Error when using a peptide level entrapment database for peptide-level FDP estimation

[TMin-relax]

Dear FDRBench maintainers,

I'm trying to perform precursor-level entrapment analysis for a search.

1. I generated a peptide level entrapment database using the human target database UP000005640_9606.fasta by taking peptides from Arabidopsis thaliana and Saccharomyces cerevisiae as entrapments.
   Here is the command I used:
   java -jar fdrbench-0.0.4.jar java -db UP000005640_9606.fasta -o UP000005640_9606_I2L_foreign_species_entrapment_peptide.txt -I2L -diann -uniprot -ms uniprotkb_Saccharomyces_cerevisiae_AND_2025_07_15.fasta,uniprotkb_Arabidopsis_AND_model_organis_2025_07_15.fasta -enzyme 2 -miss_c 1 -minLength 7 -maxLength 35 -level peptide -fold 1

2. After obtaining the identifications with DIA-NN (version 2.1.0), I proceeded with peptide-level results and prepared two input tables (https://github.com/TMin-relax/SupportingInformation.git).

3. Here is the command I used: java -jar fdrbench-0.0.4.jar -i pep_fdp.tsv -fold 1 -pep UP000005640_9606_I2L_foreign_species_entrapment_peptide.txt -level precursor -o peptide-openspec_fdp_precursor.csv -score score:0.

However, I encountered the following error:

WARNING: A terminally deprecated method in sun.misc.Unsafe has been called
WARNING: sun.misc.Unsafe::invokeCleaner has been called by nonapi.io.github.classgraph.utils.FileUtils (file:/D:/TMin/ArticalResponse/FDR/fdrbench-0.0.4/lib/classgraph-4.8.60.jar)
WARNING: Please consider reporting this to the maintainers of class nonapi.io.github.classgraph.utils.FileUtils
WARNING: sun.misc.Unsafe::invokeCleaner will be removed in a future release
Use both q_value and score for ranking
of peptides: 155268
Exception in thread "main" java.lang.NullPointerException: Cannot invoke "java.util.ArrayList.get(int)" because the return value of "java.util.HashMap.get(Object)" is null
at main.java.FDR.PeptidePair.get_paired_target_peptide(PeptidePair.java:151)
at main.java.FDR.FDREval.calc_fdp_fast_kfold(FDREval.java:2892)
at main.java.FDR.FDREval.main(FDREval.java:525)

Could you help check what might be causing this issue? Please let me know if I can provide more details.
Thanks!

[wenbostar]

Since you're using foreign species as the source of entrapments, only the combined and lower bound methods are available for FDP estimation in FDRBench. In this case, you could use the following command line for FDP calculation:

java -jar fdrbench-0.0.4.jar -i pep_fdp.tsv -r 1 -pep UP000005640_9606_I2L_foreign_species_entrapment_peptide.txt -level precursor -o peptide-openspec_fdp_precursor.csv -score score:0.

[TMin-relax]

I tried using this command line, but it still reported an error:

FDP level: precursor
Use combined entrapment method ...
WARNING: A terminally deprecated method in sun.misc.Unsafe has been called
WARNING: sun.misc.Unsafe::invokeCleaner has been called by nonapi.io.github.classgraph.utils.FileUtils (file:/D:/TMin/ArticalResponse/FDR/fdrbench-0.0.4/lib/classgraph-4.8.60.jar)
WARNING: Please consider reporting this to the maintainers of class nonapi.io.github.classgraph.utils.FileUtils
WARNING: sun.misc.Unsafe::invokeCleaner will be removed in a future release
Use both q_value and score for ranking
of peptides: 158137
Peptide file:UP000005640_9606_I2L_foreign_species_entrapment_peptide.txt
Exception in thread "main" java.lang.NullPointerException: Cannot invoke "String.equals(Object)" because the return value of "java.util.HashMap.get(Object)" is null
at main.java.FDR.FDREval.calc_fdp_fast_combined_entrapment_method(FDREval.java:3142)
at main.java.FDR.FDREval.main(FDREval.java:516)

[wenbostar]

Could you share the input files with me?

[TMin-relax]

I always fail to upload file here, so I uploaded it to my own GitHub. The URL is
https://github.com/TMin-relax/SupportingInformation.git.

[wenbostar]

The file in the git repo is empty.

