PIgLET: Program for Ig clusters
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.
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.
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 fromighvDistance.generateReferenceSet: Generate the allele clusters reference set.plotAlleleCluster: Plots the Hierarchical clustering.artificialFRW1Germline: Artificially create an IGHV reference set with framework1 (FWR1) primers.
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.
Allele Similarity Cluster
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.
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. @ref(fig:plot-amplicon) 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 |
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.
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:
- The IGHV germline reference - this reference set was download from IMGT in July 2022.
- The allele functionality table - the table contains functionality information for each of the alleles. Download from IMGT in July 2022
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. @ref(fig:asc-plot).
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.
Artificial framework 1 reference set
As described in section @ref(sec-amplicon-Length), 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-FR1:3We can use the artificial germline set to infer the ASC clusters in the same fashion as in section @ref(sec-asc).
Allele based genotype
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.
ASC-based thresholds
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.
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
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 \(0.0001\).
Inferring ASC-based genotype
Genotype inference has an increasing importance in downstream analysis, as described in @ref(sec-genotype) 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.
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(iuis_allele = paste0(sort(unique(iuis_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*03If 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.
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 | iuis_alleles | counts | absolute_fraction | absolute_threshold | genotyped_alleles | genotype_iuis_alleles |
|---|---|---|---|---|---|---|---|
| allele cluster | the present alleles | the IUIS nomenclature | the number of reads | the absolute fraction | the population driven allele | the alleles which | the IUIS 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
#> iuis_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_iuis_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*01For 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$genotyped_iuis_alleles, ","))
# get the genes
genes <- gsub("[*][0-9]+", "", alleles)
# extract the alleles
alleles <- sapply(strsplit(alleles, "[*]"), "[[", 2)
# make sure to extract only alleles
alleles <- gsub("([0-9]+).*$", "\\1", alleles)
# create the genotype
genotype <- data.frame(alleles = alleles, gene = genes)
# plot the genotype
tigger::plotGenotype(genotype = genotype)