FDRBench

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Estimation of false discovery proportion for mass spectrometry proteomics data using entrapment

FDRBench 최신버젼 다운로드

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# [FDRBench](https://www.nature.com/articles/s41592-025-02719-x)

![Downloads](https://img.shields.io/github/downloads/Noble-Lab/FDRBench/total.svg) ![Release](https://img.shields.io/github/release/Noble-Lab/FDRBench.svg)![Downloads](https://img.shields.io/github/downloads/Noble-Lab/FDRBench/latest/total)

**FDRBench** is a tool for false discovery rate (FDR) control evaluation in proteomics. It provides two main functions: (1) build entrapment databases using randomly shuffled target sequences or using sequences from foreign species, and (2) estimate the false discovery proportion (FDP) using the lower bound, combined, and paired methods. FDRBench has been tested on identification results from a wide range of search engines, including DIA-NN, MSFragger, Spectronaut, MaxQuant, Tide, Sage and others. More details about how FDRBench works are provided in the following manuscript:

Wen, B., Freestone, J., Riffle, M. et al. [Assessment of false discovery rate control in tandem mass spectrometry analysis using entrapment](https://doi.org/10.1038/s41592-025-02719-x). *Nat Methods* 22, 1454–1463 (2025).

[![FDRBench](https://media.springernature.com/full/springer-static/image/art%3A10.1038%2Fs41592-025-02719-x/MediaObjects/41592_2025_2719_Fig1_HTML.png)](https://www.nature.com/articles/s41592-025-02719-x/figures/1)

## Installation

### Using the FDRBench graphical user interface (recommended)

FDRBench provides a standalone graphical user interface (GUI) for Windows, Linux and macOS. You can install it from the `.msi` (Windows) package available on the [GitHub Releases](https://github.com/Noble-Lab/FDRBench/releases) page. For Linux and macOS, use the ZIP package instead. After installation on Windows, launch FDRBench from the Start menu and use the GUI to configure inputs, parameters, and output locations for entrapment database generation and FDP estimation without using the command line. No Java is required if FDRBench is installed using the `.msi` installer. For Linux and macOS, FDRBench can be run using the command line (see below) to launch the GUI.

### Using FDRBench through the command line

FDRBench is written in Java and can run on Windows, macOS, and Linux. To run FDRBench from the command line, Java 11 or newer must be installed. If Java is not installed, please install it by following the instructions at https://openjdk.org/install/ or https://www.oracle.com/java/technologies/downloads/. After Java is installed, FDRBench can be downloaded from https://github.com/Noble-Lab/FDRBench/releases.

## Usage

### Using the FDRBench graphical user interface (recommended)

#### Launch the FDRBench GUI

##### Windows

After FDRBench is installed using the `.msi` installer, launch the GUI by clicking the FDRBench icon in the Start menu or the desktop shortcut.

##### Linux or macOS

On Linux or macOS, launch the GUI from the command line:

```shell
java -jar fdrbench-1.0.0.jar
```

The FDRBench GUI:

In the FDRBench GUI, hover the mouse pointer over a field label, input box, or button to display a tooltip with additional help about that option.

The GUI supports the two main FDRBench workflows:

1. Entrapment database generation;
2. FDP estimation / FDR control evaluation.

#### Workflow 1: Entrapment database generation

Use this workflow to build an entrapment database from a target protein FASTA. Two sequence-generation modes are supported, selected from the **Sequence Generation** dropdown:

- **Random Shuffling** — entrapment sequences are derived from the target database by random shuffling. Both peptide-level and protein-level entrapment databases can be generated.
- **Foreign Species** — entrapment sequences are taken from one or more foreign-species FASTA files supplied by the user. Both peptide-level and protein-level entrapment databases can be generated.

The main inputs:

1. **Protein Database**: target protein database in FASTA format. The **Download** button opens a dialog for downloading a UniProt reference proteome directly inside the GUI.
2. **Foreign Species** (Foreign Species mode only): one or more FASTA files used as entrapment sources. Multiple files can be selected.
3. **Output Folder**: directory where the entrapment FASTA and peptide pair file (for peptide level entrapment database generation only) are written. The output file formats are described in the [Build entrapment databases](#Build-entrapment-databases) section below.

Key parameters in the workflow include the **Level** (protein or peptide), **Enzyme**, **Missed Cleavages**, **Peptide Length**, **Fold** (number of entrapment copies per target), **Fix Peptide N/C Terminal**, **Fix Protein N/C Terminal**, **Convert I to L**, **DIA-NN Format**, **UniProt Format**, **Add Decoys**, and **Check Duplicates**. The available options change automatically based on the **Level** and **Sequence Generation** selections — only the parameters that are relevant for the chosen combination are shown.

