Evidence map›Paper›PMID 32915251›Full record

ArticleHuman genetics2021

Resolving misalignment interference for NGS-based clinical diagnostics.

Che-Yu Lee, Hai-Yun Yen, Alan W Zhong, Hanlin Gao

Abstract read
PubMed Publisher
In one paragraph

Article in Human genetics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed, 1 pooled it
3.1field-weighted citation impact, top 8% of its field
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

18 citing papers in PubMed, 1 synthesis or guideline pooled it, 26 citations in OpenAlex.

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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

4 authors at 1 institution in 1 country.

Che-Yu Lee *Fulgent Genetics, Temple City, CA, 91780, USA. cheyulee74@gmail.com.
Hai-Yun Yen *Fulgent Genetics, Temple City, CA, 91780, USA.
Alan W ZhongFulgent Genetics, Temple City, CA, 91780, USA.
Hanlin GaoFulgent Genetics, Temple City, CA, 91780, USA.
Temple College · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Next-generation sequencing (NGS) is an incredibly useful tool for genetic disease diagnosis. However, the most commonly used bioinformatics methods for analyzing sequence reads insufficiently discriminate genomic regions with extensive sequence identity, such as gene families and pseudogenes, complicating diagnostics. This problem has been recognized for specific genes, including many involved in human disease, and diagnostic labs must perform additional costly steps to guarantee accurate diagnosis in these cases. Here we report a new data analysis method based on the comparison of read depth between highly homologous regions to identify misalignment. Analyzing six clinically important genes-CYP21A2, GBA, HBA1/2, PMS2, and SMN1-each exhibiting misalignment issues related to homology, we show that our technique can correctly identify potential misalignment events and be used to make appropriate calls. Combined with long-range PCR and/or MLPA orthogonal testing, our clinical laboratory can improve variant calling with minimal additional cost. We propose an accurate and cost-efficient NGS testing procedure that will benefit disease diagnostics, carrier screening, and research-based population studies.

Indexed as

AlgorithmsGenetic Diseases, InbornHigh-Throughput Nucleotide SequencingHumansPolymerase Chain ReactionPolymorphism, Single NucleotidePseudogenes

Identifiers

PMID32915251
OpenAlexW3085125475

What OpenQuestion holds

Textmetadata
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.