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immunogenetr is a comprehensive toolkit for clinical HLA informatics. It is built on tidyverse principles and makes use of genotype list string (GL string, https://glstring.org/) for storing and using HLA genotype data.
Specific functionalities of this library include:
You may install immunogenetr from GitHub with the below lines of
code. Devtools is necessary for installation. If devtools is not
installed, you may run install.packages("devtools")
first.
::install_github("k96nb01/immunogenetr_package") devtools
To demonstrate some functionality of immunogenetr
we
will use an internal dataset to perform match grades for a putative
recipient/donor pair.
library(immunogenetr)
library(tidyverse)
# The "HLA_typing_1" dataset is installed with immunogenetr, and contains high resolution typing at all classical
# HLA loci for ten individuals.
print(HLA_typing_1)
patient | A1 | A2 | C1 | C2 | B1 | B2 | DRB345_1 | DRB345_2 | DRB1_1 | DRB1_2 | DQA1_1 | DQA1_2 | DQB1_1 | DQB1_2 | DPA1_1 | DPA1_2 | DPB1_1 | DPB1_2 |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1 | A*24:02 | A*29:02 | C*07:04 | C*16:01 | B*44:02 | B*44:03 | DRB5*01:01 | DRB5*01:01 | DRB1*15:01 | DRB1*15:01 | DQA1*01:02 | DQA1*01:02 | DQB1*06:02 | DQB1*06:02 | DPA1*01:03 | DPA1*01:03 | DPB1*03:01 | DPB1*04:01 |
2 | A*02:01 | A*11:05 | C*07:01 | C*07:02 | B*07:02 | B*08:01 | DRB3*01:01 | DRB4*01:03 | DRB1*03:01 | DRB1*04:01 | DQA1*03:03 | DQA1*05:01 | DQB1*02:01 | DQB1*03:01 | DPA1*01:03 | DPA1*01:03 | DPB1*04:01 | DPB1*04:01 |
3 | A*02:01 | A*26:18 | C*02:02 | C*03:04 | B*27:05 | B*54:01 | DRB3*02:02 | DRB4*01:03 | DRB1*04:04 | DRB1*14:54 | DQA1*01:04 | DQA1*03:01 | DQB1*03:02 | DQB1*05:02 | DPA1*01:03 | DPA1*02:02 | DPB1*02:01 | DPB1*05:01 |
4 | A*29:02 | A*30:02 | C*06:02 | C*07:01 | B*08:01 | B*13:02 | DRB4*01:03 | DRB4*01:03 | DRB1*04:01 | DRB1*07:01 | DQA1*02:01 | DQA1*03:01 | DQB1*02:02 | DQB1*03:02 | DPA1*01:03 | DPA1*02:01 | DPB1*01:01 | DPB1*16:01 |
5 | A*02:05 | A*24:02 | C*07:18 | C*12:03 | B*35:03 | B*58:01 | DRB3*02:02 | DRB3*02:02 | DRB1*03:01 | DRB1*14:54 | DQA1*01:04 | DQA1*05:01 | DQB1*02:01 | DQB1*05:03 | DPA1*01:03 | DPA1*02:01 | DPB1*10:01 | DPB1*124:01 |
6 | A*01:01 | A*24:02 | C*07:01 | C*14:02 | B*49:01 | B*51:01 | DRB3*03:01 | DRBX*NNNN | DRB1*08:01 | DRB1*13:02 | DQA1*01:02 | DQA1*04:01 | DQB1*04:02 | DQB1*06:04 | DPA1*01:03 | DPA1*01:04 | DPB1*04:01 | DPB1*15:01 |
