Workflow Presets in pepVet • pepVet

Why presets exist

Most digest-scoring tools expose a fixed set of assumptions. Real experiments do not. A targeted assay, a membrane-protein workflow, and an FFPE rescue experiment do not value the same peptide properties to the same degree.

pepvet_preset() packages three related decisions into one object.

the valid peptide-length window
the acceptable GRAVY window
the component weight vector

The returned object keeps the scoring settings explicit and editable.

score_peptides() records the resolved workflow label in preset_used. When a call exactly matches one of pepVet’s shipped presets, the function reports that preset name. Otherwise it reports "custom".

Available presets

pepvet_preset("standard")
#> $gravy_range
#> [1] -1.0  0.6
#> 
#> $length_range
#> [1]  7 25
#> 
#> $weights
#>   S_length S_coverage    S_count    S_hydro   S_charge   S_unique 
#>      0.200      0.348      0.226      0.138      0.088      0.000 
#> 
#> $include_pI
#> [1] FALSE

Preset	Intended use	Key change
`standard`	Routine DDA	Default expert-prior weights
`dia`	DIA and SWATH	`[7,30]` aa, GRAVY `[-1,0.8]`, high coverage weight
`targeted`	SRM, PRM, MRM	`[8,20]` aa, GRAVY `[-0.8,0.4]`, `S_unique` 30%
`membrane`	Hydrophobic proteins	Length `[7,30]`, GRAVY `[-1.0,2.0]`, `S_hydro` 5%
`ffpe_degraded`	Degraded material	`[6,30]` aa, higher `S_count` weight
`fractionated`	Offline fractionation	Standard settings with pI annotation

Standard workflow

The standard preset contains the package defaults for routine peptide-centric proteomics.

standard <- pepvet_preset("standard")

do.call(
  evaluate_digest,
  c(list(sequence = bsa_path, enzyme = "trypsin", missed_cleavages = 1L), standard)
)$scores
#> # A tibble: 1 × 12
#>   protein_id        S_length S_coverage S_count S_hydro S_charge composite_score
#>   <chr>                <dbl>      <dbl>   <dbl>   <dbl>    <dbl>           <dbl>
#> 1 sp|P02769|ALBU_B…    0.688      0.997       1   0.769    0.778           0.885
#> # ℹ 5 more variables: verdict <chr>, median_peptide_length <dbl>,
#> #   preset_used <chr>, n_high_efficiency_sites <int>,
#> #   n_low_efficiency_sites <int>

DIA workflow

The dia preset assigns more weight to coverage, widens the valid length window to 30 aa, and extends the GRAVY window to $-1.0, 0.8$ . These values encode a broader inclusion prior for DIA workflows. They are not calibrated detection boundaries.

syn_proteome <- digest_protein(syn_path, enzyme = "trypsin")
dia <- pepvet_preset("dia")

do.call(
  evaluate_digest,
  c(list(sequence = syn_path, enzyme = "trypsin", proteome = syn_proteome), dia)
)$scores
#> # A tibble: 3 × 13
#>   protein_id               S_length S_coverage S_count S_hydro S_charge S_unique
#>   <chr>                       <dbl>      <dbl>   <dbl>   <dbl>    <dbl>    <dbl>
#> 1 sp|P37840|SYUA_HUMAN Al…    0.548      0.693       1   1        0.706    0    
#> 2 sp|P37840-2|SYUA_HUMAN …    0.613      1           1   0.895    0.684    0.105
#> 3 sp|P37840-3|SYUA_HUMAN …    0.519      0.659       1   1        0.643    0.214
#> # ℹ 6 more variables: composite_score <dbl>, verdict <chr>,
#> #   median_peptide_length <dbl>, preset_used <chr>,
#> #   n_high_efficiency_sites <int>, n_low_efficiency_sites <int>

Targeted workflow

The targeted preset assigns more weight to S_unique and requires a relevant comparison proteome. It uses a narrower length and GRAVY window than the standard preset.

proteome <- digest_protein(syn_path, enzyme = "trypsin")
targeted <- pepvet_preset("targeted")

do.call(
  evaluate_digest,
  c(list(sequence = syn_path, enzyme = "trypsin", proteome = proteome), targeted)
)$scores
#> # A tibble: 3 × 13
#>   protein_id               S_length S_coverage S_count S_hydro S_charge S_unique
#>   <chr>                       <dbl>      <dbl>   <dbl>   <dbl>    <dbl>    <dbl>
#> 1 sp|P37840|SYUA_HUMAN Al…    0.516      0.693       1   0.812    0.688    0    
#> 2 sp|P37840-2|SYUA_HUMAN …    0.581      1           1   0.722    0.667    0.111
#> 3 sp|P37840-3|SYUA_HUMAN …    0.481      0.659       1   0.692    0.615    0.231
#> # ℹ 6 more variables: composite_score <dbl>, verdict <chr>,
#> #   median_peptide_length <dbl>, preset_used <chr>,
#> #   n_high_efficiency_sites <int>, n_low_efficiency_sites <int>

Without a proteome digest, pepVet cannot calculate a non-zero uniqueness component and raises an error.

Membrane workflow

The membrane preset extends the GRAVY window to $-1.0, 2.0$ , extends the length window to $7, 30$ , and lowers the S_hydro weight to 5%. These settings reduce the score penalty for hydrophobic peptides. They remain workflow priors and do not predict peptide observation.

pepvet_preset("membrane")
#> $gravy_range
#> [1] -1  2
#> 
#> $length_range
#> [1]  7 30
#> 
#> $weights
#>   S_length S_coverage    S_count    S_hydro   S_charge   S_unique 
#>       0.25       0.25       0.20       0.05       0.15       0.10 
#> 
#> $include_pI
#> [1] FALSE

The preset changes how the score treats hydrophobic peptides. It does not predict whether the digest will succeed experimentally.

