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Reactome pathway analysis: a high-performance in-memory approach

Overview of attention for article published in BMC Bioinformatics, March 2017
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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 (84th percentile)
  • High Attention Score compared to outputs of the same age and source (91st percentile)

Mentioned by

twitter
19 tweeters

Citations

dimensions_citation
426 Dimensions

Readers on

mendeley
420 Mendeley
citeulike
3 CiteULike
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Title
Reactome pathway analysis: a high-performance in-memory approach
Published in
BMC Bioinformatics, March 2017
DOI 10.1186/s12859-017-1559-2
Pubmed ID
Authors

Antonio Fabregat, Konstantinos Sidiropoulos, Guilherme Viteri, Oscar Forner, Pablo Marin-Garcia, Vicente Arnau, Peter D’Eustachio, Lincoln Stein, Henning Hermjakob

Abstract

Reactome aims to provide bioinformatics tools for visualisation, interpretation and analysis of pathway knowledge to support basic research, genome analysis, modelling, systems biology and education. Pathway analysis methods have a broad range of applications in physiological and biomedical research; one of the main problems, from the analysis methods performance point of view, is the constantly increasing size of the data samples. Here, we present a new high-performance in-memory implementation of the well-established over-representation analysis method. To achieve the target, the over-representation analysis method is divided in four different steps and, for each of them, specific data structures are used to improve performance and minimise the memory footprint. The first step, finding out whether an identifier in the user's sample corresponds to an entity in Reactome, is addressed using a radix tree as a lookup table. The second step, modelling the proteins, chemicals, their orthologous in other species and their composition in complexes and sets, is addressed with a graph. The third and fourth steps, that aggregate the results and calculate the statistics, are solved with a double-linked tree. Through the use of highly optimised, in-memory data structures and algorithms, Reactome has achieved a stable, high performance pathway analysis service, enabling the analysis of genome-wide datasets within seconds, allowing interactive exploration and analysis of high throughput data. The proposed pathway analysis approach is available in the Reactome production web site either via the AnalysisService for programmatic access or the user submission interface integrated into the PathwayBrowser. Reactome is an open data and open source project and all of its source code, including the one described here, is available in the AnalysisTools repository in the Reactome GitHub ( https://github.com/reactome/ ).

Twitter Demographics

The data shown below were collected from the profiles of 19 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 420 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 <1%
Israel 1 <1%
Sweden 1 <1%
Unknown 417 99%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 83 20%
Student > Master 59 14%
Researcher 51 12%
Student > Bachelor 49 12%
Student > Doctoral Student 26 6%
Other 60 14%
Unknown 92 22%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 122 29%
Agricultural and Biological Sciences 49 12%
Medicine and Dentistry 35 8%
Computer Science 22 5%
Immunology and Microbiology 15 4%
Other 62 15%
Unknown 115 27%

Attention Score in Context

This research output has an Altmetric Attention Score of 13. 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 09 January 2019.
All research outputs
#2,340,824
of 22,958,253 outputs
Outputs from BMC Bioinformatics
#688
of 7,307 outputs
Outputs of similar age
#46,851
of 310,726 outputs
Outputs of similar age from BMC Bioinformatics
#12
of 138 outputs
Altmetric has tracked 22,958,253 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 7,307 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one has done particularly well, scoring higher than 90% 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 310,726 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 84% of its contemporaries.
We're also able to compare this research output to 138 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 91% of its contemporaries.