[TMin-relax]

Could you try this link?
https://github.com/TMin-relax/SupportingInformation/releases/tag/SupportingFile

[wenbostar]

This link works. I looked at the files and it looks like there are many peptides present in the "pep_fdp.tsv" file but not present in the peptide mapping file "uniprot-human_20422_foreign_species_entrapment_peptide.txt". For example:

"AGPESDAQYQFTGLKK"  "PSQMEHAMETMMFTFHK" "PTTQQSPQDEQEK"    
"PSETPQAEVGPTGCPHR" "PNLVLFSGSSHQDLSQR" "PVAVGPYGQSQPSCFDR"

[wenbostar]

Could you please double check if the database you used is matched with "uniprot-human_20422_foreign_species_entrapment_peptide.txt"? If it matches, could you share your DIA-NN search command line here?

[TMin-relax]

I sincerely apologize for the error in the previously uploaded file. I have re-uploaded the input file.
https://github.com/TMin-relax/SupportingInformation/releases/tag/SupportingFile

I checked the DIA-NN parameters, and the database is correctly matched, but the same error still occurs after re-running the analysis.

DIA-NN 2.1.0 Academia  (Data-Independent Acquisition by Neural Networks)
Compiled on Mar 25 2025 10:59:54
Current date and time: Thu Jul 24 16:17:39 2025
CPU: GenuineIntel Intel(R) Xeon(R) Silver 4216 CPU @ 2.10GHz
SIMD instructions: AVX AVX2 AVX512CD AVX512F FMA SSE4.1 SSE4.2 
Logical CPU cores: 32
diann.exe --f D:\TMin\fdrbench-0.0.4\1000ngHeK-30min_2Th_01.mzML  --lib  --threads 32 --verbose 1 --out D:\TMin\fdrbench-0.0.4\report.parquet --qvalue 0.1 --matrices --out-lib D:\TMin\fdrbench-0.0.4\library.parquet --gen-spec-lib --predictor --reannotate --fasta D:\TMin\fdrbench-0.0.4\UP000005640_9606_I2L_foreign_species_entrapment_peptide.fasta --fasta-search --min-fr-mz 200 --max-fr-mz 1800 --met-excision --min-pep-len 7 --max-pep-len 35 --min-pr-mz 300 --max-pr-mz 1800 --min-pr-charge 2 --max-pr-charge 4 --cut K*,R*,!*P --missed-cleavages 1 --unimod4 --var-mods 1 --peptidoforms --rt-profiling 

Thread number set to 32
Output will be filtered at 0.1 FDR
Precursor/protein x samples expression level matrices will be saved along with the main report
A spectral library will be generated
Deep learning will be used to generate a new in silico spectral library from peptides provided
Library precursors will be reannotated using the FASTA database
DIA-NN will carry out FASTA digest for in silico lib generation
Min fragment m/z set to 200
Max fragment m/z set to 1800
N-terminal methionine excision enabled
Min peptide length set to 7
Max peptide length set to 35
Min precursor m/z set to 300
Max precursor m/z set to 1800
Min precursor charge set to 2
Max precursor charge set to 4
In silico digest will involve cuts at K*,R*
But excluding cuts at *P
Maximum number of missed cleavages set to 1
Cysteine carbamidomethylation enabled as a fixed modification
Maximum number of variable modifications set to 1
Peptidoform scoring enabled
The spectral library (if generated) will retain the original spectra but will include empirically-aligned RTs
DIA-NN will automatically optimise the mass accuracy for the first run of the experiment, use this mode for preliminary analyses only
WARNING: incorrect settings, the in silico-predicted library must be generated in a separate pipeline step and then used to process the raw data, now without activating FASTA digest
WARNING: protein inference will only be performed for precursors identified with global q-value <= 0.05 or q-value <= 0.05.
WARNING: it is strongly recommended to set the q-value threshold to 5% or lower when generating an empirical library.