7 | A*03:01 | A*03:01 | C*03:03 | C*16:01 | B*15:01 | B*51:01 | DRB4*01:01 | DRBX*NNNN | DRB1*01:01 | DRB1*07:01 | DQA1*01:01 | DQA1*02:01 | DQB1*02:02 | DQB1*05:01 | DPA1*01:03 | DPA1*01:03 | DPB1*04:01 | DPB1*04:01 |
8 | A*01:01 | A*32:01 | C*06:02 | C*07:02 | B*08:01 | B*37:01 | DRB3*02:02 | DRB5*01:01 | DRB1*03:01 | DRB1*15:01 | DQA1*01:02 | DQA1*05:01 | DQB1*02:01 | DQB1*06:02 | DPA1*01:03 | DPA1*02:01 | DPB1*04:01 | DPB1*14:01 |
9 | A*03:01 | A*30:01 | C*07:02 | C*12:03 | B*07:02 | B*38:01 | DRB3*01:01 | DRB5*01:01 | DRB1*03:01 | DRB1*15:01 | DQA1*01:02 | DQA1*05:01 | DQB1*02:01 | DQB1*06:02 | DPA1*01:03 | DPA1*01:03 | DPB1*04:01 | DPB1*04:01 |
10 | A*02:05 | A*11:01 | C*07:18 | C*16:02 | B*51:01 | B*58:01 | DRB3*03:01 | DRB5*01:01 | DRB1*13:02 | DRB1*15:01 | DQA1*01:02 | DQA1*01:03 | DQB1*06:01 | DQB1*06:09 | DPA1*01:03 | DPA1*01:03 | DPB1*02:01 | DPB1*104:01 |
immunogenetr uses genotype list strings (GL strings) for most
functions, including the matching and mismatching functions. To easily
convert the genotypes found in “HLA_typing_1” to GL strings we can use
the HLA_columns_to_GLstring
function:
<- HLA_typing_1 %>%
HLA_typing_1_GLstring mutate(GL_string = HLA_columns_to_GLstring(., HLA_typing_columns = A1:DPB1_2), .after = patient) %>%
# Note the syntax for the `HLA_columns_to_GLstring` arguments - when this function is used inside
# of a `mutate` function to make a new column in a data frame, "." is used in the first argument
# to tell the function to use the working data frame as the source of the HLA typing columns.
select(patient, GL_string)
print(HLA_typing_1_GLstring)
patient | GL_string |
---|---|
1 | HLA-A*24:02+HLA-A*29:02HLA-C*07:04+HLA-C*16:01HLA-B*44:02+HLA-B*44:03HLA-DRB3*01:01+HLA-DRB3*01:01HLA-DRB1*15:01+HLA-DRB1*15:01HLA-DQA1*01:02+HLA-DQA1*01:02HLA-DQB1*06:02+HLA-DQB1*06:02HLA-DPA1*01:03+HLA-DPA1*01:03HLA-DPB1*03:01+HLA-DPB1*04:01 |
2 | HLA-A*02:01+HLA-A*11:05HLA-C*07:01+HLA-C*07:02HLA-B*07:02+HLA-B*08:01HLA-DRB3*01:01+HLA-DRB3*01:03HLA-DRB1*03:01+HLA-DRB1*04:01HLA-DQA1*03:03+HLA-DQA1*05:01HLA-DQB1*02:01+HLA-DQB1*03:01HLA-DPA1*01:03+HLA-DPA1*01:03HLA-DPB1*04:01+HLA-DPB1*04:01 |
3 | HLA-A*02:01+HLA-A*26:18HLA-C*02:02+HLA-C*03:04HLA-B*27:05+HLA-B*54:01HLA-DRB3*02:02+HLA-DRB3*01:03HLA-DRB1*04:04+HLA-DRB1*14:54HLA-DQA1*01:04+HLA-DQA1*03:01HLA-DQB1*03:02+HLA-DQB1*05:02HLA-DPA1*01:03+HLA-DPA1*02:02HLA-DPB1*02:01+HLA-DPB1*05:01 |
