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HLA Typing

Overview of attention for book
Cover of 'HLA Typing'

Table of Contents

  1. Altmetric Badge
    Book Overview
  2. Altmetric Badge
    Chapter 1 The Past, Present, and Future of HLA Typing in Transplantation
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    Chapter 2 Role of Human Leukocyte Antigens (HLA) in Autoimmune Diseases
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    Chapter 3 The IPD Databases: Cataloguing and Understanding Allele Variants
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    Chapter 4 Allele Frequency Net Database
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    Chapter 5 High-Resolution HLA-Typing by Next-Generation Sequencing of Randomly Fragmented Target DNA
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    Chapter 6 High-Throughput Contiguous Full-Length Next-Generation Sequencing of HLA Class I and II Genes from 96 Donors in a Single MiSeq Run
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    Chapter 7 Application of High-Throughput Next-Generation Sequencing for HLA Typing on Buccal Extracted DNA
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    Chapter 8 Super High Resolution for Single Molecule-Sequence-Based Typing of Classical HLA Loci Using Ion Torrent PGM
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    Chapter 9 High-Resolution Full-Length HLA Typing Method Using Third Generation (Pac-Bio SMRT) Sequencing Technology
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    Chapter 10 Full-Length HLA Class I Genotyping with the MinION Nanopore Sequencer
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    Chapter 11 Imputation-Based HLA Typing with SNPs in GWAS Studies
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    Chapter 12 In Silico Typing of Classical and Non-classical HLA Alleles from Standard RNA-Seq Reads
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    Chapter 13 PHLAT: Inference of High-Resolution HLA Types from RNA and Whole Exome Sequencing
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    Chapter 14 Using Exome and Amplicon-Based Sequencing Data for High-Resolution HLA Typing with ATHLATES
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    Chapter 15 HLA Typing from Short-Read Sequencing Data with OptiType
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    Chapter 16 Comprehensive HLA Typing from a Current Allele Database Using Next-Generation Sequencing Data
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    Chapter 17 Accurate Assembly and Typing of HLA using a Graph-Guided Assembler Kourami
  19. Altmetric Badge
    Chapter 18 AmpliSAS and AmpliHLA: Web Server Tools for MHC Typing of Non-Model Species and Human Using NGS Data
  20. Altmetric Badge
    Chapter 19 HLA Haplotype Frequency Estimation from Real-Life Data with the Hapl-o-Mat Software
Attention for Chapter 11: Imputation-Based HLA Typing with SNPs in GWAS Studies
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Chapter title
Imputation-Based HLA Typing with SNPs in GWAS Studies
Chapter number 11
Book title
HLA Typing
Published in
Methods in molecular biology, January 2018
DOI 10.1007/978-1-4939-8546-3_11
Pubmed ID
29858808
Book ISBNs
978-1-4939-8545-6, 978-1-4939-8546-3
Authors

Xiuwen Zheng, Zheng, Xiuwen

Abstract

SNP-based imputation approaches for human leukocyte antigen (HLA) typing take advantage of the extended haplotype structure within the major histocompatibility complex (MHC) to predict classical HLA alleles using dense SNP genotypes, such as those available on chip panels of genome-wide association study (GWAS). These methods enable HLA analyses of classical alleles on existing SNP datasets genotyped in GWAS studies at no extra cost. Here, I describe the workflow of HIBAG, an imputation method with attribute bagging, for obtaining a sample's HLA class I and II genotypes of two-field resolution using SNP data. Two examples are provided to illustrate with a publicly available HLA and SNP dataset: genotype imputation with pre-fit classifiers in GWAS, and model training to build a new classifier.

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X Demographics

The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
 Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 25 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 25 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 6 24%
Student > Master 5 20%
Professor 3 12%
Student > Ph. D. Student 3 12%
Student > Doctoral Student 1 4%
Other 3 12%
Unknown 4 16%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 10 40%
Immunology and Microbiology 4 16%
Agricultural and Biological Sciences 3 12%
Environmental Science 1 4%
Medicine and Dentistry 1 4%
Other 1 4%
Unknown 5 20%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 03 June 2018.
All research outputs
#17,932,284
of 26,017,215 outputs
Outputs from Methods in molecular biology
#6,206
of 14,425 outputs
Outputs of similar age
#290,705
of 455,808 outputs
Outputs of similar age from Methods in molecular biology
#597
of 1,488 outputs
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So far Altmetric has tracked 14,425 research outputs from this source. They receive a mean Attention Score of 3.5. This one is in the 40th percentile – i.e., 40% of its peers scored the same or lower than it.
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We're also able to compare this research output to 1,488 others from the same source and published within six weeks on either side of this one. This one is in the 41st percentile – i.e., 41% of its contemporaries scored the same or lower than it.

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