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The somatic autosomal mutation matrix in cancer genomes

Overview of attention for article published in Human Genetics, May 2015
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  • Good Attention Score compared to outputs of the same age (70th percentile)
  • Good Attention Score compared to outputs of the same age and source (72nd percentile)

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1 X user
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1 Wikipedia page

Citations

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15 Dimensions

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32 Mendeley
Title
The somatic autosomal mutation matrix in cancer genomes
Published in
Human Genetics, May 2015
DOI 10.1007/s00439-015-1566-1
Pubmed ID
Authors

Nuri A. Temiz, Duncan E. Donohue, Albino Bacolla, Karen M. Vasquez, David N. Cooper, Uma Mudunuri, Joseph Ivanic, Regina Z. Cer, Ming Yi, Robert M. Stephens, Jack R. Collins, Brian T. Luke

Abstract

DNA damage in somatic cells originates from both environmental and endogenous sources, giving rise to mutations through multiple mechanisms. When these mutations affect the function of critical genes, cancer may ensue. Although identifying genomic subsets of mutated genes may inform therapeutic options, a systematic survey of tumor mutational spectra is required to improve our understanding of the underlying mechanisms of mutagenesis involved in cancer etiology. Recent studies have presented genome-wide sets of somatic mutations as a 96-element vector, a procedure that only captures the immediate neighbors of the mutated nucleotide. Herein, we present a 32 × 12 mutation matrix that captures the nucleotide pattern two nucleotides upstream and downstream of the mutation. A somatic autosomal mutation matrix (SAMM) was constructed from tumor-specific mutations derived from each of 909 individual cancer genomes harboring a total of 10,681,843 single-base substitutions. In addition, mechanistic template mutation matrices (MTMMs) representing oxidative DNA damage, ultraviolet-induced DNA damage, (5m)CpG deamination, and APOBEC-mediated cytosine mutation, are presented. MTMMs were mapped to the individual tumor SAMMs to determine the maximum contribution of each mutational mechanism to the overall mutation pattern. A Manhattan distance across all SAMM elements between any two tumor genomes was used to determine their relative distance. Employing this metric, 89.5 % of all tumor genomes were found to have a nearest neighbor from the same tissue of origin. When a distance-dependent 6-nearest neighbor classifier was used, 86.9 % of all SAMMs were assigned to the correct tissue of origin. Thus, although tumors from different tissues may have similar mutation patterns, their SAMMs often display signatures that are characteristic of specific tissues.

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X Demographics

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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 %
Norway 1 3%
Unknown 31 97%

Demographic breakdown

Readers by professional status Count As %
Researcher 12 38%
Student > Ph. D. Student 7 22%
Student > Master 3 9%
Student > Doctoral Student 2 6%
Other 2 6%
Other 3 9%
Unknown 3 9%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 10 31%
Agricultural and Biological Sciences 10 31%
Medicine and Dentistry 3 9%
Computer Science 3 9%
Mathematics 1 3%
Other 2 6%
Unknown 3 9%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 16 September 2018.
All research outputs
#6,418,622
of 22,807,037 outputs
Outputs from Human Genetics
#807
of 2,953 outputs
Outputs of similar age
#76,790
of 267,813 outputs
Outputs of similar age from Human Genetics
#7
of 25 outputs
Altmetric has tracked 22,807,037 research outputs across all sources so far. This one has received more attention than most of these and is in the 70th percentile.
So far Altmetric has tracked 2,953 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.2. This one has gotten more attention than average, scoring higher than 71% 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 267,813 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 70% of its contemporaries.
We're also able to compare this research output to 25 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 72% of its contemporaries.