4 | HLA-A*29:02+HLA-A*30:02HLA-C*06:02+HLA-C*07:01HLA-B*08:01+HLA-B*13:02HLA-DRB3*01:03+HLA-DRB3*01:03HLA-DRB1*04:01+HLA-DRB1*07:01HLA-DQA1*02:01+HLA-DQA1*03:01HLA-DQB1*02:02+HLA-DQB1*03:02HLA-DPA1*01:03+HLA-DPA1*02:01HLA-DPB1*01:01+HLA-DPB1*16:01 |
5 | HLA-A*02:05+HLA-A*24:02HLA-C*07:18+HLA-C*12:03HLA-B*35:03+HLA-B*58:01HLA-DRB3*02:02+HLA-DRB3*02:02HLA-DRB1*03:01+HLA-DRB1*14:54HLA-DQA1*01:04+HLA-DQA1*05:01HLA-DQB1*02:01+HLA-DQB1*05:03HLA-DPA1*01:03+HLA-DPA1*02:01HLA-DPB1*10:01+HLA-DPB1*124:01 |
6 | HLA-A*01:01+HLA-A*24:02HLA-C*07:01+HLA-C*14:02HLA-B*49:01+HLA-B*51:01HLA-DRB3*03:01HLA-DRB1*08:01+HLA-DRB1*13:02HLA-DQA1*01:02+HLA-DQA1*04:01HLA-DQB1*04:02+HLA-DQB1*06:04HLA-DPA1*01:03+HLA-DPA1*01:04HLA-DPB1*04:01+HLA-DPB1*15:01 |
7 | HLA-A*03:01+HLA-A*03:01HLA-C*03:03+HLA-C*16:01HLA-B*15:01+HLA-B*51:01HLA-DRB3*01:01HLA-DRB1*01:01+HLA-DRB1*07:01HLA-DQA1*01:01+HLA-DQA1*02:01HLA-DQB1*02:02+HLA-DQB1*05:01HLA-DPA1*01:03+HLA-DPA1*01:03HLA-DPB1*04:01+HLA-DPB1*04:01 |
8 | HLA-A*01:01+HLA-A*32:01HLA-C*06:02+HLA-C*07:02HLA-B*08:01+HLA-B*37:01HLA-DRB3*02:02+HLA-DRB3*01:01HLA-DRB1*03:01+HLA-DRB1*15:01HLA-DQA1*01:02+HLA-DQA1*05:01HLA-DQB1*02:01+HLA-DQB1*06:02HLA-DPA1*01:03+HLA-DPA1*02:01HLA-DPB1*04:01+HLA-DPB1*14:01 |
9 | HLA-A*03:01+HLA-A*30:01HLA-C*07:02+HLA-C*12:03HLA-B*07:02+HLA-B*38:01HLA-DRB3*01:01+HLA-DRB3*01:01HLA-DRB1*03:01+HLA-DRB1*15:01HLA-DQA1*01:02+HLA-DQA1*05:01HLA-DQB1*02:01+HLA-DQB1*06:02HLA-DPA1*01:03+HLA-DPA1*01:03HLA-DPB1*04:01+HLA-DPB1*04:01 |
10 | HLA-A*02:05+HLA-A*11:01HLA-C*07:18+HLA-C*16:02HLA-B*51:01+HLA-B*58:01HLA-DRB3*03:01+HLA-DRB3*01:01HLA-DRB1*13:02+HLA-DRB1*15:01HLA-DQA1*01:02+HLA-DQA1*01:03HLA-DQB1*06:01+HLA-DQB1*06:09HLA-DPA1*01:03+HLA-DPA1*01:03HLA-DPB1*02:01+HLA-DPB1*104:01 |
The “HLA_typing_1_GLstring” data frame now contains a row with a GL string for each individual, containing their full HLA genotype in a single string. Let’s select one individual to act as a recipient, and one to act as a donor.
# Select one case each for recipient and donor.
<- HLA_typing_1_GLstring %>%
HLA_typing_1_GLstring_recipient filter(patient == 7) %>%
rename(GL_string_recipient = GL_string, case = patient)
<- HLA_typing_1_GLstring %>%
HLA_typing_1_GLstring_donor filter(patient == 9) %>%
rename(GL_string_donor = GL_string) %>%
select(-patient)
