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PathOS: a decision support system for reporting high throughput sequencing of cancers in clinical diagnostic laboratories

Overview of attention for article published in Genome Medicine, April 2017
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  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (73rd percentile)

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Title
PathOS: a decision support system for reporting high throughput sequencing of cancers in clinical diagnostic laboratories
Published in
Genome Medicine, April 2017
DOI 10.1186/s13073-017-0427-z
Pubmed ID
Authors

Kenneth D. Doig, Andrew Fellowes, Anthony H. Bell, Andrei Seleznev, David Ma, Jason Ellul, Jason Li, Maria A. Doyle, Ella R. Thompson, Amit Kumar, Luis Lara, Ravikiran Vedururu, Gareth Reid, Thomas Conway, Anthony T. Papenfuss, Stephen B. Fox

Abstract

The increasing affordability of DNA sequencing has allowed it to be widely deployed in pathology laboratories. However, this has exposed many issues with the analysis and reporting of variants for clinical diagnostic use. Implementing a high-throughput sequencing (NGS) clinical reporting system requires a diverse combination of capabilities, statistical methods to identify variants, global variant databases, a validated bioinformatics pipeline, an auditable laboratory workflow, reproducible clinical assays and quality control monitoring throughout. These capabilities must be packaged in software that integrates the disparate components into a useable system. To meet these needs, we developed a web-based application, PathOS, which takes variant data from a patient sample through to a clinical report. PathOS has been used operationally in the Peter MacCallum Cancer Centre for two years for the analysis, curation and reporting of genetic tests for cancer patients, as well as the curation of large-scale research studies. PathOS has also been deployed in cloud environments allowing multiple institutions to use separate, secure and customisable instances of the system. Increasingly, the bottleneck of variant curation is limiting the adoption of clinical sequencing for molecular diagnostics. PathOS is focused on providing clinical variant curators and pathology laboratories with a decision support system needed for personalised medicine. While the genesis of PathOS has been within cancer molecular diagnostics, the system is applicable to NGS clinical reporting generally. The widespread availability of genomic sequencers has highlighted the limited availability of software to support clinical decision-making in molecular pathology. PathOS is a system that has been developed and refined in a hospital laboratory context to meet the needs of clinical diagnostics. The software is available as a set of Docker images and source code at https://github.com/PapenfussLab/PathOS .

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The data shown below were collected from the profiles of 12 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 68 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 68 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 21 31%
Student > Ph. D. Student 13 19%
Student > Master 9 13%
Other 7 10%
Student > Bachelor 3 4%
Other 5 7%
Unknown 10 15%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 18 26%
Agricultural and Biological Sciences 12 18%
Medicine and Dentistry 9 13%
Computer Science 7 10%
Neuroscience 3 4%
Other 9 13%
Unknown 10 15%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 7. 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 January 2018.
All research outputs
#4,981,096
of 24,286,850 outputs
Outputs from Genome Medicine
#928
of 1,500 outputs
Outputs of similar age
#82,546
of 313,531 outputs
Outputs of similar age from Genome Medicine
#23
of 31 outputs
Altmetric has tracked 24,286,850 research outputs across all sources so far. Compared to these this one has done well and is in the 79th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,500 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 26.6. This one is in the 38th percentile – i.e., 38% 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 313,531 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 73% of its contemporaries.
We're also able to compare this research output to 31 others from the same source and published within six weeks on either side of this one. This one is in the 29th percentile – i.e., 29% of its contemporaries scored the same or lower than it.