Peptide Library Metadata

Overview

This vignette describes the metadata fields available for the peptide libraries used in phiper’s PhIP-seq analyses. The annotations described here apply to the Agilent, Twist, and Corona2 libraries where available, and can be retrieved for any phip_data object via phiperio’s get_peptide_library():

library(phiperio)
library(phiper)
library(dplyr)

peplib <- get_peptide_library() %>%
  collect()
#> [10:54:00] INFO  Retrieving peptide metadata into DuckDB cache
#>                  -> get_peptide_library(force_refresh = FALSE)
#> [10:54:00] INFO  Opened DuckDB connection
#>                    - cache dir:
#>                      /github/home/.cache/R/phiperio/peptide_meta/phip_cache.duckdb
#>                    - table: peptide_meta
#> [10:54:00] OK    Using cached peptide_meta (fast path)
#> [10:54:00] OK    Retrieving peptide metadata into DuckDB cache - done
#>                  -> elapsed: 0.039s

General library information

The following columns describe peptide-level and protein-level information.

Column	Description
`peptide_id`	Peptide alphanumeric ID. Format: `"${Library}_${PeptideNumber}"`, e.g. `"agilent_1"`, `"twist_2"`, `"corona2_3"`.
`aa_seq`	Amino acid sequence of the peptide.
`pos`	Start position of the peptide within the source protein.
`len_seq`	Length of the source protein from which the peptide is derived.
`Fullname`	Full name of the protein, derived from several databases.
`full_aa_seq`	Full amino acid sequence of the source protein. Optional column; not included in `phiper` because of large file size.

Taxonomic information

Taxonomic information was derived from NCBI where possible. For Agilent, UniRef source IDs were used; for Twist, Allergome database IDs were also used where applicable.

Column	Description
`Description`	Protein description, usually based on UniRef annotations. If UniRef information is missing, other columns are used for lookup.
`domain`	Taxonomic domain.
`kingdom`	Taxonomic kingdom.
`phylum`	Taxonomic phylum.
`class`	Taxonomic class.
`order`	Taxonomic order.
`family`	Taxonomic family.
`genus`	Taxonomic genus.
`species`	Taxonomic species.
`common`	Common organism name, based on NCBI standard guidelines.

Additional annotations

Original annotations from Vogl et al. 2021

Column	Description
`is_IEDB_or_cntrl`	Peptides from the Immune Epitope Database or control peptides.
`is_auto`	Autoantigens.
`is_infect`	Infectious disease antigens; subgroup of IEDB.
`is_EBV`	Epstein-Barr virus antigens.
`is_toxin`	Virulence Factor Database proteins.
`is_PNP`	Metagenomic antigens.
`is_EM`	Microbiota strains.
`is_MPA`	Microbiota genes.
`is_patho`	Gut pathogens.
`is_probio`	Probiotic strains.
`is_IgA`	IgA-covered strains.
`is_flagellum`	Flagellar proteins.
`signalp6_slow`	Signal peptides annotated with SignalP6, run in 2023.
`is_topgraph_new`	Membrane proteins annotated with the TopGraph tool, 2023.
`is_allergens`	Peptides derived from allergen databases.

Manual annotations from Sasha Zhernakova

The following 14 manually curated anno_* columns classify enriched peptides into broader biological groups: bacteria & archaea, viruses & bacteriophages, human proteins, food (with subcategory and item), fungi, eukaryotic pathogens, insects/arthropods, plants & grasses, animals, and other.

Column	Description
`anno_other`	Other manually curated category.
`anno_eucaryotic_pathogens`	Eukaryotic pathogens.
`anno_animals`	Animal-derived proteins or antigens.
`anno_fly_worm_bee_cocroach_mite_mosquito`	Insects, worms, mites, mosquitoes, cockroaches, bees, or flies.
`anno_plant_grass`	Plant or grass-derived annotations.
`anno_is_fungi`	Fungal annotations.
`anno_fungi_type`	Fungal subtype or category.
`anno_is_food`	Food-related peptides.
`anno_food_subcategory`	Food subcategory.
`anno_food_item`	Specific food item.
`anno_bacteria_archaea`	Bacterial or archaeal annotations.
`anno_is_homo_sapiens`	Human proteins, Homo sapiens.
`anno_viruses_bacteriophage`	Viruses or bacteriophages.
`anno_is_lacto_phage`	Lactobacillus-associated phages.

Note: these anno_* columns were curated for the set of peptides detected in at least one individual in a previous cohort/measurement (97,866 peptides), and may differ from the peptide set detected in your own cohort.

Notes on taxonomic harmonization

The original metadata contained several inconsistencies and dispersed identifiers:

Inconsistent organism names.
Missing or variable taxonomic ranks.
Organism or species names sometimes scattered across multiple columns, or embedded in protein names/comments.
UniRef IDs, protein accessions, representative IDs, and taxonomic IDs scattered across different columns.

Carlos Reyna-Blanco resolved these issues through the following harmonization steps:

Step	Description
Standardize metadata	Parsed and cleaned organism and protein names.
Consolidate names and IDs	Combined names and identifiers from UniRef, accession numbers, representative IDs, or taxonomic IDs.
Retrieve and map IDs	Queried NCBI, UniProt, and AllergomeDB APIs for UniRef IDs, accession numbers, representative IDs, and taxonomic IDs.
Resolve ambiguities	Manually reviewed outdated or conflicting entries.
Enrich taxonomy	Retrieved taxonomic lineages from NCBI, from domain to species and common name.

Not all taxonomic fields are always available or returned by NCBI.

Practical usage

The peptide library metadata can be used to:

filter peptides by taxonomic group;
group peptides by species, genus, family, or broader taxonomic rank;
select allergen, pathogen, microbiota, or food-derived peptides;
compare enriched peptides across biological categories;
summarize reactivity patterns at peptide, protein, species, or annotation level.

# Select Helicobacter pylori peptides
hpylori_peptides <- peplib %>% filter(species == "Helicobacter pylori")

# Select allergen-derived peptides
allergen_peptides <- peplib %>% filter(is_allergens == TRUE)

# Select food-related manually curated peptides
food_peptides <- peplib %>% filter(anno_is_food == TRUE)

# Count peptides per species
table(peplib$species)

# Count peptides per manually curated category
table(peplib$anno_bacteria_archaea)

Ranks such as species can also be passed directly to phiper’s analysis functions, e.g. via rank_cols in compute_pop() or compute_delta() (see the typical workflow vignette).

Credits

This documentation of the peptide library metadata fields was written by Gabriel Innocenti. The underlying library annotations themselves draw on the original framework from Vogl et al. 2021, manual curation of the anno_* columns by Sasha Zhernakova, and taxonomic harmonization by Carlos Reyna-Blanco.