# Combine the tables so recipient and donor are on the same row.
<- bind_cols(
HLA_typing_1_recip_donor
HLA_typing_1_GLstring_recipient,
HLA_typing_1_GLstring_donor
)
print(HLA_typing_1_recip_donor)
case | GL_string_recipient | GL_string_donor |
---|---|---|
7 | HLA-A*03:01+HLA-A*03:01HLA-C*03:03+HLA-C*16:01HLA-B*15:01+HLA-B*51:01HLA-DRB3*01:01HLA-DRB1*01:01+HLA-DRB1*07:01HLA-DQA1*01:01+HLA-DQA1*02:01HLA-DQB1*02:02+HLA-DQB1*05:01HLA-DPA1*01:03+HLA-DPA1*01:03HLA-DPB1*04:01+HLA-DPB1*04:01 | HLA-A*03:01+HLA-A*30:01HLA-C*07:02+HLA-C*12:03HLA-B*07:02+HLA-B*38:01HLA-DRB3*01:01+HLA-DRB3*01:01HLA-DRB1*03:01+HLA-DRB1*15:01HLA-DQA1*01:02+HLA-DQA1*05:01HLA-DQB1*02:01+HLA-DQB1*06:02HLA-DPA1*01:03+HLA-DPA1*01:03HLA-DPB1*04:01+HLA-DPB1*04:01 |
We now have a data frame with a recipient and donor HLA genotype on one row. Let’s try out some of the mismatching functions on this data.
<- HLA_typing_1_recip_donor %>%
HLA_typing_1_recip_donor_mismatches mutate(A_MM_GvH = HLA_mismatch_logical(
GL_string_recipient,
GL_string_donor, "HLA-A",
direction = "GvH"),
.after = case) %>%
mutate(A_MM_HvG = HLA_mismatch_logical(
GL_string_recipient,
GL_string_donor, "HLA-A",
direction = "HvG"),
.after = A_MM_GvH)
print(HLA_typing_1_recip_donor_mismatches)
case | A_MM_GvH | A_MM_HvG | GL_string_recipient | GL_string_donor |
---|---|---|---|---|
7 | TRUE | TRUE | HLA-A*03:01+HLA-A*03:01HLA-C*03:03+HLA-C*16:01HLA-B*15:01+HLA-B*51:01HLA-DRB3*01:01HLA-DRB1*01:01+HLA-DRB1*07:01HLA-DQA1*01:01+HLA-DQA1*02:01HLA-DQB1*02:02+HLA-DQB1*05:01HLA-DPA1*01:03+HLA-DPA1*01:03HLA-DPB1*04:01+HLA-DPB1*04:01 | HLA-A*03:01+HLA-A*30:01HLA-C*07:02+HLA-C*12:03HLA-B*07:02+HLA-B*38:01HLA-DRB3*01:01+HLA-DRB3*01:01HLA-DRB1*03:01+HLA-DRB1*15:01HLA-DQA1*01:02+HLA-DQA1*05:01HLA-DQB1*02:01+HLA-DQB1*06:02HLA-DPA1*01:03+HLA-DPA1*01:03HLA-DPB1*04:01+HLA-DPB1*04:01 |
The HLA_mismatch_logical
function determines if there
are any mismatches at a particular locus. We’ve determined that at the
HLA-A locus there are not any mismatches in the graft-versus-host
direction, but are in the host-versus-graft direction. We can use the
HLA_mismatched_alleles
function to tell us what those
mismatches are:
<- HLA_typing_1_recip_donor %>%
HLA_typing_1_recip_donor_mismatched_allles mutate(A_HvG_MMs = HLA_mismatched_alleles(
GL_string_recipient,
GL_string_donor, "HLA-A",
direction = "HvG"),
.after = case)
print(HLA_typing_1_recip_donor_mismatched_allles)
case | A_HvG_MMs | GL_string_recipient | GL_string_donor |
---|---|---|---|
7 | HLA-A*30:01 | HLA-A*03:01+HLA-A*03:01HLA-C*03:03+HLA-C*16:01HLA-B*15:01+HLA-B*51:01HLA-DRB3*01:01HLA-DRB1*01:01+HLA-DRB1*07:01HLA-DQA1*01:01+HLA-DQA1*02:01HLA-DQB1*02:02+HLA-DQB1*05:01HLA-DPA1*01:03+HLA-DPA1*01:03HLA-DPB1*04:01+HLA-DPB1*04:01 | HLA-A*03:01+HLA-A*30:01HLA-C*07:02+HLA-C*12:03HLA-B*07:02+HLA-B*38:01HLA-DRB3*01:01+HLA-DRB3*01:01HLA-DRB1*03:01+HLA-DRB1*15:01HLA-DQA1*01:02+HLA-DQA1*05:01HLA-DQB1*02:01+HLA-DQB1*06:02HLA-DPA1*01:03+HLA-DPA1*01:03HLA-DPB1*04:01+HLA-DPB1*04:01 |
The HLA_mismatched_alleles
function reported that the
“HLA-A*30:01” allele was mismatched in the HvG direction. Sometimes,
however, we simply want to know how many mismatches are at a particular
locus. We can do that with the HLA_mismatch_number
function:
