Evidence map›Paper›PMID 31730669›Full record

ArticlePloS one2019

Ion torrent high throughput mitochondrial genome sequencing (HTMGS).

N R Harvey, C L Albury, S Stuart, M C Benton, D A Eccles, J R Connell, H G Sutherland, R J N Allcock, R A Lea, L M Haupt and 1 more

Abstract read
In one paragraph

Article in PloS one, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

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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

11 authors.

N R HarveyGenomics Research Centre, School of Biomedical Sciences, Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, Queensland, Australia.
C L AlburyGenomics Research Centre, School of Biomedical Sciences, Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, Queensland, Australia.ORCID 0000-0001-7556-6998
S StuartGenomics Research Centre, School of Biomedical Sciences, Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, Queensland, Australia.
M C BentonGenomics Research Centre, School of Biomedical Sciences, Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, Queensland, Australia.
D A EcclesGenomics Research Centre, School of Biomedical Sciences, Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, Queensland, Australia.
J R ConnellGenomics Research Centre, School of Biomedical Sciences, Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, Queensland, Australia.ORCID 0000-0003-4155-5750
H G SutherlandGenomics Research Centre, School of Biomedical Sciences, Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, Queensland, Australia.
R J N AllcockSchool of Biomedical Sciences, University of Western Australia (M504), Crawley, Western Australia, Australia.
R A LeaGenomics Research Centre, School of Biomedical Sciences, Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, Queensland, Australia.
L M HauptGenomics Research Centre, School of Biomedical Sciences, Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, Queensland, Australia.
L R GriffithsGenomics Research Centre, School of Biomedical Sciences, Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, Queensland, Australia.ORCID 0000-0002-6774-5475

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The implementation and popularity of next generation sequencing (NGS) has led to the development of various rapid whole mitochondrial genome sequencing techniques. We summarise an efficient and cost-effective NGS approach for mitochondrial genomic DNA in humans using the Ion Torrent platform, and further discuss our bioinformatics pipeline for streamlined variant calling. Ion 316 chips were utilised with the Ion Torrent semi-conductor platform Personal Genome Machine (PGM) to perform tandem sequencing of mitochondrial genomes from the core pedigree (n = 315) of the Norfolk Island Health Study. Key improvements from commercial methods focus on the initial PCR step, which currently requires extensive optimisation to ensure the accurate and reproducible elongation of each section of the complete mitochondrial genome. Dual-platform barcodes were incorporated into our protocol thereby extending its potential application onto Illumina-based systems. Our bioinformatics pipeline consists of a modified version of GATK best practices tailored for mitochondrial genomic data. When compared with current commercial methods, our method, termed high throughput mitochondrial genome sequencing (HTMGS), allows high multiplexing of samples and the use of alternate library preparation reagents at a lower cost per sample (~1.7 times) when compared to current commercial methodologies. Our HTMGS methodology also provides robust mitochondrial sequencing data (>450X average coverage) that can be applied and modified to suit various study designs. On average, we were able to identify ~30 variants per sample with 572 variants observed across 315 samples. We have developed a high throughput sequencing and analysis method targeting complete mitochondrial genomes; with the potential to be platform agnostic with analysis options that adhere to current best practices.

Indexed as

Genome, MitochondrialHigh-Throughput Nucleotide SequencingDNA, MitochondrialGenetic VariationHumansQuality ControlDNA, Mitochondrial

Identifiers

PMID31730669
PMCPMC6857855

What OpenQuestion holds

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LicenceCC BY
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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.