After all the inputs and parameters are set, click the **Run FDRBench** button to start the run. The **Preview Command** button shows the exact command line that will be executed, which is useful for reproducibility and for switching to the command line later. During the run, console output will be shown in the **Console** tab. After the run is finished, the output folder will contain the generated entrapment FASTA file, the peptide pair `*.txt` file (peptide-level runs), and `fdrbench_log.txt`, which mirrors the console output for the run.

#### Workflow 2: FDP estimation / FDR control evaluation

Use this workflow to estimate the FDP of a peptide or protein detection result using the lower bound, combined, and paired methods.

The main inputs:

1. **Input File**: PSM / peptide / precursor / protein level identification file. The column delimiter (tab or comma) is detected automatically from the header line, so both tab-separated (`.tsv`/`.txt`) and comma-separated (`.csv`) files are accepted. Decoy hits should be removed from the input file before FDP calculation. The format is described in the [FDP estimation](#FDP-calculation) section below.
2. **Peptide Pair File** (optional): the peptide pair `*.txt` file generated in Workflow 1. Required for the paired method at the peptide or precursor level.
3. **Output Folder**: directory where the FDP estimation result and FDP-vs-FDR plot are written. The output file format is described in the [FDP estimation](#FDP-calculation) section below..

Key parameters in the workflow include the **Level** (precursor, peptide, protein, or PSM), the **Sequence Generation** mode used to build the entrapment database (Random Shuffling or Foreign Species), the **Score Column** and ranking **Direction**, the **Fold** (Random Shuffling) or **R Ratio** (Foreign Species), the **Pick Method** for protein-group handling, and the **Entrapment Label** and **Label Position** that identify entrapment sequences in the input file.

After all the inputs and parameters are set, click the **Run FDRBench** button to start the run. During the run, console output will be shown in the **Console** tab. After the run is finished, the output folder will contain a CSV file with the estimated FDP values from each method (lower bound, combined, paired when using random shuffling for entrapment generation), and `fdrbench_log.txt`. The **Plot** tab in the GUI displays the FDP-vs-FDR plot and supports interactive customization of the plot (FDR range, etc.) and exporting in PNG or PDF format.

### Using FDRBench through the command line

<details>
<summary>FDRBench command line options</summary>

```
$ java -jar fdrbench-1.2.0.jar -h
usage: Options
 -db <arg> Protein database file
 -crux <arg> The peptide list generated by Crux
 -target_pep <arg> The target peptide list to consider
 -decoy Add decoy or not
 -clip_n_m When digesting a protein starting with amino acid M, two copies of the
 leading peptides (with and without the N-terminal M) are considered or
 not. Default is false.
 -fix_nc <arg> Fix N/C terminal amino acid. n/N: only N terminal, c/C: only C terminal
 and nc/NC/cn/CN: both (default)
 -fix_protein_nc <arg> Also fix the protein N/C terminal amino acid when building protein-level
 entrapment proteins (in addition to -fix_nc). n: protein N-term, c:
 protein C-term, nc/cn: both, off: neither. Default: n
 -o <arg> Output file
 -I2L Convert I to L
 -diann For DIA-NN
 -uniprot For Uniprot
 -prosit Generate Prosit input file
 -charge <arg> For prosit input: charge range, 2,3,4
 -nce <arg> For prosit input: NCE
 -enzyme <arg> Enzyme used for protein digestion. 0:Non-specific, 1:Trypsin, 2:Trypsin (no
 P rule) (default), 3:Arg-C, 4:Arg-C (no P rule), 5:Arg-N, 6:Glu-C,
 7:Lys-C, 8: NoCut
 -miss_c <arg> The max missed cleavages, default is 1
 -minLength <arg> The minimum length of peptide to consider, default is 7
 -maxLength <arg> The maximum length of peptide to consider, default is 35
 -export_db Export protein database or not
 -seed <arg> Random seed for generating decoy peptides
 -fix_seed Use a fixed random seed for all decoy peptides generation
 -fold <arg> The number of folds for generating entrapment proteins/peptides
 -r <arg> For FDP calculation: #entrapment/#target
 -use_v1 <arg> Use the first version of FDP calculation for 1-fold
 -pick <arg> If a group has multiple proteins, how to peak one protein: first
 (default),last,random
 -a Generate entrapment protein(s) for each target protein independently
 -check Checking for duplicates and random shuffling
 -method <arg> The method to generate a random peptide: 0:shuffle (default), 1:swap
 -ms <arg> Multiple species entrapment: Fasta files of foreign species
 -ns no shared peptides between entrapment and target protein
 -swap Reverse the order of generated random peptide sequences
 -i <arg> PSM/peptide/precursor/protein file
 -score <arg> The score name for ranking precursor/peptide/protein for FDP calculation.
 The format could be "score", "score:0" or "score:1". The second part is 0
 or 1, 0: lower is better, 1: higher is better
 -level <arg> PSM, peptide, precursor or protein
 -pep <arg> peptide/protein pair file
 -decoy_label <arg> Label for decoy: rev_ in default
 -decoy_pos <arg> Position of decoy label: 0 (start, in default); 1 (end)
 -entrapment_label <arg> Label for entrapment: _p_target in default
 -entrapment_pos <arg> Position of entrapment label: 0 (start); 1 (end, in default)
 -debug Print detailed information for debugging
 -h Print this usage information
```
</details>