# Determine the number of bidirectional mismatches at several loci.
<- HLA_typing_1_recip_donor %>%
HLA_typing_1_recip_donor_MM_number mutate(ABCDRB1_MM = HLA_mismatch_number(
GL_string_recipient,
GL_string_donor, c("HLA-A", "HLA-B", "HLA-C", "HLA-DRB1"),
direction = "bidirectional"),
.after = case)
print(HLA_typing_1_recip_donor_MM_number)
case | ABCDRB1_MM | GL_string_recipient | GL_string_donor |
---|---|---|---|
7 | HLA-A=1 | HLA-A*03:01+HLA-A*03:01HLA-C*03:03+HLA-C*16:01HLA-B*15:01+HLA-B*51:01HLA-DRB3*01:01HLA-DRB1*01:01+HLA-DRB1*07:01HLA-DQA1*01:01+HLA-DQA1*02:01HLA-DQB1*02:02+HLA-DQB1*05:01HLA-DPA1*01:03+HLA-DPA1*01:03HLA-DPB1*04:01+HLA-DPB1*04:01 | HLA-A*03:01+HLA-A*30:01HLA-C*07:02+HLA-C*12:03HLA-B*07:02+HLA-B*38:01HLA-DRB3*01:01+HLA-DRB3*01:01HLA-DRB1*03:01+HLA-DRB1*15:01HLA-DQA1*01:02+HLA-DQA1*05:01HLA-DQB1*02:01+HLA-DQB1*06:02HLA-DPA1*01:03+HLA-DPA1*01:03HLA-DPB1*04:01+HLA-DPB1*04:01 |
We might want to calculate an HLA match summary for stem cell
transplantation. We can use the HLA_match_summarry_HCT
function for this:
# The match_grade argument of "Xof8" will return the number of matches at the HLA-A, B, C, and DRB1 loci.
<- HLA_typing_1_recip_donor %>%
HLA_typing_1_recip_donor_8of8_matching mutate(ABCDRB1_matching = HLA_match_summary_HCT(
GL_string_recipient,
GL_string_donor, direction = "bidirectional",
match_grade = "Xof8"),
.after = case)
print(HLA_typing_1_recip_donor_8of8_matching)
case | ABCDRB1_matching | GL_string_recipient | GL_string_donor |
---|---|---|---|
7 | 1 | HLA-A*03:01+HLA-A*03:01HLA-C*03:03+HLA-C*16:01HLA-B*15:01+HLA-B*51:01HLA-DRB3*01:01HLA-DRB1*01:01+HLA-DRB1*07:01HLA-DQA1*01:01+HLA-DQA1*02:01HLA-DQB1*02:02+HLA-DQB1*05:01HLA-DPA1*01:03+HLA-DPA1*01:03HLA-DPB1*04:01+HLA-DPB1*04:01 | HLA-A*03:01+HLA-A*30:01HLA-C*07:02+HLA-C*12:03HLA-B*07:02+HLA-B*38:01HLA-DRB3*01:01+HLA-DRB3*01:01HLA-DRB1*03:01+HLA-DRB1*15:01HLA-DQA1*01:02+HLA-DQA1*05:01HLA-DQB1*02:01+HLA-DQB1*06:02HLA-DPA1*01:03+HLA-DPA1*01:03HLA-DPB1*04:01+HLA-DPB1*04:01 |
Clearly, this recipient and donor are not a great match. Let’s see how we could use this workflow to find the best-matched donor from several options. To do this, we’ll choose a case from “HLA_typing_1” and compare it to all the cases in that data set:
