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Dove Medical Press

Next-generation sequencing: advances and applications in cancer diagnosis

Overview of attention for article published in OncoTargets and therapy, December 2016
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (88th percentile)
  • High Attention Score compared to outputs of the same age and source (92nd percentile)

Mentioned by

policy
2 policy sources
twitter
8 X users
patent
5 patents
facebook
1 Facebook page

Readers on

mendeley
302 Mendeley
Title
Next-generation sequencing: advances and applications in cancer diagnosis
Published in
OncoTargets and therapy, December 2016
DOI 10.2147/ott.s99807
Pubmed ID
Authors

Simona Serratì, Simona De Summa, Brunella Pilato, Daniela Petriella, Rosanna Lacalamita, Stefania Tommasi, Rosamaria Pinto

Abstract

Technological advances have led to the introduction of next-generation sequencing (NGS) platforms in cancer investigation. NGS allows massive parallel sequencing that affords maximal tumor genomic assessment. NGS approaches are different, and concern DNA and RNA analysis. DNA sequencing includes whole-genome, whole-exome, and targeted sequencing, which focuses on a selection of genes of interest for a specific disease. RNA sequencing facilitates the detection of alternative gene-spliced transcripts, posttranscriptional modifications, gene fusion, mutations/single-nucleotide polymorphisms, small and long noncoding RNAs, and changes in gene expression. Most applications are in the cancer research field, but lately NGS technology has been revolutionizing cancer molecular diagnostics, due to the many advantages it offers compared to traditional methods. There is greater knowledge on solid cancer diagnostics, and recent interest has been shown also in the field of hematologic cancer. In this review, we report the latest data on NGS diagnostic/predictive clinical applications in solid and hematologic cancers. Moreover, since the amount of NGS data produced is very large and their interpretation is very complex, we briefly discuss two bioinformatic aspects, variant-calling accuracy and copy-number variation detection, which are gaining a lot of importance in cancer-diagnostic assessment.

X Demographics

X Demographics

The data shown below were collected from the profiles of 8 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 302 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Spain 1 <1%
China 1 <1%
Unknown 300 99%

Demographic breakdown

Readers by professional status Count As %
Student > Master 41 14%
Student > Bachelor 40 13%
Researcher 39 13%
Student > Ph. D. Student 39 13%
Other 18 6%
Other 46 15%
Unknown 79 26%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 79 26%
Medicine and Dentistry 45 15%
Agricultural and Biological Sciences 30 10%
Computer Science 11 4%
Engineering 8 3%
Other 31 10%
Unknown 98 32%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 15. 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 18 April 2023.
All research outputs
#2,473,805
of 25,837,817 outputs
Outputs from OncoTargets and therapy
#63
of 3,024 outputs
Outputs of similar age
#46,146
of 420,468 outputs
Outputs of similar age from OncoTargets and therapy
#4
of 57 outputs
Altmetric has tracked 25,837,817 research outputs across all sources so far. Compared to these this one has done well and is in the 89th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 3,024 research outputs from this source. They receive a mean Attention Score of 3.0. This one has done particularly well, scoring higher than 97% 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 420,468 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 88% of its contemporaries.
We're also able to compare this research output to 57 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 92% of its contemporaries.