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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
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    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 13: PHLAT: Inference of High-Resolution HLA Types from RNA and Whole Exome Sequencing
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (75th percentile)
  • High Attention Score compared to outputs of the same age and source (90th percentile)

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Chapter title
PHLAT: Inference of High-Resolution HLA Types from RNA and Whole Exome Sequencing
Chapter number 13
Book title
HLA Typing
Published in
Methods in molecular biology, January 2018
DOI 10.1007/978-1-4939-8546-3_13
Pubmed ID
Book ISBNs
978-1-4939-8545-6, 978-1-4939-8546-3
Authors

Yu Bai, David Wang, Wen Fury, Bai, Yu, Wang, David, Fury, Wen

Abstract

Inferring HLA types from genome-wide sequencing data has gained growing attention with the development of new cost-efficient sequencing technologies and the increasing need to integrate HLA types with transcriptomic or other genomic information for insights into immune-mediated diseases, vaccination, and cancer immunotherapy. PHLAT is a computational tool designed for high-resolution (4-digit) typing of the major class I and class II HLA genes using RNAseq or exome sequencing data as input. We illustrate here how PHLAT can be installed, configured, and executed. This document also provides guidance for how to read and interpret the output results. Finally, the best practices of using PHLAT are also discussed.

X Demographics

X Demographics

The data shown below were collected from the profiles of 12 X users 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 43 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 43 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 11 26%
Student > Ph. D. Student 8 19%
Other 4 9%
Student > Postgraduate 4 9%
Student > Bachelor 2 5%
Other 6 14%
Unknown 8 19%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 15 35%
Immunology and Microbiology 6 14%
Medicine and Dentistry 5 12%
Computer Science 4 9%
Agricultural and Biological Sciences 4 9%
Other 0 0%
Unknown 9 21%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 6. 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 04 June 2018.
All research outputs
#5,782,971
of 23,923,788 outputs
Outputs from Methods in molecular biology
#1,564
of 13,508 outputs
Outputs of similar age
#110,888
of 447,933 outputs
Outputs of similar age from Methods in molecular biology
#136
of 1,479 outputs
Altmetric has tracked 23,923,788 research outputs across all sources so far. Compared to these this one has done well and is in the 75th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 13,508 research outputs from this source. They receive a mean Attention Score of 3.5. This one has done well, scoring higher than 88% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 447,933 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 75% of its contemporaries.
We're also able to compare this research output to 1,479 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 90% of its contemporaries.