Generating Allele Similarity Clusters (ASC) for BIOMED IGHV Reference

Ayelet Peres & William D. Lees & Gur Yaari

Last modified 2026-02-13

1 Overview

This tutorial demonstrates how to generate Allele Similarity Clusters (ASC) for a BIOMED-style V reference using the OGRDB IGHV reference. You will:

  1. Load and Process the Reference Sets
url <- "https://bitbucket.org/yaarilab/piglet/raw/70b7d4491e25e7197e2a94bd890ce5b6e3b506a8/data-raw/HVGERM_OGRDB.fasta"
tmp_dest_file <- file.path(tempdir(), "HVGERM_OGRDB.fasta")
download.file(url, tmp_dest_file, mode = "wb")

ref_ogrdb <- readIgFasta(tmp_dest_file)

ref_ogrdb_frw1 <- piglet::artificialFRW1Germline(ref_ogrdb)
  1. Infer Allele Similarity Clusters (ASC)
asc_frw1 <- inferAlleleClusters(ref_ogrdb_frw1)
allele_table_frw1 <- setDT(asc_frw1@alleleClusterTable)[, .(imgt_allele, new_allele)]
setnames(allele_table_frw1, c("allele", "asc_allele"))
  1. Merge with Threshold Table from PIgLET
allele_table_piglet <- fread("https://bitbucket.org/yaarilab/piglet/raw/70b7d4491e25e7197e2a94bd890ce5b6e3b506a8/data-raw/allele_threshold_table.tsv")

allele_table_frw1$threshold <- 1e-04
allele_table_frw1$threshold <- apply(allele_table_frw1, 1, function(x){
  gene <- unlist(strsplit(x[["allele"]],"[*]"))
  alleles <- unlist(strsplit(gene[2],"_"))
  gene <- gene[1]
  alleles <- paste0(gene,"*",alleles)
  thresh <- allele_table_piglet[allele %in% alleles, sum(threshold)]
  thresh
})

allele_table_frw1 <- rbind(
  allele_table_frw1[,.(allele,asc_allele,threshold)],
  allele_table_piglet[!grepl("V",allele),]
)
allele_table_frw1[,tag:=substr(allele, 4, 4)]
  1. Load Precomputed Partial BIOMED Reference
url <- "https://bitbucket.org/yaarilab/piglet/raw/70b7d4491e25e7197e2a94bd890ce5b6e3b506a8/data-raw/HVGERM_ogrdb_asc_partial.fasta"
tmp_dest_file <- file.path(tempdir(), "HVGERM_ogrdb_asc_partial.fasta")
download.file(url, tmp_dest_file, mode = "wb")

asc_germline <- readIgFasta(tmp_dest_file)

allele_table <- fread("https://bitbucket.org/yaarilab/piglet/raw/70b7d4491e25e7197e2a94bd890ce5b6e3b506a8/data-raw/allele_threshold_table_ogrdb_partial.tsv")
  1. Convert Annotations to ASC format
data <- tigger::AIRRDb
data$v_call_or <- data$v_call

allele_table_split <- allele_table[, {
  parts <- strsplit(allele, "\\*")[[1]]
  gene <- parts[1]
  alleles <- strsplit(parts[2], "_")[[1]]
  expanded <- paste0(gene, "*", alleles)
  .(allele = expanded, asc_allele, threshold, tag)
}, by = .I]

allele_table_split[, I := NULL]

asc_data <- assignAlleleClusters(data, allele_table_split, v_call = "v_call", from_col = "allele", to_col = "asc_allele")
  1. Infer Genotypes

# using the asc annotations

asc_genotype <- inferGenotypeAllele(
  asc_data,
  allele_threshold_table = allele_table,
  call = "v_call", # change to the column call you want to genotype
  asc_annotation = TRUE, # if you use iuis names then set to FALSE
  single_assignment = TRUE, # if you want to use the single assignment algorithm
  find_unmutated = FALSE # change to TRUE to filter mutated reads
  # germline_db = asc_germline # Uncomment if you want to filter mutated reads
)

# using the biomed annotations, make sure to convert the v_call to the collapsed biomed annotations
allele_table_biomed <- allele_table
allele_table_biomed[, asc_allele := allele]

allele_table_split <- allele_table_biomed[, {
  parts <- strsplit(allele, "\\*")[[1]]
  gene <- parts[1]
  alleles <- strsplit(parts[2], "_")[[1]]
  expanded <- paste0(gene, "*", alleles)
  .(allele = expanded, asc_allele, threshold, tag)
}, by = .I]

allele_table_split[, I := NULL]

biomed_data <- assignAlleleClusters(data, allele_table_split, v_call = "v_call", from_col = "allele", to_col = "asc_allele")

asc_genotype_biomed <- inferGenotypeAllele(
  biomed_data,
  allele_threshold_table = allele_table,
  call = "v_call", # change to the column call you want to genotype
  asc_annotation = FALSE, # if you use iuis names then set to FALSE
  single_assignment = TRUE
)