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fastQ_brew: module for analysis, preprocessing, and reformatting of FASTQ sequence data

Overview of attention for article published in BMC Research Notes, July 2017
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Title
fastQ_brew: module for analysis, preprocessing, and reformatting of FASTQ sequence data
Published in
BMC Research Notes, July 2017
DOI 10.1186/s13104-017-2616-7
Pubmed ID
Authors

Damien M. O’Halloran

Abstract

Next generation sequencing datasets are stored as FASTQ formatted files. In order to avoid downstream artefacts, it is critical to implement a robust preprocessing protocol of the FASTQ sequence in order to determine the integrity and quality of the data. Here I describe fastQ_brew which is a package that provides a suite of methods to evaluate sequence data in FASTQ format and efficiently implements a variety of manipulations to filter sequence data by size, quality and/or sequence. fastQ_brew allows for mismatch searches to adapter sequences, left and right end trimming, removal of duplicate reads, as well as reads containing non-designated bases. fastQ_brew also returns summary statistics on the unfiltered and filtered FASTQ data, and offers FASTQ to FASTA conversion as well as FASTQ reverse complement and DNA to RNA manipulations. fastQ_brew is open source and freely available to all users at the following webpage: https://github.com/dohalloran/fastQ_brew .

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The data shown below were collected from the profile of 1 X user 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 23 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 23 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 5 22%
Student > Master 4 17%
Student > Bachelor 3 13%
Researcher 3 13%
Professor 2 9%
Other 1 4%
Unknown 5 22%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 7 30%
Environmental Science 2 9%
Agricultural and Biological Sciences 2 9%
Computer Science 2 9%
Immunology and Microbiology 2 9%
Other 2 9%
Unknown 6 26%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 31 March 2018.
All research outputs
#18,345,702
of 23,577,761 outputs
Outputs from BMC Research Notes
#2,853
of 4,305 outputs
Outputs of similar age
#225,967
of 313,745 outputs
Outputs of similar age from BMC Research Notes
#82
of 142 outputs
Altmetric has tracked 23,577,761 research outputs across all sources so far. This one is in the 19th percentile – i.e., 19% of other outputs scored the same or lower than it.
So far Altmetric has tracked 4,305 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.7. This one is in the 29th percentile – i.e., 29% 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 313,745 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 23rd percentile – i.e., 23% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 142 others from the same source and published within six weeks on either side of this one. This one is in the 38th percentile – i.e., 38% of its contemporaries scored the same or lower than it.