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.

3.1 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)
#> Registered S3 methods overwritten by 'ggalt':
#>   method                  from   
#>   grid.draw.absoluteGrob  ggplot2
#>   grobHeight.absoluteGrob ggplot2
#>   grobWidth.absoluteGrob  ggplot2
#>   grobX.absoluteGrob      ggplot2
#>   grobY.absoluteGrob      ggplot2
#> PIgLET version: 1.0.1
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:

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 quite=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 will look at one sequence, IGHV1-8*01 to see the masking (Fig. 3.3)

#> Scale for x is already present.
#> Adding another scale for x, which will replace the existing scale.
#> Coordinate system already present. Adding new coordinate system, which will
#> replace the existing one.

Figure 3.3: IGHV1-8*01 germline sequence. The allele’s germline sequence before and after masking. The colors represents the different nucleotides.

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

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

zenodo_doi <- "10.5281/zenodo.7401189"
asc_archive <-
  recentAlleleClusters(doi = zenodo_doi, get_file = TRUE)
#> Files will be downloaded to tmp directory: /tmp/RtmpkknRsj

allele_cluster_table <- extractASCTable(archive_file = asc_archive)

#> # A tibble: 6 × 3
#>   new_allele   imgt_allele.piglet imgt_allele.zenodo
#>   <chr>        <chr>              <chr>             
#> 1 IGHVF1-G1*01 IGHV3-72*01        IGHV3-72*01       
#> 2 IGHVF1-G2*01 IGHV3-73*01        IGHV3-73*01       
#> 3 IGHVF1-G2*02 IGHV3-73*02        IGHV3-73*02       
#> 4 IGHVF1-G3*01 IGHV3-49*02        IGHV3-49*02       
#> 5 IGHVF1-G3*02 IGHV3-49*01        IGHV3-49*01       
#> 6 IGHVF1-G3*03 IGHV3-49*05        IGHV3-49*05

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

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

head(asc_genotype)
#>          gene     alleles
#> 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
#> 1:                                                  IGHV1-24*01           105
#> 2:                                                IGHV1-69-2*01            31
#> 3:                                      IGHV1-58*01,IGHV1-58*02         23,18
#> 4: IGHV1-69*01/IGHV1-69D*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
#> 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
#>    genotyped_alleles
#> 1:                01
#> 2:                01
#> 3:             02,03
#> 4:       15,07,10,01
#> 5:                02
#> 6:                03
#>                                          genotyped_imgt_alleles
#> 1:                                                  IGHV1-24*01
#> 2:                                                IGHV1-69-2*01
#> 3:                                      IGHV1-58*01,IGHV1-58*02
#> 4: IGHV1-69*01/IGHV1-69D*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)