#### Build entrapment databases

##### Build entrapment databases using randomly shuffled target sequences - peptide level:

Generate a **peptide level** entrapment database using the human target database `UP000005640_9606.fasta` (~20k human proteins). The database can be downloaded from [UniProt](https://ftp.uniprot.org/pub/databases/uniprot/current_release/knowledgebase/reference_proteomes/Eukaryota/UP000005640/UP000005640_9606.fasta.gz).

```shell
java -jar fdrbench-1.0.0.jar -I2L -level peptide -db UP000005640_9606.fasta -o UP000005640_9606_entrapment_pep.txt -uniprot -diann -fix_nc c
```
Using the above command line, a peptide level entrapment database in FASTA format "UP000005640_9606_entrapment_pep.fasta" and a peptide tsv format file "UP000005640_9606_entrapment_pep.txt" will be generated:

The format of the Fasta file "UP000005640_9606_entrapment_pep.fasta" looks like below. The Fasta header of each peptide sequence in the file contains "_target" when the sequence is an original target or "_p_target" when the sequence is an entrapment peptide.

The format of the tsv file "UP000005640_9606_entrapment_pep.txt" looks like below. This file is used in FDP calculation.

Below please find the description of each column in the output tsv file "UP000005640_9606_entrapment_pep.txt":

| Column name  | Description |
| ------------ | ----------- |
| sequence | peptide sequence (original target peptide or entrapment peptide) |
| decoy | decoy peptide (**Yes**) or not (**No**)  |
| proteins | protein ID (multiple IDs are separated by ";") |
| peptide_type | peptide type, original target (**target**) or entrapment (**p_target**) |
| peptide_pair_index | peptide pair index: an original target peptide and its paired entrapment have the same index |

The above example command line took about 30 seconds to run on a Mac MacBook computer.

##### Build entrapment databases using randomly shuffled target sequences - protein level:

Generate a **protein level** entrapment database using the human target database `UP000005640_9606.fasta` (~20k human proteins). The database can be downloaded from [UniProt](https://ftp.uniprot.org/pub/databases/uniprot/current_release/knowledgebase/reference_proteomes/Eukaryota/UP000005640/UP000005640_9606.fasta.gz):
```shell
java -jar fdrbench-1.0.0.jar -level protein -db UP000005640_9606.fasta -o UP000005640_9606_I2L_entrapment.fasta -I2L -diann -uniprot -fix_nc c -check
```
Using the above command line, a protein level entrapment database in FASTA format "UP000005640_9606_I2L_entrapment.fasta" will be generated. For each target human protein, an entrapment protein is generated.

The format of the Fasta file "UP000005640_9606_I2L_entrapment.fasta" looks like below. The first protein as shown below is a target human protein while the second protein is its paired entrapment protein. Both command line parameters `-diann` and `-uniprot` are set in the command line, so an entrapment label "_p_target" is added in three positions of the Fasta protein header. This format is compatible with [DIA-NN](https://github.com/vdemichev/DiaNN) search.

