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HLA Typing

Overview of attention for book
Cover of 'HLA Typing'

Table of Contents

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    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 12: In Silico Typing of Classical and Non-classical HLA Alleles from Standard RNA-Seq Reads
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Chapter title
In Silico Typing of Classical and Non-classical HLA Alleles from Standard RNA-Seq Reads
Chapter number 12
Book title
HLA Typing
Published in
Methods in molecular biology, January 2018
DOI 10.1007/978-1-4939-8546-3_12
Pubmed ID
Book ISBNs
978-1-4939-8545-6, 978-1-4939-8546-3
Authors

Sebastian Boegel, Thomas Bukur, John C. Castle, Ugur Sahin, Boegel, Sebastian, Bukur, Thomas, Castle, John C., Sahin, Ugur

Abstract

Next-Generation Sequencing (NGS) enables the rapid generation of billions of short nucleic acid sequence fragments (i.e., "sequencing reads"). Especially, the adoption of gene expression profiling using whole transcriptome sequencing (i.e., "RNA-Seq") has been rapid. Here, we describe an in silico method, seq2HLA, that takes standard RNA-Seq reads as input and determines a sample's (classical and non-classical) HLA class I and class II types as well as HLA expression. We demonstrate the application of seq2HLA using publicly available RNA-Seq data from the Burkitt's lymphoma cell line DAUDI and the choriocarcinoma cell line JEG-3.

X Demographics

X Demographics

The data shown below were collected from the profiles of 4 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 22 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 22 100%

Demographic breakdown

Readers by professional status Count As %
Professor 4 18%
Researcher 2 9%
Student > Master 2 9%
Student > Postgraduate 1 5%
Unknown 13 59%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 2 9%
Computer Science 2 9%
Agricultural and Biological Sciences 2 9%
Immunology and Microbiology 2 9%
Arts and Humanities 1 5%
Other 1 5%
Unknown 12 55%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 September 2018.
All research outputs
#14,120,688
of 24,143,470 outputs
Outputs from Methods in molecular biology
#3,720
of 13,616 outputs
Outputs of similar age
#221,944
of 450,188 outputs
Outputs of similar age from Methods in molecular biology
#341
of 1,483 outputs
Altmetric has tracked 24,143,470 research outputs across all sources so far. This one is in the 40th percentile – i.e., 40% of other outputs scored the same or lower than it.
So far Altmetric has tracked 13,616 research outputs from this source. They receive a mean Attention Score of 3.5. This one has gotten more attention than average, scoring higher than 71% 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 450,188 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 49th percentile – i.e., 49% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 1,483 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 75% of its contemporaries.