# PIgLET: Program for Ig clusters

#### Ayelet Peres & William D. Lees & Gur Yaari

#### Last modified 2026-02-13

# 1 Introduction

In adaptive immune receptor repertoire analysis, determining the germline variable (V) allele associated with each T- and B-cell receptor sequence is a crucial step. This process is highly impacted by allele annotations. Aligning sequences, assigning them to specific germline alleles, and inferring individual genotypes are challenging when the repertoire is highly mutated, or sequence reads do not cover the whole V region.

PIgLET was created to provide a solution for this challenge. The package includes two main tools. The first, creates an alternative naming scheme for the V alleles, based on the proposed approach in Peres at. el [1]. The second, is an allele based genotype, that determined the presence of an allele based on a threshold derived from a naive population.

The naming scheme is compatible with current annotation tools and pipelines. Analysis results can be converted from the proposed naming scheme to the nomenclature determined by the International Union of Immunological Societies (IUIS). The package genotype inference method is accompanied by an online interactive website, to allow researchers to further explore the approach on real data IGHV reference book.

# 2 Package Overview

PIgLET is a suite of computational tools that improves genotype inference and downstream AIRR-seq data analysis. The package as two main tools. The first is Allele Clusters, this tool is designed to reduce the ambiguity within the IGHV alleles. The ambiguity is caused by duplicated or similar alleles which are shared among different genes. The second tool is an allele based genotype, that determined the presence of an allele based on a threshold derived from a naive population.

## 2.1 Allele Similarity Cluster:

This section provides the functions that support the main tool of creating the allele similarity cluster form an IGHV germline set.

- `inferAlleleClusters`: The main function of the section to create the allele clusters based on a germline set.
- `ighvDistance`: Calculate the distance between IGHV aligned germline sequences.
- `ighvClust`: Hierarchical clustering of the distance matrix from `ighvDistance`.
- `generateReferenceSet`: Generate the allele clusters reference set.
- `plotAlleleCluster`: Plots the Hierarchical clustering.
- `artificialFRW1Germline`: Artificially create an IGHV reference set with framework1 (FWR1) primers.

## 2.2 Allele based genotype:

This section provides the functions to infer the IGHV genotype using the allele based method and the allele clusters thresholds

- `inferGenotypeAllele`: Infer the IGHV genotype using the allele based method.
- `assignAlleleClusters`: Renames the v allele calls based on the new allele clusters.
- `germlineASC`: Converts IGHV germline set to ASC germline set.
- `recentAlleleClusters`: Download the most recent version of the allele clusters table archive from Zenodo.
- `extractASCTable`: Extracts the allele cluster table from the Zenodo archive file.
- `zenodoArchive`: An R6 object to query the Zenodo api.

# 3 Allele Similarity Cluster

## 3.1 Introduction

The term Allele similarity clusters (ASC), defines alleles that have a degree of germline proximity. The proximity is defined as the Levenshtein distance between the coding region of the alleles’ germline sequences. A distance matrix of all alleles’ Levenshtein distance is constructed and the hierarchical tree is calculated. The tree leaves are then clustered by 95% similarity which creates the alleles clusters.

### 3.1.1 Library amplicon length

Even though, we wish that all repertoires data available will cover the entire V region this is not always the case. Hence, we adapted our protocols to fit partial V coverage libraries. For the beginning we chose two library amplicon length, BIOMED-2 primers and Adaptive region coverage. The table below summaries the naming for each of the amplicon lengths and see Fig. 3.1 for coverage illustration:

Library amplicon length | Coverage | Similar known protocol
--- | --- | ---
S1 | Full length - 1 to 318 (IMGT numbering) | 5’ Race
S2 | Starting within the framework 1 region | BIOMED-2
S3 | End of the V region | Adaptive

Figure 3.1: V library amplicon length. Each row is a different V coverage, S1 for full length, S2 for BIOMED-2 primers, and S3 for adaptive coverage. The colors indicates the V regions according to IMGT numbering, where dark gray represents the IMGT gaps.

## 3.2 Inferring Allele Similarity Clusters

The main function in this section `inferAlleleClusters` returns an S4 object that includes the ASC allele cluster table `alleleClusterTable` with the new names and the default thresholds, the renamed germline set `alleleClusterSet`, and the germline set hierarchical clustering `hclustAlleleCluster`, and the similarity threshold parameters `threshold`. Further by using the plot function on the returned object, a colorful visualization of the allele clusters dendrogram and threshold is received.

The function receives as an input a germline reference set of allele sequences, the filtration parameters for the 3’ and 5’ regions, and two similarity thresholds for the ASC clusters and families.