<code>
<pre>
>sp|A0A0G2JMI3|HV692_HUMAN Immunoglobulin heavy variable 1-69-2 OS=Homo sapiens OX=9606 GN=IGHV1-69-2 PE=3 SV=2
MDCTWRLLLLVAAATGTHAEVQLVQSGAEVKKPGATVKLSCKVSGYTFTDYYMHWVQQAPGKGLEWMGLVDPEDGETLYAEKFQGRVTLTADTSTDTAYMELSSLRSEDTAVYYCAT
>sp|A0A0G2JMI3_p_target|HV692_HUMAN_p_target Immunoglobulin heavy variable 1-69-2 OS=Homo sapiens OX=9606 GN=IGHV1-69-2_p_target PE=3 SV=2
MTDWCRHLGTTQVALALEVGVASAQLVELAKKPTGVAKLCSKDTYMYFTGAVPYWSGHQQVKVDLEWGATMGGDLEPELYEKFGQREDLTLSSLTVTTTAYMASDRSVEATADCYYT
</code>
</pre>

Note that the entrapment protein keeps the same N-terminal residue as the target protein: protein N-terminus fixing is enabled by default (equivalent to `-fix_protein_nc n`). Use `-fix_protein_nc off` to disable it.

The above example command line took about 10 seconds to run on a Mac MacBook computer.

##### Build entrapment databases using foreign species - peptide level:

Generate a **peptide level** entrapment database using the human target database `UP000005640_9606.fasta` (~20k human proteins) by taking peptides from *Arabidopsis thaliana* (`UP000002311_559292.fasta`) and *Saccharomyces cerevisiae* (`UP000006548_3702.fasta`) as entrapments. The database can be downloaded from [UniProt](https://ftp.uniprot.org/pub/databases/uniprot/current_release/knowledgebase/reference_proteomes/Eukaryota/UP000005640/UP000005640_9606.fasta.gz).

```shell
java -jar fdrbench-1.0.0.jar java -db UP000005640_9606.fasta -o UP000005640_9606_I2L_foreign_species_entrapment_peptide.txt -I2L -diann -uniprot -ms UP000002311_559292.fasta,UP000006548_3702.fasta -enzyme 2 -miss_c 1 -minLength 7 -maxLength 35 -level peptide -fold 1
```

##### Build entrapment databases using foreign species - protein level:

Generate a **protein level** entrapment database using the human target database `UP000005640_9606.fasta` (~20k human proteins) by taking proteins from *Arabidopsis thaliana* (`UP000002311_559292.fasta`) and *Saccharomyces cerevisiae* (`UP000006548_3702.fasta`) as entrapments. The database can be downloaded from [UniProt](https://ftp.uniprot.org/pub/databases/uniprot/current_release/knowledgebase/reference_proteomes/Eukaryota/UP000005640/UP000005640_9606.fasta.gz).

```shell
java -jar fdrbench-1.0.0.jar java -db UP000005640_9606.fasta -o UP000005640_9606_I2L_foreign_species_entrapment_protein.fasta -I2L -diann -uniprot -ms UP000002311_559292.fasta,UP000006548_3702.fasta -enzyme 2 -miss_c 1 -minLength 7 -maxLength 35 -level protein -fold 1
```

#### FDP calculation

##### Peptide (or precursor) level FDR control evaluation

For **precursor level** FDP calculation, an example input is shown below:

```
run                                                peptide                             mod_peptide                                     charge  q_value  PEP  protein                                    score
20230406_OLEP08_MMCC_1ug_MB_24min_AS_10ms_4Th_I_1  AAAPAPEEEMDECEQALAAEPK              AAAPAPEEEMDEC(UniMod:4)EQALAAEPK                3       1e-9     0    AAAPAPEEEMDECEQALAAEPK_target              1
20230406_OLEP08_MMCC_1ug_MB_24min_AS_10ms_4Th_I_1  AAASGQPRPEMQCPAEHEEDMYR             AAASGQPRPEMQC(UniMod:4)PAEHEEDMYR               4       1e-9     0    AAASGQPRPEMQCPAEHEEDMYR_target             2
20230406_OLEP08_MMCC_1ug_MB_24min_AS_10ms_4Th_I_1  AAEVWMDEYKNFYYAAVPSAR               AAEVWMDEYKNFYYAAVPSAR                           3       1e-9     0    AAEVWMDEYKNFYYAAVPSAR_target               3
20230406_OLEP08_MMCC_1ug_MB_24min_AS_10ms_4Th_I_1  AAFTVSLDPGPLEQFPHSMEPQLR            AAFTVSLDPGPLEQFPHSMEPQLR                        3       1e-9     0    AAFTVSLDPGPLEQFPHSMEPQLR_target            4
20230406_OLEP08_MMCC_1ug_MB_24min_AS_10ms_4Th_I_1  AAHVLMPHESTVEHTHVDLNEMESPLATR       AAHVLMPHESTVEHTHVDLNEMESPLATR                   4       1e-9     0    AAHVLMPHESTVEHTHVDLNEMESPLATR_target       5
20230406_OLEP08_MMCC_1ug_MB_24min_AS_10ms_4Th_I_1  AALLSPGDPALWAGLMAACHADDKLALVNNTQPK  AALLSPGDPALWAGLMAAC(UniMod:4)HADDKLALVNNTQPK    4       1e-9     0    AALLSPGDPALWAGLMAACHADDKLALVNNTQPK_target  6
20230406_OLEP08_MMCC_1ug_MB_24min_AS_10ms_4Th_I_1  AALQQKENLPVSSDGNLPQQAASAPSR         AALQQKENLPVSSDGNLPQQAASAPSR                     3       1e-9     0    AALQQKENLPVSSDGNLPQQAASAPSR_target         7
20230406_OLEP08_MMCC_1ug_MB_24min_AS_10ms_4Th_I_1  ADMLCNSQSNDLLQHQGSNCGGTSNK          ADMLC(UniMod:4)NSQSNDLLQHQGSNC(UniMod:4)GGTSNK  3       1e-9     0    ADMLCNSQSNDLLQHQGSNCGGTSNK_target          8
20230406_OLEP08_MMCC_1ug_MB_24min_AS_10ms_4Th_I_1  AFLADPSAFVAAAPVAAATTAAPAAAAAPAK     AFLADPSAFVAAAPVAAATTAAPAAAAAPAK                 3       1e-9     0    AFLADPSAFVAAAPVAAATTAAPAAAAAPAK_target     9
```

