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NEAT: a framework for building fully automated NGS pipelines and analyses

Overview of attention for article published in BMC Bioinformatics, February 2016
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  • Good Attention Score compared to outputs of the same age (75th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (60th percentile)

Mentioned by

10 tweeters


7 Dimensions

Readers on

89 Mendeley
3 CiteULike
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NEAT: a framework for building fully automated NGS pipelines and analyses
Published in
BMC Bioinformatics, February 2016
DOI 10.1186/s12859-016-0902-3
Pubmed ID

Patrick Schorderet


The analysis of next generation sequencing (NGS) has become a standard task for many laboratories in the life sciences. Though there exists several tools to support users in the manipulation of such datasets on various levels, few are built on the basis of vertical integration. Here, we present the NExt generation Analysis Toolbox (NEAT) that allows non-expert users including wet-lab scientists to comprehensively build, run and analyze NGS data through double-clickable executables without the need of any programming experience. In comparison to many publicly available tools including Galaxy, NEAT provides three main advantages: (1) Through the development of double-clickable executables, NEAT is efficient (completes within <24 hours), easy to implement and intuitive; (2) Storage space, maximum number of job submissions, wall time and cluster-specific parameters can be customized as NEAT is run on the institution's cluster; (3) NEAT allows users to visualize and summarize NGS data rapidly and efficiently using various built-in exploratory data analysis tools including metagenomic and differentially expressed gene analysis. To simplify the control of the workflow, NEAT projects are built around a unique and centralized file containing sample names, replicates, conditions, antibodies, alignment-, filtering- and peak calling parameters as well as cluster-specific paths and settings. Moreover, the small-sized files produced by NEAT allow users to easily manipulate, consolidate and share datasets from different users and institutions. NEAT provides biologists and bioinformaticians with a robust, efficient and comprehensive tool for the analysis of massive NGS datasets. Frameworks such as NEAT not only allow novice users to overcome the increasing number of technical hurdles due to the complexity of manipulating large datasets, but provide more advance users with tools that ensure high reproducibility standards in the NGS era. NEAT is publically available at https://github.com/pschorderet/NEAT .

Twitter Demographics

The data shown below were collected from the profiles of 10 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 89 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 4 4%
Netherlands 1 1%
Germany 1 1%
Italy 1 1%
Luxembourg 1 1%
Unknown 81 91%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 22 25%
Researcher 22 25%
Student > Master 10 11%
Student > Bachelor 7 8%
Student > Postgraduate 7 8%
Other 17 19%
Unknown 4 4%
Readers by discipline Count As %
Agricultural and Biological Sciences 36 40%
Biochemistry, Genetics and Molecular Biology 23 26%
Computer Science 13 15%
Immunology and Microbiology 4 4%
Engineering 4 4%
Other 3 3%
Unknown 6 7%

Attention Score in Context

This research output has an Altmetric Attention Score of 5. 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 08 February 2016.
All research outputs
of 22,842,950 outputs
Outputs from BMC Bioinformatics
of 7,289 outputs
Outputs of similar age
of 397,369 outputs
Outputs of similar age from BMC Bioinformatics
of 130 outputs
Altmetric has tracked 22,842,950 research outputs across all sources so far. This one has received more attention than most of these and is in the 73rd percentile.
So far Altmetric has tracked 7,289 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 gotten more attention than average, scoring higher than 69% 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 397,369 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 75% of its contemporaries.
We're also able to compare this research output to 130 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 60% of its contemporaries.