To create the clusters we will first load data from the package:

1. The IGHV germline reference - this reference set was download from IMGT in July 2022.

    library(piglet)
    data(HVGERM)

2. The allele functionality table - the table contains functionality information for each of the alleles. Download from IMGT in July 2022

    data(hv_functionality)

Before clustering the germline set, we will remove non functional alleles, alleles that do not start on the first 5’ nucleotide, and those that are shorter than 318 bases.

    germline <- HVGERM
    ## keep only functional alleles
    germline <- germline[hv_functionality$allele[hv_functionality$functional=="F"]]
    ## keep only alleles that start from the first position of the V sequence
    germline <- germline[!grepl("^[.]", germline)]
    ## keep only alleles that are at minimum 318 nucleotide long
    germline <- germline[nchar(germline) >= 318]
    ## keep only localized alleles (remove NL)
    germline <- germline[!grepl("NL", names(germline))]

    germline <- HVGERM
    ## keep only functional alleles
    germline <- germline[hv_functionality$allele[hv_functionality$functional=="F"]]
    ## keep only alleles that start from the first position of the V sequence
    germline <- germline[!grepl("^[.]", germline)]
    ## keep only alleles that are at minimum 318 nucleotide long
    germline <- germline[nchar(germline) >= 318]
    ## keep only localized alleles (remove NL)
    germline <- germline[!grepl("NL", names(germline))]

Then we will create the ASC clusters using the `inferAlleleClusters` function. For better clustering results with the human IGHV reference set, it is recommended to set the `trim_3prime_side` parameter to 318. Here, we will use the default similarity thresholds 75% for the family and 95% for the clusters.

    asc <- inferAlleleClusters(
      germline_set = germline,
      trim_3prime_side = 318,
      mask_5prime_side = 0,
      family_threshold = 75,
      allele_cluster_threshold = 95)

The output of `inferAlleleClusters` is an `S4` object of type `GermlineCluster` that contains several slots:

Slot | Description
--- | ---
germlineSet | The input germline set with the 3’ and 5’ modifications (If defined)
alleleClusterSet | The input germline set with the ASC name scheme, if exists without duplicated sequences
alleleClusterTable | The allele similarity cluster with the new names and the default thresholds
threshold | The input family and allele cluster similarity thresholds
hclustAlleleCluster | Germline set hierarchical clustering, an hclust object

We can use the S4 plot method to plot the hierarchical clustering of the germline set as seen below in Fig. 3.2.

    plot(asc)

Figure 3.2: Allele similarity clusters. The out most circle is the allele names, the second layer are the ASC groups, each group is labeled and colored. The third circle is the clustering dendrogram, the branches are colored by the ASC families. The blue and orange dashed lines are the 95% and 75% similarity ASC threshold.

### 3.2.1 Artificial framework 1 reference set

As described in section 3.1.1, not all repertoires data available covers the entire V region. Hence, a modified reference set for the sequenced region can help us further understand the results we can obtain from certain library protocols.

Hence, we created the function `artificialFRW1Germline`, to mimic the seen coding region of targeted framework 1 (FRW1) primers for a given reference set. The primers were obtained from BIOMED-2 protocol [2].

Essentially the function matches the primer to each of the germline set sequences and either mask or trim the region. The returned object is a character vector with the named sequence in the desire length (Trimmed/Masked).

To demonstrate the use of the function, we can use the cleaned germline set from above (block 1). In this case we will mask the FRW1 region, this will return the sequences with the Ns instead of DNA nucleotide. The function output a log of the process, this output can be repressed using the `quiet=TRUE` flag.

    germline_frw1 <- artificialFRW1Germline(germline, mask_primer = T)
    #> 282/286 germline sequences have passed
    #> Counts by primers:
    #> VH1-FR1:53,VH2-FR1:25,VH3-FR1:122,VH4-FR1:69,VH5-FR1:10,VH6-FR1:3

We can use the artificial germline set to infer the ASC clusters in the same fashion as in section 3.2.

# 4 Allele based genotype

## 4.1 Introduction

Genotyping an Individual’s repertoire is becoming a common practice in down stream analysis. There are several tools nowadays to achieve such inference, namely TIgGER [3] and IgDiscover [4]. Though the methods are doing a fine job at inferring the genotype in high accuracy, they often neglect to detect lowly frequent alleles. The set of restriction the methods operates under enhance the specificity over the sensitivity.

Aside from low frequent alleles, another limitation that can hinder genotype inference is sequence multiple assignment. Each sequence in the repertoire is assigned its inferred V(D)J alleles for each of the segments. The assignments can be influenced by several factors, such as sequencing errors, somatic hyper mutations, amplicon length, and the initial reference set. This confounding factors can results in assigning more than a single allele per sequence segment. This multiple assignment has a downstream affect on the genotype inference. Each tool tries to deal with this effect in various ways.

In PIgLET the Allele based genotype section is dedicated to the ASC-based genotype inference.