The required columns are described below. The columns can be separated by either tabs or commas; FDRBench detects the delimiter automatically from the header line. Decoy hits shouldn't be included in the input file for FDP calcualtion.
| Column name  | Description |
| ------------ | ----------- |
| peptide | peptide sequence |
| mod_peptide | peptide modification (optional for peptide level FDR control evaluation) |
| charge | precursor charge (optional for peptide level FDR control evaluation) |
| q_value | FDR reported by the search engine used to generate the result |
| score | precursor or peptide score used to rank precursor or peptide for FDR calculation |

Below is an example command line to run precursor FDP calculation:

```shell
java -jar fdrbench-1.0.0.jar  -i peptide-fdp_precursor_input.tsv -fold 1 -pep UP000005640_9606_entrapment_pep.txt -level precursor -o peptide-diann_fdp_precursor.csv -score 'score:0'

```

The output file "peptide-diann_fdp_precursor.csv" contains estimated FDP values using different methods. The file looks like below:

```
run                                                peptide                             mod_peptide                                     charge  q_value  PEP  protein                                    score  combined_fdp  n_t  n_p  paired_fdp  n_p_t_s  n_p_s_t  vt  lower_bound_fdp
20230406_OLEP08_MMCC_1ug_MB_24min_AS_10ms_4Th_I_1  AAAPAPEEEMDECEQALAAEPK              AAAPAPEEEMDEC(UniMod:4)EQALAAEPK                3       1.0E-9   0.0  AAAPAPEEEMDECEQALAAEPK_target              1      0.0           393  0    0.0         0        0        0   0.0
20230406_OLEP08_MMCC_1ug_MB_24min_AS_10ms_4Th_I_1  AAASGQPRPEMQCPAEHEEDMYR             AAASGQPRPEMQC(UniMod:4)PAEHEEDMYR               4       1.0E-9   0.0  AAASGQPRPEMQCPAEHEEDMYR_target             2      0.0           393  0    0.0         0        0        0   0.0
20230406_OLEP08_MMCC_1ug_MB_24min_AS_10ms_4Th_I_1  AAEVWMDEYKNFYYAAVPSAR               AAEVWMDEYKNFYYAAVPSAR                           3       1.0E-9   0.0  AAEVWMDEYKNFYYAAVPSAR_target               3      0.0           393  0    0.0         0        0        0   0.0
20230406_OLEP08_MMCC_1ug_MB_24min_AS_10ms_4Th_I_1  AAFTVSLDPGPLEQFPHSMEPQLR            AAFTVSLDPGPLEQFPHSMEPQLR                        3       1.0E-9   0.0  AAFTVSLDPGPLEQFPHSMEPQLR_target            4      0.0           393  0    0.0         0        0        0   0.0
20230406_OLEP08_MMCC_1ug_MB_24min_AS_10ms_4Th_I_1  AAHVLMPHESTVEHTHVDLNEMESPLATR       AAHVLMPHESTVEHTHVDLNEMESPLATR                   4       1.0E-9   0.0  AAHVLMPHESTVEHTHVDLNEMESPLATR_target       5      0.0           393  0    0.0         0        0        0   0.0
20230406_OLEP08_MMCC_1ug_MB_24min_AS_10ms_4Th_I_1  AALLSPGDPALWAGLMAACHADDKLALVNNTQPK  AALLSPGDPALWAGLMAAC(UniMod:4)HADDKLALVNNTQPK    4       1.0E-9   0.0  AALLSPGDPALWAGLMAACHADDKLALVNNTQPK_target  6      0.0           393  0    0.0         0        0        0   0.0
20230406_OLEP08_MMCC_1ug_MB_24min_AS_10ms_4Th_I_1  AALQQKENLPVSSDGNLPQQAASAPSR         AALQQKENLPVSSDGNLPQQAASAPSR                     3       1.0E-9   0.0  AALQQKENLPVSSDGNLPQQAASAPSR_target         7      0.0           393  0    0.0         0        0        0   0.0
20230406_OLEP08_MMCC_1ug_MB_24min_AS_10ms_4Th_I_1  ADMLCNSQSNDLLQHQGSNCGGTSNK          ADMLC(UniMod:4)NSQSNDLLQHQGSNC(UniMod:4)GGTSNK  3       1.0E-9   0.0  ADMLCNSQSNDLLQHQGSNCGGTSNK_target          8      0.0           393  0    0.0         0        0        0   0.0
20230406_OLEP08_MMCC_1ug_MB_24min_AS_10ms_4Th_I_1  AFLADPSAFVAAAPVAAATTAAPAAAAAPAK     AFLADPSAFVAAAPVAAATTAAPAAAAAPAK                 3       1.0E-9   0.0  AFLADPSAFVAAAPVAAATTAAPAAAAAPAK_target     9      0.0           393  0    0.0         0        0        0   0.0
```