# Select one case to be the recipient.
<- HLA_typing_1_GLstring %>%
HLA_typing_1_GLstring_candidate filter(patient == 3) %>%
select(GL_string) %>%
rename(GL_string_recip = GL_string)
# Join the recipient to the 10-donor list and perform matching
<- HLA_typing_1_GLstring %>%
HLA_typing_1_GLstring_donors rename(GL_string_donor = GL_string, donor = patient) %>%
cross_join(HLA_typing_1_GLstring_candidate) %>%
mutate(ABCDRB1_matching = HLA_match_summary_HCT(
GL_string_recip,
GL_string_donor, direction = "bidirectional",
match_grade = "Xof8"),
.after = donor) %>%
arrange(desc(ABCDRB1_matching))
print(HLA_typing_1_GLstring_donors)
donor | ABCDRB1_matching | GL_string_donor | GL_string_recip |
---|---|---|---|
3 | 8 | HLA-A*02:01+HLA-A*26:18HLA-C*02:02+HLA-C*03:04HLA-B*27:05+HLA-B*54:01HLA-DRB3*02:02+HLA-DRB3*01:03HLA-DRB1*04:04+HLA-DRB1*14:54HLA-DQA1*01:04+HLA-DQA1*03:01HLA-DQB1*03:02+HLA-DQB1*05:02HLA-DPA1*01:03+HLA-DPA1*02:02HLA-DPB1*02:01+HLA-DPB1*05:01 | HLA-A*02:01+HLA-A*26:18HLA-C*02:02+HLA-C*03:04HLA-B*27:05+HLA-B*54:01HLA-DRB3*02:02+HLA-DRB3*01:03HLA-DRB1*04:04+HLA-DRB1*14:54HLA-DQA1*01:04+HLA-DQA1*03:01HLA-DQB1*03:02+HLA-DQB1*05:02HLA-DPA1*01:03+HLA-DPA1*02:02HLA-DPB1*02:01+HLA-DPB1*05:01 |
2 | 1 | HLA-A*02:01+HLA-A*11:05HLA-C*07:01+HLA-C*07:02HLA-B*07:02+HLA-B*08:01HLA-DRB3*01:01+HLA-DRB3*01:03HLA-DRB1*03:01+HLA-DRB1*04:01HLA-DQA1*03:03+HLA-DQA1*05:01HLA-DQB1*02:01+HLA-DQB1*03:01HLA-DPA1*01:03+HLA-DPA1*01:03HLA-DPB1*04:01+HLA-DPB1*04:01 | HLA-A*02:01+HLA-A*26:18HLA-C*02:02+HLA-C*03:04HLA-B*27:05+HLA-B*54:01HLA-DRB3*02:02+HLA-DRB3*01:03HLA-DRB1*04:04+HLA-DRB1*14:54HLA-DQA1*01:04+HLA-DQA1*03:01HLA-DQB1*03:02+HLA-DQB1*05:02HLA-DPA1*01:03+HLA-DPA1*02:02HLA-DPB1*02:01+HLA-DPB1*05:01 |
