Evidence map›Paper›PMID 27955616›Full record

ArticleBMC genomics2016

The ability of human nuclear DNA to cause false positive low-abundance heteroplasmy calls varies across the mitochondrial genome.

Levent Albayrak, Kamil Khanipov, Maria Pimenova, George Golovko, Mark Rojas, Ioannis Pavlidis, Sergei Chumakov, Gerardo Aguilar, Arturo Chávez, William R Widger and 1 more

Open access · goldAbstract read
In one paragraph

Article in BMC genomics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed
1.0field-weighted citation impact, top 21% 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

19 citing papers in PubMed, 43 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

11 authors at 3 institutions in 2 countries.

Levent AlbayrakDepartment of Pharmacology and Toxicology, University of Texas Medical Branch, 301 University Boulevard, Galveston, TX, 77555-0144, USA.
Kamil KhanipovDepartment of Pharmacology and Toxicology, University of Texas Medical Branch, 301 University Boulevard, Galveston, TX, 77555-0144, USA.
Maria PimenovaDepartment of Pharmacology and Toxicology, University of Texas Medical Branch, 301 University Boulevard, Galveston, TX, 77555-0144, USA.
George GolovkoDepartment of Pharmacology and Toxicology, University of Texas Medical Branch, 301 University Boulevard, Galveston, TX, 77555-0144, USA.
Mark RojasDepartment of Pharmacology and Toxicology, University of Texas Medical Branch, 301 University Boulevard, Galveston, TX, 77555-0144, USA.
Ioannis PavlidisDepartment of Computer Science, University of Houston, Houston, TX, USA.
Sergei ChumakovDepartment of Physics, University of Guadalajara, Guadalajara, Jalisco, Mexico.
Gerardo AguilarDepartment of Physics, University of Guadalajara, Guadalajara, Jalisco, Mexico.
Arturo ChávezDepartment of Physics, University of Guadalajara, Guadalajara, Jalisco, Mexico.
William R WidgerDepartment of Biology and Biochemistry, University of Houston, Houston, TX, USA.
Yuriy FofanovDepartment of Pharmacology and Toxicology, University of Texas Medical Branch, 301 University Boulevard, Galveston, TX, 77555-0144, USA. yufofano@UTMB.EDU.
The University of Texas Medical Branch at Galveston · USUniversidad de Guadalajara · MXUniversity of Houston · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundLow-abundance mutations in mitochondrial populations (mutations with minor allele frequency ≤ 1%), are associated with cancer, aging, and neurodegenerative disorders. While recent progress in high-throughput sequencing technology has significantly improved the heteroplasmy identification process, the ability of this technology to detect low-abundance mutations can be affected by the presence of similar sequences originating from nuclear DNA (nDNA). To determine to what extent nDNA can cause false positive low-abundance heteroplasmy calls, we have identified mitochondrial locations of all subsequences that are common or similar (one mismatch allowed) between nDNA and mitochondrial DNA (mtDNA).

resultsPerformed analysis revealed up to a 25-fold variation in the lengths of longest common and longest similar (one mismatch allowed) subsequences across the mitochondrial genome. The size of the longest subsequences shared between nDNA and mtDNA in several regions of the mitochondrial genome were found to be as low as 11 bases, which not only allows using these regions to design new, very specific PCR primers, but also supports the hypothesis of the non-random introduction of mtDNA into the human nuclear DNA.

conclusionAnalysis of the mitochondrial locations of the subsequences shared between nDNA and mtDNA suggested that even very short (36 bases) single-end sequencing reads can be used to identify low-abundance variation in 20.4% of the mitochondrial genome. For longer (76 and 150 bases) reads, the proportion of the mitochondrial genome where nDNA presence will not interfere found to be 44.5 and 67.9%, when low-abundance mutations at 100% of locations can be identified using 417 bases long single reads. This observation suggests that the analysis of low-abundance variations in mitochondria population can be extended to a variety of large data collections such as NCBI Sequence Read Archive, European Nucleotide Archive, The Cancer Genome Atlas, and International Cancer Genome Consortium.

Indexed as

DNA ContaminationGenome, HumanGenome, MitochondrialGenes, MitochondrialHigh-Throughput Nucleotide SequencingHumansPolymerase Chain ReactionReproducibility of ResultsHeteroplasmyHigh throughput sequencingLow-abundance mutationMinor alleleMitochondriaNUMTRare variant

Identifiers

PMID27955616
PMCPMC5153897
OpenAlexW2562939826

What OpenQuestion holds

Textmetadata
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.