Below please find the description of the main columns in the output tsv file "peptide-diann_fdp_precursor.csv":

| Column name  | Description |
| ------------ | ----------- |
| peptide | peptide sequence |
| mod_peptide | peptide modification |
| charge | precursor charge |
| q_value | FDR reported by the search engine used to generate the result |
| score | precursor score used to rank precursor for FDR calculation |
| combined_fdp | the FDP estimated using the combined method |
| paired_fdp | the FDP estimated using the paired method |
| lower_bound_fdp | the lower bound FDP |

The above example command line took about 30 seconds to run on a Mac MacBook computer.

##### Protein level FDR control evaluation

For **protein level** FDP calculation, an example input is shown below. Only the three columns **protein**, **q_value** and **score** are required. Decoy hits shouldn't be included in the input file for FDP calcualtion.

```
Protein.Group  PG.Q.Value  q_value     protein  score
P62857         1.64447e-4  1.64447e-4  P62857   1
P29590         1.64447e-4  1.64447e-4  P29590   2
O95989         1.64447e-4  1.64447e-4  O95989   3
P61006         1.64447e-4  1.64447e-4  P61006   4
Q70J99         1.64447e-4  1.64447e-4  Q70J99   5
P36404         1.64447e-4  1.64447e-4  P36404   6
Q71SY5         1.64447e-4  1.64447e-4  Q71SY5   7
Q9BPW8         1.64447e-4  1.64447e-4  Q9BPW8   8
Q8WUP2         1.64447e-4  1.64447e-4  Q8WUP2   9
```

Below is an example command line to run protein FDP calculation. The database used to generate the identification result must be generated using FDRBench. The command line parameter **-score** is set to use the column "score" as a secondary ranking score in FDP calculation. The number **0** indicates lower score is better (more confident). The example file **protein-fdp_protein_input.tsv** is available in the [example](https://github.com/Noble-Lab/FDRBench/tree/master/example) folder.

```shell
java -jar fdrbench-1.0.0.jar -i example/protein-fdp_protein_input.tsv -level protein -o protein-diann_fdp_protein.csv -score 'score:0' -fold 1 -pick first
```

The output file "protein-diann_fdp_protein.csv" contains estimated FDP values using different methods. The file looks like below:

```
Protein.Group  PG.Q.Value  q_value     protein  score  combined_fdp  n_t   n_p  paired_fdp  n_p_t_s  n_p_s_t  vt  lower_bound_fdp
P62857         1.64447E-4  1.64447E-4  P62857   1      0.0           6079  0    0.0         0        0        0   0.0
P29590         1.64447E-4  1.64447E-4  P29590   2      0.0           6079  0    0.0         0        0        0   0.0
O95989         1.64447E-4  1.64447E-4  O95989   3      0.0           6079  0    0.0         0        0        0   0.0
P61006         1.64447E-4  1.64447E-4  P61006   4      0.0           6079  0    0.0         0        0        0   0.0
Q70J99         1.64447E-4  1.64447E-4  Q70J99   5      0.0           6079  0    0.0         0        0        0   0.0
```