5 | 1 | HLA-A*02:05+HLA-A*24:02HLA-C*07:18+HLA-C*12:03HLA-B*35:03+HLA-B*58:01HLA-DRB3*02:02+HLA-DRB3*02:02HLA-DRB1*03:01+HLA-DRB1*14:54HLA-DQA1*01:04+HLA-DQA1*05:01HLA-DQB1*02:01+HLA-DQB1*05:03HLA-DPA1*01:03+HLA-DPA1*02:01HLA-DPB1*10:01+HLA-DPB1*124:01 | HLA-A*02:01+HLA-A*26:18HLA-C*02:02+HLA-C*03:04HLA-B*27:05+HLA-B*54:01HLA-DRB3*02:02+HLA-DRB3*01:03HLA-DRB1*04:04+HLA-DRB1*14:54HLA-DQA1*01:04+HLA-DQA1*03:01HLA-DQB1*03:02+HLA-DQB1*05:02HLA-DPA1*01:03+HLA-DPA1*02:02HLA-DPB1*02:01+HLA-DPB1*05:01 |
1 | 0 | HLA-A*24:02+HLA-A*29:02HLA-C*07:04+HLA-C*16:01HLA-B*44:02+HLA-B*44:03HLA-DRB3*01:01+HLA-DRB3*01:01HLA-DRB1*15:01+HLA-DRB1*15:01HLA-DQA1*01:02+HLA-DQA1*01:02HLA-DQB1*06:02+HLA-DQB1*06:02HLA-DPA1*01:03+HLA-DPA1*01:03HLA-DPB1*03:01+HLA-DPB1*04:01 | HLA-A*02:01+HLA-A*26:18HLA-C*02:02+HLA-C*03:04HLA-B*27:05+HLA-B*54:01HLA-DRB3*02:02+HLA-DRB3*01:03HLA-DRB1*04:04+HLA-DRB1*14:54HLA-DQA1*01:04+HLA-DQA1*03:01HLA-DQB1*03:02+HLA-DQB1*05:02HLA-DPA1*01:03+HLA-DPA1*02:02HLA-DPB1*02:01+HLA-DPB1*05:01 |
4 | 0 | HLA-A*29:02+HLA-A*30:02HLA-C*06:02+HLA-C*07:01HLA-B*08:01+HLA-B*13:02HLA-DRB3*01:03+HLA-DRB3*01:03HLA-DRB1*04:01+HLA-DRB1*07:01HLA-DQA1*02:01+HLA-DQA1*03:01HLA-DQB1*02:02+HLA-DQB1*03:02HLA-DPA1*01:03+HLA-DPA1*02:01HLA-DPB1*01:01+HLA-DPB1*16:01 | HLA-A*02:01+HLA-A*26:18HLA-C*02:02+HLA-C*03:04HLA-B*27:05+HLA-B*54:01HLA-DRB3*02:02+HLA-DRB3*01:03HLA-DRB1*04:04+HLA-DRB1*14:54HLA-DQA1*01:04+HLA-DQA1*03:01HLA-DQB1*03:02+HLA-DQB1*05:02HLA-DPA1*01:03+HLA-DPA1*02:02HLA-DPB1*02:01+HLA-DPB1*05:01 |
6 | 0 | HLA-A*01:01+HLA-A*24:02HLA-C*07:01+HLA-C*14:02HLA-B*49:01+HLA-B*51:01HLA-DRB3*03:01HLA-DRB1*08:01+HLA-DRB1*13:02HLA-DQA1*01:02+HLA-DQA1*04:01HLA-DQB1*04:02+HLA-DQB1*06:04HLA-DPA1*01:03+HLA-DPA1*01:04HLA-DPB1*04:01+HLA-DPB1*15:01 | HLA-A*02:01+HLA-A*26:18HLA-C*02:02+HLA-C*03:04HLA-B*27:05+HLA-B*54:01HLA-DRB3*02:02+HLA-DRB3*01:03HLA-DRB1*04:04+HLA-DRB1*14:54HLA-DQA1*01:04+HLA-DQA1*03:01HLA-DQB1*03:02+HLA-DQB1*05:02HLA-DPA1*01:03+HLA-DPA1*02:02HLA-DPB1*02:01+HLA-DPB1*05:01 |