## 4.2 ASC-based thresholds

### 4.2.1 Introduction

Briefly, the ASC-based threshold were determined based on a population of a large naive IGH repertoire cohort. For each allele a specific threshold was determined based on the population usage, the haplotype information (if available) and based on the alleles presented in the individual. The thresholds were adjusted based on a genomic validation approach with a coupled dataset, of both repertoire and long read data. At base the default threshold for any allele is \(0.0001\), this value is also what the function `inferAlleleClusters` returns for each of the alleles in the germline set. For more information on the specific threshold please review the manuscript Peres at al. [1] and the IGHV reference book.

### 4.2.2 Retriving Zenodo archive

The ASC-based threshold, found in the manuscript and the IGHV reference book are archived in Zenodo and can be retrieved using PIgLET.

To retrieve the archive files we can use the `recentAlleleClusters` function. The function can get a path value for locally saving the archive files with the `path` flag, if non is supplied then the function save the files in a temporary directory. The flag `get_file=TRUE`, will return the downloaded file full path.

    zenodo_doi <- "10.5281/zenodo.7401189"
    asc_archive <-
      recentAlleleClusters(doi = zenodo_doi, get_file = TRUE)

To extract the ASC threshold table we can use the `extractASCTable` function

    allele_cluster_table <- extractASCTable(archive_file = asc_archive)

The table is has identical ASC clusters to the table we created above (block 2).

We can now extract the threshold from the Zenodo archive table and fill the table created using the PIgLET. We recommend that in case an allele does not have a threshold in the archive to keep the default threshold of \(0.0001\).

## 4.3 Inferring ASC-based genotype

Genotype inference has an increasing importance in downstream analysis, as described in 4.2.1 an individual genotype inference can help reduce bias within the repertoire annotations. Based on the reference book, the ASC clusters, and the ASC-based threshold we developed in PIgLET a genotype inference function which is based on the ASC-based genotype.

The function `inferGenotypeAllele` infer an subject genotype using the absolute fraction and the allele based threshold. Essentially, for each unique allele that is found in the repertoire, its absolute fraction is calculated and compared to the population derived threshold. In case the allele’s fraction is above the threshold then it is inferred into the subject genotype.

Recommendations:

- For naive repertoires:
  - Filter the repertoire for up to 3 mutation within the V region
  - Setting the flag `find_unmutated=T`. Not needed if the above mutation filter is applied
- For non-naive repertoires:
  - Cloning the repertoire and selecting a single clonal representative with the least amount of mutations
  - Setting the flag `single_assignment=F`. In this case the function treats cases of multiple allele call assignment as belonging to all groups.

Below is a demonstration of inferring the genotype for an example dataset taken from TIgGER [3] package.

The data is b cell repertoire data from individual (PGP1) in AIRR format. The records were annotated with by IMGT/HighV-QUEST.

    # loading TIgGER AIRR-seq b cell data
    data <- tigger::AIRRDb

For using the genotype inference function on non ASC name scheme annotations, we first need to transform the `v_call` column to the ASC alleles. We will use the ASC-table downloaded from Zenodo archive and the example data

First we will collapse allele duplication in the ASC-table

    allele_cluster_table <-
      allele_cluster_table %>% dplyr::group_by(new_allele, func_group, thresh) %>%
      dplyr::summarise(imgt_allele = paste0(sort(unique(imgt_allele)), collapse = "/"),
                       .groups = "keep")

Now, we can transform the data

    # storing original v_call values
    data$v_call_or <- data$v_call
    # assigning the ASC alleles
    asc_data <- assignAlleleClusters(data, allele_cluster_table)

    head(asc_data[, c("v_call", "v_call_or")])
    #> # A tibble: 6 × 2
    #>   v_call                      v_call_or
    #>   <chr>                       <chr>
    #> 1 IGHVF5-G29*03               IGHV1-2*02
    #> 2 IGHVF5-G30*02               IGHV1-18*01
    #> 3 IGHVF5-G26*10               IGHV1-69*06
    #> 4 IGHVF5-G26*07               IGHV1-69*04
    #> 5 IGHVF5-G27*02               IGHV1-8*01
    #> 6 IGHVF5-G28*03,IGHVF5-G28*02 IGHV1-46*01,IGHV1-46*03

If we have not inferred the ASC clustered and generated the renamed germline set, we can use the `germlineASC` to obtain it. We need to supply the function the ASC-table and an IGHV germline set.