Below please find the description of the main columns in the output tsv file "protein-diann_fdp_protein.csv":

| Column name  | Description |
| ------------ | ----------- |
| protein | protein ID (multiple IDs are separated by ";"). Each row is a protein group |
| q_value | FDR reported by the search engine used to generate the result  |
| score | protein score used to rank protein for FDR calculation |
| combined_fdp | the FDP estimated using the combined method |
| paired_fdp | the FDP estimated using the paired method |
| lower_bound_fdp | the lower bound FDP |

The above example command line took about 5 seconds to run on a Mac MacBook computer.

#### Visualization of FDR control evaluation result

<details>
<summary>R function "plot_fdp_fdr" used to generate an FDP vs FDR plot. The result can also be visualized using the GUI introduced above.</summary>
 
```R
library(tidyverse)
library(ggpubr)

plot_fdp_fdr=function(fdp_file="", # the FDP (in csv format) estimation file generated by FDRBench
 fdr_max=NULL,
 fig_title=NULL,
 scale_xy=TRUE,
 add_numbers=FALSE,
 numbers_position=NULL,
 numbers_font_size=11,
 r=1,
 fixed_fdr_max=FALSE,
 max_x=NA,
 max_y=NA,
 color_mapping=NULL,
 legend_position=c(0.7, 0.16),
 legend_font_size=11,
 fdr_decimal_place=2,
 return_data=FALSE,
 add_max_qvalue=FALSE) {
 if(is.null(color_mapping)){
 color_mapping <- c("Paired method" = "#7CAE00", "Sample method" = "#C77CFF", "Lower bound" = "#00BFC4", "Combined method" = "#F8766D")
 }
 x <- read_csv(fdp_file)
 if("paired_fdp" %in% names(x)){
 if(r>=2){
 dat <- x %>% select(q_value,combined_fdp,paired_fdp,lower_bound_fdp) %>% distinct() %>% 
 rename(`Combined method`=combined_fdp,`Matched method`=paired_fdp,`Lower bound`=lower_bound_fdp) %>%
 gather(key = "Method",value = "FDP",-`q_value`) %>% select(q_value,FDP,Method)
 dat$Method <- factor(dat$Method, levels = c("Combined method","Matched method","Lower bound"))
 }else{
 dat <- x %>% select(q_value,combined_fdp,paired_fdp,lower_bound_fdp) %>% distinct() %>% 
 rename(`Combined method`=combined_fdp,`Paired method`=paired_fdp,`Lower bound`=lower_bound_fdp) %>%
 gather(key = "Method",value = "FDP",-`q_value`) %>% select(q_value,FDP,Method)
 dat$Method <- factor(dat$Method, levels = c("Combined method","Paired method","Lower bound"))
 }
 }else{
 dat <- x %>% select(q_value,combined_fdp,lower_bound_fdp) %>% distinct() %>% 
 rename(`Combined method`=combined_fdp,`Lower bound`=lower_bound_fdp) %>%
 gather(key = "Method",value = "FDP",-`q_value`) %>% select(q_value,FDP,Method)
 dat$Method <- factor(dat$Method, levels = c("Combined method","Lower bound"))
 }
 
 
 max_fdp <- max(c(dat$FDP,dat$q_value))
 if(!is.null(fdr_max)){
 if(fixed_fdr_max){
 max_fdp <- fdr_max
 }else{
 max_fdp <- min(c(fdr_max,max_fdp))
 }
 }
 
 gg1 <- ggplot(dat,aes(x=q_value,y=FDP,color=Method)) + 
 geom_abline(slope = 1,intercept = 0,color="gray")+
 geom_line()+
 xlab("FDR threshold")+
 ylab("Estimated FDP")+
 theme_bw()+
 theme_pubr(base_size = 12,border = TRUE)
 
 if(!is.null(color_mapping)){
 gg1 <- gg1 + scale_color_manual(values = color_mapping)
 }
 
 if(scale_xy){
 
 if(!is.na(max_x) || !is.na(max_y)){
 gg1 <- gg1 + geom_vline(xintercept = 0.01,linetype=2,color="blue")
 if(!is.na(max_x)){
 gg1 <- gg1 + xlim(0,max_x) + scale_x_continuous(labels = scales::percent,limits =c(0,max_x))
 }else{
 gg1 <- gg1 + xlim(0,max_fdp)+ scale_x_continuous(labels = scales::percent,limits =c(0,max_fdp))
 }
 if(!is.na(max_y)){