7 | 0 | HLA-A*03:01+HLA-A*03:01HLA-C*03:03+HLA-C*16:01HLA-B*15:01+HLA-B*51:01HLA-DRB3*01:01HLA-DRB1*01:01+HLA-DRB1*07:01HLA-DQA1*01:01+HLA-DQA1*02:01HLA-DQB1*02:02+HLA-DQB1*05:01HLA-DPA1*01:03+HLA-DPA1*01:03HLA-DPB1*04:01+HLA-DPB1*04:01 | HLA-A*02:01+HLA-A*26:18HLA-C*02:02+HLA-C*03:04HLA-B*27:05+HLA-B*54:01HLA-DRB3*02:02+HLA-DRB3*01:03HLA-DRB1*04:04+HLA-DRB1*14:54HLA-DQA1*01:04+HLA-DQA1*03:01HLA-DQB1*03:02+HLA-DQB1*05:02HLA-DPA1*01:03+HLA-DPA1*02:02HLA-DPB1*02:01+HLA-DPB1*05:01 |
8 | 0 | HLA-A*01:01+HLA-A*32:01HLA-C*06:02+HLA-C*07:02HLA-B*08:01+HLA-B*37:01HLA-DRB3*02:02+HLA-DRB3*01:01HLA-DRB1*03:01+HLA-DRB1*15:01HLA-DQA1*01:02+HLA-DQA1*05:01HLA-DQB1*02:01+HLA-DQB1*06:02HLA-DPA1*01:03+HLA-DPA1*02:01HLA-DPB1*04:01+HLA-DPB1*14:01 | HLA-A*02:01+HLA-A*26:18HLA-C*02:02+HLA-C*03:04HLA-B*27:05+HLA-B*54:01HLA-DRB3*02:02+HLA-DRB3*01:03HLA-DRB1*04:04+HLA-DRB1*14:54HLA-DQA1*01:04+HLA-DQA1*03:01HLA-DQB1*03:02+HLA-DQB1*05:02HLA-DPA1*01:03+HLA-DPA1*02:02HLA-DPB1*02:01+HLA-DPB1*05:01 |
9 | 0 | HLA-A*03:01+HLA-A*30:01HLA-C*07:02+HLA-C*12:03HLA-B*07:02+HLA-B*38:01HLA-DRB3*01:01+HLA-DRB3*01:01HLA-DRB1*03:01+HLA-DRB1*15:01HLA-DQA1*01:02+HLA-DQA1*05:01HLA-DQB1*02:01+HLA-DQB1*06:02HLA-DPA1*01:03+HLA-DPA1*01:03HLA-DPB1*04:01+HLA-DPB1*04:01 | HLA-A*02:01+HLA-A*26:18HLA-C*02:02+HLA-C*03:04HLA-B*27:05+HLA-B*54:01HLA-DRB3*02:02+HLA-DRB3*01:03HLA-DRB1*04:04+HLA-DRB1*14:54HLA-DQA1*01:04+HLA-DQA1*03:01HLA-DQB1*03:02+HLA-DQB1*05:02HLA-DPA1*01:03+HLA-DPA1*02:02HLA-DPB1*02:01+HLA-DPB1*05:01 |
10 | 0 | HLA-A*02:05+HLA-A*11:01HLA-C*07:18+HLA-C*16:02HLA-B*51:01+HLA-B*58:01HLA-DRB3*03:01+HLA-DRB3*01:01HLA-DRB1*13:02+HLA-DRB1*15:01HLA-DQA1*01:02+HLA-DQA1*01:03HLA-DQB1*06:01+HLA-DQB1*06:09HLA-DPA1*01:03+HLA-DPA1*01:03HLA-DPB1*02:01+HLA-DPB1*104:01 | HLA-A*02:01+HLA-A*26:18HLA-C*02:02+HLA-C*03:04HLA-B*27:05+HLA-B*54:01HLA-DRB3*02:02+HLA-DRB3*01:03HLA-DRB1*04:04+HLA-DRB1*14:54HLA-DQA1*01:04+HLA-DQA1*03:01HLA-DQB1*03:02+HLA-DQB1*05:02HLA-DPA1*01:03+HLA-DPA1*02:02HLA-DPB1*02:01+HLA-DPB1*05:01 |
We can see that donor 3 is the only donor with an 8/8 match for the recipient.
This project is licensed under the GNU General Public License v3.0.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.