↓ Skip to main content

Detection and quantification of mitochondrial DNA deletions from next-generation sequence data

Overview of attention for article published in BMC Bioinformatics, October 2017
Altmetric Badge

About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (51st percentile)
  • Above-average Attention Score compared to outputs of the same age and source (57th percentile)

Mentioned by

twitter
7 X users
facebook
1 Facebook page

Citations

dimensions_citation
30 Dimensions

Readers on

mendeley
66 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
Detection and quantification of mitochondrial DNA deletions from next-generation sequence data
Published in
BMC Bioinformatics, October 2017
DOI 10.1186/s12859-017-1821-7
Pubmed ID
Authors

Colleen M. Bosworth, Sneha Grandhi, Meetha P. Gould, Thomas LaFramboise

Abstract

Chromosomal deletions represent an important class of human genetic variation. Various methods have been developed to mine "next-generation" sequencing (NGS) data to detect deletions and quantify their clonal abundances. These methods have focused almost exclusively on the nuclear genome, ignoring the mitochondrial chromosome (mtDNA). Detecting mtDNA deletions requires special care. First, the chromosome's relatively small size (16,569 bp) necessitates the ability to detect extremely focal events. Second, the chromosome can be present at thousands of copies in a single cell (in contrast to two copies of nuclear chromosomes), and mtDNA deletions may be present on only a very small percentage of chromosomes. Here we present a method, termed MitoDel, to detect mtDNA deletions from NGS data. We validate the method on simulated and real data, and show that MitoDel can detect novel and previously-reported mtDNA deletions. We establish that MitoDel can find deletions such as the "common deletion" at heteroplasmy levels well below 1%. MitoDel is a tool for detecting large mitochondrial deletions at low heteroplasmy levels. The tool can be downloaded at http://mendel.gene.cwru.edu/laframboiselab/ .

X Demographics

X Demographics

The data shown below were collected from the profiles of 7 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 66 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 66 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 13 20%
Researcher 13 20%
Student > Bachelor 6 9%
Student > Master 5 8%
Other 4 6%
Other 9 14%
Unknown 16 24%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 20 30%
Agricultural and Biological Sciences 9 14%
Neuroscience 7 11%
Medicine and Dentistry 6 9%
Computer Science 3 5%
Other 3 5%
Unknown 18 27%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 23 November 2017.
All research outputs
#13,056,998
of 23,005,189 outputs
Outputs from BMC Bioinformatics
#3,809
of 7,312 outputs
Outputs of similar age
#153,859
of 325,925 outputs
Outputs of similar age from BMC Bioinformatics
#48
of 122 outputs
Altmetric has tracked 23,005,189 research outputs across all sources so far. This one is in the 42nd percentile – i.e., 42% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,312 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one is in the 45th percentile – i.e., 45% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 325,925 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 51% of its contemporaries.
We're also able to compare this research output to 122 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 57% of its contemporaries.