Evidence map›Paper›PMID 25543015›Full record

ArticleHuman immunology2015

Impact of three Illumina library construction methods on GC bias and HLA genotype calling.

James H Lan, Yuxin Yin, Elaine F Reed, Kevin Moua, Kimberly Thomas, Qiuheng Zhang

Abstract readComparative Study
In one paragraph

Article in Human immunology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.

0numbers the graph read from it
0cells of the map it votes in
34citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

34 citing papers in PubMed.

  1. Article
  2. Advancements in HLA Typing Techniques and Their Impact on Transplantation Medicine.Medical principles and practice : international journal of the Kuwait University, Health Science Centre · 2024
    Review
  3. Article
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  16. Review
  17. Comparison of the sequencing bias of currently available library preparation kits for Illumina sequencing of bacterial genomes and metagenomes.DNA research : an international journal for rapid publication of reports on genes and genomes · 2019
    Article
  18. Article
  19. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

James H LanUCLA Immunogenetics Center, Department of Pathology & Laboratory Medicine, Los Angeles, CA, USA; University of British Columbia, Clinician Investigator Program, Vancouver, BC, Canada.
Yuxin YinUCLA Immunogenetics Center, Department of Pathology & Laboratory Medicine, Los Angeles, CA, USA.
Elaine F ReedUCLA Immunogenetics Center, Department of Pathology & Laboratory Medicine, Los Angeles, CA, USA.
Kevin MouaUCLA Immunogenetics Center, Department of Pathology & Laboratory Medicine, Los Angeles, CA, USA.
Kimberly ThomasUCLA Immunogenetics Center, Department of Pathology & Laboratory Medicine, Los Angeles, CA, USA.
Qiuheng ZhangUCLA Immunogenetics Center, Department of Pathology & Laboratory Medicine, Los Angeles, CA, USA.

Funding

UCLA Clinical and Translational Science InstituteUL1TR000124 · NCATS · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI DUBINETT, STEVEN M. · 2012 to 2015
$57.0M
NCATS NIH HHS UL1 TR000124NCATS NIH HHS UL1TR000124
6 · The paper itself

Abstract

Next-generation sequencing (NGS) is increasingly recognized for its ability to overcome allele ambiguity and deliver high-resolution typing in the HLA system. Using this technology, non-uniform read distribution can impede the reliability of variant detection, which renders high-confidence genotype calling particularly difficult to achieve in the polymorphic HLA complex. Recently, library construction has been implicated as the dominant factor in instigating coverage bias. To study the impact of this phenomenon on HLA genotyping, we performed long-range PCR on 12 samples to amplify HLA-A, -B, -C, -DRB1, and -DQB1, and compared the relative contribution of three Illumina library construction methods (TruSeq Nano, Nextera, Nextera XT) in generating downstream bias. Here, we show high GC% to be a good predictor of low sequencing depth. Compared to standard TruSeq Nano, GC bias was more prominent in transposase-based protocols, particularly Nextera XT, likely through a combination of transposase insertion bias being coupled with a high number of PCR enrichment cycles. Importantly, our findings demonstrate non-uniform read depth can have a direct and negative impact on the robustness of HLA genotyping, which has clinical implications for users when choosing a library construction strategy that aims to balance cost and throughput with data quality.

Indexed as

Gene LibraryHistocompatibility TestingAllelesCost-Benefit AnalysisGenotypeHigh-Throughput Nucleotide SequencingHLA AntigensHumansReproducibility of ResultsTransposasesHLA AntigensTransposasesGC biasHLA genotypingIllumina NexteraNGSTransposase

Identifiers

PMID25543015
PMCPMC5089167

What OpenQuestion holds

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Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.