRescore

2. Install Docker (>=19.03).

3. Install Nextflow. More information can be found in the Nextflow get started page.

4. Install nvidia-docker (>=2.2.2) for AutoRT and pDeep2 by following the instruction at https://github.com/NVIDIA/nvidia-docker. Please note GPU is required to run DeepRescore.

All other tools used by DeepRescore have been dockerized and will be automatically installed when DeepRescore is run in the first time on a computer. DeepRescore has been tested on Linux.

Usage

```sh ○ → nextflow run DeepRescore.nf --help N E X T F L O W ~ version 19.10.0

Launching deeprescore.nf [special_hamilton] - revision: 2817bc64da

DeepRescore => Rescore PSMs

Usage: nextflow run DeepRescore.nf Arguments: --idfile Identification result. --msfile MS/MS data in MGF format. If the search engine is MaxQuant, this parameter is not useful. --se The name of search engine, msgf:MS-GF+, xtandem:X!Tandem, comet:Comet or maxquant:MaxQuant. Default is "msgf" (MS-GF+). --msinstrument The MS instrument used to generate the MS/MS data. This is used by pDeep2 for MS/MS spectrum prediction. Default is "Lumos". --msenergy The energy used in MS/MS data generation. This is used by pDeep2 for MS/MS spectrum prediction. Default is 0.34. --outdir Output folder, default is "./output" --prefix The prefix of output file(s). --decoyprefix The prefix of decoy proteins. Default is "XXX_". --cpu The number of CPUs --mem The memory for processing the data, default is 8. The unit is G. --help Print help message

```

Input

In general, the main inputs to run DeepRescore are identification result from one of the four search engines (MS-GF+, X!Tandem, Comet and MaxQuant) and the MS/MS data used for searching. If the identification software is MaxQuant, then the MS/MS data is not needed because MS/MS data is included in MaxQuant search result ( folder combined, mqpar.xml is also required to be present in the combined folder). Below is the table showing the detailed search result format and MS/MS data format supported for each search engine. Using MS-GF+, X!Tandem or Comet, raw MS/MS data must be converted to MGF format using ProteoWizard. Multiple MGF files (different fractions) from the sample or same TMT/iTRAQ experiment should be combined into one MGF file. Only oxidation of M is supported as variable modification. Please note if DeepRescore is used to rescore MaxQuant result, the FDR cutoff should be set as 100% when performing the MaxQuant search, otherwise target PSMs may be filtered by MaxQuant's FDR calculation before rescoring using DeepRescore.

For MGF file conversion, we recommend to use the following command line:

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

| Search engine | Identification format | MS/MS data format | |---|---|---| | Comet | .pepxml | MGF | | MS-GF+ | .mzid | MGF | | X!Tandem | .xml | MGF | | MaxQuant | /combined/ | - |

Below is an example: sh nextflow run DeepRescore.nf --id_file ./example_data/A1101.pep.xml \ --ms_file ./example_data/A1101.mgf \ --se comet \ --ms_instrument Lumos \ --ms_energy 0.34 \ --out_dir out \ --prefix d2 \ --decoy_prefix XXX_ \ --cpu 4 \ --mem 8 It took about one and half hour to run the example on a Linux server (12 threads, 64 RAM, GPU: TITAN Xp). The example data can be downloaded through this link: test_data.

Output

The final output data can be found in this folder out_dir/DeepRescore_results. Here out_dir is the output directory specified through parameter --out_dir. There are two files in this folder: *_psm_final.tsv and *_pep_final.tsv. The first one is the result controled FDR at PSM level and the second one is the result controled FDR at peptide level. Below is an example of *_psm_final.tsv. The format of *_pep_final.tsv is the same with *_psm_final.tsv. Users can filter the result based on the column q-value (for example, q-value <= 0.01). The result files (*_psm_final.tsv or *_pep_final.tsv + the MS/MS data in MGF format) can be imported into PDV for visualization.

| spectrumtitle | Percolatorscore | qvalue | modification | ModSequence | Label | RT | Mass | AbsMassError | LnTotalIntensity | MatchIonsIntensity | RelMatchIonsIntensity | MaxMatchIonIntensity | Score | Pep | DeltaScore | charge | peptide | Proteins | DeltaRT | SA | mz | |----------------------------------------------------------|------------------|-------------|-------------------------------|--------------------------|-------|--------|------------------|----------------|--------------------|----------------------|--------------------------|-------------------------|--------|-------------|-------------|--------|--------------------------|-----------------------|-------------------|-------------------|------------------| | YE20180517SKHLAA11013Ipsa50mioR102.25098.25098.2 | 1.43274 | 5.43478e-05 | Carbamidomethyl of C@23[0.0]; | QVADEGDALVAGGVSQTPSYLSCK | 1 | 59.187 | 2451.16256793088 | 0 | 14.0435918544821 | 11.9698824268126 | 0.125718571751381 | 24026.38671875 | 339.58 | 4.3576e-114 | 283.38 | 2 | QVADEGDALVAGGVSQTPSYLSCK | uc003kfu.4 | 0.130932000000001 | 0.775038384865853 | 1226.58908396544 | | YE20180517SKHLAA11013IPsa50mioR101.21936.21936.3 | 1.36464 | 5.43478e-05 | - | PLFVNVNDQTNEGIMHESK | 1 | 52.926 | 2171.03328724 | 0 | 16.183782828437 | 15.7096998670651 | 0.622455610654202 | 520227.46875 | 268.55 | 1.5772e-35 | 235.24 | 3 | PLFVNVNDQTNEGIMHESK | uc010fur.3;uc002vee.4 | 0.23695 | 0.669018716842519 | 724.685562413335 | | YE20180517SKHLAA11013Ipsa50mioR201.21952.21952.3 | 1.34864 | 5.43478e-05 | - | PLFVNVNDQTNEGIMHESK | 1 | 52.495 | 2171.03151690128 | 0 | 14.6959014960537 | 14.1948714045837 | 0.605906199334659 | 119562.108398438 | 284.15 | 1.4821e-46 | 248.85 | 3 | PLFVNVNDQTNEGIMHESK | uc010fur.3;uc002vee.4 | 0.855877999999997 | 0.677688435645182 | 724.684972300427 | | AC20171011BroadHLAA1101R1Rep01.3055.3055.3 | 1.32707 | 5.43478e-05 | - | RTLDAKMPRK | 1 | 11.999 | 1214.69196592591 | 0 | 15.3894885906454 | 14.7015468520804 | 0.502609506923564 | 815314.75 | 201.84 | 0.0048553 | 201.84 | 3 | RTLDAKMPRK | uc003lvo.4;uc021ygh.2 | 0.168488 | 0.884456468503026 | 405.905121975303 | | YE20180517SKHLAA11013IPsa50mioR1_01.19078.19078.2 | 1.29334 | 5.43478e-05 | - | GILAADESVGTMGNR | 1 | 46.712 | 1489.71904591213 | 0 | 15.0714146779268 | 14.7645814265636 | 0.735773279870115 | 423094.538085938 | 305.7 | 1.1819e-36 | 222.7 | 2 | GILAADESVGTMGNR | uc004bbk.2 | 0.180505999999994 | 0.862802817911802 | 745.867322956064 |

How to cite:

Kai Li, Antrix Jain, Anna Malovannaya, Bo Wen, Bing Zhang (2020), DeepRescore: Leveraging Deep Learning to Improve Peptide Identification in Immunopeptidomics. Proteomics. doi:10.1002/pmic.201900334