    # reforming the germline set
    asc_germline <- germlineASC(allele_cluster_table, germline = HVGERM)

Once we have both the modified dataset and germline reference set, we can infer the genotype. The function returns the genotype table with the following columns

gene | alleles | imgt_alleles | counts | absolute_fraction | absolute_threshold | genotyped_alleles | genotype_imgt_alleles
--- | --- | --- | --- | --- | --- | --- | ---
allele cluster | the present alleles | the imgt nomenclature | the number of reads | the absolute fraction | the population driven allele | the alleles which | the imgt nomenclature

    # inferring the genotype
    asc_genotype <- inferGenotypeAllele_asc(
      asc_data,
      alleleClusterTable = allele_cluster_table,
      germline_db = asc_germline,
      find_unmutated = T
    )

    head(asc_genotype)
    #>          gene     alleles
    #>        <char>      <char>
    #> 1: IGHVF5-G22          01
    #> 2: IGHVF5-G23          01
    #> 3: IGHVF5-G24       02,03
    #> 4: IGHVF5-G26 15,07,10,01
    #> 5: IGHVF5-G27          02
    #> 6: IGHVF5-G28          03
    #>                                                    imgt_alleles        counts
    #>                                                          <char>        <char>
    #> 1:                                                  IGHV1-24*01           105
    #> 2:                                                IGHV1-69-2*01            31
    #> 3:                                      IGHV1-58*01,IGHV1-58*02         23,18
    #> 4: IGHV1-69D*01/IGHV1-69*01,IGHV1-69*04,IGHV1-69*06,IGHV1-69*02 515,469,280,9
    #> 5:                                                   IGHV1-8*01           467
    #> 6:                                                  IGHV1-46*01           624
    #>                          absolute_fraction          absolute_threshold
    #>                                     <char>                      <char>
    #> 1:                               0.0221613                      0.0001
    #> 2:                               0.0065429                      0.0001
    #> 3:                     0.0048544,0.0037991               0.0001,0.0001
    #> 4: 0.1086956,0.0989869,0.0590967,0.0018995 0.0010,0.0010,0.0010,0.0010
    #> 5:                               0.0985648                      0.0001
    #> 6:                               0.1317011                      0.0010
    #>                  genotype_confidence genotyped_alleles
    #>                               <char>            <char>
    #> 1:                          151.8621                01
    #> 2:                           44.3503                01
    #> 3:                   32.7274,25.4631             02,03
    #> 4: 234.5379,213.3943,126.5220,1.9590       15,07,10,01
    #> 5:                          677.7979                02
    #> 6:                          284.6388                03
    #>                                          genotyped_imgt_alleles
    #>                                                          <char>
    #> 1:                                                  IGHV1-24*01
    #> 2:                                                IGHV1-69-2*01
    #> 3:                                      IGHV1-58*01,IGHV1-58*02
    #> 4: IGHV1-69D*01/IGHV1-69*01,IGHV1-69*04,IGHV1-69*06,IGHV1-69*02
    #> 5:                                                   IGHV1-8*01
    #> 6:                                                  IGHV1-46*01

For plotting the genotype with TIgGER `plotGenotype`, we need to do a small modification to our genotype table

    # get the genotype alleles
    alleles <- unlist(strsplit(asc_genotype$genotyped_imgt_alleles, ","))
    # get the genes
    genes <- gsub("[*][0-9]+", "", alleles)
    # extract the alleles
    alleles <- sapply(strsplit(alleles, "[*]"), "[[", 2)
    # make sure to extract only alleles
    alleles <- gsub("([0-9]+).*$", "\\1", alleles)
    # create the genotype
    genotype <- data.frame(alleles = alleles, gene = genes)
    # plot the genotype
    tigger::plotGenotype(genotype = genotype)

# 5 Contact

For help, questions, or suggestions, please contact:

- Ayelet Peres
- Gur Yaari
- Issue tracker

# References

[1]
A. Peres et al., “IGHV allele similarity clustering improves genotype inference from adaptive immune receptor repertoire sequencing data,” bioRxiv, pp. 2022–12, 2022.
[2]
J. Van Dongen et al., “Design and standardization of PCR primers and protocols for detection of clonal immunoglobulin and t-cell receptor gene recombinations in suspect lymphoproliferations: Report of the BIOMED-2 concerted action BMH4-CT98-3936,” Leukemia, vol. 17, no. 12, pp. 2257–2317, 2003.
[3]
D. Gadala-Maria, G. Yaari, M. Uduman, and S. H. Kleinstein, “Automated analysis of high-throughput b-cell sequencing data reveals a high frequency of novel immunoglobulin v gene segment alleles,” Proceedings of the National Academy of Sciences, vol. 112, no. 8, pp. E862–E870, 2015 [Online]. Available: https://doi.org/10.1073/pnas.1417683112
[4]
M. M. Corcoran et al., “Production of individualized v gene databases reveals high levels of immunoglobulin genetic diversity,” Nature Communications, vol. 7, no. 1, p. 13642, Dec. 2016 [Online]. Available: https://doi.org/10.1038/ncomms13642