 gg1 <- gg1 + ylim(0,max_y) + scale_y_continuous(labels = scales::percent,limits =c(0,max_y))
 }else{
 gg1 <- gg1 + ylim(0,max_fdp) + scale_y_continuous(labels = scales::percent,limits =c(0,max_fdp))
 }
 }else{
 gg1 <- gg1 + geom_vline(xintercept = 0.01,linetype=2,color="blue")+
 xlim(0,max_fdp)+
 ylim(0,max_fdp)+
 scale_y_continuous(labels = scales::percent,limits =c(0,max_fdp))+
 scale_x_continuous(labels = scales::percent,limits =c(0,max_fdp))
 }
 }
 
 gg1 <- gg1 + theme(legend.position="inside",
 legend.position.inside = legend_position,
 legend.background = element_blank(),
 legend.text=element_text(size=legend_font_size),
 legend.title=element_text(size=legend_font_size),
 plot.margin = unit(2*c(0.1, 0.1, 0.1, 0.1),"inches"),
 axis.text.y = element_text(angle = 90, hjust = 0.5)) 
 
 
 if(!is.null(fig_title)){
 gg1 <- gg1 + ggtitle(fig_title)
 }
 
 added_numbers <- NULL
 if(add_numbers){
 if(fdr_decimal_place==1){
 y <- dat %>% filter(q_value<=0.01) %>% group_by(Method) %>% arrange(desc(q_value)) %>% filter(row_number()==1) %>% summarise(FDP001=max(FDP)) %>% mutate(ratio=sprintf("%.1f%%",FDP001*100))
 }else if(fdr_decimal_place==2){
 y <- dat %>% filter(q_value<=0.01) %>% group_by(Method) %>% arrange(desc(q_value)) %>% filter(row_number()==1) %>% summarise(FDP001=max(FDP)) %>% mutate(ratio=sprintf("%.2f%%",FDP001*100))
 }else{
 y <- dat %>% filter(q_value<=0.01) %>% group_by(Method) %>% arrange(desc(q_value)) %>% filter(row_number()==1) %>% summarise(FDP001=max(FDP)) %>% mutate(ratio=sprintf("%.4f%%",FDP001*100))
 }
 if(abs(max_fdp-0.01)<=0.02){
 added_numbers <- paste("Total discoveries:",nrow(x %>% filter(q_value<=0.01)),"\n",paste(y$Method,y$ratio,sep=":",collapse = "\n"),sep="")
 if(add_max_qvalue){
 added_numbers <- paste(added_numbers,"\n","Max q-value:",sprintf("%.2e",max(x$q_value)),sep="")
 }
 if(is.null(numbers_position)){
 gg1 <- gg1 + annotate("text", x = max_fdp*0.1, y = 0.9*max_fdp, label = added_numbers, color = "black", size = numbers_font_size/.pt,hjust = 0)
 }else{
 gg1 <- gg1 + annotate("text", x = numbers_position[1], y = numbers_position[2], label = added_numbers, color = "black", size = numbers_font_size/.pt,hjust = 0)
 }
 
 }else{
 added_numbers <- paste("Total discoveries:",nrow(x %>% filter(q_value<=0.01)),"\n",paste(y$Method,y$ratio,sep=":",collapse = "\n"),sep="")
 if(add_max_qvalue){
 added_numbers <- paste(added_numbers,"\n","Max q-value:",sprintf("%.2e",max(x$q_value)),sep="")
 }
 if(is.null(numbers_position)){
 gg1 <- gg1 + annotate("text", x = 0.01*1.05, y = 0.9*max_fdp, label = added_numbers, color = "black", size = numbers_font_size/.pt,hjust = 0)
 }else{
 gg1 <- gg1 + annotate("text", x = numbers_position[1], y = numbers_position[2], label = added_numbers, color = "black", size = numbers_font_size/.pt,hjust = 0)
 }
 }
 }

if(return_data){
 return(list(gg=gg1,data=dat,added_numbers=added_numbers))
 }else{
 return(gg1)
 }
 
}
```
</details>

The following R code can be used to generate an FDR vesus FDP plot as shown below:

```R
## load the above R function first before running the following R code.
fdp_plot <- plot_fdp_fdr("tide-peptide-percolator_fdp_peptide.csv",fdr_max = NULL,add_numbers = TRUE, fixed_fdr_max=TRUE)
pdf("fdr-fdp.pdf",width = 4.5,height = 4.5)
print(fdp_plot)
dev.off()
```

The dashed vertical line is at the 1% FDR threshold, as are the numbers reported in text in the figure.

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