Degraded samples

The ffpe_degraded preset lowers the minimum length to 6 aa and increases the S_count weight. This configuration includes more short products and gives peptide count more influence on the composite score. It does not model formalin chemistry or degradation directly.

pepvet_preset("ffpe_degraded")
#> $gravy_range
#> [1] -1.0  0.8
#> 
#> $length_range
#> [1]  6 30
#> 
#> $weights
#>   S_length S_coverage    S_count    S_hydro   S_charge   S_unique 
#>        0.2        0.2        0.3        0.1        0.1        0.1 
#> 
#> $include_pI
#> [1] FALSE

Use this preset only when the sample context justifies it. A wider window changes the meaning of S_length, S_coverage, and S_count.

Fractionated workflows

The fractionated preset keeps the standard score settings and requests pI annotation for planning offline fractionation.

Editing a preset

Presets are plain R lists. Copy one and change the fields required by the workflow.

custom_fractionated <- pepvet_preset("fractionated")
custom_fractionated$weights["S_coverage"] <- 0.30
custom_fractionated$weights["S_hydro"] <- 0.10
custom_fractionated$weights <-
  custom_fractionated$weights / sum(custom_fractionated$weights)

do.call(
  evaluate_digest,
  c(list(sequence = bsa_path, enzyme = "chymotrypsin-high"), custom_fractionated)
)$scores
#> # A tibble: 1 × 13
#>   protein_id        S_length S_coverage S_count S_hydro S_charge composite_score
#>   <chr>                <dbl>      <dbl>   <dbl>   <dbl>    <dbl>           <dbl>
#> 1 sp|P02769|ALBU_B…    0.477      0.590   0.840   0.549    0.863           0.649
#> # ℹ 6 more variables: verdict <chr>, median_peptide_length <dbl>,
#> #   preset_used <chr>, n_high_efficiency_sites <int>,
#> #   n_low_efficiency_sites <int>, pI <list>

Presets are data, not hidden state. You can inspect, store, version, and modify them without changing package internals.

Interpreting presets

The preset name records the assumptions used for a score. It does not make scores from different configurations directly comparable.

When presets help

Presets provide a recorded starting configuration when:

You need a starting point for a new assay.
You want comparisons to stay consistent across a panel of proteins.
You need to explain why a score changed when the workflow changed.

If a membrane target receives a higher score only after widening the GRAVY window, the recorded configuration shows which assumption changed.

Cross-preset interpretation

Do not compare composite scores across different presets as if they were on one universal scale. A standard score and a targeted score can differ because the weight vector, peptide-length window, and GRAVY window changed, not because the protein became intrinsically better or worse.

Use preset_used when saving or comparing results. Compare scores only when the resolved scoring configuration matches.

Presets are starting points, not experimentally fitted parameter sets. Modify them when the workflow requires different assumptions.

Session info

sessionInfo()
#> R version 4.6.1 (2026-06-24)
#> Platform: x86_64-pc-linux-gnu
#> Running under: Ubuntu 24.04.4 LTS
#> 
#> Matrix products: default
#> BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 
#> LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.26.so;  LAPACK version 3.12.0
#> 
#> locale:
#>  [1] LC_CTYPE=C.UTF-8       LC_NUMERIC=C           LC_TIME=C.UTF-8       
#>  [4] LC_COLLATE=C.UTF-8     LC_MONETARY=C.UTF-8    LC_MESSAGES=C.UTF-8   
#>  [7] LC_PAPER=C.UTF-8       LC_NAME=C              LC_ADDRESS=C          
#> [10] LC_TELEPHONE=C         LC_MEASUREMENT=C.UTF-8 LC_IDENTIFICATION=C   
#> 
#> time zone: UTC
#> tzcode source: system (glibc)
#> 
#> attached base packages:
#> [1] stats     graphics  grDevices utils     datasets  methods   base     
#> 
#> other attached packages:
#> [1] pepVet_0.99.1
#> 
#> loaded via a namespace (and not attached):
#>  [1] vctrs_0.7.3          crayon_1.5.3         cli_3.6.6           
#>  [4] knitr_1.51           rlang_1.3.0          xfun_0.60           
#>  [7] otel_0.2.0           generics_0.1.4       textshaping_1.0.5   
#> [10] jsonlite_2.0.0       glue_1.8.1           S4Vectors_0.51.5    
#> [13] Biostrings_2.81.5    htmltools_0.5.9      stats4_4.6.1        
#> [16] ragg_1.5.2           sass_0.4.10          cleaver_1.51.0      
#> [19] rmarkdown_2.31       Seqinfo_1.3.0        tibble_3.3.1        
#> [22] evaluate_1.0.5       jquerylib_0.1.4      fastmap_1.2.0       
#> [25] IRanges_2.47.2       yaml_2.3.12          lifecycle_1.0.5     
#> [28] compiler_4.6.1       fs_2.1.0             pkgconfig_2.0.3     
#> [31] XVector_0.53.0       systemfonts_1.3.2    digest_0.6.39       
#> [34] R6_2.6.1             utf8_1.2.6           pillar_1.11.1       
#> [37] magrittr_2.0.5       bslib_0.11.0         tools_4.6.1         
#> [40] pkgdown_2.2.1        BiocGenerics_0.59.10 cachem_1.1.0        
#> [43] desc_1.4.3