PMID- 37169596
OWN - NLM
STAT- MEDLINE
DCOM- 20230927
LR  - 20240216
IS  - 1549-5469 (Electronic)
IS  - 1088-9051 (Print)
IS  - 1088-9051 (Linking)
VI  - 33
IP  - 6
DP  - 2023 Jun
TI  - Efficient and accurate KIR and HLA genotyping with massively parallel sequencing 
      data.
PG  - 923-931
LID - 10.1101/gr.277585.122 [doi]
AB  - Killer cell immunoglobulin like receptor (KIR) genes and human leukocyte antigen 
      (HLA) genes play important roles in innate and adaptive immunity. They are highly 
      polymorphic and cannot be genotyped with standard variant calling pipelines. 
      Compared with HLA genes, many KIR genes are similar to each other in sequences 
      and may be absent in the chromosomes. Therefore, although many tools have been 
      developed to genotype HLA genes using common sequencing data, none of them work 
      for KIR genes. Even specialized KIR genotypers could not resolve all the KIR 
      genes. Here we describe T1K, a novel computational method for the efficient and 
      accurate inference of KIR or HLA alleles from RNA-seq, whole-genome sequencing, 
      or whole-exome sequencing data. T1K jointly considers alleles across all 
      genotyped genes, so it can reliably identify present genes and distinguish 
      homologous genes, including the challenging KIR2DL5A/KIR2DL5B genes. This model 
      also benefits HLA genotyping, where T1K achieves high accuracy in benchmarks. 
      Moreover, T1K can call novel single-nucleotide variants and process single-cell 
      data. Applying T1K to tumor single-cell RNA-seq data, we found that KIR2DL4 
      expression was enriched in tumor-specific CD8(+) T cells. T1K may open the 
      opportunity for HLA and KIR genotyping across various sequencing applications.
CI  - (c) 2023 Song et al.; Published by Cold Spring Harbor Laboratory Press.
FAU - Song, Li
AU  - Song L
AD  - Department of Data Science, Dana-Farber Cancer Institute, Boston, Massachusetts 
      02215, USA.
AD  - Department of Biomedical Informatics, Harvard Medical School, Boston, 
      Massachusetts 02115, USA.
FAU - Bai, Gali
AU  - Bai G
AD  - Department of Data Science, Dana-Farber Cancer Institute, Boston, Massachusetts 
      02215, USA.
FAU - Liu, X Shirley
AU  - Liu XS
AD  - Department of Data Science, Dana-Farber Cancer Institute, Boston, Massachusetts 
      02215, USA.
FAU - Li, Bo
AU  - Li B
AD  - Lyda Hill Department of Bioinformatics, University of Texas Southwestern Medical 
      Center, Dallas, Texas 75390, USA lib3@chop.edu hli@ds.dfci.harvard.edu.
FAU - Li, Heng
AU  - Li H
AUID- ORCID: 0000-0003-4874-2874
AD  - Department of Data Science, Dana-Farber Cancer Institute, Boston, Massachusetts 
      02215, USA; lib3@chop.edu hli@ds.dfci.harvard.edu.
AD  - Department of Biomedical Informatics, Harvard Medical School, Boston, 
      Massachusetts 02115, USA.
LA  - eng
GR  - U01 HG010961/HG/NHGRI NIH HHS/United States
GR  - R01 HG010040/HG/NHGRI NIH HHS/United States
GR  - R01 CA258524/CA/NCI NIH HHS/United States
GR  - P20 GM130454/GM/NIGMS NIH HHS/United States
GR  - R01 HG011139/HG/NHGRI NIH HHS/United States
GR  - R01 CA245318/CA/NCI NIH HHS/United States
GR  - U01 CA226196/CA/NCI NIH HHS/United States
PT  - Journal Article
PT  - Research Support, N.I.H., Extramural
DEP - 20230511
PL  - United States
TA  - Genome Res
JT  - Genome research
JID - 9518021
RN  - 0 (Receptors, KIR)
RN  - 0 (KIR2DL5B protein, human)
RN  - 0 (Receptors, KIR2DL5)
SB  - IM
MH  - Humans
MH  - Genotype
MH  - *CD8-Positive T-Lymphocytes
MH  - *Receptors, KIR/genetics
MH  - Alleles
MH  - High-Throughput Nucleotide Sequencing/methods
MH  - Receptors, KIR2DL5/genetics
PMC - PMC10519407
EDAT- 2023/05/12 01:07
MHDA- 2023/09/27 06:43
PMCR- 2023/12/01
CRDT- 2023/05/11 21:38
PHST- 2022/12/11 00:00 [received]
PHST- 2023/05/04 00:00 [accepted]
PHST- 2023/09/27 06:43 [medline]
PHST- 2023/05/12 01:07 [pubmed]
PHST- 2023/05/11 21:38 [entrez]
PHST- 2023/12/01 00:00 [pmc-release]
AID - gr.277585.122 [pii]
AID - 10.1101/gr.277585.122 [doi]
PST - ppublish
SO  - Genome Res. 2023 Jun;33(6):923-931. doi: 10.1101/gr.277585.122. Epub 2023 May 11.