1 files will be processed
[0:00] Loading FASTA D:\TMin\fdrbench-0.0.4\UP000005640_9606_I2L_foreign_species_entrapment_peptide.fasta
[1:11] Processing FASTA
[1:17] Assembling elution groups
[2:43] 8187216 precursors generated
[2:47] Gene names missing for some isoforms
[2:47] Library contains 2781107 proteins, and 0 genes
[2:58] [3:23] [34:36] [37:46] [37:57] [37:58] Saving the library to D:\TMin\fdrbench-0.0.4\library.predicted.speclib
[38:21] Initialising library
[39:10] Loading spectral library D:\TMin\fdrbench-0.0.4\library.predicted.speclib
[39:25] Library annotated with sequence database(s): D:\TMin\fdrbench-0.0.4\UP000005640_9606_I2L_foreign_species_entrapment_peptide.fasta
[39:27] Spectral library loaded: 2781107 protein isoforms, 2890676 protein groups and 8187216 precursors in 3174529 elution groups.
[39:27] Loading protein annotations from FASTA D:\TMin\fdrbench-0.0.4\UP000005640_9606_I2L_foreign_species_entrapment_peptide.fasta
[40:20] Annotating library proteins with information from the FASTA database
[40:23] Gene names missing for some isoforms
[40:23] Library contains 2781107 proteins, and 0 genes
[40:34] Initialising library

[41:18] File #1/1
[41:18] Loading run D:\TMin\fdrbench-0.0.4\1000ngHeK-30min_2Th_01.mzML
[48:16] Pre-processing...
[48:47] 4355 MS1 and 472447 MS2 scans in 1452 (inferred) and 1452 (encoded) cycles, 5665239 precursors in range
[48:55] Calibrating with mass accuracies 25 (MS1), 25 (MS2)
[50:07] RT window set to 2.47361
[50:07] Peak width: 3.768
[50:07] Scan window radius set to 8
[50:07] Recommended MS1 mass accuracy setting: 9 ppm
[53:03] Optimised mass accuracy: 14 ppm
[54:10] Searching decoys
[63:15] Main search
[80:56] Removing low confidence identifications
[81:58] Removing interfering precursors
[82:35] Training neural networks on 275579 target and 190152 decoy PSMs
[86:45] Training neural networks on 275579 target and 186550 decoy PSMs
[90:36] Number of IDs at 0.01 FDR: 156948
[90:38] Precursors at 1% peptidoform FDR: 151458
[90:40] Calculating protein q-values
[90:41] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[90:41] Quantification
[90:54] Quantification information saved to D:\TMin\fdrbench-0.0.4\1000ngHeK-30min_2Th_01.mzML.quant

[90:54] Cross-run analysis
[90:54] Reading quantification information: 1 files
[91:26] Quantifying peptides
[91:27] Assembling protein groups
[91:41] Quantifying proteins
[91:42] Calculating q-values for protein and gene groups
[91:43] Calculating global q-values for protein and gene groups
[91:43] Protein groups with global q-value <= 0.01: 103986
[91:46] Compressed report saved to D:\TMin\fdrbench-0.0.4\report.parquet. Use R 'arrow' or Python 'PyArrow' package to process
[91:46] Saving precursor levels matrix
[91:46] Precursor levels matrix (1% precursor and protein group FDR) saved to D:\TMin\fdrbench-0.0.4\report.pr_matrix.tsv.
[91:46] Saving protein group levels matrix
[91:47] Protein groups matrix saved to D:\TMin\fdrbench-0.0.4\report.pg_matrix.tsv.
[91:47] Saving gene group levels matrix
[91:47] Gene groups matrix saved to D:\TMin\fdrbench-0.0.4\report.gg_matrix.tsv.
[91:47] Saving unique genes levels matrix
[91:47] Unique genes matrix saved to D:\TMin\fdrbench-0.0.4\report.unique_genes_matrix.tsv.
[91:49] Manifest saved to D:\TMin\fdrbench-0.0.4\report.manifest.txt
[91:49] Stats report saved to D:\TMin\fdrbench-0.0.4\report.stats.tsv
[91:49] Generating spectral library:
[91:57] 188877 target and 18992 decoy precursors saved
[91:57] Spectral library saved to D:\TMin\fdrbench-0.0.4\library.parquet 

[wenbostar]

Your input fasta file is a peptide level fasta file, so you don't need to do trypsin digestion again in DIA-NN search. You need to add "--cut" using the way as shown below if you use its GUI interface to run the analysis:

or change "--cut K*,R*,!*P" to "--cut " if you run the analysis through its command line interface.

[wenbostar]

You also need to remove --met-excision setting: