↓ Skip to main content

MATCHCLIP: locate precise breakpoints for copy number variation using CIGAR string by matching soft clipped reads

Overview of attention for article published in Frontiers in Genetics, January 2013
Altmetric Badge

About this Attention Score

  • Average Attention Score compared to outputs of the same age
  • Above-average Attention Score compared to outputs of the same age and source (63rd percentile)

Mentioned by

twitter
6 X users

Readers on

mendeley
39 Mendeley
citeulike
2 CiteULike
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
MATCHCLIP: locate precise breakpoints for copy number variation using CIGAR string by matching soft clipped reads
Published in
Frontiers in Genetics, January 2013
DOI 10.3389/fgene.2013.00157
Pubmed ID
Authors

Yinghua Wu, Lifeng Tian, Mario Pirastu, Dwight Stambolian, Hongzhe Li

Abstract

Copy number variations (CNVs) are associated with many complex diseases. Next generation sequencing data enable one to identify precise CNV breakpoints to better under the underlying molecular mechanisms and to design more efficient assays. Using the CIGAR strings of the reads, we develop a method that can identify the exact CNV breakpoints, and in cases when the breakpoints are in a repeated region, the method reports a range where the breakpoints can slide. Our method identifies the breakpoints of a CNV using both the positions and CIGAR strings of the reads that cover breakpoints of a CNV. A read with a long soft clipped part (denoted as S in CIGAR) at its 3'(right) end can be used to identify the 5'(left)-side of the breakpoints, and a read with a long S part at the 5' end can be used to identify the breakpoint at the 3'-side. To ensure both types of reads cover the same CNV, we require the overlapped common string to include both of the soft clipped parts. When a CNV starts and ends in the same repeated regions, its breakpoints are not unique, in which case our method reports the left most positions for the breakpoints and a range within which the breakpoints can be incremented without changing the variant sequence. We have implemented the methods in a C++ package intended for the current Illumina Miseq and Hiseq platforms for both whole genome and exon-sequencing. Our simulation studies have shown that our method compares favorably with other similar methods in terms of true discovery rate, false positive rate and breakpoint accuracy. Our results from a real application have shown that the detected CNVs are consistent with zygosity and read depth information. The software package is available at http://statgene.med.upenn.edu/softprog.html.

X Demographics

X Demographics

The data shown below were collected from the profiles of 6 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 39 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 3%
Sweden 1 3%
France 1 3%
Brazil 1 3%
Unknown 35 90%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 14 36%
Researcher 13 33%
Other 3 8%
Student > Bachelor 2 5%
Student > Doctoral Student 1 3%
Other 1 3%
Unknown 5 13%
Readers by discipline Count As %
Agricultural and Biological Sciences 16 41%
Biochemistry, Genetics and Molecular Biology 11 28%
Computer Science 3 8%
Mathematics 1 3%
Immunology and Microbiology 1 3%
Other 1 3%
Unknown 6 15%
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 25 December 2013.
All research outputs
#12,880,448
of 22,716,996 outputs
Outputs from Frontiers in Genetics
#2,716
of 11,756 outputs
Outputs of similar age
#152,705
of 280,757 outputs
Outputs of similar age from Frontiers in Genetics
#116
of 319 outputs
Altmetric has tracked 22,716,996 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 11,756 research outputs from this source. They receive a mean Attention Score of 3.7. This one has done well, scoring higher than 75% of its peers.
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 280,757 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 44th percentile – i.e., 44% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 319 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 63% of its contemporaries.