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Segmentum: a tool for copy number analysis of cancer genomes

Overview of attention for article published in BMC Bioinformatics, April 2017
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (79th percentile)
  • Good Attention Score compared to outputs of the same age and source (76th percentile)

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Title
Segmentum: a tool for copy number analysis of cancer genomes
Published in
BMC Bioinformatics, April 2017
DOI 10.1186/s12859-017-1626-8
Pubmed ID
Authors

Ebrahim Afyounian, Matti Annala, Matti Nykter

Abstract

Somatic alterations, including loss of heterozygosity, can affect the expression of oncogenes and tumor suppressor genes. Whole genome sequencing enables detailed characterization of such aberrations. However, due to the limitations of current high throughput sequencing technologies, this task remains challenging. Hence, accurate and reliable detection of such events is crucial for the identification of cancer-related alterations. We introduce a new tool called Segmentum for determining somatic copy numbers using whole genome sequencing from paired tumor/normal samples. In our approach, read depth and B-allele fraction signals are smoothed, and double sliding windows are used to detect breakpoints, which makes our approach fast and straightforward. Because the breakpoint detection is performed simultaneously at different scales, it allows accurate detection as suggested by the evaluation results from simulated and real data. We applied Segmentum to paired tumor/normal whole genome sequencing samples from 38 patients with low-grade glioma from the TCGA dataset and were able to confirm the recurrence of copy-neutral loss of heterozygosity in chromosome 17p in low-grade astrocytoma characterized by IDH1/2 mutation and lack of 1p/19q co-deletion, which was previously reported using SNP array data. Segmentum is an accurate, user-friendly tool for somatic copy number analysis of tumor samples. We demonstrate that this tool is suitable for the analysis of large cohorts, such as the TCGA dataset.

X Demographics

X Demographics

The data shown below were collected from the profiles of 17 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 32 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Mexico 1 3%
China 1 3%
Unknown 30 94%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 10 31%
Researcher 8 25%
Student > Bachelor 4 13%
Student > Doctoral Student 2 6%
Student > Master 2 6%
Other 2 6%
Unknown 4 13%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 10 31%
Agricultural and Biological Sciences 8 25%
Computer Science 5 16%
Nursing and Health Professions 1 3%
Social Sciences 1 3%
Other 1 3%
Unknown 6 19%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 10. 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 27 April 2017.
All research outputs
#3,844,106
of 25,736,439 outputs
Outputs from BMC Bioinformatics
#1,280
of 7,739 outputs
Outputs of similar age
#66,569
of 325,550 outputs
Outputs of similar age from BMC Bioinformatics
#29
of 124 outputs
Altmetric has tracked 25,736,439 research outputs across all sources so far. Compared to these this one has done well and is in the 85th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,739 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.6. This one has done well, scoring higher than 83% 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 325,550 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 79% of its contemporaries.
We're also able to compare this research output to 124 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 76% of its contemporaries.