Evidence map›Paper›PMID 28392550›Full record

ArticleCancer research and treatment2018

Comparison of Ion Personal Genome Machine Platforms for the Detection of Variants in

Sang Mee Hwang, Ki Chan Lee, Min Seob Lee, Kyoung Un Park

Abstract read
In one paragraph

Article in Cancer research and treatment, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

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4 · The record

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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.

Sang Mee HwangDepartment of Laboratory Medicine, Seoul National University Bundang Hospital, Seongnam, Korea.
Ki Chan LeeEone-Diagnomics Genome Center, Incheon, Korea.
Min Seob LeeEone-Diagnomics Genome Center, Incheon, Korea.
Kyoung Un ParkDepartment of Laboratory Medicine, Seoul National University Bundang Hospital, Seongnam, Korea.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeTransition to next generation sequencing (NGS) for MATERIALS AND

methodsThe results of Ion PGM with OTG-snpcaller, a pipeline based on Torrent mapping alignment program and Genome Analysis Toolkit, from 75 clinical samples and 14 reference DNA samples were compared with Sanger sequencing for

resultsFifty types of variants including 18 pathogenic or variants of unknown significance were identified from 75 clinical samples and known variants of the reference samples were confirmed by Sanger sequencing and/or NGS. One false-negative results were present for Ion PGM/OTG-snpcaller for an indel variant misidentified as a single nucleotide variant. However, eight discordant results were present for Ion PGM Dx/Torrent Suite with both false-positive and -negative results. A 40-bp deletion, a 4-bp deletion and a 1-bp deletion variant was not called and a false-positive deletion was identified. Four other variants were misidentified as another variant.

conclusionIon PGM/OTG-snpcaller showed acceptable performance with good concordance with Sanger sequencing. However, Ion PGM Dx/Torrent Suite showed many discrepant results not suitable for use in a clinical laboratory, requiring further optimization of the data analysis for calling variants.

Indexed as

BRCA1 ProteinBRCA2 ProteinBreast NeoplasmsFemaleGenome, HumanHigh-Throughput Nucleotide SequencingHumansBRCA1 ProteinBRCA1 protein, humanBRCA2 ProteinBRCA2 protein, humanBRCA1BRCA2GenesHigh-throughput nucleotide sequencingIon PGMIon PGM Dx

Identifiers

PMID28392550
PMCPMC5784618

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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.